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09_coloring/requirements.txt create mode 100644 09_coloring/run_coloring_pipeline.py create mode 100644 PROJECT_CONTEXT.md create mode 100644 README.md create mode 100644 config.py create mode 100644 requirements.txt diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ce2e35e --- /dev/null +++ b/.gitignore @@ -0,0 +1,45 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.egg-info/ +.eggs/ +dist/ +build/ +*.egg +.venv/ +venv/ +env/ +.env +*.log + +# IDE / editor +.cursor/ +.vscode/ +.idea/ +*.swp +*.swo +*~ +.DS_Store + +# libSGM — compiled locally on each machine (see README setup) +05_disparity/libsgm/build/ + +# Optional local test output +06_Pointcloud/output/ +**/output/ + +# Data directories — live OUTSIDE this repo under ~/ +# (listed here in case someone copies data into the clone by mistake) +3D-Scans/ +Calib-data/ +Speckle-Scanner_Processing_data/ + +# Large / generated artifacts +*.o +*.so +*.a +*.ply +*.bmp +*.npy +!05_disparity/libsgm/sample/** diff --git a/02_Calibration/README.md b/02_Calibration/README.md new file mode 100644 index 0000000..315dedc --- /dev/null +++ b/02_Calibration/README.md @@ -0,0 +1,408 @@ +# 02 Calibration + +Two-step calibration pipeline: + +| Step | Script | What it does | +|------|--------|--------------| +| **1. Detection** | `detect_features.py` | Chessboard corners / IR ellipses → **JSON next to each image** | +| **2. Calibration** | `calibrate.py` | Mono intrinsics per camera + stereo **lc vs rc/rg/ir** | + +`main.py` runs both steps by default (`--step all`). + +--- + +## Troubleshooting flag + +All calibration scripts accept `--troubleshooting` (default: **off**). + +| `--troubleshooting` | Logs | Disk output | +|---------------------|------|-------------| +| **False** (default) | Minimal summary per camera / stereo pair | Step 1: `*.json` only (required for step 2). Step 2: **`params/` only** | +| **True** | Detailed per-image / per-pair logs, progress bars | Step 1: + `corners//` overlays. Step 2: + `pairing_reports/`, `rectified/` | + +```bash +# Default — minimal logs, only params/ from step 2 +python main.py --project Olsen_wings --date 2026-05-12 --calib_name calib1 + +# Debug — verbose logs + intermediate folders +python main.py --project Olsen_wings --date 2026-05-12 --calib_name calib1 --troubleshooting +``` + +Legacy mode (`--legacy`) also respects `--troubleshooting` (corners, local_coords, images_ncb, rectified). + +--- + +## All CLI parameters (reference) + +| Parameter | Default | Used in | +|-----------|---------|---------| +| `--project` | required | all | +| `--date` | required | all | +| `--calib_name` | `calib1` | all | +| `--chessboard_size` | `8,7` | all | +| `--square_size` | `0.045` | all | +| `--left_chessboard_size` | = `--chessboard_size` | all | +| `--right_chessboard_size` | = `--chessboard_size` | all | +| `--left_square_size` | = `--square_size` | all | +| `--right_square_size` | = `--square_size` | all | +| `--preprocessing` | `None` | step 1 (`G`, `C`, `T` chain) | +| `--cameras` | all present | `detect_features.py` | +| `--ir_mode` | `auto` | step 1 (`auto` / `chessboard` / `ellipse`) | +| `--step` | `all` | `main.py` (`detect`/`calibrate`/`all`); `calibrate.py` (`mono`/`stereo`/`all`) | +| `--left_camera` | `lc` | step 2 stereo (`lc` / `lc-ir`) | +| `--time_window` | `0.1` | step 2 stereo (seconds) | +| `--partners` | `rc,rg,ir` | step 2 stereo | +| `--legacy` | off | `main.py` only | +| `--right_camera` | `rc` | `main.py --legacy` only | +| `--troubleshooting` | off | all (`False` = minimal; `True` = debug output) | + +--- + +## Folder structure + +``` +~/Calib-data//// +├── lc/ +│ ├── lc_1778599872850705.bmp +│ └── lc_1778599872850705.json ← step 1 (always) +├── rc/ +├── rg/ (or rgb/) +├── ir/ (or IR/) +├── corners/ ← step 1, only with --troubleshooting +├── pairing_reports/ ← step 2, only with --troubleshooting +├── rectified/ ← step 2, only with --troubleshooting +└── params/ ← step 2 (always) + ├── lc_intrinsics.npz + ├── rc_intrinsics.npz + ├── lc-rc_parameters.npz + ├── lc-rc_stereo_cam_model.yaml + ├── lc-rc_Q.cvstore + ├── lc-rg_* + └── lc-ir_* +``` + +Nested layout (`/images/lc/`, …) is also supported. + +--- + +## Quick start (full pipeline) + +```bash +cd ~/Speckle-Scanner/02_Calibration + +python main.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --chessboard_size 8,7 --square_size 0.045 +``` + +Or run steps separately: + +```bash +# Step 1 — detect features, write JSON +python detect_features.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --chessboard_size 8,7 --square_size 0.045 + +# Step 2 — calibrate from JSON (writes params/ only) +python calibrate.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --chessboard_size 8,7 --square_size 0.045 \ + --time_window 0.1 +``` + +--- + +## Step 1 — Feature detection (per camera) + +For every image in each camera folder (`lc`, `rc`, `rg`, `ir`, `lc-ir`): + +- Detects **chessboard corners** (default for lc/rc/rg) +- For **IR**: tries chessboard first (`--ir_mode auto`), falls back to **ellipse center** +- Writes `.json` in the **same folder** as the image (always, even without `--troubleshooting`) + +### LC only + +```bash +python detect_features.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --cameras lc \ + --chessboard_size 8,7 --square_size 0.045 \ + --left_chessboard_size 8,7 --left_square_size 0.045 \ + --preprocessing None +``` + +### RC only + +```bash +python detect_features.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --cameras rc \ + --chessboard_size 8,7 --square_size 0.045 \ + --right_chessboard_size 8,7 --right_square_size 0.045 \ + --preprocessing None +``` + +### RG only + +```bash +python detect_features.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --cameras rg \ + --chessboard_size 8,7 --square_size 0.045 \ + --right_chessboard_size 8,7 --right_square_size 0.045 \ + --preprocessing None +``` + +### IR only + +```bash +python detect_features.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --cameras ir \ + --chessboard_size 8,7 --square_size 0.045 \ + --right_chessboard_size 8,7 --right_square_size 0.045 \ + --preprocessing C \ + --ir_mode auto +``` + +### LC-IR folder only + +```bash +python detect_features.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --cameras lc-ir \ + --chessboard_size 8,7 --square_size 0.045 \ + --left_chessboard_size 8,7 --left_square_size 0.045 \ + --preprocessing None +``` + +### All cameras + +```bash +python detect_features.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --chessboard_size 8,7 --square_size 0.045 \ + --left_chessboard_size 8,7 --left_square_size 0.045 \ + --right_chessboard_size 8,7 --right_square_size 0.045 \ + --preprocessing None \ + --ir_mode auto +``` + +### Step 1 with troubleshooting + +```bash +python detect_features.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --cameras lc,rc,ir \ + --chessboard_size 8,7 --square_size 0.045 \ + --preprocessing C \ + --ir_mode auto \ + --troubleshooting +``` + +### JSON contents (chessboard example) + +```json +{ + "version": 1, + "image": "lc_1778599872850705.bmp", + "camera_folder": "lc", + "feature_type": "chessboard", + "success": true, + "board_size": [8, 7], + "square_size": 0.045, + "timestamp_sec": 1778599872.850705, + "pair_key": "1778599872850705", + "corners": [[412.3, 287.1], ...] +} +``` + +--- + +## Step 2 — Calibration + +### 2a. Mono intrinsics + +Reads chessboard JSONs from each camera folder, runs `cv2.calibrateCamera`, saves: + +- `params/_intrinsics.npz` +- `params/_intrinsics.yaml` + +Requires **≥ 3** successful chessboard detections per camera. + +### 2b. Stereo calibration + +- **Left camera:** `lc` by default (`--left_camera`) +- **Partners:** `rc`, `rg`, `ir` — each available folder is calibrated against lc +- **Pairing:** time-window match (`--time_window`, default **0.1 s**), then filename `pair_key` fallback for IR scan ids +- Uses mono intrinsics with `CALIB_FIX_INTRINSIC` +- Saves `lc-rc_*`, `lc-rg_*`, `lc-ir_*` under `params/` + +### Full step 2 (mono + all stereo pairs) + +```bash +python calibrate.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --step all \ + --left_camera lc \ + --partners rc,rg,ir \ + --time_window 0.1 \ + --chessboard_size 8,7 --square_size 0.045 \ + --left_chessboard_size 8,7 --left_square_size 0.045 \ + --right_chessboard_size 8,7 --right_square_size 0.045 +``` + +### Stereo: LC ↔ RC only + +```bash +python calibrate.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --step stereo \ + --left_camera lc \ + --partners rc \ + --time_window 0.1 \ + --chessboard_size 8,7 --square_size 0.045 +``` + +### Stereo: LC ↔ RG only + +```bash +python calibrate.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --step stereo \ + --left_camera lc \ + --partners rg \ + --time_window 0.1 \ + --chessboard_size 8,7 --square_size 0.045 +``` + +### Stereo: LC ↔ IR only + +```bash +python calibrate.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --step stereo \ + --left_camera lc \ + --partners ir \ + --time_window 0.1 \ + --chessboard_size 8,7 --square_size 0.045 +``` + +### Stereo: left = LC-IR folder + +```bash +python calibrate.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --step stereo \ + --left_camera lc-ir \ + --partners rc,rg,ir \ + --time_window 0.1 \ + --chessboard_size 8,7 --square_size 0.045 \ + --left_chessboard_size 8,7 --left_square_size 0.045 \ + --right_chessboard_size 8,7 --right_square_size 0.045 +``` + +### Mono intrinsics only + +```bash +python calibrate.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --step mono \ + --chessboard_size 8,7 --square_size 0.045 \ + --left_chessboard_size 8,7 --left_square_size 0.045 \ + --right_chessboard_size 8,7 --right_square_size 0.045 +``` + +### Step 2 with troubleshooting + +```bash +python calibrate.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --step all \ + --time_window 0.1 \ + --chessboard_size 8,7 --square_size 0.045 \ + --troubleshooting +``` + +Writes `params/` plus `pairing_reports/.txt` and `rectified//`. + +--- + +## Full pipeline (`main.py`) + +```bash +python main.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --step all \ + --left_camera lc \ + --partners rc,rg,ir \ + --time_window 0.1 \ + --chessboard_size 8,7 --square_size 0.045 \ + --left_chessboard_size 8,7 --left_square_size 0.045 \ + --right_chessboard_size 8,7 --right_square_size 0.045 \ + --preprocessing None \ + --ir_mode auto +``` + +With troubleshooting: + +```bash +python main.py \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --troubleshooting \ + --chessboard_size 8,7 --square_size 0.045 +``` + +--- + +## Legacy one-shot mode + +The old in-memory flow (single `--right_camera`, filename pairing) still works: + +```bash +# LC-RC +python main.py --legacy \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --left_camera lc --right_camera rc \ + --chessboard_size 8,7 --square_size 0.045 + +# LC-RG +python main.py --legacy \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --left_camera lc --right_camera rg \ + --chessboard_size 8,7 --square_size 0.045 + +# LC-IR +python main.py --legacy \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --left_camera lc --right_camera ir \ + --chessboard_size 8,7 --square_size 0.045 \ + --preprocessing C + +# LC-IR folder + IR partner (with debug output) +python main.py --legacy \ + --project Olsen_wings --date 2026-05-12 --calib_name calib1 \ + --left_camera lc-ir --right_camera ir \ + --chessboard_size 8,7 --square_size 0.045 \ + --preprocessing C --troubleshooting +``` + +--- + +## Dependencies + +```bash +pip install -r ~/Speckle-Scanner/02_Calibration/requirements.txt +# or full pipeline: +pip install -r ~/Speckle-Scanner/requirements.txt +``` + +--- + +## Notes + +- Stereo pairing uses **timestamps** parsed from filenames (`ts…` tokens or long numeric ids); `ck…` suffixes are ignored. +- **Ellipse-only** IR JSONs are stored but cannot produce mono intrinsics (need full chessboard grids). Use chessboard IR images for calibration. +- Per-camera board overrides apply to detection and calibration (`--left_chessboard_size`, etc.). +- Re-run **step 1** if images change; re-run **step 2** freely when tuning `time_window` or partners. +- With `--troubleshooting` off, step 2 writes **only** `params/` (no `pairing_reports/`, no `rectified/`). diff --git a/02_Calibration/calibrate.py b/02_Calibration/calibrate.py new file mode 100644 index 0000000..3006a05 --- /dev/null +++ b/02_Calibration/calibrate.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python3 +""" +Step 2 — Calibration from per-image JSON feature files. + + 2a. Mono intrinsics per camera folder + 2b. Stereo calibration: left camera vs each available partner (rc, rg, ir) + with time-window pairing (default 0.1 s) +""" + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path.home() / "Speckle-Scanner")) +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +import argparse + +from calibrationclasses.calibration_engine import ( + run_mono_calibration, + run_stereo_calibration, +) +from calibrationclasses.cli_common import ( + add_board_args, + add_session_args, + add_troubleshooting_arg, + build_board_config, + resolve_input_path, +) +from calibrationclasses.session import STEREO_PARTNERS + + +def main(): + parser = argparse.ArgumentParser( + description="Calibration step 2: mono + stereo calibration from JSON" + ) + add_session_args(parser) + add_board_args(parser) + parser.add_argument( + "--step", + choices=("mono", "stereo", "all"), + default="all", + help="Run mono intrinsics, stereo pairs, or both (default: all)", + ) + parser.add_argument( + "--left_camera", + default="lc", + choices=("lc", "lc-ir", "lc_ir"), + help="Left camera for stereo calibration (default: lc)", + ) + parser.add_argument( + "--time_window", + type=float, + default=0.1, + help="Max |t_left - t_right| in seconds for stereo pairing (default: 0.1)", + ) + parser.add_argument( + "--partners", + type=str, + default="rc,rg,ir", + help="Comma-separated right cameras for stereo (default: rc,rg,ir)", + ) + add_troubleshooting_arg(parser) + args = parser.parse_args() + + left_camera = args.left_camera.lower().replace("_", "-") + partners = tuple(p.strip() for p in args.partners.split(",") if p.strip()) + board_sizes, square_sizes = build_board_config(args) + input_path = resolve_input_path(args) + + print(f"[calibrate] session: {input_path}") + + mono_results = {} + if args.step in ("mono", "all"): + print("\n=== Step 2a: Mono intrinsics ===") + mono_results = run_mono_calibration( + input_path, + board_sizes, + square_sizes, + troubleshooting=args.troubleshooting, + ) + + if args.step in ("stereo", "all"): + print("\n=== Step 2b: Stereo calibration ===") + if not mono_results and args.step == "stereo": + mono_results = run_mono_calibration( + input_path, + board_sizes, + square_sizes, + troubleshooting=args.troubleshooting, + ) + run_stereo_calibration( + input_path, + left_camera=left_camera, + mono_results=mono_results, + board_sizes=board_sizes, + square_sizes=square_sizes, + time_window_sec=args.time_window, + partners=partners or STEREO_PARTNERS, + troubleshooting=args.troubleshooting, + ) + + print("[calibrate] done") + + +if __name__ == "__main__": + main() diff --git a/02_Calibration/calibrationclasses/__init__.py b/02_Calibration/calibrationclasses/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/02_Calibration/calibrationclasses/calibration.py b/02_Calibration/calibrationclasses/calibration.py new file mode 100644 index 0000000..30c4c50 --- /dev/null +++ b/02_Calibration/calibrationclasses/calibration.py @@ -0,0 +1,1572 @@ +from typing import List, Optional, Tuple +import re +import cv2 +import numpy as np +import os +import shutil +from tqdm import tqdm +from calibrationclasses.preprocessing import Preprocessing +from pathlib import Path + + +class StereoCalibration: + """Stereo calibration class. + + Parameters + ---------- + input_path : str + path to input data + chessboard_size : Tuple + default inner corner grid (width, height) for both cameras + square_size : float + default chessboard square size in meters for both cameras + chessboard_size_left, chessboard_size_right : Tuple, optional + per-camera inner corner grids (default: chessboard_size) + square_size_left, square_size_right : float, optional + per-camera square sizes in meters (default: square_size) + preprocessing: str + default = none, G = Gray, C = Clahe, T = Threshold + left_camera: str + Which folder to use as the left view: lc or lc-ir (folder may be lc-ir or lc_ir on disk). + right_camera: str + Which folder to use as the stereo 'right' view: rc, rg/rgb, or ir (IR folder may be named IR). + troubleshooting: bool + If True, save debug folders (corners, local_coords, images_ncb, rectified). + """ + + def __init__( + self, + input_path: str, + chessboard_size: Tuple, + square_size: float, + preprocessing: str, + calibration_mode: str = "checkerboard", + chessboard_size_left: Optional[Tuple] = None, + chessboard_size_right: Optional[Tuple] = None, + square_size_left: Optional[float] = None, + square_size_right: Optional[float] = None, + left_camera: str = "lc", + right_camera: str = "rc", + troubleshooting: bool = False, + ) -> None: + self.input_path = input_path + requested_left_camera = left_camera.lower().replace("_", "-") + if requested_left_camera not in ("lc", "lc-ir"): + raise ValueError( + f"left_camera must be one of lc, lc-ir; got {left_camera!r}" + ) + self.left_camera = requested_left_camera + self._left_folder_aliases = { + "lc": ["lc"], + "lc-ir": ["lc-ir", "lc_ir", "LC-IR"], + }[self.left_camera] + + images_dir = os.path.join(input_path, "images") + nested_left = any( + os.path.isdir(os.path.join(images_dir, folder)) + for folder in self._left_folder_aliases + ) + flat_left = any( + os.path.isdir(os.path.join(input_path, folder)) + for folder in self._left_folder_aliases + ) + if nested_left: + self.image_path = images_dir + elif flat_left: + self.image_path = input_path + else: + raise FileNotFoundError( + f"Could not find left camera folder for {self.left_camera!r}. " + f"Tried under {images_dir} (nested) and {input_path} (flat): " + f"{self._left_folder_aliases}" + ) + self.chessboard_size = chessboard_size + self.square_size = square_size + self.chessboard_size_left = chessboard_size_left or chessboard_size + self.chessboard_size_right = chessboard_size_right or chessboard_size + self.square_size_left = ( + square_size if square_size_left is None else square_size_left + ) + self.square_size_right = ( + square_size if square_size_right is None else square_size_right + ) + self.preprocessing = preprocessing + self.calibration_mode = calibration_mode + requested_right_camera = right_camera.lower() + if requested_right_camera not in ("rc", "rgb", "rg", "ir"): + raise ValueError( + f"right_camera must be one of rc, rgb, rg, ir; got {right_camera!r}" + ) + # Normalize rgb/rg to a single partner label for params naming. + if requested_right_camera in ("rgb", "rg"): + self.right_camera = "rg" + else: + self.right_camera = requested_right_camera + self._right_subdir = self.right_camera + self.troubleshooting = troubleshooting + self.corners_path = ( + os.path.join(input_path, "corners") if self.troubleshooting else None + ) + self.local_coords_path = ( + os.path.join(input_path, "local_coords") if self.troubleshooting else None + ) + self.ncb_path = ( + os.path.join(input_path, "images_ncb") if self.troubleshooting else None + ) + self._preprocessor = Preprocessing() + self._initialize() + self._init_paths() + + def _preprocessing_enabled(self) -> bool: + spec = (self.preprocessing or "").strip().lower() + return bool(spec) and spec not in ("none", "off", "false", "0") + + def _preprocess_for_detection(self, image: np.ndarray) -> np.ndarray: + """ + Apply optional preprocessing before chessboard corner detection. + + Chain letters in order (case-insensitive): G=grayscale, C=CLAHE, T=Otsu threshold + (T uses CLAHE internally per Preprocessing.threshold). Examples: C, GC, GCT. + """ + if image is None or not self._preprocessing_enabled(): + return image + spec = ( + (self.preprocessing or "") + .strip() + .lower() + .replace("none", "") + .replace(",", "") + .replace(" ", "") + ) + if not spec: + return image + out = image + pp = self._preprocessor + for ch in spec: + if ch == "g": + g = pp.gray(out) + out = cv2.cvtColor(g, cv2.COLOR_GRAY2BGR) + elif ch == "c": + c = pp.clahe(out) + out = cv2.cvtColor(c, cv2.COLOR_GRAY2BGR) + elif ch == "t": + t = pp.threshold(out) + out = cv2.cvtColor(t, cv2.COLOR_GRAY2BGR) + return out + + @staticmethod + def _to_gray_for_subpix(image: np.ndarray) -> np.ndarray: + if image is None: + return None + if len(image.shape) == 2: + return image + if image.shape[2] == 1: + return np.squeeze(image, axis=2) + return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + + @staticmethod + def _refresh_detection_progress_bar( + pbar, idx: int, total: int, count: int, label: str = "detected" + ) -> None: + """Live status on the tqdm bar (avoid print() inside loops).""" + fmt = getattr(pbar, "format_dict", None) or {} + rate = fmt.get("rate") + if rate is None or not isinstance(rate, (int, float)) or not np.isfinite(rate) or rate <= 0: + rate_str = "…" + else: + rate_str = f"{rate:.1f}" + pbar.set_description( + f"Processing image {idx}/{total} | {label}: {count} | {rate_str} it/s", + refresh=True, + ) + + def _left_filename_prefixes(self) -> Tuple[str, ...]: + if self.left_camera == "lc-ir": + return ("lc-ir", "lcir", "lc_ir", "lc") + return ("lc",) + + def _strip_left_filename_prefix(self, filename: str) -> str: + name = filename.lower() + for prefix in sorted(self._left_filename_prefixes(), key=len, reverse=True): + if name.startswith(prefix): + return name[len(prefix) :].lstrip("_-.") + return name + + @staticmethod + def _pair_key_after_prefix(name: str, prefix: str) -> str: + """Strip camera prefix and normalize so lc_123 matches ir_123 / rc_123.""" + return Path(name[len(prefix) :].lstrip("_-.")).stem + + def extract_key_lc(self, path: str): + """Filename key to pair the left camera with rc/rgb/ir.""" + name = Path(path).name.lower() + for prefix in sorted(self._left_filename_prefixes(), key=len, reverse=True): + if name.startswith(prefix): + return self._pair_key_after_prefix(name, prefix) + m = re.search(r"(scan\d{6})", name) + if m: + return m.group(1) + return None + + def extract_key_other(self, path: str): + name = Path(path).name.lower() + # IR_scan_000001.png → same scan id as scan000001_* in LC folder + m = re.match(r"^ir_scan_(\d+)", name) + if m: + return f"scan{int(m.group(1)):06d}" + for prefix in ("rc", "rgb", "rg", "ir"): + if name.startswith(prefix): + return self._pair_key_after_prefix(name, prefix) + return None + + def get_camera_type(path): + name = os.path.basename(path).lower() + if name.startswith("rc"): + return "rc" + elif name.startswith("rg"): + return "rg" + elif name.startswith("rgb"): + return "rgb" + elif name.startswith("ir"): + return "ir" + return "unknown" + + def _init_paths(self) -> None: + """Initialize paths for the selected left and right camera folders.""" + + left_dir = None + for folder in self._left_folder_aliases: + path = os.path.join(self.image_path, folder) + if os.path.isdir(path): + left_dir = path + break + if left_dir is None: + raise FileNotFoundError( + f"Left camera folder for {self.left_camera!r} not found under " + f"{self.image_path}. Tried: {self._left_folder_aliases}" + ) + self._left_image_path = left_dir + self._left_subdir = os.path.basename(left_dir) + + self._right_image_paths_all = [] + + right_folder_aliases = { + "rc": ["rc"], + "rg": ["rg", "rgb"], + "ir": ["ir", "IR"], + } + aliases = right_folder_aliases[self.right_camera] + right_dir = None + for folder in aliases: + path = os.path.join(self.image_path, folder) + if os.path.isdir(path): + right_dir = path + break + if right_dir is None: + raise FileNotFoundError( + f"Right camera folder for {self.right_camera!r} not found under " + f"{self.image_path}. Tried: {aliases}" + ) + # Use discovered folder name for writing corners/local_coords/rectified outputs. + self._right_subdir = os.path.basename(right_dir) + + names = [ + name + for name in os.listdir(right_dir) + if name.lower().endswith((".bmp", ".png", ".jpg")) + ] + names.sort() + self._right_image_paths_all = [os.path.join(right_dir, name) for name in names] + + # --- Load left camera images --- + self._left_image_names = [ + name for name in os.listdir(self._left_image_path) + if name.lower().endswith((".bmp", ".png", ".jpg")) + ] + self._left_image_names.sort() + + self._left_image_paths = [ + os.path.join(self._left_image_path, name) + for name in self._left_image_names + ] + + # --- Initialize containers --- + self._valid_left_image_paths = [] + self._valid_right_image_paths = [] + + self._left_image_paths_pairs = [] + self._right_image_paths_all_pairs = [] + + def _initialize(self) -> None: + self._chessboard_3d_left_points: List[np.ndarray] = [] + self._chessboard_2d_left_points: List[np.ndarray] = [] + self._chessboard_3d_right_points: List[np.ndarray] = [] + self._chessboard_2d_right_points: List[np.ndarray] = [] + + self._chessboard_3d_left_points_pairs: List[np.ndarray] = [] + self._chessboard_2d_left_points_pairs: List[np.ndarray] = [] + self._chessboard_3d_right_points_pairs: List[np.ndarray] = [] + self._chessboard_2d_right_points_pairs: List[np.ndarray] = [] + + self._stereo_map_left: Tuple[np.ndarray, np.ndarray] = [] + self._stereo_map_right: Tuple[np.ndarray, np.ndarray] = [] + + self._left_image_size: Tuple[int, int] = None + self._right_image_size: Tuple[int, int] = None + self._Parameters: dict = {} + + def create_3d_chessboard_points( + self, + chessboard_size: Optional[Tuple] = None, + square_size: Optional[float] = None, + ) -> np.ndarray: + """Create 3D chessboard points for a given chessboard size and square size. + + Returns + ------- + np.ndarray + Nx3 array of 3D points: N = number of squares in chessboard + """ + board = chessboard_size if chessboard_size is not None else self.chessboard_size_left + square = square_size if square_size is not None else self.square_size_left + + # create Nx3 array of 3D points: N = number of squares in chessboard + chessboard_points = np.zeros((np.prod(board), 3), np.float32) + # fill x, y coordinates with indices of chessboard points + chessboard_points[:, :2] = np.indices(board).T.reshape(-1, 2) + # scale coordinates by square size + chessboard_points *= square + return chessboard_points + + def create_2d_chessboard_points( + self, image: np.ndarray, chessboard_size: Optional[Tuple] = None + ) -> np.ndarray: + """Create 2D chessboard points for a given image and chessboard size. + + Parameters + ---------- + image : np.ndarray + image of chessboard + + Returns + ------- + np.ndarray + Nx1x2 array of 2D points: N = number of corners found in the image + """ + board = ( + chessboard_size + if chessboard_size is not None + else self.chessboard_size_left + ) + # termination criteria + criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) + # find chessboard corners + found, corners = cv2.findChessboardCorners(image, board, None) + if not found: + return None + # refine corner positions + corners = cv2.cornerSubPix( + self._to_gray_for_subpix(image), + corners, + (11, 11), + (-1, -1), + criteria, + ) + return corners + + def plot_chessboard( + self, + image: np.ndarray, + corners: np.ndarray, + chessboard_size: Optional[Tuple] = None, + ) -> np.ndarray: + """Plot chessboard corners on the image. + + Parameters + ---------- + image : np.ndarray + image of chessboard + corners : np.ndarray + Nx1x2 array of 2D points: N = number of corners found in the image + """ + board = ( + chessboard_size + if chessboard_size is not None + else self.chessboard_size_left + ) + image = cv2.drawChessboardCorners(image, board, corners, True) + return image + + def plot_local_coordinates( + self, + image: np.ndarray, + corners: np.ndarray, + chessboard_size: Optional[Tuple] = None, + ) -> np.ndarray: + """Plot local coordinates on the image. + + Parameters + ---------- + image : np.ndarray + image of chessboard + corners : np.ndarray + Nx1x2 array of 2D points: N = number of corners found in the image + """ + board = ( + chessboard_size + if chessboard_size is not None + else self.chessboard_size_left + ) + corners = np.int32(corners) + cv2.arrowedLine(image, tuple(corners[0,0]), tuple(corners[4,0]), (255,0,0), 3, tipLength=0.05) + cv2.arrowedLine(image, tuple(corners[0,0]), tuple(corners[board[0]*2,0]), (255,0,0), 3, tipLength=0.05) + cv2.circle(image, tuple(corners[0,0]), 8, (0,255,0), 3) + cv2.putText(image, '0,0', (corners[0,0,0]-35, corners[0,0,1]-15), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255,255,255), 2, cv2.LINE_AA) + cv2.putText(image, 'X', (corners[4,0,0]-25, corners[4,0,1]-15), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255,255,255), 2, cv2.LINE_AA) + cv2.putText(image, 'Y', (corners[board[0]*2,0,0]-25, corners[board[0]*2,0,1]-15), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255,255,255), 2, cv2.LINE_AA) + return image + + def create_chessboard_points_left(self) -> None: + """Create 3D and 2D chessboard points for left images.""" + + last_loaded_shape = None + paths = self._left_image_paths + n_total = len(paths) + detected = 0 + with tqdm(paths, unit="img", dynamic_ncols=True, smoothing=0.05) as pbar: + for idx, left_image_path in enumerate(pbar, start=1): + left_image = cv2.imread(left_image_path) + + left_name = left_image_path.split("/")[-1] + + if left_image is None: + tqdm.write(f"Warning: Failed to load image {left_name}. Skipping.") + self._refresh_detection_progress_bar(pbar, idx, n_total, detected) + continue + last_loaded_shape = left_image.shape + + proc_image = self._preprocess_for_detection(left_image) + left_corners = self.create_2d_chessboard_points( + proc_image, self.chessboard_size_left + ) + + if left_corners is not None: + detected += 1 + self._chessboard_3d_left_points.append( + self.create_3d_chessboard_points( + self.chessboard_size_left, self.square_size_left + ) + ) + self._chessboard_2d_left_points.append(left_corners) + self._valid_left_image_paths.append(left_image_path) + if self.corners_path is not None: + left_image = self.plot_chessboard( + left_image, + left_corners, + self.chessboard_size_left, + ) + left_image_name = os.path.basename(left_image_path) + left_image_path = os.path.join( + self.corners_path, self._left_subdir, left_image_name + ) + os.makedirs(os.path.dirname(left_image_path), exist_ok=True) + cv2.imwrite(left_image_path, left_image) + if self.local_coords_path is not None: + left_image = self.plot_local_coordinates( + left_image, left_corners, self.chessboard_size_left + ) + left_image_name = os.path.basename(left_image_path) + left_image_path = os.path.join( + self.local_coords_path, self._left_subdir, left_image_name + ) + os.makedirs(os.path.dirname(left_image_path), exist_ok=True) + cv2.imwrite(left_image_path, left_image) + + self._refresh_detection_progress_bar(pbar, idx, n_total, detected) + + if last_loaded_shape is not None: + self._left_image_size = (last_loaded_shape[1], last_loaded_shape[0]) + elif self._left_image_paths: + probe = cv2.imread(self._left_image_paths[0]) + self._left_image_size = ( + (probe.shape[1], probe.shape[0]) if probe is not None else (0, 0) + ) + else: + self._left_image_size = (0, 0) + print( + f"Number of images with detected chessboards for left ({self.left_camera}): ", + len(self._chessboard_2d_left_points), + ) + + def create_chessboard_points_right(self) -> None: + """Create 3D and 2D chessboard points for right images.""" + + paths = self._right_image_paths_all + n_total = len(paths) + detected = 0 + with tqdm(paths, unit="img", dynamic_ncols=True, smoothing=0.05) as pbar: + for idx, right_image_path in enumerate(pbar, start=1): + right_image = cv2.imread(right_image_path) + + right_name = right_image_path.split("/")[-1] + if right_image is None: + tqdm.write(f"Warning: Failed to load image {right_name}. Skipping.") + self._refresh_detection_progress_bar(pbar, idx, n_total, detected) + continue + + proc_image = self._preprocess_for_detection(right_image) + right_corners = self.create_2d_chessboard_points( + proc_image, self.chessboard_size_right + ) + + if right_corners is not None: + detected += 1 + self._chessboard_3d_right_points.append( + self.create_3d_chessboard_points( + self.chessboard_size_right, self.square_size_right + ) + ) + self._chessboard_2d_right_points.append(right_corners) + self._valid_right_image_paths.append(right_image_path) + if self.corners_path is not None: + right_image = self.plot_chessboard( + right_image, + right_corners, + self.chessboard_size_right, + ) + right_image_name = os.path.basename(right_image_path) + right_image_path = os.path.join( + self.corners_path, self._right_subdir, right_image_name + ) + os.makedirs(os.path.dirname(right_image_path), exist_ok=True) + cv2.imwrite(right_image_path, right_image) + if self.local_coords_path is not None: + right_image = self.plot_local_coordinates( + right_image, + right_corners, + self.chessboard_size_right, + ) + right_image_name = os.path.basename(right_image_path) + right_image_path = os.path.join( + self.local_coords_path, self._right_subdir, right_image_name + ) + os.makedirs(os.path.dirname(right_image_path), exist_ok=True) + cv2.imwrite(right_image_path, right_image) + + self._refresh_detection_progress_bar(pbar, idx, n_total, detected) + + if self._valid_right_image_paths: + probe = cv2.imread(self._valid_right_image_paths[-1]) + self._right_image_size = ( + (probe.shape[1], probe.shape[0]) if probe is not None else (0, 0) + ) + elif self._right_image_paths_all: + probe = cv2.imread(self._right_image_paths_all[0]) + self._right_image_size = ( + (probe.shape[1], probe.shape[0]) if probe is not None else (0, 0) + ) + else: + self._right_image_size = (0, 0) + + print( + f"Number of images with detected chessboards for right ({self.right_camera}): ", + len(self._chessboard_2d_right_points), + ) + + def detect_ellipse_center(self, image: np.ndarray) -> Tuple[float, float]: + """Detect ellipse center in infrared image. + + Parameters + ---------- + image : np.ndarray + infrared image + + Returns + ------- + Tuple[float, float] + (cx, cy) center coordinates of detected ellipse, or None if not found + """ + gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + + # --- Step 1: CLAHE for contrast enhancement --- + clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) + enhanced = clahe.apply(gray) + + # --- Step 2: Smooth to remove small noise --- + blurred = cv2.GaussianBlur(enhanced, (5, 5), 0) + + # --- Step 3: Binarization (Adaptive or Otsu) --- + _, binary = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) + # If the circle is dark instead of bright, invert + white_ratio = np.sum(binary == 255) / binary.size + if white_ratio > 0.5: + binary = cv2.bitwise_not(binary) + + # --- Step 4: Morphological close to fill small holes --- + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 9)) + closed = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel) + + # --- Step 5: Contour detection --- + contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + if not contours: + return None + + # --- Step 6: Filter for circle-like contours --- + valid_contours = [] + for cnt in contours: + area = cv2.contourArea(cnt) + if area < 100: # Skip tiny noise + continue + x, y, w, h = cv2.boundingRect(cnt) + aspect_ratio = w / h + if 0.75 < aspect_ratio < 1.25: # roughly circular + valid_contours.append(cnt) + + if not valid_contours: + return None + + # --- Step 7: Choose largest circular contour --- + best_contour = max(valid_contours, key=cv2.contourArea) + + # --- Step 8: Fit ellipse --- + if len(best_contour) < 5: + return None + + ellipse = cv2.fitEllipse(best_contour) + (cx, cy), (major, minor), angle = ellipse + + return (cx, cy), ellipse + + def plot_ellipse(self, image: np.ndarray, ellipse: Tuple) -> np.ndarray: + """Plot ellipse on the image. + + Parameters + ---------- + image : np.ndarray + image + ellipse : Tuple + ellipse parameters from cv2.fitEllipse + """ + (cx, cy), (major, minor), angle = ellipse + result = image.copy() + cv2.ellipse(result, ellipse, (0, 255, 0), 2) + cv2.circle(result, (int(cx), int(cy)), 5, (0, 0, 255), -1) + cv2.putText(result, f"({int(cx)}, {int(cy)})", (int(cx)+10, int(cy)-10), + cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2) + return result + + def create_ellipse_points_left(self) -> None: + """Create calibration points for left infrared images using ellipse detection.""" + + last_loaded_shape = None + paths = self._left_image_paths + n_total = len(paths) + detected = 0 + with tqdm(paths, unit="img", dynamic_ncols=True, smoothing=0.05) as pbar: + for idx, left_image_path in enumerate(pbar, start=1): + left_image = cv2.imread(left_image_path) + + left_name = left_image_path.split("/")[-1] + + if left_image is None: + tqdm.write(f"Warning: Failed to load image {left_name}. Skipping.") + self._refresh_detection_progress_bar(pbar, idx, n_total, detected) + continue + last_loaded_shape = left_image.shape + + ellipse_result = self.detect_ellipse_center(left_image) + + if ellipse_result is not None: + detected += 1 + (cx, cy), ellipse = ellipse_result + ellipse_center = np.array([[[cx, cy]]], dtype=np.float32) + + chessboard_3d = np.array([[0.0, 0.0, 0.0]], dtype=np.float32) + + self._chessboard_3d_left_points.append(chessboard_3d) + self._chessboard_2d_left_points.append(ellipse_center) + self._valid_left_image_paths.append(left_image_path) + + if self.corners_path is not None: + left_image = self.plot_ellipse(left_image, ellipse) + left_image_name = os.path.basename(left_image_path) + left_image_path_out = os.path.join( + self.corners_path, self._left_subdir, left_image_name + ) + os.makedirs(os.path.dirname(left_image_path_out), exist_ok=True) + cv2.imwrite(left_image_path_out, left_image) + + self._refresh_detection_progress_bar(pbar, idx, n_total, detected) + + if last_loaded_shape is not None: + self._left_image_size = (last_loaded_shape[1], last_loaded_shape[0]) + elif self._left_image_paths: + probe = cv2.imread(self._left_image_paths[0]) + self._left_image_size = ( + (probe.shape[1], probe.shape[0]) if probe is not None else (0, 0) + ) + else: + self._left_image_size = (0, 0) + print("Number of images with detected ellipses for LC: ", len(self._chessboard_2d_left_points)) + + def create_ellipse_points_right(self) -> None: + """Create calibration points for right infrared images using ellipse detection.""" + + last_loaded_shape = None + paths = self._right_image_paths_all + n_total = len(paths) + detected = 0 + with tqdm(paths, unit="img", dynamic_ncols=True, smoothing=0.05) as pbar: + for idx, right_image_path in enumerate(pbar, start=1): + right_image = cv2.imread(right_image_path) + + right_name = right_image_path.split("/")[-1] + if right_image is None: + tqdm.write(f"Warning: Failed to load image {right_name}. Skipping.") + self._refresh_detection_progress_bar(pbar, idx, n_total, detected) + continue + last_loaded_shape = right_image.shape + + ellipse_result = self.detect_ellipse_center(right_image) + + if ellipse_result is not None: + detected += 1 + (cx, cy), ellipse = ellipse_result + ellipse_center = np.array([[[cx, cy]]], dtype=np.float32) + + chessboard_3d = np.array([[0.0, 0.0, 0.0]], dtype=np.float32) + + self._chessboard_3d_right_points.append(chessboard_3d) + self._chessboard_2d_right_points.append(ellipse_center) + self._valid_right_image_paths.append(right_image_path) + + if self.corners_path is not None: + right_image = self.plot_ellipse(right_image, ellipse) + right_image_name = os.path.basename(right_image_path) + right_image_path_out = os.path.join( + self.corners_path, self._right_subdir, right_image_name + ) + os.makedirs(os.path.dirname(right_image_path_out), exist_ok=True) + cv2.imwrite(right_image_path_out, right_image) + + self._refresh_detection_progress_bar(pbar, idx, n_total, detected) + + if last_loaded_shape is not None: + self._right_image_size = (last_loaded_shape[1], last_loaded_shape[0]) + elif self._right_image_paths_all: + probe = cv2.imread(self._right_image_paths_all[0]) + self._right_image_size = ( + (probe.shape[1], probe.shape[0]) if probe is not None else (0, 0) + ) + else: + self._right_image_size = (0, 0) + + print( + f"Number of images with detected ellipses for right ({self.right_camera}): ", + len(self._chessboard_2d_right_points), + ) + + def _dedupe_left_one_per_scan_for_ir(self) -> None: + """With IR_scan_NNN (one IR per scan), keep one LC frame per scan ID.""" + if self.right_camera != "ir": + return + packed = list( + zip( + self._valid_left_image_paths, + self._chessboard_2d_left_points, + self._chessboard_3d_left_points, + ) + ) + packed.sort(key=lambda x: x[0]) + seen = set() + kept = [] + for path, p2d, p3d in packed: + key = self.extract_key_lc(path) + if key is None: + continue + if key in seen: + continue + seen.add(key) + kept.append((path, p2d, p3d)) + if not kept: + return + self._valid_left_image_paths = [x[0] for x in kept] + self._chessboard_2d_left_points = [x[1] for x in kept] + self._chessboard_3d_left_points = [x[2] for x in kept] + + def build_pairs_cal(self) -> None: + """ + Build pairs of LC ↔ (RC | RGB | IR) images based on filenames, + and align their corresponding chessboard points. + """ + self._dedupe_left_one_per_scan_for_ir() + warned_stereo_board_model = False + # --- Build lookup for RIGHT images --- + right_lookup = {} + for j, path in enumerate(self._valid_right_image_paths): + key = self.extract_key_other(path) # use @staticmethod via self + if key is not None: + right_lookup[key] = (path, j) + + # --- Match LC against RIGHT --- + for i, left_path in enumerate(self._valid_left_image_paths): + key = self.extract_key_lc(left_path) # use @staticmethod via self + if key is None: + continue + + if key in right_lookup: + right_path, j = right_lookup[key] + + left_corners = self._chessboard_2d_left_points[i] + right_corners = self._chessboard_2d_right_points[j] + if left_corners.shape != right_corners.shape: + tqdm.write( + f"Skipping pair {key}: corner count mismatch " + f"({self.left_camera} {left_corners.shape[0]} vs " + f"{self.right_camera} {right_corners.shape[0]}). " + "Use the same board geometry for stereo pairs." + ) + continue + if not warned_stereo_board_model and ( + self.chessboard_size_left != self.chessboard_size_right + or self.square_size_left != self.square_size_right + ): + tqdm.write( + "Per-camera board sizes differ; stereo object points use left: " + f"{self.chessboard_size_left} @ {self.square_size_left}m." + ) + warned_stereo_board_model = True + + # --- Append image paths --- + self._left_image_paths_pairs.append(left_path) + self._right_image_paths_all_pairs.append(right_path) + + # --- Append chessboard 2D points --- + self._chessboard_2d_left_points_pairs.append(left_corners) + self._chessboard_2d_right_points_pairs.append(right_corners) + + # --- Append