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‎.gitignore‎

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@@ -172,3 +172,5 @@ GEMstack/offboard/calibration/calibration_by_segmentation/init_proj.png
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GEMstack/offboard/calibration/calibration_by_segmentation/refined_proj_seg.png
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GEMstack/offboard/calibration/calibration_by_segmentation/refined_proj.png
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GEMstack/offboard/calibration/calibration_by_segmentation/extrinsic.txt
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repo/

‎GEMstack/offboard/calibration/README.md‎

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## Table of Contents
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- [img2pc.py](#img2pcpy) - Camera-to-LiDAR extrinsic calibration
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- [get_intrinsic_by_chessboard.py](#get_intrinsic_by_chessboardpy) - Intrinsic calibration using chessboard
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- [intrinsic_calibration_chessboard.py](#intrinsic_calibration_chessboardpy) - Intrinsic calibration using SfM
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---
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## img2pc.py
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**Camera-to-LiDAR Extrinsic Calibration Tool**
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Compute camera extrinsic parameters by manually selecting corresponding features in an image and point cloud.
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### Usage
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```bash
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python3 img2pc.py \
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--img_path IMG_PATH \
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--pc_path PC_PATH \
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--img_intrinsics_path IMG_INTRINSICS_PATH \
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[--pc_transform_path PC_TRANSFORM_PATH] \
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[--out_path OUT_PATH] \
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[--n_features N_FEATURES]
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```
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#### Parameters
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| Parameter | Description | Format | Required | Default |
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|-----------|-------------|--------|----------|---------|
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| `--img_path` | Input image path | .png | Yes | - |
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| `--pc_path` | Point cloud path | .npz | Yes | - |
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| `--img_intrinsics_path` | Camera intrinsics file | .yaml | Yes | - |
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| `--pc_transform_path` | LiDAR extrinsic transform | .yaml | No | Identity |
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| `--out_path` | Output extrinsic path | .yaml | No | None |
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| `--n_features` | Manual feature points | int | No | 8 |
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### Example
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```bash
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root='/mnt/GEMstack'
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python3 img2pc.py \
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--img_path $root/data/calib1/img/fl/fl16.png \
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--pc_path $root/data/calib1/pc/ouster16.npz \
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--pc_transform_path $root/GEMstack/knowledge/calibration/gem_e4_ouster.yaml \
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--img_intrinsics_path $root/GEMstack/knowledge/calibration/gem_e4_oak_in.yaml \
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--n_features 4 \
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--out_path $root/GEMstack/knowledge/calibration/gem_e4_oak.yaml
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```
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## get_intrinsic_by_chessboard.py
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Chessboard-based Intrinsic Calibration
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Compute camera intrinsic parameters using multiple images of a chessboard pattern.
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### Usage
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```bash
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python3 get_intrinsic_by_chessboard.py IMG_DIR WIDTH HEIGHT [OUTPUT_PATH]
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```
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### Parameters
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| Parameter | Description | Required |
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|-----------|-------------|----------|
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| `IMG_DIR` | Calibration images folder | Yes |
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| `WIDTH` | Chessboard width (squares-1) | Yes |
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| `HEIGHT` | Chessboard height (squares-1) | Yes |
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| `OUTPUT_PATH` | Output .yaml path | No |
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## get_intrinsic_by_SfM.py
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Compute camera intrinsic parameters using Structure-from-Motion on a sequence of images.
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### Usage
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```bash
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python3 intrinsic_calibration_chessboard.py \
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--input_imgs INPUT_IMGS [INPUT_IMGS ...] \
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--workspace WORKSPACE \
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[--out_file OUT_FILE]
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```
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### Parameters
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| Parameter | Description | Required |
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|-----------|-------------|----------|
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| `--input_imgs` | Input images (glob pattern) | Yes |
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| `--workspace` | Temporary directory path | Yes |
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| `--out_file` | Output .yaml path | No |
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### Example
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```bash
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root='/mnt/GEMstack'
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python3 intrinsic_calibration_chessboard.py \
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--input_imgs data/fl/images/0000[0-8][147].png \
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--workspace /tmp/SfM_intrinsic_fl \
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--out_file $root/GEMstack/knowledge/calibration/camera_intrinsics2/gem_e4_fl_in.yaml
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```