chessboard 3D points (stereo requires one object model) --- + pts3d = self.create_3d_chessboard_points( + self.chessboard_size_left, self.square_size_left + ) + self._chessboard_3d_left_points_pairs.append(pts3d) + self._chessboard_3d_right_points_pairs.append(pts3d) + + print(f"Found {len(self._left_image_paths_pairs)} common image pairs with valid chessboard corners.") + + + + + def build_pairs_rec(self) -> None: + def strip_prefix(filename): + # Assuming the prefix is always the first two characters (like "lc" or "rc") + return filename[2:] # Remove the first two characters + + + valid_left_images = set([strip_prefix(path.split('/')[-1]) for path in self._left_image_paths]) + valid_right_images = set([strip_prefix(path.split('/')[-1]) for path in self._right_image_paths_all]) + common_images = valid_left_images.intersection(valid_right_images) + # Filter for common image pairs and corresponding points + for i, left_path in enumerate(self._left_image_paths): + left_name = strip_prefix(left_path.split('/')[-1]) + if left_name in common_images: + # Find the corresponding right image and its index + for j, right_path in enumerate(self._right_image_paths_all): + right_name = strip_prefix(right_path.split('/')[-1]) + if left_name == right_name: + # Append the common left and right paths and points + self._left_image_paths_pairs.append(left_path) + self._right_image_paths_all_pairs.append(right_path) + break + + # Print or return the number of common image pairs and points + print(f"Found {len(self._left_image_paths_pairs)} common image pairs.") + + left_image_paths = set([path for path in self._left_image_paths]) + right_image_paths = set(self._valid_right_image_paths) + left_image_paths_pairs = set([path for path in self._left_image_paths_pairs]) + right_image_paths_pairs = set([path for path in self._right_image_paths_all_pairs]) + unmatched_left_paths = left_image_paths - left_image_paths_pairs + unmatched_right_paths = right_image_paths - right_image_paths_pairs + for path in unmatched_left_paths: + if self.ncb_path is not None: + left_image_path = os.path.join( + self.ncb_path, self._left_subdir, os.path.basename(path) + ) + os.makedirs(os.path.dirname(left_image_path), exist_ok=True) + shutil.copy(path, left_image_path) + + + + for path in unmatched_right_paths: + if self.ncb_path is not None: + right_image_path = os.path.join( + self.ncb_path, self._right_subdir, os.path.basename(path) + ) + os.makedirs(os.path.dirname(right_image_path), exist_ok=True) + shutil.copy(path, right_image_path) + + def create_chessboard_points_pairs(self) -> Tuple[np.ndarray, np.ndarray]: + """Create 3D and 2D chessboard points for left and right images.""" + + # Match left/right images by filename after stripping camera prefixes + left_dict = { + self._strip_left_filename_prefix(os.path.basename(p)): p + for p in self._left_image_paths + } + right_dict = {os.path.basename(p).replace("rc", ""): p for p in self._right_image_paths_all} + # Find common keys by matching filenames without prefixes + common_keys = set(left_dict.keys()) & set(right_dict.keys()) + keys_sorted = sorted(common_keys, key=str) + n_total = len(keys_sorted) + detected = 0 + last_left_shape = None + last_right_shape = None + + with tqdm(keys_sorted, unit="pair", dynamic_ncols=True, smoothing=0.05) as pbar: + for idx, key in enumerate(pbar, start=1): + left_name = left_dict[key] + right_name = right_dict[key] + + left_image = cv2.imread(left_name) + right_image = cv2.imread(right_name) + + if left_image is None or right_image is None: + tqdm.write( + f"Warning: Failed to load images {left_name} or {right_name}. Skipping this pair." + ) + self._refresh_detection_progress_bar(pbar, idx, n_total, detected) + continue + + last_left_shape = left_image.shape + last_right_shape = right_image.shape + + left_proc = self._preprocess_for_detection(left_image) + right_proc = self._preprocess_for_detection(right_image) + left_corners = self.create_2d_chessboard_points( + left_proc, self.chessboard_size_left + ) + right_corners = self.create_2d_chessboard_points( + right_proc, self.chessboard_size_right + ) + + if left_corners is not None and right_corners is not None: + if left_corners.shape != right_corners.shape: + tqdm.write( + f"Skipping pair {key}: corner count mismatch " + f"({self.left_camera} {left_corners.shape[0]} vs " + f"{self.right_camera} {right_corners.shape[0]})." + ) + self._refresh_detection_progress_bar( + pbar, idx, n_total, detected + ) + continue + detected += 1 + second_elements_array1 = left_corners[:, :, 1] + second_elements_array2 = right_corners[:, :, 1] + + difference = second_elements_array1 - second_elements_array2 + + updated_second_elements_array2 = second_elements_array2 + difference + + right_corners2 = np.copy(right_corners) + + right_corners2[:, :, 1] = updated_second_elements_array2 + + pts3d = self.create_3d_chessboard_points( + self.chessboard_size_left, self.square_size_left + ) + self._chessboard_3d_left_points_pairs.append(pts3d) + self._chessboard_2d_left_points_pairs.append(left_corners) + self._chessboard_3d_right_points_pairs.append(pts3d) + self._chessboard_2d_right_points_pairs.append(right_corners) + if self.corners_path is not None: + left_image = self.plot_chessboard( + left_image, left_corners, self.chessboard_size_left + ) + + right_image = self.plot_chessboard( + right_image, right_corners, self.chessboard_size_right + ) + right_image = self.plot_chessboard( + right_image, right_corners2, self.chessboard_size_right + ) + + right_image_name = os.path.basename(right_name) + right_image_path = os.path.join( + self.corners_path, self._right_subdir, right_image_name + ) + os.makedirs(os.path.dirname(right_image_path), exist_ok=True) + cv2.imwrite(right_image_path, right_image) + + left_image_name = os.path.basename(left_name) + left_image_path = os.path.join( + self.corners_path, self._left_subdir, left_image_name + ) + os.makedirs(os.path.dirname(left_image_path), exist_ok=True) + cv2.imwrite(left_image_path, left_image) + else: + if self.ncb_path is not None: + left_image_path = os.path.join( + self.ncb_path, self._left_subdir, os.path.basename(left_name) + ) + right_image_path = os.path.join( + self.ncb_path, self._right_subdir, os.path.basename(right_name) + ) + os.makedirs(os.path.dirname(left_image_path), exist_ok=True) + os.makedirs(os.path.dirname(right_image_path), exist_ok=True) + tqdm.write( + f"Chessboard not detected in both {left_name} and {right_name}. " + f"Moving to {os.path.dirname(left_image_path)} and {os.path.dirname(right_image_path)}." + ) + shutil.copy(left_name, left_image_path) + shutil.copy(right_name, right_image_path) + + self._refresh_detection_progress_bar(pbar, idx, n_total, detected) + + if last_left_shape is not None and last_right_shape is not None: + self._left_image_size = (last_left_shape[1], last_left_shape[0]) + self._right_image_size = (last_right_shape[1], last_right_shape[0]) + else: + self._left_image_size = (0, 0) + self._right_image_size = (0, 0) + unmatched_left = set(left_dict.keys()) - common_keys + unmatched_right = set(right_dict.keys()) - common_keys + + for key in unmatched_left: + if self.ncb_path is not None: + left_image_path = os.path.join( + self.ncb_path, self._left_subdir, os.path.basename(left_dict[key]) + ) + os.makedirs(os.path.dirname(left_image_path), exist_ok=True) + shutil.copy(left_dict[key], left_image_path) + + + + for key in unmatched_right: + if self.ncb_path is not None: + right_image_path = os.path.join( + self.ncb_path, self._right_subdir, os.path.basename(right_dict[key]) + ) + os.makedirs(os.path.dirname(right_image_path), exist_ok=True) + shutil.copy(right_dict[key], right_image_path) + + self._left_image_names = os.listdir(self._left_image_path) + self._right_image_names = os.listdir(self._right_image_path) + self._left_image_names = [ + name for name in self._left_image_names if name.endswith((".bmp", '.png', '.jpg')) + ] + self._right_image_names = [ + name for name in self._right_image_names if name.endswith((".bmp", '.png', '.jpg')) + ] + self._left_image_names.sort() + self._right_image_names.sort() + self._left_image_paths_pairs = [ + os.path.join(self._left_image_path, name) for name in self._left_image_names + ] + self._right_image_paths_all_pairs = [ + os.path.join(self._right_image_path, name) + for name in self._right_image_names + ] + + print("Number of image pairs: ", len(self._chessboard_2d_left_points_pairs)) + return self._chessboard_2d_left_points_pairs, self._chessboard_2d_right_points_pairs + + def calibrate(self) -> dict: + """Calibrate stereo camera. + + The calibration process consists of the following steps: + 1. Calibrate left camera + 2. Calibrate right camera + 3. Stereo calibration + + + Returns + ------- + dict_keys(['Transformation', 'Essential', 'Fundamental', 'MeanError', 'SquareSize', 'BoardSize', 'Objpoints', 'L_Intrinsic', 'L_Distortion', 'L_DistortionROI', 'L_DistortionIntrinsic', 'L_RotVektor', 'L_RotMatrix', 'L_Extrinsics', 'L_TransVektor', 'L_Errors', 'L_MeanError', 'R_Intrinsic', 'R_Distortion', 'R_DistortionROI', 'R_DistortionIntrinsic', 'R_RotVektor', 'R_RotMatrix', 'R_Extrinsics', 'R_TransVektor', 'R_Errors', 'R_MeanError', 'L_Imgpoints', 'R_Imgpoints']) + """ + if len(self._left_image_paths_pairs) == 0: + raise RuntimeError( + "No stereo pairs with detected chessboard corners. " + "Check --chessboard_size (or per-camera sizes), preprocessing, and that the board is visible in both cameras. " + f"For {self.left_camera}+IR, left files like scan000001_* pair with " + "IR_scan_000001.png by scan id; only one left frame per scan is used " + "when multiple sequences exist." + ) + def CalibrateCamera(image_size, chessboard_2d_points, chessboard_3d_points): + CameraParams = {} + flags = 0 + (ret, mtx, dist, rvecs, tvecs) = cv2.calibrateCamera(chessboard_3d_points, chessboard_2d_points, image_size, None, None, flags=flags) + + Rmtx = []; Tmtx = []; k = 0 + for r in rvecs: + Rmtx.append(cv2.Rodrigues(r)[0]) + Tmtx.append(np.vstack((np.hstack((Rmtx[k],tvecs[k])),np.array([0,0,0,1])))) + k += 1 + + newmtx, roi = cv2.getOptimalNewCameraMatrix(mtx,dist,image_size,1,image_size) + + if np.sum(roi) == 0: + roi = (0,0,image_size[0]-1,image_size[1]-1) + + CameraParams['Intrinsic'] = mtx + CameraParams['Distortion'] = dist + CameraParams['DistortionROI'] = roi + CameraParams['DistortionIntrinsic'] = newmtx + CameraParams['RotVektor'] = rvecs + CameraParams['RotMatrix'] = Rmtx + CameraParams['Extrinsics'] = Tmtx + CameraParams['TransVektor'] = tvecs + + return CameraParams + + def CalculateErrors( + params, chessboard_2d_points, chessboard_size, square_size + ): + imgp = np.array(chessboard_2d_points) + imgp = imgp.reshape((imgp.shape[0], imgp.shape[1], imgp.shape[3])) + objp = np.array( + self.create_3d_chessboard_points(chessboard_size, square_size) + ) + K = np.array(params['Intrinsic']) + D = np.array(params['Distortion']) + R = np.array(params['RotVektor']) + T = np.array(params['TransVektor']) + N = imgp.shape[0] + + imgpNew = [] + for i in range(N): + temp, _ = cv2.projectPoints(objp, R[i], T[i], K, D) + imgpNew.append(temp.reshape((temp.shape[0], temp.shape[2]))) + imgpNew = np.array(imgpNew) + + err = [] + for i in range(N): + err.append(imgp[i] - imgpNew[i]) + err = np.array(err) + + def RMSE(err): + return np.sqrt(np.mean(np.sum(err**2, axis=1))) + + errall = np.copy(err[0]) + rmsePerView = [RMSE(err[0])] + for i in range(1,N): + errall = np.vstack((errall, err[i])) + rmsePerView.append(RMSE(err[i])) + + rmseAll = RMSE(errall) + return rmsePerView, rmseAll + + # left camera calibration + print("Calibrating left camera...") + if self._chessboard_2d_left_points == [] : + Left_Params = CalibrateCamera(self._left_image_size, self._chessboard_2d_left_points_pairs, self._chessboard_3d_left_points_pairs) + Left_Errors, Left_MeanError = CalculateErrors( + Left_Params, + self._chessboard_2d_left_points_pairs, + self.chessboard_size_left, + self.square_size_left, + ) + Left_Params['Imgpoints'] = self._chessboard_2d_left_points_pairs + else : + Left_Params = CalibrateCamera(self._left_image_size, self._chessboard_2d_left_points, self._chessboard_3d_left_points) + Left_Errors, Left_MeanError = CalculateErrors( + Left_Params, + self._chessboard_2d_left_points, + self.chessboard_size_left, + self.square_size_left, + ) + Left_Params['Imgpoints'] = self._chessboard_2d_left_points + np.set_printoptions(suppress=True, precision=5) + print('Intrinsic Matrix:') + print(Left_Params['Intrinsic']) + print('\nDistortion Parameters:') + print(Left_Params['Distortion']) + print('\nExtrinsic Matrix from 1.Image:') + print(Left_Params['Extrinsics'][0]) + print('Reprojection Error Left: {:.4f}'.format(Left_MeanError)) + + + # right camera calibration + print("Calibrating right camera...") + if self._chessboard_2d_right_points == [] : + Right_Params = CalibrateCamera(self._right_image_size, self._chessboard_2d_right_points_pairs, self._chessboard_3d_right_points_pairs) + Right_Errors, Right_MeanError = CalculateErrors( + Right_Params, + self._chessboard_2d_right_points_pairs, + self.chessboard_size_right, + self.square_size_right, + ) + Right_Params['Imgpoints'] = self._chessboard_2d_right_points_pairs + else : + Right_Params = CalibrateCamera(self._right_image_size, self._chessboard_2d_right_points, self._chessboard_3d_right_points) + Right_Errors, Right_MeanError = CalculateErrors( + Right_Params, + self._chessboard_2d_right_points, + self.chessboard_size_right, + self.square_size_right, + ) + Right_Params['Imgpoints'] = self._chessboard_2d_right_points + np.set_printoptions(suppress=True, precision=5) + print('Intrinsic Matrix:') + print(Right_Params['Intrinsic']) + print('\nDistortion Parameters:') + print(Right_Params['Distortion']) + print('\nExtrinsic Matrix from 1.Image:') + print(Right_Params['Extrinsics'][0]) + print('Reprojection Error Right: {:.4f}'.format(Right_MeanError)) + + + Left_Params['Errors'] = Left_Errors + Left_Params['MeanError'] = Left_MeanError + + Right_Params['Errors'] = Right_Errors + Right_Params['MeanError'] = Right_MeanError + + + # --- stereo calibration --- + print("Calibrating stereo camera...") + + flags = cv2.CALIB_FIX_INTRINSIC + criteria = (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS, 30, 0.001) + + ( + ret_stereo, + newcameramtx_left, + dist_left, + newcameramtx_right, + dist_right, + rot, + trans, + essential, + fundamental, + ) = cv2.stereoCalibrate( + self._chessboard_3d_left_points_pairs, + self._chessboard_2d_left_points_pairs, + self._chessboard_2d_right_points_pairs, + Left_Params['Intrinsic'], + Left_Params['Distortion'], + Right_Params['Intrinsic'], + Right_Params['Distortion'], + self._left_image_size, + criteria=criteria, + flags=flags, + ) + + T = np.vstack((np.hstack((rot, trans)), np.array([0, 0, 0, 1]))) + + print("\n--- Stereo Calibration Results ---") + print("Translation vector:\n", trans) + print("Rotation matrix:\n", rot) + print("Mean reprojection error:", ret_stereo) + + # --- Compute Q --- + print("\nComputing stereo rectification + Q matrix...") + + R1, R2, P1, P2, Q, roi1, roi2 = cv2.stereoRectify( + Left_Params['Intrinsic'], + Left_Params['Distortion'], + Right_Params['Intrinsic'], + Right_Params['Distortion'], + self._left_image_size, + rot, + trans, + flags=0, + alpha=1 + ) + + # ensure clean numpy array + self._Q = np.array(Q, dtype=np.float64) + + print("\n--- Q MATRIX ---") + print(self._Q) + print("Q shape:", self._Q.shape) + print("Q dtype:", self._Q.dtype) + + # --- sanity checks --- + print("\n--- Q sanity check ---") + print("Q[2,3] (focal length term):", self._Q[2, 3]) + print("Q[3,2] (baseline term):", self._Q[3, 2]) + + if self._Q.shape != (4, 4): + raise RuntimeError("[ERROR] Q is not 4x4!") + + if self._Q[3, 2] == 0: + print("[WARNING] Baseline is ZERO → depth will fail!") + + # --- store stereo params --- + Parameters = { + 'Translation': trans, + 'Rotation': rot, + 'Transformation': T, + 'Essential': essential, + 'Fundamental': fundamental, + 'MeanError': ret_stereo, + 'SquareSize': self.square_size_left, + 'BoardSize': self.chessboard_size_left, + 'L_SquareSize': self.square_size_left, + 'R_SquareSize': self.square_size_right, + 'L_BoardSize': self.chessboard_size_left, + 'R_BoardSize': self.chessboard_size_right, + 'Objpoints': self.create_3d_chessboard_points( + self.chessboard_size_left, self.square_size_left + ), + 'Q': self._Q # ✅ store Q in parameters (for NPZ) + } + + # add left params + for key, value in Left_Params.items(): + Parameters[f"L_{key}"] = value + + # add right params + for key, value in Right_Params.items(): + Parameters[f"R_{key}"] = value + + self._Parameters = Parameters + + return Parameters + + + def save_stereo_calibration(self) -> None: + print("[DEBUG] save_stereo_calibration() CALLED") + """Save stereo calibration to pair-specific NPZ/YAML/Q (including Q).""" + print("\nSaving stereo calibration...") + if not hasattr(self, "_Q") or self._Q is None: + raise RuntimeError("[ERROR] Q missing! Run calibration first.") + # ensure Q is clean + Q_clean = np.array(self._Q, dtype=np.float64) + self._Parameters['Q'] = Q_clean + + pair_tag = f"{self.left_camera}-{self.right_camera}" + + # --- Save pair-specific NPZ --- + npz_path = os.path.join(self.input_path, "params", f"{pair_tag}_parameters.npz") + os.makedirs(os.path.dirname(npz_path), exist_ok=True) + np.savez(npz_path, **self._Parameters) + print(f"[INFO] Saved NPZ → {npz_path}") + + # --- Save pair-specific YAML --- + yaml_path = os.path.join(self.input_path, "params", f"{pair_tag}_stereo_cam_model.yaml") + fs = cv2.FileStorage(yaml_path, cv2.FILE_STORAGE_WRITE) + fs.write("L_DistortionIntrinsic", self._Parameters['L_DistortionIntrinsic']) + fs.write("L_Intrinsic", self._Parameters['L_Intrinsic']) + fs.write("L_Distortion", self._Parameters['L_Distortion']) + fs.write("R_DistortionIntrinsic", self._Parameters['R_DistortionIntrinsic']) + fs.write("R_Intrinsic", self._Parameters['R_Intrinsic']) + fs.write("R_Distortion", self._Parameters['R_Distortion']) + fs.write("Rotation", self._Parameters['Transformation'][:3, :3]) + fs.write("Translation", self._Parameters['Transformation'][:3, 3:]) + # --- Save Q (FIXED) --- + fs.write("Q", Q_clean) + print("[INFO] Q written to YAML") + fs.release() + print(f"[INFO] Saved YAML → {yaml_path}") + + # --- Save pair-specific Q-matrix for use in disparity --- + cvstore_path = os.path.join(os.path.dirname(npz_path), f"{pair_tag}_Q.cvstore") + fs2 = cv2.FileStorage(cvstore_path, cv2.FILE_STORAGE_WRITE) + fs2.write("Q", Q_clean) + fs2.release() + print(f"[INFO] Saved Q matrix separately → {cvstore_path}") + + + def read_stereo_calibration_yaml(self, yaml_path: str) -> None: + """Read stereo calibration from YAML file safely.""" + + print("\nReading stereo calibration YAML...") + + fs = cv2.FileStorage(yaml_path, cv2.FILE_STORAGE_READ) + + self._Parameters = {} + + for key in fs.root().keys(): + node = fs.getNode(key) + + if node.empty(): + print(f"[WARNING] Empty node: {key}") + continue + + mat = node.mat() + + if mat is None: + print(f"[WARNING] Could not read: {key}") + continue + + self._Parameters[key] = mat + print(f"[INFO] Loaded: {key}, shape={mat.shape}") + + fs.release() + + # --- Check Q --- + if "Q" in self._Parameters: + Q = self._Parameters["Q"] + + print("\n--- Loaded Q MATRIX ---") + print(Q) + print("Shape:", Q.shape) + print("dtype:", Q.dtype) + + print("\n--- Q sanity check ---") + print("Q[2,3]:", Q[2, 3]) + print("Q[3,2]:", Q[3, 2]) + + if Q.shape != (4, 4): + print("[ERROR] Q is not 4x4!") + + if Q[3, 2] == 0: + print("[ERROR] Baseline is zero → INVALID Q") + + else: + print("[ERROR] Q NOT FOUND in YAML!") + + def read_stereo_calibration_npz(self, npz_path: str) -> None: + """Read stereo calibration from npz file. + + Parameters + ---------- + npz_path : str + path to npz file + + """ + + print("Reading stereo calibration...") + self._Parameters = dict(np.load(npz_path)) + print(self._Parameters.keys()) + + def rectify_images( + self, left_image: np.ndarray, right_image: np.ndarray, left_image_size, right_image_size + ) -> Tuple[np.ndarray, np.ndarray]: + """Rectify left and right images using stereo mapping. + + Parameters + ---------- + left_image : np.ndarray + left image + right_image : np.ndarray + right image + + Returns + ------- + Tuple[np.ndarray, np.ndarray] + left_rectified, right_rectified + """ + + # stereo rectification + print("Rectifying stereo camera...") + rect_left, rect_right, proj_left, proj_right, Q, roi_left, roi_right = ( + cv2.stereoRectify( + self._Parameters['L_Intrinsic'], + self._Parameters['L_Distortion'], + self._Parameters['R_Intrinsic'], + self._Parameters['R_Distortion'], + left_image_size, + self._Parameters['Rotation'], + self._Parameters['Translation'], + flags=0, + alpha=1 + ) + ) + self._Q = Q + print ("roi_left: ", roi_left) + print ("roi_right: ", roi_right) + + # stereo mapping + print("Mapping left and right cameras...") + stereo_map_left = cv2.initUndistortRectifyMap( + self._Parameters['L_Intrinsic'], + self._Parameters['L_Distortion'], + rect_left, + proj_left, + left_image_size, + cv2.CV_32FC1, + ) + stereo_map_right = cv2.initUndistortRectifyMap( + self._Parameters['R_Intrinsic'], + self._Parameters['R_Distortion'], + rect_right, + proj_right, + right_image_size, + cv2.CV_32FC1, + ) + + + print("Calibration complete!") + self._stereo_map_left = stereo_map_left + self._stereo_map_right = stereo_map_right + self._roi_left = roi_left + self._roi_right = roi_right + + # rectify images + left_rectified = cv2.remap( + left_image, + self._stereo_map_left[0], + self._stereo_map_left[1], + cv2.INTER_LINEAR + ) + right_rectified = cv2.remap( + right_image, + self._stereo_map_right[0], + self._stereo_map_right[1], + cv2.INTER_LINEAR + ) + + return left_rectified, right_rectified + + def rectify_calibration_images(self) -> None: + """Rectify calibration images and save them to disk. + + The rectified images are stored in separate directories as follows: + - input_path + - rectified + - lc + - lc_0000.bmp + - lc_0001.bmp + - ... + - rc + - rc_0000.bmp + - rc_0001.bmp + - ... + """ + if not self.troubleshooting: + print("Troubleshooting is disabled; skipping rectified debug image export.") + return + print("Rectifying images...") + pairs = list( + zip(self._left_image_paths_pairs, self._right_image_paths_all_pairs) + ) + n_total = len(pairs) + saved = 0 + with tqdm(pairs, unit="pair", dynamic_ncols=True, smoothing=0.05) as pbar: + for idx, (left_image_path, right_image_path) in enumerate(pbar, start=1): + left_image = cv2.imread(left_image_path) + right_image = cv2.imread(right_image_path) + if left_image is None or right_image is None: + tqdm.write( + f"Warning: Failed to load rectification pair " + f"{os.path.basename(left_image_path)} / {os.path.basename(right_image_path)}." + ) + self._refresh_detection_progress_bar( + pbar, idx, n_total, saved, label="saved" + ) + continue + left_image_size = (left_image.shape[1], left_image.shape[0]) + right_image_size = (right_image.shape[1], right_image.shape[0]) + left_rectified, right_rectified = self.rectify_images( + left_image, right_image, left_image_size, right_image_size + ) + left_image_name = os.path.basename(left_image_path) + right_image_name = os.path.basename(right_image_path) + left_image_path = os.path.join( + self.input_path, "rectified", self._left_subdir, left_image_name + ) + right_image_path = os.path.join( + self.input_path, "rectified", self._right_subdir, right_image_name + ) + os.makedirs(os.path.dirname(left_image_path), exist_ok=True) + os.makedirs(os.path.dirname(right_image_path), exist_ok=True) + cv2.imwrite(left_image_path, left_rectified) + cv2.imwrite(right_image_path, right_rectified) + saved += 1 + self._refresh_detection_progress_bar( + pbar, idx, n_total, saved, label="saved" + ) \ No newline at end of file diff --git a/02_Calibration/calibrationclasses/calibration_engine.py b/02_Calibration/calibrationclasses/calibration_engine.py new file mode 100644 index 0000000..ce2022c --- /dev/null +++ b/02_Calibration/calibrationclasses/calibration_engine.py @@ -0,0 +1,424 @@ +"""Step 2: mono and stereo calibration from per-image JSON feature files.""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import cv2 +import numpy as np + +from calibrationclasses.feature_json import FeatureRecord, load_folder_features +from calibrationclasses.pairing import StereoPair, build_stereo_pairs +from calibrationclasses.session import ( + CameraFolder, + discover_camera_folder, + resolve_session_root, + STEREO_PARTNERS, +) + + +def create_3d_board_points(board_size: Tuple[int, int], square_size: float) -> np.ndarray: + pts = np.zeros((np.prod(board_size), 3), np.float32) + pts[:, :2] = np.indices(board_size).T.reshape(-1, 2) + pts *= square_size + return pts + + +def _image_size_from_records(records: List[FeatureRecord]) -> Tuple[int, int]: + for record in records: + img = cv2.imread(str(record.image_path)) + if img is not None: + return img.shape[1], img.shape[0] + raise RuntimeError("Could not determine image size from feature JSONs") + + +def calibrate_camera_intrinsics( + records: List[FeatureRecord], + board_size: Tuple[int, int], + square_size: float, +) -> Dict: + chess_records = [r for r in records if r.is_chessboard] + if len(chess_records) < 3: + raise RuntimeError( + f"Need at least 3 chessboard detections for mono calibration, got {len(chess_records)}" + ) + + image_size = _image_size_from_records(chess_records) + objp = create_3d_board_points(board_size, square_size) + obj_points = [objp for _ in chess_records] + img_points = [r.corners for r in chess_records] + + ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera( + obj_points, img_points, image_size, None, None, flags=0 + ) + + rmtx = [] + tmtx = [] + for k, r in enumerate(rvecs): + rmtx.append(cv2.Rodrigues(r)[0]) + tmtx.append(np.vstack((np.hstack((rmtx[k], tvecs[k])), np.array([0, 0, 0, 1])))) + + newmtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, image_size, 1, image_size) + if np.sum(roi) == 0: + roi = (0, 0, image_size[0] - 1, image_size[1] - 1) + + return { + "Intrinsic": mtx, + "Distortion": dist, + "DistortionROI": roi, + "DistortionIntrinsic": newmtx, + "RotVektor": rvecs, + "RotMatrix": rmtx, + "Extrinsics": tmtx, + "TransVektor": tvecs, + "MeanError": float(ret), + "image_size": image_size, + "num_views": len(chess_records), + } + + +def save_mono_intrinsics( + params_dir: Path, + camera_name: str, + intrinsics: Dict, + *, + troubleshooting: bool = False, +) -> None: + params_dir.mkdir(parents=True, exist_ok=True) + tag = camera_name.replace("/", "-") + npz_path = params_dir / f"{tag}_intrinsics.npz" + np.savez( + npz_path, + Intrinsic=intrinsics["Intrinsic"], + Distortion=intrinsics["Distortion"], + DistortionIntrinsic=intrinsics["DistortionIntrinsic"], + DistortionROI=intrinsics["DistortionROI"], + MeanError=intrinsics["MeanError"], + image_size=np.array(intrinsics["image_size"]), + num_views=intrinsics["num_views"], + ) + yaml_path = params_dir / f"{tag}_intrinsics.yaml" + fs = cv2.FileStorage(str(yaml_path), cv2.FILE_STORAGE_WRITE) + fs.write("Intrinsic", intrinsics["Intrinsic"]) + fs.write("Distortion", intrinsics["Distortion"]) + fs.write("DistortionIntrinsic", intrinsics["DistortionIntrinsic"]) + fs.release() + if troubleshooting: + print(f"[INFO] Saved mono intrinsics → {npz_path} and {yaml_path}") + + +def run_mono_calibration( + input_path: str | Path, + board_sizes: Dict[str, Tuple[int, int]], + square_sizes: Dict[str, float], + cameras: Optional[List[str]] = None, + troubleshooting: bool = False, +) -> Dict[str, Dict]: + session_root = resolve_session_root(input_path) + params_dir = Path(input_path) / "params" + results = {} + + for logical_name, board_size in board_sizes.items(): + if cameras and logical_name not in cameras: + continue + cam = discover_camera_folder(session_root, logical_name) + if cam is None: + continue + records = load_folder_features(cam.path) + square_size = square_sizes[logical_name] + try: + intrinsics = calibrate_camera_intrinsics(records, board_size, square_size) + save_mono_intrinsics( + params_dir, logical_name, intrinsics, troubleshooting=troubleshooting + ) + results[logical_name] = intrinsics + print( + f"[mono:{logical_name}] views={intrinsics['num_views']} " + f"reproj_err={intrinsics['MeanError']:.4f}" + ) + except RuntimeError as exc: + if troubleshooting: + print(f"[SKIP mono:{logical_name}] {exc}") + else: + print(f"[mono:{logical_name}] skipped") + + return results + + +def calibrate_stereo_pair( + pairs: List[StereoPair], + left_intrinsics: Dict, + right_intrinsics: Dict, + board_size: Tuple[int, int], + square_size: float, + image_size: Tuple[int, int], +) -> Dict: + if not pairs: + raise RuntimeError("No stereo pairs available") + + objp = create_3d_board_points(board_size, square_size) + obj_points = [objp for _ in pairs] + left_img_points = [p.left.corners for p in pairs] + right_img_points = [p.right.corners for p in pairs] + + flags = cv2.CALIB_FIX_INTRINSIC + criteria = (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS, 30, 0.001) + + ret_stereo, _, _, _, _, rot, trans, essential, fundamental = cv2.stereoCalibrate( + obj_points, + left_img_points, + right_img_points, + left_intrinsics["Intrinsic"], + left_intrinsics["Distortion"], + right_intrinsics["Intrinsic"], + right_intrinsics["Distortion"], + image_size, + criteria=criteria, + flags=flags, + ) + + R1, R2, P1, P2, Q, roi1, roi2 = cv2.stereoRectify( + left_intrinsics["Intrinsic"], + left_intrinsics["Distortion"], + right_intrinsics["Intrinsic"], + right_intrinsics["Distortion"], + image_size, + rot, + trans, + flags=0, + alpha=1, + ) + + T = np.vstack((np.hstack((rot, trans)), np.array([0, 0, 0, 1]))) + Q_clean = np.array(Q, dtype=np.float64) + + parameters = { + "Translation": trans, + "Rotation": rot, + "Transformation": T, + "Essential": essential, + "Fundamental": fundamental, + "MeanError": float(ret_stereo), + "SquareSize": square_size, + "BoardSize": board_size, + "Objpoints": objp, + "Q": Q_clean, + "num_pairs": len(pairs), + "L_Intrinsic": left_intrinsics["Intrinsic"], + "L_Distortion": left_intrinsics["Distortion"], + "L_DistortionIntrinsic": left_intrinsics["DistortionIntrinsic"], + "R_Intrinsic": right_intrinsics["Intrinsic"], + "R_Distortion": right_intrinsics["Distortion"], + "R_DistortionIntrinsic": right_intrinsics["DistortionIntrinsic"], + "L_Imgpoints": left_img_points, + "R_Imgpoints": right_img_points, + "R1": R1, + "R2": R2, + "P1": P1, + "P2": P2, + "image_size": image_size, + } + return parameters + + +def save_stereo_calibration( + input_path: str | Path, + pair_tag: str, + parameters: Dict, + *, + troubleshooting: bool = False, +) -> None: + params_dir = Path(input_path) / "params" + params_dir.mkdir(parents=True, exist_ok=True) + Q_clean = np.array(parameters["Q"], dtype=np.float64) + + npz_path = params_dir / f"{pair_tag}_parameters.npz" + save_kwargs = {k: v for k, v in parameters.items() if k not in ("R1", "R2", "P1", "P2")} + np.savez(npz_path, **save_kwargs) + if troubleshooting: + print(f"[INFO] Saved NPZ → {npz_path}") + + yaml_path = params_dir / f"{pair_tag}_stereo_cam_model.yaml" + fs = cv2.FileStorage(str(yaml_path), cv2.FILE_STORAGE_WRITE) + fs.write("L_DistortionIntrinsic", parameters["L_DistortionIntrinsic"]) + fs.write("L_Intrinsic", parameters["L_Intrinsic"]) + fs.write("L_Distortion", parameters["L_Distortion"]) + fs.write("R_DistortionIntrinsic", parameters["R_DistortionIntrinsic"]) + fs.write("R_Intrinsic", parameters["R_Intrinsic"]) + fs.write("R_Distortion", parameters["R_Distortion"]) + fs.write("Rotation", parameters["Transformation"][:3, :3]) + fs.write("Translation", parameters["Transformation"][:3, 3:]) + fs.write("Q", Q_clean) + fs.release() + if troubleshooting: + print(f"[INFO] Saved YAML → {yaml_path}") + + cvstore_path = params_dir / f"{pair_tag}_Q.cvstore" + fs2 = cv2.FileStorage(str(cvstore_path), cv2.FILE_STORAGE_WRITE) + fs2.write("Q", Q_clean) + fs2.release() + if troubleshooting: + print(f"[INFO] Saved Q → {cvstore_path}") + + +def save_pairing_report( + input_path: str | Path, + pair_tag: str, + pairs: List[StereoPair], +) -> Path: + report_dir = Path(input_path) / "pairing_reports" + report_dir.mkdir(parents=True, exist_ok=True) + report_path = report_dir / f"{pair_tag}.txt" + lines = [ + f"# stereo pairs for {pair_tag}", + f"# total={len(pairs)}", + "left_image\tright_image\tdelta_sec\tmethod", + ] + for pair in pairs: + lines.append( + f"{pair.left.image_path.name}\t{pair.right.image_path.name}\t" + f"{pair.delta_sec:.6f}\t{pair.method}" + ) + report_path.write_text("\n".join(lines) + "\n", encoding="utf-8") + print(f"[INFO] Pairing report → {report_path}") + return report_path + + +def save_rectified_pairs( + input_path: str | Path, + pair_tag: str, + pairs: List[StereoPair], + parameters: Dict, + left_folder: str, + right_folder: str, +) -> None: + image_size = parameters["image_size"] + R1, R2, P1, P2 = parameters["R1"], parameters["R2"], parameters["P1"], parameters["P2"] + map_left = cv2.initUndistortRectifyMap( + parameters["L_Intrinsic"], + parameters["L_Distortion"], + R1, + P1, + image_size, + cv2.CV_32FC1, + ) + map_right = cv2.initUndistortRectifyMap( + parameters["R_Intrinsic"], + parameters["R_Distortion"], + R2, + P2, + image_size, + cv2.CV_32FC1, + ) + + out_left = Path(input_path) / "rectified" / pair_tag / left_folder + out_right = Path(input_path) / "rectified" / pair_tag / right_folder + out_left.mkdir(parents=True, exist_ok=True) + out_right.mkdir(parents=True, exist_ok=True) + + saved = 0 + for pair in pairs: + left_img = cv2.imread(str(pair.left.image_path)) + right_img = cv2.imread(str(pair.right.image_path)) + if left_img is None or right_img is None: + continue + left_rect = cv2.remap(left_img, map_left[0], map_left[1], cv2.INTER_LINEAR) + right_rect = cv2.remap(right_img, map_right[0], map_right[1], cv2.INTER_LINEAR) + cv2.imwrite(str(out_left / pair.left.image_path.name), left_rect) + cv2.imwrite(str(out_right / pair.right.image_path.name), right_rect) + saved += 1 + print(f"[INFO] Rectified {saved}/{len(pairs)} pairs → {out_left.parent}") + + +def run_stereo_calibration( + input_path: str | Path, + left_camera: str, + mono_results: Dict[str, Dict], + board_sizes: Dict[str, Tuple[int, int]], + square_sizes: Dict[str, float], + time_window_sec: float = 0.1, + partners: Tuple[str, ...] = STEREO_PARTNERS, + troubleshooting: bool = False, +) -> None: + session_root = resolve_session_root(input_path) + left_cam = discover_camera_folder(session_root, left_camera) + if left_cam is None: + raise FileNotFoundError(f"Left camera folder {left_camera!r} not found") + + if left_camera not in mono_results: + raise RuntimeError( + f"No mono intrinsics for {left_camera}. Run mono calibration first." + ) + + left_records = load_folder_features(left_cam.path) + left_board = board_sizes[left_camera] + left_square = square_sizes[left_camera] + image_size = mono_results[left_camera]["image_size"] + + for partner in partners: + right_cam = discover_camera_folder(session_root, partner) + if right_cam is None: + if troubleshooting: + print(f"[SKIP stereo:{left_camera}-{partner}] folder not found") + continue + if partner not in mono_results: + if troubleshooting: + print( + f"[SKIP stereo:{left_camera}-{partner}] " + f"no mono intrinsics for {partner}" + ) + continue + + right_records = load_folder_features(right_cam.path) + pairs = build_stereo_pairs(left_records, right_records, time_window_sec) + pair_tag = f"{left_camera}-{partner}" + + if not pairs: + if troubleshooting: + print( + f"[SKIP stereo:{pair_tag}] no valid pairs " + f"(time_window={time_window_sec}s)" + ) + continue + + time_n = sum(1 for p in pairs if p.method == "time_window") + key_n = sum(1 for p in pairs if p.method == "pair_key") + if troubleshooting: + print( + f"[stereo:{pair_tag}] {len(pairs)} pairs " + f"(time_window={time_n}, pair_key={key_n})" + ) + save_pairing_report(input_path, pair_tag, pairs) + + try: + params = calibrate_stereo_pair( + pairs, + mono_results[left_camera], + mono_results[partner], + left_board, + left_square, + image_size, + ) + save_stereo_calibration( + input_path, pair_tag, params, troubleshooting=troubleshooting + ) + print( + f"[stereo:{pair_tag}] pairs={params['num_pairs']} " + f"reproj_err={params['MeanError']:.4f}" + ) + if troubleshooting: + save_rectified_pairs( + input_path, + pair_tag, + pairs, + params, + left_cam.folder_name, + right_cam.folder_name, + ) + except RuntimeError as exc: + if troubleshooting: + print(f"[FAIL stereo:{pair_tag}] {exc}") + else: + print(f"[stereo:{pair_tag}] failed") diff --git a/02_Calibration/calibrationclasses/camera_pose_visualizer.py b/02_Calibration/calibrationclasses/camera_pose_visualizer.py new file mode 100644 index 0000000..3569dec --- /dev/null +++ b/02_Calibration/calibrationclasses/camera_pose_visualizer.py @@ -0,0 +1,50 @@ +import numpy as np +import matplotlib as mpl +import matplotlib.pyplot as plt +from matplotlib.patches import Patch +from mpl_toolkits.mplot3d.art3d import Poly3DCollection + +class CameraPoseVisualizer: + def __init__(self, xlim, ylim, zlim): + self.fig = plt.figure(figsize=(18, 7)) + self.ax = self.fig.add_subplot(projection='3d') + self.ax.set_aspect("auto") + self.ax.set_xlim(xlim) + self.ax.set_ylim(ylim) + self.ax.set_zlim(zlim) + self.ax.set_xlabel('x') + self.ax.set_ylabel('y') + self.ax.set_zlabel('z') + print('initialize camera pose visualizer') + + def extrinsic2pyramid(self, extrinsic, color='r', focal_len_scaled=5, aspect_ratio=0.3): + vertex_std = np.array([[0, 0, 0, 1], + [focal_len_scaled * aspect_ratio, -focal_len_scaled * aspect_ratio, focal_len_scaled, 1], + [focal_len_scaled * aspect_ratio, focal_len_scaled * aspect_ratio, focal_len_scaled, 1], + [-focal_len_scaled * aspect_ratio, focal_len_scaled * aspect_ratio, focal_len_scaled, 1], + [-focal_len_scaled * aspect_ratio, -focal_len_scaled * aspect_ratio, focal_len_scaled, 1]]) + vertex_transformed = vertex_std @ extrinsic.T + meshes = [[vertex_transformed[0, :-1], vertex_transformed[1][:-1], vertex_transformed[2, :-1]], + [vertex_transformed[0, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1]], + [vertex_transformed[0, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]], + [vertex_transformed[0, :-1], vertex_transformed[4, :-1], vertex_transformed[1, :-1]], + [vertex_transformed[1, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]]] + self.ax.add_collection3d( + Poly3DCollection(meshes, facecolors=color, linewidths=0.3, edgecolors=color, alpha=0.35)) + + def customize_legend(self, list_label): + list_handle = [] + for idx, label in enumerate(list_label): + color = plt.cm.rainbow(idx / len(list_label)) + patch = Patch(color=color, label=label) + list_handle.append(patch) + plt.legend(loc='right', bbox_to_anchor=(1.8, 0.5), handles=list_handle) + + def colorbar(self, max_frame_length): + cmap = mpl.cm.rainbow + norm = mpl.colors.Normalize(vmin=0, vmax=max_frame_length) + self.fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), orientation='vertical', label='Frame Number') + + def show(self): + plt.title('Extrinsic Parameters') + plt.show() \ No newline at end of file diff --git a/02_Calibration/calibrationclasses/cli_common.py b/02_Calibration/calibrationclasses/cli_common.py new file mode 100644 index 0000000..ec001e3 --- /dev/null +++ b/02_Calibration/calibrationclasses/cli_common.py @@ -0,0 +1,93 @@ +"""Shared CLI helpers for calibration scripts.""" + +from __future__ import annotations + +import argparse +from pathlib import Path +from typing import Dict, Optional, Tuple + +import config + + +def parse_chessboard_size(value: str) -> Tuple[int, int]: + parts = value.split(",") + if len(parts) != 2: + raise argparse.ArgumentTypeError( + "chessboard size must be width,height (e.g. 8,7)" + ) + return tuple(map(int, parts)) + + +def add_session_args(parser: argparse.ArgumentParser) -> None: + parser.add_argument("--project", required=True, help="Project name (e.g. Olsen_wings)") + parser.add_argument("--date", required=True, help="Date string (e.g. 2026-05-12)") + parser.add_argument( + "--calib_name", default="calib1", help="Calibration folder name (default: calib1)" + ) + + +def add_board_args(parser: argparse.ArgumentParser) -> None: + parser.add_argument( + "--chessboard_size", + type=parse_chessboard_size, + default="8,7", + help="Default inner corner grid width,height", + ) + parser.add_argument( + "--square_size", + type=float, + default=0.045, + help="Default chessboard square size in metres", + ) + parser.add_argument("--left_chessboard_size", type=parse_chessboard_size, default=None) + parser.add_argument("--right_chessboard_size", type=parse_chessboard_size, default=None) + parser.add_argument("--left_square_size", type=float, default=None) + parser.add_argument("--right_square_size", type=float, default=None) + parser.add_argument( + "--preprocessing", + type=str, + default="None", + help="Pre-detection chain: G=gray, C=CLAHE, T=threshold (e.g. C, GC)", + ) + + +def add_troubleshooting_arg(parser: argparse.ArgumentParser) -> None: + parser.add_argument( + "--troubleshooting", + action="store_true", + help=( + "Verbose logs and intermediate debug files (corners/, pairing_reports/, " + "rectified/). Default: minimal logs; step 2 writes only params/" + ), + ) + + +def resolve_input_path(args) -> Path: + return config.CALIB_DATA_DIR / args.project / args.date / args.calib_name + + +def build_board_config(args) -> Tuple[Dict[str, Tuple[int, int]], Dict[str, float]]: + default_board = args.chessboard_size + default_square = args.square_size + left_board = args.left_chessboard_size or default_board + right_board = args.right_chessboard_size or default_board + left_square = args.left_square_size if args.left_square_size is not None else default_square + right_square = ( + args.right_square_size if args.right_square_size is not None else default_square + ) + + board_sizes = { + "lc": left_board, + "lc-ir": left_board, + "rc": right_board, + "rg": right_board, + "ir": right_board, + } + square_sizes = { + "lc": left_square, + "lc-ir": left_square, + "rc": right_square, + "rg": right_square, + "ir": right_square, + } + return board_sizes, square_sizes diff --git a/02_Calibration/calibrationclasses/feature_detection.py b/02_Calibration/calibrationclasses/feature_detection.py new file mode 100644 index 0000000..db493da --- /dev/null +++ b/02_Calibration/calibrationclasses/feature_detection.py @@ -0,0 +1,280 @@ +"""Step 1: detect chessboard corners / ellipse centers and write per-image JSON.""