‎GEMstack/offboard/calibration/get_intrinsic_by_SfM.py‎

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import argparse
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import os
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import shutil
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import pycolmap
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import subprocess
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from tools.save_cali import save_in
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def run_colmap_command(args):
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result = subprocess.run(args, capture_output=True, text=True)
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if result.returncode != 0:
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print(f"Command failed: {' '.join(args)}")
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print(f"Error: {result.stderr}")
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raise RuntimeError("COLMAP command failed")
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return result
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def main(input_imgs, output_dir, out, refine=True):
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# Setup directory structure
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workspace_dir = os.path.join(output_dir, 'sfm_workspace')
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image_dir = os.path.join(workspace_dir, 'images')
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os.makedirs(image_dir, exist_ok=True)
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# Copy images to workspace
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print(f"Copying images to workspace...")
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for path in input_imgs:
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filename = path.split('/')[-1]
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dst = os.path.join(image_dir, filename)
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shutil.copy(path, dst)
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# Create database path
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database_path = os.path.join(workspace_dir, 'database.db')
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if os.path.exists(database_path):
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os.remove(database_path)
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# Feature extraction
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print("Extracting features...")
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# pycolmap.extract_features(
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# database_path, image_dir,
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# camera_mode=pycolmap.CameraMode.SINGLE,
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# camera_model=pycolmap.CameraModelId.OPENCV
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# )
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run_colmap_command([
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"colmap", "feature_extractor",
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"--database_path", database_path,
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"--image_path", image_dir,
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"--ImageReader.single_camera", "1",
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"--ImageReader.camera_model", "OPENCV",
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"--SiftExtraction.estimate_affine_shape", "1",
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"--SiftExtraction.domain_size_pooling", "1"
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])
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# Feature matching
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print("Matching features...")
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match_options = pycolmap.SequentialMatchingOptions()
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match_options.overlap = 2
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pycolmap.match_sequential(
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database_path,
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matching_options=match_options
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)
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# Run SfM reconstruction
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mapper_options = pycolmap.IncrementalPipelineOptions()
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if refine:
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mapper_options.ba_refine_focal_length = 1
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mapper_options.ba_refine_principal_point = 1
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mapper_options.ba_refine_extra_params = 1
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else:
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mapper_options.ba_refine_focal_length = 0
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mapper_options.ba_refine_principal_point = 0
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mapper_options.ba_refine_extra_params = 0
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print("Running incremental SfM...")
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output_path = os.path.join(workspace_dir, 'sparse')
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os.makedirs(output_path, exist_ok=True)
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reconstructions = pycolmap.incremental_mapping(
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database_path=database_path,
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image_path=image_dir,
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output_path=output_path,
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options=mapper_options
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)
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# Process results
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if not reconstructions:
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print("SfM failed to reconstruct the scene!")
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return
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camera:pycolmap._core.Camera = 0
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print("\nCamera calibration parameters:")
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for idx, reconstruction in reconstructions.items():
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print(f"\nReconstruction {idx + 1}:")
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for camera_id, camera in reconstruction.cameras.items():
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print(f"\nCamera ID {camera_id}:")
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print(f"Model: {camera.model}")
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print(f"Parameters: {camera.params}")
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print(f"Parameters info: {camera.params_info}")
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# print(f"Focal length: {camera.focal_length}")
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print(f"Focal length x: {camera.focal_length_x}")
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print(f"Focal length y: {camera.focal_length_y}")
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print(f"Principal point x: {camera.principal_point_x}")
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print(f"Principal point y: {camera.principal_point_y}")
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save_in(
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path=out,
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focal=[camera.focal_length_x,camera.focal_length_y],
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center=[camera.principal_point_x,camera.principal_point_y],
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distort=list(camera.params[4:])+[0.0],
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)
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print("\nCalibration complete! Results saved to:", workspace_dir)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='Camera calibration using SfM')
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parser.add_argument('--input_imgs','-i', nargs='+', help='List of input imgs', required=True)
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parser.add_argument('--workspace','-w', type=str, required=True,
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help='Output directory for results')
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parser.add_argument('--out_file','-o', type=str, required=True,
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help='output yaml file')
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args = parser.parse_args()
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main(args.input_imgs, args.workspace, args.out_file)