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import Optional, Tuple + +import cv2 +import numpy as np +from tqdm import tqdm + +from calibrationclasses.feature_json import FeatureRecord, save_feature_json +from calibrationclasses.preprocessing import Preprocessing +from calibrationclasses.session import ( + CameraFolder, + json_path_for_image, + list_cameras_present, + list_image_paths, + resolve_session_root, +) +from calibrationclasses.timestamp import parse_pair_key, parse_timestamp_sec + + +@dataclass +class DetectionConfig: + chessboard_size: Tuple[int, int] = (8, 7) + square_size: float = 0.045 + preprocessing: str = "None" + ir_mode: str = "auto" # auto | chessboard | ellipse + troubleshooting: bool = False + + +class FeatureDetector: + def __init__(self, config: DetectionConfig, corners_root: Optional[Path] = None): + self.config = config + self._preprocessor = Preprocessing() + self.corners_root = corners_root + + def _preprocessing_enabled(self) -> bool: + spec = (self.config.preprocessing or "").strip().lower() + return bool(spec) and spec not in ("none", "off", "false", "0") + + def _preprocess(self, image: np.ndarray) -> np.ndarray: + if image is None or not self._preprocessing_enabled(): + return image + spec = ( + (self.config.preprocessing or "") + .strip() + .lower() + .replace("none", "") + .replace(",", "") + .replace(" ", "") + ) + out = image + pp = self._preprocessor + for ch in spec: + if ch == "g": + g = pp.gray(out) + out = cv2.cvtColor(g, cv2.COLOR_GRAY2BGR) + elif ch == "c": + c = pp.clahe(out) + out = cv2.cvtColor(c, cv2.COLOR_GRAY2BGR) + elif ch == "t": + t = pp.threshold(out) + out = cv2.cvtColor(t, cv2.COLOR_GRAY2BGR) + return out + + @staticmethod + def _to_gray(image: np.ndarray) -> np.ndarray: + if len(image.shape) == 2: + return image + return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + + def detect_chessboard( + self, image: np.ndarray, board_size: Tuple[int, int] + ) -> Optional[np.ndarray]: + criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) + found, corners = cv2.findChessboardCorners(image, board_size, None) + if not found: + return None + corners = cv2.cornerSubPix( + self._to_gray(image), corners, (11, 11), (-1, -1), criteria + ) + return corners + + def detect_ellipse(self, image: np.ndarray): + gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) + enhanced = clahe.apply(gray) + blurred = cv2.GaussianBlur(enhanced, (5, 5), 0) + _, binary = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) + if np.sum(binary == 255) / binary.size > 0.5: + binary = cv2.bitwise_not(binary) + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 9)) + closed = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel) + contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + if not contours: + return None + valid = [] + for cnt in contours: + area = cv2.contourArea(cnt) + if area < 100: + continue + x, y, w, h = cv2.boundingRect(cnt) + if 0.75 < (w / h) < 1.25: + valid.append(cnt) + if not valid: + return None + best = max(valid, key=cv2.contourArea) + if len(best) < 5: + return None + ellipse = cv2.fitEllipse(best) + (cx, cy), (major, minor), angle = ellipse + return (cx, cy), { + "center": [float(cx), float(cy)], + "axes": [float(major), float(minor)], + "angle": float(angle), + } + + def _save_corner_overlay( + self, + image: np.ndarray, + record: FeatureRecord, + board_size: Tuple[int, int], + ) -> None: + if self.corners_root is None: + return + out_dir = self.corners_root / record.camera_folder + out_dir.mkdir(parents=True, exist_ok=True) + vis = image.copy() + if record.feature_type == "chessboard" and record.corners is not None: + vis = cv2.drawChessboardCorners(vis, board_size, record.corners, True) + elif record.feature_type == "ellipse" and record.center is not None: + cx, cy = record.center + cv2.circle(vis, (int(cx), int(cy)), 12, (0, 255, 0), 2) + out_path = out_dir / record.image_path.name + cv2.imwrite(str(out_path), vis) + + def process_image( + self, + image_path: Path, + camera: CameraFolder, + board_size: Optional[Tuple[int, int]] = None, + square_size: Optional[float] = None, + ) -> FeatureRecord: + board_size = board_size or self.config.chessboard_size + square_size = square_size if square_size is not None else self.config.square_size + json_path = json_path_for_image(image_path) + + base = FeatureRecord( + image_path=image_path, + json_path=json_path, + camera_folder=camera.folder_name, + feature_type="unknown", + success=False, + preprocessing=self.config.preprocessing, + timestamp_sec=parse_timestamp_sec(image_path.name), + pair_key=parse_pair_key(image_path.name), + ) + + image = cv2.imread(str(image_path)) + if image is None: + base.error = "failed to load image" + save_feature_json(base) + return base + + proc = self._preprocess(image) + use_ellipse = camera.logical_name == "ir" and self.config.ir_mode in ( + "ellipse", + "auto", + ) + + if not use_ellipse or self.config.ir_mode == "auto": + corners = self.detect_chessboard(proc, board_size) + if corners is not None: + record = FeatureRecord( + image_path=image_path, + json_path=json_path, + camera_folder=camera.folder_name, + feature_type="chessboard", + success=True, + board_size=board_size, + square_size=square_size, + corners=corners, + preprocessing=self.config.preprocessing, + timestamp_sec=base.timestamp_sec, + pair_key=base.pair_key, + ) + save_feature_json(record) + if self.config.troubleshooting: + self._save_corner_overlay(image, record, board_size) + return record + + if use_ellipse: + result = self.detect_ellipse(image) + if result is not None: + (cx, cy), ellipse = result + record = FeatureRecord( + image_path=image_path, + json_path=json_path, + camera_folder=camera.folder_name, + feature_type="ellipse", + success=True, + center=(cx, cy), + ellipse=ellipse, + preprocessing=self.config.preprocessing, + timestamp_sec=base.timestamp_sec, + pair_key=base.pair_key, + ) + save_feature_json(record) + if self.config.troubleshooting: + self._save_corner_overlay(image, record, board_size) + return record + + base.feature_type = "chessboard" if not use_ellipse else "ellipse" + base.error = "no features detected" + save_feature_json(base) + if self.config.troubleshooting: + print(f"[detect] FAIL {image_path.name}: {base.error}") + return base + + def process_camera( + self, + camera: CameraFolder, + board_size: Optional[Tuple[int, int]] = None, + square_size: Optional[float] = None, + ) -> Tuple[int, int]: + images = list_image_paths(camera.path) + if not images: + print(f"[WARN] No images in {camera.path}") + return 0, 0 + + detected = 0 + iterator = ( + tqdm(images, unit="img", dynamic_ncols=True) + if self.config.troubleshooting + else images + ) + for image_path in iterator: + record = self.process_image( + image_path, camera, board_size=board_size, square_size=square_size + ) + if record.success: + detected += 1 + if self.config.troubleshooting and hasattr(iterator, "set_description"): + iterator.set_description( + f"{camera.logical_name} | detected {detected}/{len(images)}" + ) + print(f"[{camera.logical_name}] {detected}/{len(images)} features detected") + return detected, len(images) + + +def run_detection( + input_path: str | Path, + config: DetectionConfig, + cameras: Optional[list[str]] = None, + per_camera_board: Optional[dict] = None, +) -> None: + session_root = resolve_session_root(input_path) + present = list_cameras_present(session_root) + if cameras: + wanted = set(cameras) + present = [c for c in present if c.logical_name in wanted] + + if not present: + raise FileNotFoundError(f"No camera folders found under {session_root}") + + corners_root = None + if config.troubleshooting: + corners_root = Path(input_path) / "corners" + print(f"[detect] troubleshooting: corner overlays → {corners_root}") + + detector = FeatureDetector(config, corners_root=corners_root) + per_camera_board = per_camera_board or {} + + for camera in present: + board = per_camera_board.get(camera.logical_name, {}).get("board_size") + square = per_camera_board.get(camera.logical_name, {}).get("square_size") + detector.process_camera(camera, board_size=board, square_size=square) diff --git a/02_Calibration/calibrationclasses/feature_json.py b/02_Calibration/calibrationclasses/feature_json.py new file mode 100644 index 0000000..ed4f382 --- /dev/null +++ b/02_Calibration/calibrationclasses/feature_json.py @@ -0,0 +1,136 @@ +"""JSON schema for per-image feature detection results.""" + +from __future__ import annotations + +import json +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np + +FEATURE_JSON_VERSION = 1 + + +@dataclass +class FeatureRecord: + image_path: Path + json_path: Path + camera_folder: str + feature_type: str + success: bool + board_size: Optional[Tuple[int, int]] = None + square_size: Optional[float] = None + corners: Optional[np.ndarray] = None # Nx1x2 float32 + center: Optional[Tuple[float, float]] = None + ellipse: Optional[Dict[str, Any]] = None + timestamp_sec: Optional[float] = None + pair_key: Optional[str] = None + preprocessing: Optional[str] = None + error: Optional[str] = None + + @property + def is_chessboard(self) -> bool: + return self.success and self.feature_type == "chessboard" and self.corners is not None + + @property + def corner_count(self) -> int: + if self.corners is None: + return 0 + return int(self.corners.shape[0]) + + +def corners_to_list(corners: np.ndarray) -> List[List[float]]: + flat = corners.reshape(-1, 2) + return [[float(x), float(y)] for x, y in flat] + + +def corners_from_list(data: List[List[float]]) -> np.ndarray: + arr = np.array(data, dtype=np.float32).reshape(-1, 1, 2) + return arr + + +def save_feature_json(record: FeatureRecord) -> None: + payload: Dict[str, Any] = { + "version": FEATURE_JSON_VERSION, + "image": record.image_path.name, + "camera_folder": record.camera_folder, + "feature_type": record.feature_type, + "success": record.success, + "preprocessing": record.preprocessing, + "timestamp_sec": record.timestamp_sec, + "pair_key": record.pair_key, + } + if record.board_size is not None: + payload["board_size"] = [int(record.board_size[0]), int(record.board_size[1])] + if record.square_size is not None: + payload["square_size"] = float(record.square_size) + if record.corners is not None: + payload["corners"] = corners_to_list(record.corners) + if record.center is not None: + payload["center"] = [float(record.center[0]), float(record.center[1])] + if record.ellipse is not None: + payload["ellipse"] = record.ellipse + if record.error: + payload["error"] = record.error + + record.json_path.parent.mkdir(parents=True, exist_ok=True) + with open(record.json_path, "w", encoding="utf-8") as f: + json.dump(payload, f, indent=2) + + +def load_feature_json(json_path: Path, image_path: Optional[Path] = None) -> FeatureRecord: + with open(json_path, "r", encoding="utf-8") as f: + data = json.load(f) + + if image_path is not None: + img = Path(image_path) + else: + stem = json_path.stem + parent = json_path.parent + img = parent / data.get("image", stem) + if not img.exists(): + for ext in (".bmp", ".png", ".jpg", ".jpeg"): + candidate = parent / f"{stem}{ext}" + if candidate.exists(): + img = candidate + break + + board_size = None + if "board_size" in data and data["board_size"]: + board_size = (int(data["board_size"][0]), int(data["board_size"][1])) + + corners = None + if data.get("corners"): + corners = corners_from_list(data["corners"]) + + center = None + if data.get("center"): + center = (float(data["center"][0]), float(data["center"][1])) + + return FeatureRecord( + image_path=Path(img), + json_path=Path(json_path), + camera_folder=data.get("camera_folder", ""), + feature_type=data.get("feature_type", "unknown"), + success=bool(data.get("success", False)), + board_size=board_size, + square_size=data.get("square_size"), + corners=corners, + center=center, + ellipse=data.get("ellipse"), + timestamp_sec=data.get("timestamp_sec"), + pair_key=data.get("pair_key"), + preprocessing=data.get("preprocessing"), + error=data.get("error"), + ) + + +def load_folder_features(camera_dir: Path) -> List[FeatureRecord]: + records = [] + for json_path in sorted(camera_dir.glob("*.json")): + try: + records.append(load_feature_json(json_path)) + except (json.JSONDecodeError, OSError) as exc: + print(f"[WARN] Skipping invalid JSON {json_path}: {exc}") + return records diff --git a/02_Calibration/calibrationclasses/pairing.py b/02_Calibration/calibrationclasses/pairing.py new file mode 100644 index 0000000..09971e5 --- /dev/null +++ b/02_Calibration/calibrationclasses/pairing.py @@ -0,0 +1,106 @@ +"""Stereo pair building: time-window matching with filename-key fallback.""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Dict, List, Optional, Tuple + +from calibrationclasses.feature_json import FeatureRecord + + +@dataclass(frozen=True) +class StereoPair: + left: FeatureRecord + right: FeatureRecord + delta_sec: float + method: str # "time_window" | "pair_key" + + +def _chessboard_compatible(left: FeatureRecord, right: FeatureRecord) -> bool: + if not left.is_chessboard or not right.is_chessboard: + return False + return left.corner_count == right.corner_count + + +def pair_by_time_window( + left_records: List[FeatureRecord], + right_records: List[FeatureRecord], + window_sec: float, +) -> List[StereoPair]: + """Match each left image to the closest unused right image within window_sec.""" + pairs: List[StereoPair] = [] + used_right: set[int] = set() + + left_sorted = sorted( + [r for r in left_records if r.is_chessboard and r.timestamp_sec is not None], + key=lambda r: r.timestamp_sec, + ) + right_candidates = [ + (i, r) + for i, r in enumerate(right_records) + if r.is_chessboard and r.timestamp_sec is not None + ] + + for left in left_sorted: + best_idx = None + best_dt = None + for idx, right in right_candidates: + if idx in used_right: + continue + if not _chessboard_compatible(left, right): + continue + dt = abs(left.timestamp_sec - right.timestamp_sec) + if dt <= window_sec and (best_dt is None or dt < best_dt): + best_idx = idx + best_dt = dt + if best_idx is not None: + used_right.add(best_idx) + right = right_candidates[best_idx][1] + pairs.append(StereoPair(left, right, best_dt, "time_window")) + + return pairs + + +def pair_by_key( + left_records: List[FeatureRecord], + right_records: List[FeatureRecord], +) -> List[StereoPair]: + """Legacy exact pair_key matching (IR scan ids, shared numeric suffix).""" + right_lookup: Dict[str, FeatureRecord] = {} + for right in right_records: + if right.is_chessboard and right.pair_key: + right_lookup[right.pair_key] = right + + pairs: List[StereoPair] = [] + used_right: set[str] = set() + for left in left_records: + if not left.is_chessboard or not left.pair_key: + continue + right = right_lookup.get(left.pair_key) + if right is None or left.pair_key in used_right: + continue + if not _chessboard_compatible(left, right): + continue + used_right.add(left.pair_key) + pairs.append(StereoPair(left, right, 0.0, "pair_key")) + + return pairs + + +def build_stereo_pairs( + left_records: List[FeatureRecord], + right_records: List[FeatureRecord], + time_window_sec: float = 0.1, +) -> List[StereoPair]: + """ + Prefer time-window pairs; fill remaining with pair_key matches not already paired. + """ + time_pairs = pair_by_time_window(left_records, right_records, time_window_sec) + paired_left = {p.left.image_path for p in time_pairs} + paired_right = {p.right.image_path for p in time_pairs} + + remaining_left = [r for r in left_records if r.image_path not in paired_left] + remaining_right = [r for r in right_records if r.image_path not in paired_right] + key_pairs = pair_by_key(remaining_left, remaining_right) + + return time_pairs + key_pairs diff --git a/02_Calibration/calibrationclasses/preprocessing.py b/02_Calibration/calibrationclasses/preprocessing.py new file mode 100644 index 0000000..4312782 --- /dev/null +++ b/02_Calibration/calibrationclasses/preprocessing.py @@ -0,0 +1,82 @@ +from typing import List, Tuple +import cv2 +import numpy as np + + +class Preprocessing: + """Preprocessing class. + + Parameters + ---------- + clipLimit: float + default = 5.0 + tileGridSize: Tuple[int, int] + default = (15, 15) + thresh1: int + default = 0 + thresh2: int + default = 255 + """ + + def __init__( + self, + tileGridSize: Tuple[int, int] = (15, 15), + clipLimit: float = 5.0, + thresh1: int = 0, + thresh2: int = 255, + ) -> None: + self.tileGridSize = tileGridSize + self.clipLimit = clipLimit + self.thresh1 = thresh1 + self.thresh2 = thresh2 + + + def gray(self, image: np.ndarray) -> np.ndarray: + """Convert to GRAY for a given image. + + Parameters + ---------- + image : np.ndarray + image of chessboard + + Returns + ------- + np.ndarray + image of chessboard converted to GRAY + """ + gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) + return gray + + def clahe(self, image: np.ndarray) -> np.ndarray: + """Apply Clahe to GRAY Shimage. + + Parameters + ---------- + image : np.ndarray + image of chessboard + + Returns + ------- + np.ndarray + image of chessboard converted to GRAY and applied CLAHE + """ + clahe = cv2.createCLAHE(clipLimit = self.clipLimit, tileGridSize = self.tileGridSize) + clahed = clahe.apply(self.gray(image)) + return clahed + + def threshold(self, image: np.ndarray) -> np.ndarray: + """Apply Clahe to GRAY Shimage. + + Parameters + ---------- + image : np.ndarray + image of chessboard + + Returns + ------- + np.ndarray + image of chessboard converted to GRAY applied CLAHE and applied THRESHOLD + """ + criteria = cv2.THRESH_BINARY + cv2.THRESH_OTSU+1 + ret, threshold = cv2.threshold(self.clahe(image), self.thresh1, self.thresh2, criteria) + return threshold \ No newline at end of file diff --git a/02_Calibration/calibrationclasses/session.py b/02_Calibration/calibrationclasses/session.py new file mode 100644 index 0000000..293d48c --- /dev/null +++ b/02_Calibration/calibrationclasses/session.py @@ -0,0 +1,72 @@ +"""Calibration session path resolution and camera folder discovery.""" + +from __future__ import annotations + +import os +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +IMAGE_EXTENSIONS = (".bmp", ".png", ".jpg", ".jpeg") + +# Logical camera name -> folder aliases on disk +CAMERA_FOLDER_ALIASES: Dict[str, Tuple[str, ...]] = { + "lc": ("lc",), + "lc-ir": ("lc-ir", "lc_ir", "LC-IR"), + "rc": ("rc",), + "rg": ("rg", "rgb"), + "ir": ("ir", "IR"), +} + +STEREO_PARTNERS = ("rc", "rg", "ir") + + +@dataclass(frozen=True) +class CameraFolder: + logical_name: str + path: Path + folder_name: str + + +def resolve_session_root(input_path: str | Path) -> Path: + """Return flat or nested `images/` root containing camera folders.""" + input_path = Path(input_path) + images_dir = input_path / "images" + if images_dir.is_dir(): + return images_dir + return input_path + + +def discover_camera_folder( + session_root: Path, logical_name: str +) -> Optional[CameraFolder]: + aliases = CAMERA_FOLDER_ALIASES.get(logical_name) + if not aliases: + return None + for folder in aliases: + path = session_root / folder + if path.is_dir(): + return CameraFolder(logical_name, path, folder) + return None + + +def list_image_paths(camera_dir: Path) -> List[Path]: + paths = [ + camera_dir / name + for name in os.listdir(camera_dir) + if name.lower().endswith(IMAGE_EXTENSIONS) + ] + return sorted(paths) + + +def json_path_for_image(image_path: Path) -> Path: + return image_path.with_suffix(".json") + + +def list_cameras_present(session_root: Path) -> List[CameraFolder]: + found = [] + for logical in CAMERA_FOLDER_ALIASES: + cam = discover_camera_folder(session_root, logical) + if cam is not None: + found.append(cam) + return found diff --git a/02_Calibration/calibrationclasses/timestamp.py b/02_Calibration/calibrationclasses/timestamp.py new file mode 100644 index 0000000..690e3a7 --- /dev/null +++ b/02_Calibration/calibrationclasses/timestamp.py @@ -0,0 +1,80 @@ +"""Parse timestamps and pairing keys from calibration image filenames.""" + +from __future__ import annotations + +import re +from pathlib import Path +from typing import Optional, Tuple + +_TS_TOKEN = re.compile(r"ts(\d+)", re.IGNORECASE) +_SCAN_TOKEN = re.compile(r"scan(\d{6})", re.IGNORECASE) +_IR_SCAN = re.compile(r"^ir_scan_(\d+)", re.IGNORECASE) + + +def _digits_after_prefix(name: str, prefixes: Tuple[str, ...]) -> Optional[str]: + lower = name.lower() + for prefix in sorted(prefixes, key=len, reverse=True): + if lower.startswith(prefix): + remainder = lower[len(prefix) :].lstrip("_-.") + m = re.match(r"(\d+)", remainder) + if m: + return m.group(1) + return None + + +def parse_timestamp_sec(filename: str) -> Optional[float]: + """ + Normalize filename timestamps to seconds for time-window pairing. + + Supports: + - lc_ts1634840093_ck.... -> ms since epoch + - lc_1778599872850705.bmp -> µs since epoch (16+ digits) + - lc_1778599872850.bmp -> ms (13 digits) + """ + name = Path(filename).name + m = _TS_TOKEN.search(name) + if m: + digits = m.group(1) + if len(digits) >= 16: + return int(digits) / 1_000_000.0 + if len(digits) >= 13: + return int(digits) / 1_000.0 + return int(digits) / 1_000.0 + + prefixes = ("lc-ir", "lcir", "lc_ir", "lc", "rc", "rg", "rgb", "ir") + digits = _digits_after_prefix(name, prefixes) + if digits is None: + return None + + if len(digits) >= 16: + return int(digits) / 1_000_000.0 + if len(digits) >= 13: + return int(digits) / 1_000.0 + return float(digits) + + +def parse_pair_key(filename: str) -> Optional[str]: + """ + Filename key for legacy exact matching (IR scan ids, shared numeric tails). + """ + name = Path(filename).name + lower = name.lower() + + m = _IR_SCAN.match(lower) + if m: + return f"scan{int(m.group(1)):06d}" + + m = _SCAN_TOKEN.search(lower) + if m: + return m.group(0).lower() + + m = _TS_TOKEN.search(lower) + if m: + return f"ts{m.group(1)}" + + prefixes = ("lc-ir", "lcir", "lc_ir", "lc", "rc", "rg", "rgb", "ir") + digits = _digits_after_prefix(lower, prefixes) + if digits: + return digits + + return Path(lower).stem diff --git a/02_Calibration/detect_features.py b/02_Calibration/detect_features.py new file mode 100644 index 0000000..8cf0891 --- /dev/null +++ b/02_Calibration/detect_features.py @@ -0,0 +1,73 @@ +#!