‎GEMstack/offboard/calibration/get_intrinsic_by_chessborad.py‎

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import cv2
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import numpy as np
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from tools.save_cali import save_in
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import glob
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def estimate_intrinsics(image_dir, checkerboard_size):
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criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
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w,h = checkerboard_size
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objp = np.zeros((w*h,3), np.float32)
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objp[:,:2] = np.mgrid[0:w,0:h].T.reshape(-1,2)
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objpoints = []
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imgpoints = []
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images = glob.glob(image_dir + '*.png')
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for fname in images:
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img = cv2.imread(fname)
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gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
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ret, corners = cv2.findChessboardCorners(gray, (w,h),None)
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if ret == True:
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objpoints.append(objp)
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corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
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imgpoints.append(corners2)
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img = cv2.drawChessboardCorners(img, (w,h), corners2,ret)
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cv2.imshow('img',img)
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cv2.waitKey(500)
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cv2.destroyAllWindows()
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ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
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return {
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'fx': mtx[0,0],
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'fy': mtx[1,1],
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'cx': mtx[0,2],
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'cy': mtx[1,2],
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'distortion_coeffs': dist[0].tolist()
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}
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if __name__ == "__main__":
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import sys
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if len(sys.argv) < 4:
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print(f"Usage: python3 this.py <image_path> <checkerboard_w> <checkerboard_h> [output_path]")
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sys.exit(1)
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try:
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image_path = sys.argv[1]
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w = int(sys.argv[2])
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h = int(sys.argv[3])
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params = estimate_intrinsics(image_path, (w,h))
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print("Camera Intrinsics:")
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print(f"Focal Length (fx, fy): {params['fx']:.2f}, {params['fy']:.2f}")
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print(f"Principal Point (cx, cy): {params['cx']:.2f}, {params['cy']:.2f}")
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print(f"Distortion Coefficients: {params['distortion_coeffs']}")
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except Exception as e:
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print(f"Error: {str(e)}")
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if len(sys.argv) == 5:
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out_path = sys.argv[4]
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save_in(out_path, focal=[params['fx'],params['fy']], center=[params['cx'],params['cy']],distort=params['distortion_coeffs'],skew=0)

‎GEMstack/offboard/calibration/intrinsic_calibration.py‎

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This file was deleted.

‎GEMstack/offboard/calibration/tools/save_cali.py‎

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@@ -61,7 +61,8 @@ def load_in(path,mode='matrix',return_distort=False):
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'skew':np.array(y['skew']),
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'distort':np.array(y['distort'])}
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def save_in(path,focal=None,center=None,skew=None,distort=None,matrix=None):
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from collections.abc import Iterable
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def save_in(path,focal=None,center=None,skew=0,distort=[0.0]*5,matrix=None):
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if matrix is not None:
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focal = matrix.diagonal()[0,-1]
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skew = matrix[0,1]
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ret = {}
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ret['focal'] = focal
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ret['center'] = center
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ret['skew'] = skew or 0
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ret['distort'] = distort or [0,0,0,0,0]
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ret['skew'] = skew
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assert len(distort) in [4,5]
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ret['distort'] = distort
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if len(ret['distort']) == 4:
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ret['distort'] = list(ret['distort'])+[0.0]
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for i in ret:
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if type(ret[i]) == np.ndarray:
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ret[i] = ret[i].tolist()
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if isinstance(ret[i],Iterable):
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ret[i] = [*map(float,ret[i])]
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print(yaml.dump(ret,Dumper=SafeDumper,default_flow_style=False))
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with open(path,'w') as stream:
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yaml.dump(ret,stream,Dumper=SafeDumper,default_flow_style=False)

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