/usr/bin/env python3 +""" +Step 1 — Feature detection for calibration. + +Detects chessboard corners (and ellipse centers for IR when needed) and writes +one JSON per image next to the source file in the same camera folder. +""" + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path.home() / "Speckle-Scanner")) +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +import argparse + +from calibrationclasses.cli_common import ( + add_board_args, + add_session_args, + add_troubleshooting_arg, + build_board_config, + resolve_input_path, +) +from calibrationclasses.feature_detection import DetectionConfig, run_detection + + +def main(): + parser = argparse.ArgumentParser( + description="Calibration step 1: detect features and save per-image JSON" + ) + add_session_args(parser) + add_board_args(parser) + parser.add_argument( + "--cameras", + type=str, + default=None, + help="Comma-separated camera folders to process (default: all present)", + ) + parser.add_argument( + "--ir_mode", + choices=("auto", "chessboard", "ellipse"), + default="auto", + help="IR detection: try chessboard first (auto), or force one mode", + ) + add_troubleshooting_arg(parser) + args = parser.parse_args() + + board_sizes, square_sizes = build_board_config(args) + per_camera_board = { + name: {"board_size": board_sizes[name], "square_size": square_sizes[name]} + for name in board_sizes + } + + cameras = None + if args.cameras: + cameras = [c.strip() for c in args.cameras.split(",") if c.strip()] + + config = DetectionConfig( + chessboard_size=args.chessboard_size, + square_size=args.square_size, + preprocessing=args.preprocessing, + ir_mode=args.ir_mode, + troubleshooting=args.troubleshooting, + ) + + input_path = resolve_input_path(args) + print(f"[detect] session: {input_path}") + run_detection(input_path, config, cameras=cameras, per_camera_board=per_camera_board) + print("[detect] done") + + +if __name__ == "__main__": + main() diff --git a/02_Calibration/main.py b/02_Calibration/main.py new file mode 100644 index 0000000..fe7f5ae --- /dev/null +++ b/02_Calibration/main.py @@ -0,0 +1,205 @@ +#!/usr/bin/env python3 +""" +Calibration entry point. + +Default (2-step pipeline): + 1. detect_features.py — corners/ellipses → per-image JSON + 2. calibrate.py — mono intrinsics + stereo (lc vs rc/rg/ir) + +Legacy one-shot mode: --legacy (detect + calibrate in memory, single partner) +""" + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path.home() / "Speckle-Scanner")) +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +import argparse +import threading +from typing import Optional, Tuple + +import config +from calibrationclasses.calibration import StereoCalibration +from calibrationclasses.calibration_engine import ( + run_mono_calibration, + run_stereo_calibration, +) +from calibrationclasses.cli_common import ( + add_board_args, + add_session_args, + add_troubleshooting_arg, + build_board_config, + parse_chessboard_size, + resolve_input_path, +) +from calibrationclasses.feature_detection import DetectionConfig, run_detection +from calibrationclasses.session import STEREO_PARTNERS + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Stereo camera calibration (2-step pipeline by default)" + ) + add_session_args(parser) + add_board_args(parser) + parser.add_argument( + "--step", + choices=("detect", "calibrate", "all"), + default="all", + help="Pipeline step: detect JSONs, calibrate from JSONs, or both (default)", + ) + parser.add_argument( + "--legacy", + action="store_true", + help="Old one-shot flow: detect in memory, one stereo partner only", + ) + parser.add_argument( + "--left_camera", + type=str, + default="lc", + choices=("lc", "lc-ir", "lc_ir"), + help="Left camera folder for stereo (default: lc)", + ) + parser.add_argument( + "--right_camera", + type=str, + default="rc", + choices=("rc", "rgb", "rg", "ir"), + help="Stereo partner (legacy mode only; 2-step uses lc vs all partners)", + ) + parser.add_argument( + "--time_window", + type=float, + default=0.1, + help="Stereo pair time window in seconds (default: 0.1)", + ) + parser.add_argument( + "--partners", + type=str, + default="rc,rg,ir", + help="Stereo partners in 2-step mode (default: rc,rg,ir)", + ) + parser.add_argument( + "--ir_mode", + choices=("auto", "chessboard", "ellipse"), + default="auto", + help="IR feature detection mode for step 1", + ) + add_troubleshooting_arg(parser) + return parser.parse_args() + + +def run_legacy( + input_path, + chessboard_size=(8, 7), + square_size=0.045, + chessboard_size_left: Optional[Tuple[int, int]] = None, + chessboard_size_right: Optional[Tuple[int, int]] = None, + square_size_left: Optional[float] = None, + square_size_right: Optional[float] = None, + preprocessing="None", + left_camera="lc", + right_camera="rc", + troubleshooting=False, +): + chessboard_size_left = chessboard_size_left or chessboard_size + chessboard_size_right = chessboard_size_right or chessboard_size + square_size_left = square_size if square_size_left is None else square_size_left + square_size_right = ( + square_size if square_size_right is None else square_size_right + ) + + stereo_calibrator = StereoCalibration( + input_path, + chessboard_size, + square_size, + preprocessing, + chessboard_size_left=chessboard_size_left, + chessboard_size_right=chessboard_size_right, + square_size_left=square_size_left, + square_size_right=square_size_right, + left_camera=left_camera, + right_camera=right_camera, + troubleshooting=troubleshooting, + ) + if stereo_calibrator._preprocessing_enabled(): + print(f"[INFO] Preprocessing for corner detection enabled: {preprocessing!r}") + + t1 = threading.Thread(target=stereo_calibrator.create_chessboard_points_left) + t2 = threading.Thread(target=stereo_calibrator.create_chessboard_points_right) + t1.start() + t2.start() + t1.join() + t2.join() + + stereo_calibrator.build_pairs_cal() + stereo_calibrator.calibrate() + stereo_calibrator.save_stereo_calibration() + + if troubleshooting: + stereo_calibrator.rectify_calibration_images() + + +def run_two_step(args): + input_path = resolve_input_path(args) + board_sizes, square_sizes = build_board_config(args) + per_camera_board = { + name: {"board_size": board_sizes[name], "square_size": square_sizes[name]} + for name in board_sizes + } + left_camera = args.left_camera.lower().replace("_", "-") + partners = tuple(p.strip() for p in args.partners.split(",") if p.strip()) + + if args.step in ("detect", "all"): + print("\n=== Step 1: Feature detection → JSON ===") + config_det = DetectionConfig( + chessboard_size=args.chessboard_size, + square_size=args.square_size, + preprocessing=args.preprocessing, + ir_mode=args.ir_mode, + troubleshooting=args.troubleshooting, + ) + run_detection(input_path, config_det, per_camera_board=per_camera_board) + + if args.step in ("calibrate", "all"): + print("\n=== Step 2a: Mono intrinsics ===") + mono_results = run_mono_calibration( + input_path, + board_sizes, + square_sizes, + troubleshooting=args.troubleshooting, + ) + print("\n=== Step 2b: Stereo (lc vs partners) ===") + run_stereo_calibration( + input_path, + left_camera=left_camera, + mono_results=mono_results, + board_sizes=board_sizes, + square_sizes=square_sizes, + time_window_sec=args.time_window, + partners=partners or STEREO_PARTNERS, + troubleshooting=args.troubleshooting, + ) + + +if __name__ == "__main__": + args = parse_args() + input_path = str(resolve_input_path(args)) + + if args.legacy: + run_legacy( + input_path=input_path, + chessboard_size=args.chessboard_size, + square_size=args.square_size, + chessboard_size_left=args.left_chessboard_size, + chessboard_size_right=args.right_chessboard_size, + square_size_left=args.left_square_size, + square_size_right=args.right_square_size, + preprocessing=args.preprocessing, + left_camera=args.left_camera, + right_camera=args.right_camera, + troubleshooting=args.troubleshooting, + ) + else: + run_two_step(args) diff --git a/02_Calibration/requirements.txt b/02_Calibration/requirements.txt new file mode 100644 index 0000000..806b596 --- /dev/null +++ b/02_Calibration/requirements.txt @@ -0,0 +1,8 @@ +# 02_Calibration — Python dependencies +# Install: pip install -r requirements.txt +# Full pipeline (all steps): pip install -r ~/Speckle-Scanner/requirements.txt + +numpy>=1.21 +opencv-python>=4.8 +tqdm>=4.0 +matplotlib>=3.5 diff --git a/04_Rectification/README.md b/04_Rectification/README.md new file mode 100644 index 0000000..e418314 --- /dev/null +++ b/04_Rectification/README.md @@ -0,0 +1,204 @@ +# 04 Rectification + +Stereo rectification for multi-camera scan sessions. Reads raw images from `3D-Scans`, applies calibration from `Calib-data`, and writes results into `Speckle-Scanner_Processing_data`. + +Supported stereo pairs (per scan): + +| Pair | Left | Right | Params file | +|------|------|-------|-------------| +| `lc-rc` | `lc_*` | `rc_*` | `lc-rc_parameters.npz` | +| `lc-rg` | `lc_*` | `rg_*` | `lc-rg_parameters.npz` | +| `lc-ir` | `lc_*` | `ir_*` | `lc-ir_parameters.npz` | + +Rectified LC frames are taken from the `lc-rc` run only (one LC set in `02_rect_images`). Partner cameras (`rc`, `rg`, `ir`) are saved from their own pair calibration. + +--- + +## Folder layout (general paths) + +All paths use `$HOME` — replace with your home directory on any machine. + +| Role | Path pattern | +|------|----------------| +| Source scans (RAW) | `$HOME/3D-Scans///sessionN/Scan00000X/01_raw_images/` | +| Calibration params | `$HOME/Calib-data////params/` | +| Processing output | `$HOME/Speckle-Scanner_Processing_data///` | + +Example naming: + +- **project** (Calib + processing): `Olsen_wings` (underscore) +- **raw_project** (3D-Scans): `Olsen-wings` (often hyphen; default = project with `_` → `-`) +- **date**: `2026-05-12` +- **calib_name**: `calib1` + +Per session under processing output: + +```text +$HOME/Speckle-Scanner_Processing_data/// + session53/ + params_link/ # copied lc-rc, lc-rg, lc-ir params + Scan000001/ + 01_raw_images/ # copy of source images + 02_rect_images/ # rectified lc_*, rc_*, rg_*, ir_* (single folder) + Scan000002/ + ... +``` + +Source side (same session/scan names): + +```text +$HOME/3D-Scans///session53/Scan000001/01_raw_images/ +``` + +Calibration params (once per project/date): + +```text +$HOME/Calib-data////params/ + lc-rc_parameters.npz + lc-rc_stereo_cam_model.yaml + lc-rc_Q.cvstore + lc-rg_* + lc-ir_* +``` + +--- + +## Requirements + +```bash +pip install numpy opencv-python tqdm +``` + +Use a Python environment where `import cv2` works. + +--- + +## How to run + +From anywhere: + +```bash +cd "$HOME/Speckle-Scanner/04_Rectification" +python main.py [options] +``` + +### All sessions under one date (full batch) + +Processes every `session*/Scan*/01_raw_images` under the date folder. + +```bash +python main.py \ + --project Olsen_wings \ + --raw_project Olsen-wings \ + --date 2026-05-12 \ + --calib_name calib1 +``` + +One line: + +```bash +python main.py --project Olsen_wings --raw_project Olsen-wings --date 2026-05-12 --calib_name calib1 +``` + +### One session only + +```bash +python main.py \ + --project Olsen_wings \ + --raw_project Olsen-wings \ + --date 2026-05-12 \ + --calib_name calib1 \ + --session session53 +``` + +### Custom pairs + +Default: `lc-rc,lc-rg,lc-ir`. Example — RC and IR only: + +```bash +python main.py --project Olsen_wings --date 2026-05-12 --pairs lc-rc,lc-ir +``` + +### Override paths (any project/machine) + +```bash +python main.py \ + --source_date_root "$HOME/3D-Scans/MyProject/2026-05-12" \ + --calib_params_dir "$HOME/Calib-data/MyProject/2026-05-12/calib1/params" \ + --processing_date_root "$HOME/Speckle-Scanner_Processing_data/MyProject/2026-05-12" +``` + +--- + +## CLI reference + +| Option | Default | Meaning | +|--------|---------|---------| +| `--project` | **required** | Project name in Calib-data and Processing_data (e.g. `Olsen_wings`) | +| `--date` | **required** | Date subfolder (e.g. `2026-05-12`) | +| `--raw_project` | `` with `_` → `-` | Project folder name under 3D-Scans | +| `--session` | (all) | Only this session, e.g. `session53` | +| `--calib_name` | `calib1` | Calibration run folder | +| `--pairs` | `lc-rc,lc-rg,lc-ir` | Comma-separated stereo pairs | +| `--keep_lc_from_pair` | `lc-rc` | Which pair defines rectified LC in `02_rect_images` | +| `--source_date_root` | auto | Override RAW scan root | +| `--calib_params_dir` | auto | Override params folder | +| `--processing_date_root` | auto | Override output root | + +--- + +## Pairing notes + +Images are matched by filename key (in order): + +1. `_ts` in both names (e.g. `lc_ts254303092_...` ↔ `rc_ts254303092_...`) +2. `scan000001` style / `IR_scan_000001` +3. Prefix + suffix (`lc_123` ↔ `ir_123`) + +If no key match for `lc-rg` or `lc-ir`, the script may use **index fallback** (first LC with first RG/IR). Check logs for: + +```text +[WARN] No key match for lc-rg; using index fallback with N pairs. +``` + +`lc-rc` usually matches on `_ts` when both cameras captured the same timestamps. + +--- + +## What gets created + +For each processed scan: + +- Copies `01_raw_images` into processing tree (does not delete source RAW data) +- Writes rectified images to `02_rect_images/` +- Creates `params_link/` once per session with all calibration files + +Does **not** modify files under `3D-Scans`. + +--- + +## Quick check after a run + +```bash +PROJECT=Olsen_wings +DATE=2026-05-12 +SESSION=session53 +SCAN=Scan000001 + +ls "$HOME/Speckle-Scanner_Processing_data/$PROJECT/$DATE/$SESSION/params_link" +ls "$HOME/Speckle-Scanner_Processing_data/$PROJECT/$DATE/$SESSION/$SCAN/02_rect_images" | head +``` + +--- + +## Dependencies + +```bash +# This step only +pip install -r ~/Speckle-Scanner/04_Rectification/requirements.txt + +# Or install everything for the full pipeline +pip install -r ~/Speckle-Scanner/requirements.txt +``` + +Packages: `numpy`, `opencv-python`, `tqdm`. diff --git a/04_Rectification/main.py b/04_Rectification/main.py new file mode 100644 index 0000000..6c125c2 --- /dev/null +++ b/04_Rectification/main.py @@ -0,0 +1,85 @@ +import argparse +from pathlib import Path + +from rectificationclasses.rectification import Rectification + + +def parse_args(): + parser = argparse.ArgumentParser(description="Batch stereo rectification") + parser.add_argument("--project", type=str, required=True, help="Project name used for Calib-data and processing_data (e.g. Olsen_wings)") + parser.add_argument( + "--raw_project", + type=str, + default=None, + help="Project name used in 3D-Scans (default: project with '_' replaced by '-')", + ) + parser.add_argument("--date", type=str, required=True, help="Date folder (e.g. 2026-05-12)") + parser.add_argument( + "--session", + type=str, + default=None, + help="Process only this session folder (e.g. session53). Default: all sessions under the date.", + ) + parser.add_argument("--calib_name", type=str, default="calib1", help="Calibration folder under Calib-data///") + parser.add_argument( + "--pairs", + type=str, + default="lc-rc,lc-rg,lc-ir", + help="Comma-separated pair list, e.g. lc-rc,lc-rg,lc-ir", + ) + parser.add_argument( + "--keep_lc_from_pair", + type=str, + default="lc-rc", + help="Pair whose rectified LC frames are kept in 02_rect_images.", + ) + parser.add_argument( + "--source_date_root", + type=str, + default=None, + help="Override source root (default: ~/3D-Scans//)", + ) + parser.add_argument( + "--calib_params_dir", + type=str, + default=None, + help="Override calib params dir (default: ~/Calib-data////params)", + ) + parser.add_argument( + "--processing_date_root", + type=str, + default=None, + help="Override processing target root (default: ~/Speckle-Scanner_Processing_data//)", + ) + return parser.parse_args() + + +def main(): + args = parse_args() + home = Path.home() + raw_project = args.raw_project or args.project.replace("_", "-") + pairs = tuple([p.strip() for p in args.pairs.split(",") if p.strip()]) + + source_date_root = Path(args.source_date_root) if args.source_date_root else ( + home / "3D-Scans" / raw_project / args.date + ) + calib_params_dir = Path(args.calib_params_dir) if args.calib_params_dir else ( + home / "Calib-data" / args.project / args.date / args.calib_name / "params" + ) + processing_date_root = Path(args.processing_date_root) if args.processing_date_root else ( + home / "Speckle-Scanner_Processing_data" / args.project / args.date + ) + + rectificator = Rectification( + source_date_root=str(source_date_root), + calib_params_dir=str(calib_params_dir), + processing_date_root=str(processing_date_root), + pairs=pairs, + keep_lc_from_pair=args.keep_lc_from_pair, + session_filter=args.session, + ) + rectificator.run_batch() + + +if __name__ == "__main__": + main() diff --git a/04_Rectification/rectificationclasses/__init__.py b/04_Rectification/rectificationclasses/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/04_Rectification/rectificationclasses/rectification.py b/04_Rectification/rectificationclasses/rectification.py new file mode 100644 index 0000000..a6d0d9c --- /dev/null +++ b/04_Rectification/rectificationclasses/rectification.py @@ -0,0 +1,333 @@ +from pathlib import Path +import re +import shutil +from typing import Dict, List, Optional, Tuple + +import cv2 +import numpy as np +from tqdm import tqdm + + +VALID_EXTS = {".bmp", ".png", ".jpg", ".jpeg"} + + +class Rectification: + """Batch rectification for one project/date tree. + + Reads source scans from RAW data tree, copies scans into processing tree, and + rectifies lc-rc/lc-rg/lc-ir pairs with pair-specific calibration params. + """ + + def __init__( + self, + source_date_root: str, + calib_params_dir: str, + processing_date_root: str, + pairs: Tuple[str, ...] = ("lc-rc", "lc-rg", "lc-ir"), + keep_lc_from_pair: str = "lc-rc", + session_filter: Optional[str] = None, + ) -> None: + self.source_date_root = Path(source_date_root) + self.calib_params_dir = Path(calib_params_dir) + self.processing_date_root = Path(processing_date_root) + self.pairs = pairs + self.keep_lc_from_pair = keep_lc_from_pair + self.session_filter = session_filter + + if not self.source_date_root.is_dir(): + raise FileNotFoundError(f"Source date root not found: {self.source_date_root}") + if not self.calib_params_dir.is_dir(): + raise FileNotFoundError(f"Calibration params dir not found: {self.calib_params_dir}") + + self.processing_date_root.mkdir(parents=True, exist_ok=True) + self._params_by_pair: Dict[str, Dict[str, np.ndarray]] = {} + self._rect_maps_cache: Dict[Tuple[str, int, int], Tuple[Tuple[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray], np.ndarray]] = {} + + self._load_all_pair_params() + + @staticmethod + def _extract_ts_key(filename: str) -> Optional[str]: + stem = Path(filename).stem.lower() + m = re.search(r"_ts(\d+)", stem) + return m.group(1) if m else None + + @staticmethod + def _extract_scan_key(filename: str) -> Optional[str]: + stem = Path(filename).stem.lower() + m = re.search(r"(scan\d{6})", stem) + if m: + return m.group(1) + m = re.match(r"^ir_scan_(\d+)", stem) + if m: + return f"scan{int(m.group(1)):06d}" + m = re.match(r"^ir_(\d{6})(?:_|$)", stem) + if m: + return f"scan{m.group(1)}" + return None + + @staticmethod + def _extract_generic_suffix_key(filename: str, prefix: str) -> Optional[str]: + stem = Path(filename).stem.lower() + if not stem.startswith(prefix): + return None + return stem[len(prefix):].lstrip("_-.") + + @staticmethod + def _camera_from_pair(pair_name: str) -> str: + return pair_name.split("-", 1)[1] + + def _load_pair_params(self, pair_name: str) -> Dict[str, np.ndarray]: + npz_path = self.calib_params_dir / f"{pair_name}_parameters.npz" + if not npz_path.exists(): + raise FileNotFoundError(f"Missing params file for {pair_name}: {npz_path}") + data = np.load(npz_path, allow_pickle=True) + params = dict(data) + required = [ + "L_Intrinsic", + "L_Distortion", + "R_Intrinsic", + "R_Distortion", + "Rotation", + "Translation", + ] + missing = [k for k in required if k not in params] + if missing: + raise KeyError(f"{pair_name} params missing keys: {missing}") + return params + + def _load_all_pair_params(self) -> None: + for pair_name in self.pairs: + self._params_by_pair[pair_name] = self._load_pair_params(pair_name) + print(f"[INFO] Loaded calibration params for pairs: {', '.join(self.pairs)}") + + def _copy_params_link_for_session(self, session_name: str) -> None: + target_params = self.processing_date_root / session_name / "params_link" + target_params.mkdir(parents=True, exist_ok=True) + for src in self.calib_params_dir.iterdir(): + if src.is_file() and src.suffix.lower() in (".npz", ".yaml", ".cvstore"): + shutil.copy2(src, target_params / src.name) + + @staticmethod + def _copy_raw_images(src_raw_dir: Path, dst_raw_dir: Path) -> None: + dst_raw_dir.mkdir(parents=True, exist_ok=True) + for src in src_raw_dir.iterdir(): + if src.is_file(): + shutil.copy2(src, dst_raw_dir / src.name) + + @staticmethod + def _list_images(raw_dir: Path, prefix: str) -> List[Path]: + imgs = [ + p for p in raw_dir.iterdir() + if p.is_file() + and p.suffix.lower() in VALID_EXTS + and p.name.lower().startswith(prefix.lower()) + ] + imgs.sort() + return imgs + + def _pair_images(self, left_images: List[Path], right_images: List[Path], right_camera: str) -> List[Tuple[Path, Path]]: + left_by_ts = {self._extract_ts_key(p.name): p for p in left_images if self._extract_ts_key(p.name)} + right_by_ts = {self._extract_ts_key(p.name): p for p in right_images if self._extract_ts_key(p.name)} + pairs: List[Tuple[Path, Path]] = [] + + common_ts = sorted(set(left_by_ts.keys()) & set(right_by_ts.keys())) + for ts in common_ts: + pairs.append((left_by_ts[ts], right_by_ts[ts])) + if pairs: + return pairs + + left_by_scan = {self._extract_scan_key(p.name): p for p in left_images if self._extract_scan_key(p.name)} + right_by_scan = {self._extract_scan_key(p.name): p for p in right_images if self._extract_scan_key(p.name)} + common_scan = sorted(set(left_by_scan.keys()) & set(right_by_scan.keys())) + for skey in common_scan: + pairs.append((left_by_scan[skey], right_by_scan[skey])) + if pairs: + return pairs + + left_by_suffix = { + self._extract_generic_suffix_key(p.name, "lc"): p + for p in left_images + if self._extract_generic_suffix_key(p.name, "lc") + } + right_by_suffix = { + self._extract_generic_suffix_key(p.name, right_camera): p + for p in right_images + if self._extract_generic_suffix_key(p.name, right_camera) + } + common_suffix = sorted(set(left_by_suffix.keys()) & set(right_by_suffix.keys())) + for key in common_suffix: + pairs.append((left_by_suffix[key], right_by_suffix[key])) + if pairs: + return pairs + + fallback_count = min(len(left_images), len(right_images)) + if fallback_count > 0: + print( + f"[WARN] No key match for lc-{right_camera}; " + f"using index fallback with {fallback_count} pairs." + ) + return list(zip(left_images[:fallback_count], right_images[:fallback_count])) + return [] + + def _get_rectification_maps( + self, + pair_name: str, + left_size: Tuple[int, int], + right_size: Tuple[int, int], + ) -> Tuple[Tuple[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray], np.ndarray]: + cache_key = (pair_name, left_size[0], left_size[1]) + if cache_key in self._rect_maps_cache: + return self._rect_maps_cache[cache_key] + + params = self._params_by_pair[pair_name] + rect_left, rect_right, proj_left, proj_right, q_mat, _, _ = cv2.stereoRectify( + params["L_Intrinsic"], + params["L_Distortion"], + params["R_Intrinsic"], + params["R_Distortion"], + left_size, + params["Rotation"], + params["Translation"], + alpha=1, + flags=0, + ) + + left_maps = cv2.initUndistortRectifyMap( + params["L_Intrinsic"], + params["L_Distortion"], + rect_left, + proj_left, + left_size, + cv2.CV_32FC1, + ) + right_maps = cv2.initUndistortRectifyMap( + params["R_Intrinsic"], + params["R_Distortion"], + rect_right, + proj_right, + right_size, + cv2.CV_32FC1, + ) + self._rect_maps_cache[cache_key] = (left_maps, right_maps, q_mat) + return left_maps, right_maps, q_mat + + def _rectify_pair_image( + self, + pair_name: str, + left_img: np.ndarray, + right_img: np.ndarray, + ) -> Tuple[np.ndarray, np.ndarray]: + left_size = (left_img.shape[1], left_img.shape[0]) + right_size = (right_img.shape[1], right_img.shape[0]) + left_maps, right_maps, _ = self._get_rectification_maps(pair_name, left_size, right_size) + left_rect = cv2.remap(left_img, left_maps[0], left_maps[1], cv2.INTER_AREA) + right_rect = cv2.remap(right_img, right_maps[0], right_maps[1], cv2.INTER_AREA) + return left_rect, right_rect + + def _process_scan(self, session_name: str, scan_name: str) -> Dict[str, int]: + src_raw_dir = self.source_date_root / session_name / scan_name / "01_raw_images" + dst_scan_dir = self.processing_date_root / session_name / scan_name + dst_raw_dir = dst_scan_dir / "01_raw_images" + dst_rect_dir = dst_scan_dir / "02_rect_images" + dst_rect_dir.mkdir(parents=True, exist_ok=True) + + self._copy_raw_images(src_raw_dir, dst_raw_dir) + + stats = {"pairs_total": 0, "saved": 0, "skipped": 0} + lc_written = False + ordered_pairs = list(self.pairs) + if self.keep_lc_from_pair in ordered_pairs: + ordered_pairs.remove(self.keep_lc_from_pair) + ordered_pairs.insert(0, self.keep_lc_from_pair) + + for pair_name in ordered_pairs: + right_camera = self._camera_from_pair(pair_name) + left_images = self._list_images(dst_raw_dir, "lc") + right_images = self._list_images(dst_raw_dir, right_camera) + + if not left_images or not right_images: + stats["skipped"] += 1 + print( + f"[WARN] {session_name}/{scan_name} {pair_name}: " + f"missing images (lc={len(left_images)}, {right_camera}={len(right_images)})." + ) + continue + + pairs = self._pair_images(left_images, right_images, right_camera) + if not pairs: + stats["skipped"] += 1 + print(f"[WARN] {session_name}/{scan_name} {pair_name}: no valid pairs.") + continue + + save_lc_this_pair = ( + pair_name == self.keep_lc_from_pair + or (not lc_written and pair_name != self.keep_lc_from_pair) + ) + + stats["pairs_total"] += len(pairs) + for left_path, right_path in tqdm( + pairs, + desc=f"{session_name}/{scan_name} {pair_name}", + unit="pair", + leave=False, + ): + left_img = cv2.imread(str(left_path), cv2.IMREAD_COLOR) + right_img = cv2.imread(str(right_path), cv2.IMREAD_COLOR) + if left_img is None or right_img is None: + stats["skipped"] += 1 + continue + + left_rect, right_rect = self._rectify_pair_image(pair_name, left_img, right_img) + if save_lc_this_pair: + left_out = dst_rect_dir / left_path.name + cv2.imwrite(str(left_out), left_rect) + lc_written = True + right_out = dst_rect_dir / right_path.name + cv2.imwrite(str(right_out), right_rect) + stats["saved"] += 1 + + return stats + + def _discover_session_scan_raw_dirs(self) -> List[Tuple[str, str]]: + found: List[Tuple[str, str]] = [] + session_dirs = sorted( + [p for p in self.source_date_root.iterdir() if p.is_dir() and p.name.lower().startswith("session")] + ) + for session_dir in session_dirs: + if self.session_filter and session_dir.name != self.session_filter: + continue + scan_dirs = sorted( + [p for p in session_dir.iterdir() if p.is_dir() and p.name.lower().startswith("scan")] + ) + for scan_dir in scan_dirs: + raw_dir = scan_dir / "01_raw_images" + if raw_dir.is_dir(): + found.append((session_dir.name, scan_dir.name)) + return found + + def run_batch(self) -> Dict[str, int]: + all_scans = self._discover_session_scan_raw_dirs() + if not all_scans: + raise RuntimeError(f"No scan folders found under {self.source_date_root}") + + print(f"[INFO] Found {len(all_scans)} scans under {self.source_date_root}") + totals = {"scans": 0, "pairs_total": 0, "saved": 0, "skipped": 0} + + sessions_seen = set() + for session_name, scan_name in all_scans: + if session_name not in sessions_seen: + self._copy_params_link_for_session(session_name) + sessions_seen.add(session_name) + + scan_stats = self._process_scan(session_name, scan_name) + totals["scans"] += 1 + totals["pairs_total"] += scan_stats["pairs_total"] + totals["saved"] += scan_stats["saved"] + totals["skipped"] += scan_stats["skipped"] + + print( + "[INFO] Batch rectification finished: " + f"scans={totals['scans']} pairs={totals['pairs_total']} " + f"saved={totals['saved']} skipped={totals['skipped']}" + ) + return totals \ No newline at end of file diff --git a/04_Rectification/requirements.txt b/04_Rectification/requirements.txt new file mode 100644 index 0000000..71a1d6d --- /dev/null +++ b/04_Rectification/requirements.txt @@ -0,0 +1,7 @@ +# 04_Rectification — Python dependencies +# Install: pip install -r requirements.txt +# Full pipeline (all steps): pip install -r ~/Speckle-Scanner/requirements.txt + +numpy>=1.21 +opencv-python>=4.8 +tqdm>=4.0 diff --git a/05_disparity/libsgm/.gitignore b/05_disparity/libsgm/.gitignore new file mode 100644 index 0000000..1f636f2 --- /dev/null +++ b/05_disparity/libsgm/.gitignore @@ -0,0 +1,2 @@ +include/libsgm_config.h +build/ \ No newline at end of file diff --git a/05_disparity/libsgm/.gitlab-ci.yml b/05_disparity/libsgm/.gitlab-ci.yml new file mode 100644 index 0000000..ba65e98 --- /dev/null +++ b/05_disparity/libsgm/.gitlab-ci.yml @@ -0,0 +1,66 @@ +image: adaskit/libsgm:0.3-opencv4 + +variables: + GIT_SUBMODULE_STRATEGY: recursive + +stages: + - build + - test + +.build_template: &build_definition + stage: build + tags: + - docker + script: + - ldconfig + - cmake . -DBUILD_OPENCV_WRAPPER="ON" -DENABLE_SAMPLES=${build_samples} -DLIBSGM_SHARED=${build_shared} -DENABLE_TESTS=${build_tests} + - make + +build:samples_on:shared: + variables: + build_samples: "ON" + build_shared: "ON" + build_tests: "OFF" + <<: *build_definition + +build:samples_on:static: + variables: + build_samples: "ON" + build_shared: "OFF" + build_tests: "OFF" + <<: *build_definition + +build:samples_off:shared: + variables: + build_samples: "OFF" + build_shared: "ON" + build_tests: "OFF" + <<: *build_definition + +build:samples_off:static: + variables: + build_samples: "OFF" + build_shared: "OFF" + build_tests: "OFF" + <<: *build_definition + +build:test: + variables: + build_samples: "OFF" + build_shared: "OFF" + build_tests: "ON" + artifacts: + paths: + - ./test/sgm-test + expire_in: 1d + <<: *build_definition + +test: + stage: test + tags: + - nvidia-docker + script: + - ldconfig + - cuda-memcheck --leak-check full ./test/sgm-test + dependencies: + - build:test diff --git a/05_disparity/libsgm/.gitmodules b/05_disparity/libsgm/.gitmodules new file mode 100644 index 0000000..6889aed --- /dev/null +++ b/05_disparity/libsgm/.gitmodules @@ -0,0 +1,3 @@ +[submodule "test/googletest"] + path = test/googletest + url = https://github.com/google/googletest.git diff --git a/05_disparity/libsgm/CMakeLists.txt b/05_disparity/libsgm/CMakeLists.txt new file mode 100644 index 0000000..18d526e --- /dev/null +++ b/05_disparity/libsgm/CMakeLists.txt @@ -0,0 +1,28 @@ +cmake_minimum_required(VERSION 3.18) + +option(ENABLE_ZED_DEMO "Build a Demo using ZED Camera" OFF) +option(ENABLE_SAMPLES "Build samples" OFF) +option(ENABLE_TESTS "Test library" OFF) +option(LIBSGM_SHARED "Build a shared library" OFF) +option(BUILD_OPENCV_WRAPPER "Make library compatible with cv::Mat and cv::cuda::GpuMat of OpenCV" OFF) + +if(NOT DEFINED CMAKE_CUDA_ARCHITECTURES) + set(CMAKE_CUDA_ARCHITECTURES "52;61;72;75;86") +endif() + +project(libSGM VERSION 3.1.0) + +configure_file( +${PROJECT_SOURCE_DIR}/include/libsgm_config.h.in +${PROJECT_SOURCE_DIR}/include/libsgm_config.h +) + +add_subdirectory(src) + +if(ENABLE_SAMPLES) + add_subdirectory(sample) +endif() + +if(ENABLE_TESTS) + add_subdirectory(test) +endif() diff --git a/05_disparity/libsgm/FindLibSGM.cmake b/05_disparity/libsgm/FindLibSGM.cmake new file mode 100644 index 0000000..ac950c2 --- /dev/null +++ b/05_disparity/libsgm/FindLibSGM.cmake @@ -0,0 +1,33 @@ +############################################################################### +# Find LibSGM +# +# This sets the following variables: +# LIBSGM_FOUND - True if LIBSGM was found. +# LIBSGM_INCLUDE_DIRS - Directories containing the LIBSGM include files. +# LIBSGM_LIBRARY - Libraries needed to use LIBSGM. + +# Find lib +set(LIBSGM_FOUND FALSE CACHE BOOL "" FORCE) +find_library(LIBSGM_LIBRARY + NAMES sgm libsgm + PATH_SUFFIXES lib/ +) + +# Find include +find_path(LIBSGM_INCLUDE_DIRS + NAMES libsgm.h + PATH_SUFFIXES include/ +) + +include(FindPackageHandleStandardArgs) +find_package_handle_standard_args(LibSGM DEFAULT_MSG LIBSGM_LIBRARY LIBSGM_INCLUDE_DIRS) + +message(STATUS "(LIBSGM_FOUND : ${LIBSGM_FOUND} include: ${LIBSGM_INCLUDE_DIRS}, lib: ${LIBSGM_LIBRARY})") + +mark_as_advanced(LIBSGM_FOUND) + +if(LIBSGM_FOUND) + set(LIBSGM_FOUND TRUE CACHE BOOL "" FORCE) + set(LIBSGM_LIBRARIES ${LIBSGM_LIBRARY}) + message(STATUS "LibSGM found ( include: ${LIBSGM_INCLUDE_DIRS}, lib: ${LIBSGM_LIBRARY})") +endif() diff --git a/05_disparity/libsgm/LICENSE b/05_disparity/libsgm/LICENSE new file mode 100644 index 0000000..d645695 --- /dev/null +++ b/05_disparity/libsgm/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/05_disparity/libsgm/README.md b/05_disparity/libsgm/README.md new file mode 100644 index 0000000..74fac88 --- /dev/null +++ b/05_disparity/libsgm/README.md @@ -0,0 +1,335 @@ +# installation process for US: +Install Anaconda and CUDA Toolkit (compute capability >= 3.5) +Check if cmake is installed. +``` +$ cmake --version +``` +if version is <3.18 +``` +$ sudo apt remove cmake #Only if cmake is installed with <3.18 version +$ wget https://github.com/Kitware/CMake/releases/download/v3.21.5/cmake-3.21.5.tar.gz +$ tar -xzvf cmake-3.21.5.tar.gz +$ cd cmake-3.21.5 +$ ./bootstrap +$ make +$ sudo make install +$ cmake --version +``` +Now if it is giving error of not found +``` +$ find /usr/local/bin -name cmake +``` +if path exists then close the terminal and open new terminal then again check the version. +``` +$ cmake --version +``` +## Environment: + +Create an environment (named libsgm) in conda +``` +$ conda create --name libsgm +$ conda activate libsgm +``` +Installing Fixstars LibSGM: +``` +$ git clone https://gitea.subseascanning.com/dejhost/libSGM.git +$ cd libSGM +$ git submodule update --init +$ mkdir build +$ cd build +$ cmake ../ +$ make +``` +## Sample Execution +``` +$ pwd +.../libSGM +$ cd build +$ cmake .. -DENABLE_SAMPLES=on +$ make +$ cd sample +``` +place /data folder in libSGM/build/sample/data. + +Now run the command once to confirm the installation and working of LibSGM. +For single image pair you use this stereosgm_new file +The disparity map will be saved on the same path which contains the executeable stereosgm_new file (.../libSGM/build/sample) +``` +$ ./stereosgm_new data/lc00012.bmp data/rc00012.bmp +``` +For multiple pairs one after another you can use stereosgm_image it will save disparity.xml files in output directory. +``` +$ ./stereosgm_image data/lc%05d.bmp data/rc%05d.bmp +``` + +--- + +## **Pipeline Usage (Automated Path Resolution)** + +Use `run_sgm_pipeline.py` to run libSGM across the project folder structure automatically. +It picks the **last rectified image pair** (highest timestamp) from each scan's `02_rect_images/` folder, +runs `stereosgm_new`, and saves results to `03_sgm_disp_map/`. + +### **Folder structure assumed** + +``` +~/Speckle-Scanner_Processing_data/ +└── / + └── / + └── / + └── / + ├── 02_rect_images/ ← lc_ts.png + rc_ts.png (input) + ├── 03_sgm_disp_map/ ← disparity.xml + disparity_color.png (created) + └── 05_sgm_pcl/ ← untouched +``` + +Pairs are matched on the shared `ts` token (e.g. `ts1634840093`). Both formats work: +`lc_ts1634840093_ck….png` / `rc_ts1634840093_ck….png` and `lc_ts1634840093.png` / `rc_ts1634840093.png`. + +### **Commands** + +```bash +cd ~/Speckle-Scanner/05_disparity/libsgm + +# Process ALL scans in a session +python run_sgm_pipeline.py \ + --project Olsen_wings \ + --date 2026-05-12 \ + --session session1 + +# Process ALL sessions on a date (omit --session) +python run_sgm_pipeline.py \ + --project Olsen_wings \ + --date 2026-05-12 + +# Process a SINGLE scan +python run_sgm_pipeline.py \ + --project Olsen_wings \ + --date 2026-05-12 \ + --session session1 \ + --scan Scan000001 + +# Custom SGM parameters +python run_sgm_pipeline.py \ + --project Olsen_wings \ + --date 2026-05-12 \ + --session session1 \ + --disp_size 128 \ + --P1 8 \ + --P2 32 \ + --min_disp 0 \ + --num_paths 8 \ + --census_type 1 +``` + +### **Pipeline parameters** + +| Parameter | Default | Description | +|-----------------|---------|----------------------------------------------------------------------------------| +| `--project` | — | Project name (e.g. `Olsen_wings`) | +| `--date` | — | Date string (e.g. `2026-05-12`) | +| `--session` | all | Session name (e.g. `session1`); omit to process **all sessions** on that date | +| `--scan` | all | Single scan (e.g. `Scan000001`); omit to process all scans in the session | +| `--disp_size` | `256` | Maximum disparity value (64, 128, or 256) | +| `--P1` | `10` | SGM penalty for disparity change of ±1 | +| `--P2` | `120` | SGM penalty for disparity change > 1 | +| `--uniqueness` | `0.80` | Uniqueness ratio threshold | +| `--num_paths` | `8` | Scanlines for cost aggregation (4 or 8) | +| `--min_disp` | `-160` | Minimum disparity value | +| `--LR_max_diff` | `1` | Maximum allowed left-right disparity difference | +| `--census_type` | `1` | Census transform type: 0=CENSUS_9x7, 1=SYMMETRIC_CENSUS_9x7 | + +### **What gets saved in `03_sgm_disp_map/`** + +| File | Description | +|------|-------------| +| `disparity.xml` | Raw disparity matrix (OpenCV FileStorage format, CV_16S) | +| `disparity_color.png` | Colorized disparity image (TURBO colormap, 8-bit) | + +--- + +## **Direct Binary Usage** + +Run `stereosgm_new` manually with explicit paths (must run from the build/sample directory or use full paths): + +```bash +cd ~/Speckle-Scanner/05_disparity/libsgm/build/sample + +# Default parameters, save to current directory +./stereosgm_new data/lc00012.bmp data/rc00012.bmp + +# Save to a specific output folder, no display window +./stereosgm_new \ + /path/to/lc_image.png \ + /path/to/rc_image.png \ + --output_dir=/path/to/03_sgm_disp_map \ + --no_display=1 \ + --disp_size=128 --P1=8 --P2=32 +``` + +--- + +## **Available Parameters** + +| Parameter | Default Value | Description | +| -------------------- | -------------- | -------------------------------------------------------------------------- | +| `@left-image-format` | `none` | Format string for the path to input left image (e.g., "left/img_%04d.png") | +| `@right-image-format`| `none` | Format string for the path to input right image | +| `--disp_size` | `256` | Maximum possible disparity value | +| `--P1` | `10` | Penalty for disparity change of ±1 | +| `--P2` | `120` | Penalty for disparity change > 1 | +| `--uniqueness` | `0.80` | Margin ratio for uniqueness constraint | +| `--num_paths` | `8` | Number of scanlines used in cost aggregation (4 or 8) | +| `--min_disp` | `-160` | Minimum disparity value | +| `--LR_max_diff` | `1` | Maximum allowed left-right disparity difference | +| `--census_type` | `1` | Census transform type (0: 5x5, 1: 9x7, 2: 11x9) | +| `--interval` | `1` | Polling interval (in seconds) for checking new stereo image pairs | +| `--output_dir` | `.` | Directory to save `disparity.xml` and `disparity_color.png` | +| `--no_display` | `0` | Set to `1` to skip interactive display window (required for pipeline/headless use) | +| `--help or -h` | | Show help message | + +### **Custom Parameters** + + +You can override any parameter through command-line arguments. Below is an example with some customized parameters: + +```bash +./stereosgm_image data/lc%05d.bmp data/rc%05d.bmp \ + --disp_size=128 --P1=8 --P2=32 --interval=2 +``` + + +# libSGM(Orignal) +--- +A CUDA implementation performing Semi-Global Matching. + +## Introduction +--- + +libSGM is library that implements in CUDA the Semi-Global Matching algorithm. +From a pair of appropriately calibrated input images, we can obtain the disparity map. + +## Features +--- +Because it uses CUDA, we can compute the disparity map at high speed. + +## Performance +The libSGM performance obtained from benchmark sample +### Settings +- image size : 1024 x 440 +- disparity size : 128 +- sgm path : 4 path +- subpixel : enabled + +### Results +|Device|CUDA version|Processing Time[Milliseconds]|FPS| +|---|---|---|---| +|GTX 1080 Ti|10.1|2.0|495.1| +|GeForce RTX 3080|11.1|1.5|651.3| +|Tegra X2|10.0|28.5|35.1| +|Xavier(MODE_15W)|10.2|17.3|57.7| +|Xavier(MAXN)|10.2|9.0|110.7| + +## Requirements +|Package Name|Minimum Requirements|Note +|---|---|---| +|CMake|version >= 3.18|| +|CUDA Toolkit|compute capability >= 3.5| +|OpenCV|version >= 3.4.8|for samples| +|OpenCV CUDA module|version >= 3.4.8|for OpenCV wrapper| +|ZED SDK|version >= 3.0|for ZED sample| + +## Build Instructions +``` +$ git clone https://github.com/fixstars/libSGM.git +$ cd libSGM +$ git submodule update --init # It is needed if ENABLE_TESTS option is set to ON +$ mkdir build +$ cd build +$ cmake ../ # Several options available +$ make +``` + +## Sample Execution +``` +$ pwd +.../libSGM +$ cd build +$ cmake .. -DENABLE_SAMPLES=on +$ make +$ cd sample +$ ./stereosgm_movie +left image path format: the format used for the file paths to the left input images +right image path format: the format used for the file paths to the right input images +disparity_size: the maximum number of disparities (optional) +``` + +"disparity_size" is optional. By default, it is 128. + +Next, we explain the meaning of the "left image path format" and "right image path format". +When provided with the following set of files, we should pass the "path formats" given below. +``` +left_image_0000.pgm +left_image_0001.pgm +left_image_0002.pgm +left_image_0003.pgm +... + +right_image_0000.pgm +right_image_0001.pgm +right_image_0002.pgm +right_image_0003.pgm +``` + +``` +$ ./stereosgm_movie left_image_%04d.pgm right_image_%04d.pgm +``` + +The sample images available at [Daimler Urban Scene Segmentation Benchmark Dataset 2014](http://www.6d-vision.com/scene-labeling) are used to test the software. + +## Test Execution +libSGM uses [Google Test](https://github.com/google/googletest) for tests as Git submodule. +So, we need to init submodule by following command firstly. + +``` +$ pwd +.../libSGM +$ git submodule update --init +``` + +We can run tests after a build. + +``` +$ pwd +.../libSGM +$ cd build +$ cd test +$ ./sgm-test +``` + +Test code compares our implementation of each functions to naive implementation. + +## Python pipeline runner dependencies + +`run_sgm_pipeline.py` uses only the Python standard library. Disparity is computed by the compiled `stereosgm_new` binary. + +See `requirements.txt` in this folder for system build requirements (CUDA, CMake, OpenCV C++). + +```bash +# No pip packages needed for the Python runner. +# Build the binary first (see above), then: +cd ~/Speckle-Scanner/05_disparity/libsgm +python run_sgm_pipeline.py --project --date +``` + +## Author +The "adaskit Team" + +The adaskit is an open-source project created by [Fixstars Corporation](https://www.fixstars.com/) and its subsidiary companies including [Fixstars Autonomous Technologies](https://at.fixstars.com/), aimed at contributing to the ADAS industry by developing high-performance implementations for algorithms with high computational cost. + +## License +Apache License 2.0 + + + + diff --git a/05_disparity/libsgm/include/libsgm.h b/05_disparity/libsgm/include/libsgm.h new file mode 100644 index 0000000..0cd928a --- /dev/null +++ b/05_disparity/libsgm/include/libsgm.h @@ -0,0 +1,180 @@ +/* +Copyright 2016 Fixstars Corporation + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +http ://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef __LIBSGM_H__ +#define __LIBSGM_H__ + +/** +* @mainpage stereo-sgm +* See sgm::StereoSGM +*/ + +/** +* @file libsgm.h +* stereo-sgm main header +*/ + +#include "libsgm_config.h" + +#if defined(LIBSGM_SHARED) +#if defined(WIN32) || defined(_WIN32) +#if defined sgm_EXPORTS +#define LIBSGM_API __declspec(dllexport) +#else +#define LIBSGM_API __declspec(dllimport) +#endif +#else +#define LIBSGM_API __attribute__((visibility("default"))) +#endif +#else +#define LIBSGM_API +#endif + +namespace sgm +{ + +/** +* @brief Indicates input/output pointer type. +*/ +enum ExecuteInOut +{ + EXECUTE_INOUT_HOST2HOST = (0 << 1) | 0, + EXECUTE_INOUT_HOST2CUDA = (1 << 1) | 0, + EXECUTE_INOUT_CUDA2HOST = (0 << 1) | 1, + EXECUTE_INOUT_CUDA2CUDA = (1 << 1) | 1, +}; + +/** +* @brief Indicates number of scanlines which will be used. +*/ +enum class PathType +{ + SCAN_4PATH, //>! Horizontal and vertical paths. + SCAN_8PATH //>! Horizontal, vertical and oblique paths. +}; + +/** +* @brief Indicates census type which will be used. +*/ +enum class CensusType +{ + CENSUS_9x7, + SYMMETRIC_CENSUS_9x7 +}; + +/** +* @brief StereoSGM class +*/ +class StereoSGM +{ +public: + + static const int SUBPIXEL_SHIFT = 4; + static const int SUBPIXEL_SCALE = (1 << SUBPIXEL_SHIFT); + + /** + * @brief Available options for StereoSGM + */ + struct Parameters + { + int P1; + int P2; + float uniqueness; + bool subpixel; + PathType path_type; + int min_disp; + int LR_max_diff; + CensusType census_type; + + /** + * @param P1 Penalty on the disparity change by plus or minus 1 between nieghbor pixels. + * @param P2 Penalty on the disparity change by more than 1 between neighbor pixels. + * @param uniqueness Margin in ratio by which the best cost function value should be at least second one. + * @param subpixel Disparity value has 4 fractional bits if subpixel option is enabled. + * @param path_type Number of scanlines used in cost aggregation. + * @param min_disp Minimum possible disparity value. + * @param LR_max_diff Acceptable difference pixels which is used in LR check consistency. LR check consistency will be disabled if this value is set to negative. + * @param census_type Type of census transform. + */ + LIBSGM_API Parameters(int P1 = 10, int P2 = 120, float uniqueness = 0.95f, bool subpixel = false, PathType path_type = PathType::SCAN_8PATH, + int min_disp = 0, int LR_max_diff = 1, CensusType census_type = CensusType::SYMMETRIC_CENSUS_9x7); + }; + + /** + * @param width Processed image's width. + * @param height Processed image's height. + * @param disparity_size It must be 64, 128 or 256. + * @param input_depth_bits Processed image's bits per pixel. It must be 8, 16 or 32. + * @param output_depth_bits Disparity image's bits per pixel. It must be 8 or 16. + * @param inout_type Specify input/output pointer type. See sgm::EXECUTE_TYPE. + * @attention + * output_depth_bits must be set to 16 when subpixel is enabled. + */ + LIBSGM_API StereoSGM(int width, int height, int disparity_size, int input_depth_bits, int output_depth_bits, + ExecuteInOut inout_type, const Parameters& param = Parameters()); + + /** + * @param width Processed image's width. + * @param height Processed image's height. + * @param disparity_size It must be 64, 128 or 256. + * @param input_depth_bits Processed image's bits per pixel. It must be 8, 16 or 32. + * @param output_depth_bits Disparity image's bits per pixel. It must be 8 or 16. + * @param src_pitch Source image's pitch (pixels). + * @param dst_pitch Destination image's pitch (pixels). + * @param inout_type Specify input/output pointer type. See sgm::EXECUTE_TYPE. + * @attention + * output_depth_bits must be set to 16 when subpixel is enabled. + */ + LIBSGM_API StereoSGM(int width, int height, int disparity_size, int input_depth_bits, int output_depth_bits, int src_pitch, int dst_pitch, + ExecuteInOut inout_type, const Parameters& param = Parameters()); + + LIBSGM_API virtual ~StereoSGM(); + + /** + * Execute stereo semi global matching. + * @param left_pixels A pointer stored input left image. + * @param right_pixels A pointer stored input right image. + * @param dst Output pointer. User must allocate enough memory. + * @attention + * You need to allocate dst memory at least width x height x sizeof(element_type) bytes. + * The element_type is uint8_t for output_depth_bits == 8 and uint16_t for output_depth_bits == 16. + * Note that dst element value would be multiplied StereoSGM::SUBPIXEL_SCALE if subpixel option was enabled. + * Value of Invalid disparity is equal to return value of `get_invalid_disparity` member function. + */ + LIBSGM_API void execute(const void* left_pixels, const void* right_pixels, void* dst); + + /** + * Generate invalid disparity value from Parameter::min_disp and Parameter::subpixel + * @attention + * Cast properly if you receive disparity value as `unsigned` type. + * See sample/movie for an example of this. + */ + LIBSGM_API int get_invalid_disparity() const; + +private: + + StereoSGM(const StereoSGM&); + StereoSGM& operator=(const StereoSGM&); + + class Impl; + Impl* impl_; +}; + +} // namespace sgm + +#endif // !__LIBSGM_H__ + +#include "libsgm_wrapper.h" diff --git a/05_disparity/libsgm/include/libsgm_config.h.in b/05_disparity/libsgm/include/libsgm_config.h.in new file mode 100644 index 0000000..26ac624 --- /dev/null +++ b/05_disparity/libsgm/include/libsgm_config.h.in @@ -0,0 +1,13 @@ +#ifndef __LIBSGM_CONFIG_H__ +#define __LIBSGM_CONFIG_H__ + +#cmakedefine LIBSGM_SHARED + +#define LIBSGM_VERSION @libSGM_VERSION@ +#define LIBSGM_VERSION_MAJOR @libSGM_VERSION_MAJOR@ +#define LIBSGM_VERSION_MINOR @libSGM_VERSION_MINOR@ +#define LIBSGM_VERSION_PATCH @libSGM_VERSION_PATCH@ + +#cmakedefine BUILD_OPENCV_WRAPPER + +#endif // __LIBSGM_CONFIG_H__ diff --git a/05_disparity/libsgm/include/libsgm_wrapper.h b/05_disparity/libsgm/include/libsgm_wrapper.h new file mode 100644 index 0000000..79db978 --- /dev/null +++ b/05_disparity/libsgm/include/libsgm_wrapper.h @@ -0,0 +1,84 @@ +#ifndef __LIBSGM_WRAPPER_H__ +#define __LIBSGM_WRAPPER_H__ + +#include "libsgm.h" + +#include +#ifdef BUILD_OPENCV_WRAPPER +#include +#endif + +namespace sgm +{ + +/** +* @brief LibSGMWrapper class which is wrapper for sgm::StereoSGM. +*/ +class LibSGMWrapper +{ +public: + + /** + * @param numDisparity Maximum disparity minus minimum disparity. + * @param P1 Penalty on the disparity change by plus or minus 1 between nieghbor pixels. + * @param P2 Penalty on the disparity change by more than 1 between neighbor pixels. + * @param uniquenessRatio Margin in ratio by which the best cost function value should be at least second one. + * @param subpixel Disparity value has 4 fractional bits if subpixel option is enabled. + * @param pathType Number of scanlines used in cost aggregation. + * @param minDisparity Minimum possible disparity value. + * @param lrMaxDiff Acceptable difference pixels which is used in LR check consistency. LR check consistency will be disabled if this value is set to negative. + * @param censusType Type of census transform. + */ + LIBSGM_API LibSGMWrapper(int numDisparity = 128, int P1 = 10, int P2 = 120, float uniquenessRatio = 0.95f, + bool subpixel = false, PathType pathType = PathType::SCAN_8PATH, int minDisparity = 0, int lrMaxDiff = 1, CensusType censusType = CensusType::SYMMETRIC_CENSUS_9x7); + LIBSGM_API ~LibSGMWrapper(); + + LIBSGM_API int getNumDisparities() const; + LIBSGM_API int getP1() const; + LIBSGM_API int getP2() const; + LIBSGM_API float getUniquenessRatio() const; + LIBSGM_API bool hasSubpixel() const; + LIBSGM_API PathType getPathType() const; + LIBSGM_API int getMinDisparity() const; + LIBSGM_API int getLrMaxDiff() const; + LIBSGM_API CensusType getCensusType() const; + LIBSGM_API int getInvalidDisparity() const; + +#ifdef BUILD_OPENCV_WRAPPER + + /** + * Execute stereo semi global matching via wrapper class. + * @param I1 Input left image. Image's type is must be CV_8U, CV_16U or CV_32S + * @param I2 Input right image. Image's size and type must be same with I1. + * @param disparity Output image. Its memory will be allocated automatically dependent on input image size. + * @attention + * type of output image `disparity` is CV_16S. + * Note that disparity element value would be multiplied StereoSGM::SUBPIXEL_SCALE if subpixel option was enabled. + */ + LIBSGM_API void execute(const cv::cuda::GpuMat& I1, const cv::cuda::GpuMat& I2, cv::cuda::GpuMat& disparity); + + /** + * Execute stereo semi global matching via wrapper class. + * @param I1 Input left image. Image's type is must be CV_8U, CV_16U or CV_32S. + * @param I2 Input right image. Image's size and type must be same with I1. + * @param disparity Output image. Its memory will be allocated automatically dependent on input image size. + * @attention + * type of output image `disparity` is CV_16S. + * Note that disparity element value would be multiplied StereoSGM::SUBPIXEL_SCALE if subpixel option was enabled. + */ + LIBSGM_API void execute(const cv::Mat& I1, const cv::Mat& I2, cv::Mat& disparity); + +#endif // BUILD_OPRENCV_WRAPPER + +private: + + struct Creator; + std::unique_ptr sgm_; + int numDisparity_; + sgm::StereoSGM::Parameters param_; + std::unique_ptr prev_; +}; + +} // namespace sgm + +#endif // __LIBSGM_WRAPPER_H__ diff --git a/05_disparity/libsgm/rectified/Q.cvstore b/05_disparity/libsgm/rectified/Q.cvstore new file mode 100644 index 0000000..69826e2 --- /dev/null +++ b/05_disparity/libsgm/rectified/Q.cvstore @@ -0,0 +1,9 @@ +%YAML:1.0 +--- +Q: !!opencv-matrix + rows: 4 + cols: 4 + dt: d + data: [ 1., 0., 0., -452.58969879150391, 0., 1., 0., + -732.08112335205078, 0., 0., 0., 3269.0086731896672, 0., 0., + 1.0200604866284457, 1125.7629393222996 ] diff --git a/05_disparity/libsgm/requirements.txt b/05_disparity/libsgm/requirements.txt new file mode 100644 index 0000000..13252f3 --- /dev/null +++ b/05_disparity/libsgm/requirements.txt @@ -0,0 +1,18 @@ +# 05_disparity/libsgm — dependencies +# +# The Python pipeline runner (run_sgm_pipeline.py) uses only the standard library. +# Disparity computation is done by the compiled stereosgm_new binary (CUDA C++). +# +# --- System build requirements (not installable via pip) --- +# - NVIDIA GPU with CUDA compute capability >= 3.5 +# - CUDA Toolkit 11.x or 12.x +# - CMake >= 3.18 +# - OpenCV (C++ headers + libs, for building libSGM samples) +# +# Build: +# cd ~/Speckle-Scanner/05_disparity/libsgm +# mkdir -p build && cd build +# cmake .. -DENABLE_SAMPLES=on +# make stereosgm_new -j4 +# +# No pip packages required to run run_sgm_pipeline.py after the binary is built. diff --git a/05_disparity/libsgm/run_sgm_pipeline.py b/05_disparity/libsgm/run_sgm_pipeline.py new file mode 100644 index 0000000..268f01b --- /dev/null +++ b/05_disparity/libsgm/run_sgm_pipeline.py @@ -0,0 +1,201 @@ +""" +Pipeline runner for libSGM stereo disparity. + +Resolves all paths from the project folder structure and drives +the stereosgm_new binary for each scan in a session (or all sessions on a date). + +For each scan it takes the LAST matched lc_/rc_ image pair from 02_rect_images/ +(images sorted by timestamp — highest timestamp = last acquired image). + +Output layout per scan: + ///// + 02_rect_images/ <- input (lc_ts.png + rc_ts.png) + 03_sgm_disp_map/ <- disparity.xml + disparity_color.png (created here) + 05_sgm_pcl/ <- untouched + +Binary: + ~/Speckle-Scanner/05_disparity/libsgm/build/sample/stereosgm_new +""" + +import sys +import re +import argparse +import subprocess +from pathlib import Path + +# Resolve config.py from ~/Speckle-Scanner regardless of CWD +sys.path.insert(0, str(Path.home() / "Speckle-Scanner")) +import config # noqa: E402 + +BINARY = Path(__file__).parent / "build" / "sample" / "stereosgm_new" + + +def extract_ts_token(filename, prefix="lc_"): + """Extract ts token from lc_ts1634840093.png or lc_ts1634840093_ck....png.""" + m = re.search(rf"^{re.escape(prefix)}(ts\d+)", filename, re.IGNORECASE) + if not m: + return None, None + ts_token = m.group(1).lower() + ts_int = int(re.search(r"\d+", ts_token).group()) + return ts_token, ts_int + + +def find_rc_for_ts(rect_dir, ts_token): + """Match rc image by shared ts token (ck suffix optional).""" + rc_matches = sorted(rect_dir.glob(f"rc_{ts_token}_*.png")) + if not rc_matches: + rc_matches = sorted(rect_dir.glob(f"rc_{ts_token}*.png")) + return rc_matches[0] if rc_matches else None + + +def find_last_lc_rc_pair(rect_dir): + """Return (lc_path, rc_path) for the highest-timestamp matched pair in rect_dir.""" + rect_dir = Path(rect_dir) + pairs = [] + for lc in rect_dir.glob("lc_ts*.png"): + ts_token, ts_int = extract_ts_token(lc.name, "lc_") + if ts_token is None: + continue + rc = find_rc_for_ts(rect_dir, ts_token) + if rc is None: + continue + pairs.append((ts_int, lc, rc)) + + if not pairs: + return None, None + + pairs.sort(key=lambda item: item[0]) + _, lc, rc = pairs[-1] + return lc, rc + + +def build_cmd(lc, rc, output_dir, sgm_args): + cmd = [ + str(BINARY), + str(lc), + str(rc), + f"--output_dir={output_dir}", + "--no_display=1", + ] + for key, val in sgm_args.items(): + if val is not None: + cmd.append(f"--{key}={val}") + return cmd + + +def run_scan(project, date, session, scan, sgm_args): + rect_dir = config.PROCESSING_DIR / project / date / session / scan / "02_rect_images" + if not rect_dir.exists(): + print(f"[SKIP] {session}/{scan}: 02_rect_images not found at {rect_dir}") + return False + + lc, rc = find_last_lc_rc_pair(rect_dir) + if lc is None: + print(f"[SKIP] {session}/{scan}: no lc_ts*.png images found in {rect_dir}") + return False + if rc is None: + print(f"[SKIP] {session}/{scan}: no matching rc image for {lc.name}") + return False + + output_dir = config.get_processing_step_dir(project, date, session, scan, "03_sgm_disp_map") + + print(f"\n{'='*60}") + print(f"[SCAN] {session}/{scan}") + print(f" lc : {lc.name}") + print(f" rc : {rc.name}") + print(f" output : {output_dir}") + print(f"{'='*60}") + + cmd = build_cmd(lc, rc, output_dir, sgm_args) + result = subprocess.run(cmd) + if result.returncode != 0: + print(f"[FAIL] {session}/{scan} exited with code {result.returncode}") + return False + print(f"[DONE] {session}/{scan}") + return True + + +def run_session(project, date, session, scan_arg, sgm_args): + if scan_arg: + scans = [scan_arg] + else: + scans = config.list_scan_dirs(project, date, session) + if not scans: + print(f"[WARN] No scan folders found in {project}/{date}/{session}") + return [], [] + print(f"\n Session {session}: {len(scans)} scan(s) found") + + failed = [] + for scan in scans: + ok = run_scan(project, date, session, scan, sgm_args) + if not ok: + failed.append(f"{session}/{scan}") + return scans, failed + + +def main(): + parser = argparse.ArgumentParser( + description="libSGM disparity pipeline runner — resolves paths from project structure" + ) + # Project location + parser.add_argument("--project", required=True, help="Project name (e.g. Olsen_wings)") + parser.add_argument("--date", required=True, help="Date string (e.g. 2026-05-12)") + parser.add_argument("--session", default=None, help="Session name (e.g. session1); omit to process ALL sessions on that date") + parser.add_argument("--scan", default=None, help="Single scan (e.g. Scan000001); omit to process all scans in the session") + + # SGM parameters — all optional, forwarded to stereosgm_new + parser.add_argument("--disp_size", type=int, default=None, help="Maximum disparity value (64, 128, or 256; default 256)") + parser.add_argument("--P1", type=int, default=None, help="SGM penalty for disparity change of ±1 (default 10)") + parser.add_argument("--P2", type=int, default=None, help="SGM penalty for disparity change >1 (default 120)") + parser.add_argument("--uniqueness", type=float, default=None, help="Uniqueness ratio threshold (default 0.80)") + parser.add_argument("--num_paths", type=int, default=None, choices=[4, 8], help="Scanlines for cost aggregation: 4 or 8 (default 8)") + parser.add_argument("--min_disp", type=int, default=None, help="Minimum disparity value (default -160)") + parser.add_argument("--LR_max_diff", type=int, default=None, help="Max left-right disparity difference (default 1)") + parser.add_argument("--census_type", type=int, default=None, choices=[0, 1], help="Census transform type: 0=CENSUS_9x7, 1=SYMMETRIC_CENSUS_9x7 (default 1)") + + args = parser.parse_args() + + if not BINARY.exists(): + print(f"ERROR: stereosgm_new binary not found at {BINARY}") + print("Build it first: cd ~/Speckle-Scanner/05_disparity/libsgm/build && make stereosgm_new") + sys.exit(1) + + sgm_args = { + "disp_size": args.disp_size, + "P1": args.P1, + "P2": args.P2, + "uniqueness": args.uniqueness, + "num_paths": args.num_paths, + "min_disp": args.min_disp, + "LR_max_diff": args.LR_max_diff, + "census_type": args.census_type, + } + + # Determine sessions to process + if args.session: + sessions = [args.session] + else: + sessions = config.list_session_dirs(args.project, args.date) + if not sessions: + print(f"No session folders found under {args.project}/{args.date}") + sys.exit(1) + print(f"Found {len(sessions)} session(s): {sessions}") + + total_scans = 0 + all_failed = [] + for session in sessions: + scans, failed = run_session( + args.project, args.date, session, args.scan, sgm_args + ) + total_scans += len(scans) + all_failed.extend(failed) + + print(f"\n{'='*60}") + print(f"Finished: {total_scans - len(all_failed)}/{total_scans} scans succeeded.") + if all_failed: + print(f"Failed: {all_failed}") + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/05_disparity/libsgm/sample/CMakeLists.txt b/05_disparity/libsgm/sample/CMakeLists.txt new file mode 100644 index 0000000..fd757fb --- /dev/null +++ b/05_disparity/libsgm/sample/CMakeLists.txt @@ -0,0 +1,62 @@ +cmake_minimum_required(VERSION 3.18) + +project(samples LANGUAGES CXX CUDA) + +set(CMAKE_CXX_STANDARD 17) +set(CMAKE_CXX_EXTENSIONS OFF) + +# required packages +find_package(OpenCV REQUIRED) + +set(SRCS_COMMON sample_common.cpp sample_common.h) + +# sample image +add_executable(stereosgm_image stereosgm_image.cpp ${SRCS_COMMON}) +target_include_directories(stereosgm_image PRIVATE ${OpenCV_INCLUDE_DIRS}) +target_link_libraries(stereosgm_image sgm ${OpenCV_LIBS}) + +# sample movie +add_executable(stereosgm_movie stereosgm_movie.cpp ${SRCS_COMMON}) +target_include_directories(stereosgm_movie PRIVATE ${OpenCV_INCLUDE_DIRS}) +target_link_libraries(stereosgm_movie sgm ${OpenCV_LIBS}) + +# sample mynew +add_executable(stereosgm_new stereosgm_new.cpp ${SRCS_COMMON}) +target_include_directories(stereosgm_new PRIVATE ${OpenCV_INCLUDE_DIRS}) +target_link_libraries(stereosgm_new sgm ${OpenCV_LIBS}) + +# sample benchmark +add_executable(stereosgm_benchmark stereosgm_benchmark.cpp ${SRCS_COMMON}) +target_include_directories(stereosgm_benchmark PRIVATE ${OpenCV_INCLUDE_DIRS}) +target_link_libraries(stereosgm_benchmark sgm ${OpenCV_LIBS}) + +# sample reprojection +add_executable(stereosgm_reprojection stereosgm_reprojection.cpp ${SRCS_COMMON}) +target_include_directories(stereosgm_reprojection PRIVATE ${OpenCV_INCLUDE_DIRS}) +target_link_libraries(stereosgm_reprojection sgm ${OpenCV_LIBS}) + +# sample image with cv::GpuMat +if(BUILD_OPENCV_WRAPPER) + add_executable(stereosgm_image_cv_gpumat stereosgm_image_cv_gpumat.cpp ${SRCS_COMMON}) + target_include_directories(stereosgm_image_cv_gpumat PRIVATE ${OpenCV_INCLUDE_DIRS}) + target_link_libraries(stereosgm_image_cv_gpumat sgm ${OpenCV_LIBS}) +endif() + +# sample ZED camera +if(ENABLE_ZED_DEMO) + if(WIN32) + set(ZED_SDK_LIB "C:\\Program Files (x86)\\ZED SDK\\lib\\sl_zed64.lib" CACHE STRING "ZED SDK library(sl_zed**.llb) path.") + set(ZED_SDK_INCLUDE_DIR "C:\\Program Files (x86)\\ZED SDK\\include" CACHE STRING "ZED SDK include path.") + else() + set(ZED_SDK_LIB "/usr/local/zed/lib/libsl_zed.so" CACHE STRING "ZED SDK library(sl_zed**.llb) path.") + set(ZED_SDK_INCLUDE_DIR "/usr/local/zed/include" CACHE STRING "ZED SDK include path.") + endif() + + find_package(ZED 3 REQUIRED) + string(REGEX REPLACE [[; +]] [[;]] CUDA_NPP_LIBRARIES_ZED "${CUDA_NPP_LIBRARIES_ZED}") + + add_executable(stereosgm_zed stereosgm_zed.cpp ${SRCS_COMMON}) + target_include_directories(stereosgm_zed PRIVATE ${OpenCV_INCLUDE_DIRS} ${ZED_INCLUDE_DIRS}) + target_link_directories(stereosgm_zed PRIVATE ${ZED_LIBRARY_DIR}) + target_link_libraries(stereosgm_zed sgm ${OpenCV_LIBS} ${ZED_LIBRARIES} ${CUDA_NPP_LIBRARIES_ZED}) +endif() diff --git a/05_disparity/libsgm/sample/camera_parameters/camera_daimler_gt_stixel.xml 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