152 lines
5.7 KiB
Python
152 lines
5.7 KiB
Python
import cv2
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import numpy as np
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from cv2 import aruco
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from cv2.typing import MatLike
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from enum import Enum
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from pathlib import Path
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from loguru import logger
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from itertools import chain
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from typing import Optional, Sequence, TypedDict, cast
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import awkward as ak
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from matplotlib.pyplot import stem
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from numpy import ndarray
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class ArucoDictionary(Enum):
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Dict_4X4_50 = aruco.DICT_4X4_50
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Dict_4X4_100 = aruco.DICT_4X4_100
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Dict_4X4_250 = aruco.DICT_4X4_250
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Dict_4X4_1000 = aruco.DICT_4X4_1000
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Dict_5X5_50 = aruco.DICT_5X5_50
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Dict_5X5_100 = aruco.DICT_5X5_100
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Dict_5X5_250 = aruco.DICT_5X5_250
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Dict_5X5_1000 = aruco.DICT_5X5_1000
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Dict_6X6_50 = aruco.DICT_6X6_50
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Dict_6X6_100 = aruco.DICT_6X6_100
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Dict_6X6_250 = aruco.DICT_6X6_250
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Dict_6X6_1000 = aruco.DICT_6X6_1000
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Dict_7X7_50 = aruco.DICT_7X7_50
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Dict_7X7_100 = aruco.DICT_7X7_100
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Dict_7X7_250 = aruco.DICT_7X7_250
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Dict_7X7_1000 = aruco.DICT_7X7_1000
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Dict_APRILTAG_16h5 = aruco.DICT_APRILTAG_16h5
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Dict_APRILTAG_25h9 = aruco.DICT_APRILTAG_25h9
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Dict_APRILTAG_36h10 = aruco.DICT_APRILTAG_36h10
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Dict_APRILTAG_36h11 = aruco.DICT_APRILTAG_36h11
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Dict_ArUco_ORIGINAL = aruco.DICT_ARUCO_ORIGINAL
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IMAGE_FOLDER = Path("xx")
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OUTPUT_FOLDER = Path("output")
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DICTIONARY = ArucoDictionary.Dict_4X4_50
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CALIBRATION_PARQUET: Optional[Path] = OUTPUT_FOLDER / "calibration.parquet"
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class CameraParams(TypedDict):
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camera_matrix: MatLike
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distortion_coefficients: MatLike
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rotation_vectors: MatLike
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translation_vectors: MatLike
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def main():
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OUTPUT_FOLDER.mkdir(exist_ok=True)
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images = chain(
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IMAGE_FOLDER.glob("*.jpeg"),
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IMAGE_FOLDER.glob("*.png"),
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IMAGE_FOLDER.glob("*.jpg"),
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)
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border_num_x = 10
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border_num_y = 7
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dictionary = aruco.getPredefinedDictionary(DICTIONARY.value)
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board = aruco.CharucoBoard((border_num_x, border_num_y), 0.115, 0.09, dictionary)
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detector = aruco.CharucoDetector(board)
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all_ch_corners: list[MatLike] = []
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all_ch_ids: list[MatLike] = []
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all_image_points: list[MatLike] = []
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all_object_points: list[MatLike] = []
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last_shape = np.array((0, 0))
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calibration: Optional[ak.Record] = None
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def has_cal():
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return CALIBRATION_PARQUET is not None and CALIBRATION_PARQUET.exists()
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try:
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if has_cal():
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calibration_arr = ak.from_parquet(CALIBRATION_PARQUET)
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calibration = calibration_arr[0]
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logger.info(f"Loaded calibration parameters: {calibration}")
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except Exception as e:
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logger.error(f"Failed to load calibration parameters: {e}")
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for img_path in images:
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img = cv2.imread(str(img_path))
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last_shape = img.shape
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# 10x7
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# minus 1 when dealing with normal chessboard
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# 115mm square
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# 90mm marker
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# https://docs.opencv.org/3.4/df/d4a/tutorial_charuco_detection.html
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# https://docs.opencv.org/4.x/df/d4a/tutorial_charuco_detection.html
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# https://docs.opencv.org/4.x/da/d13/tutorial_aruco_calibration.html
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# https://docs.opencv.org/4.x/dc/dbb/tutorial_py_calibration.html
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# https://docs.opencv.org/4.x/d9/d0c/group__calib3d.html#ga93efa9b0aa890de240ca32b11253dd4a
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# https://github.com/opencv/opencv/issues/22083
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# OpenCV 4.10.x
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# pylint: disable-next=unpacking-non-sequence
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ch_corners, ch_ids, markers_corners, marker_ids = detector.detectBoard(img)
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# https://docs.opencv.org/4.10.0/d9/df5/classcv_1_1aruco_1_1CharucoDetector.html
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if ch_corners is not None:
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# https://docs.opencv.org/4.x/d4/db2/classcv_1_1aruco_1_1Board.html
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aruco.drawDetectedCornersCharuco(img, ch_corners, ch_ids, (0, 255, 0))
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all_ch_corners.append(ch_corners)
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all_ch_ids.append(ch_ids)
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# pylint: disable-next=unpacking-non-sequence
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op, ip = board.matchImagePoints(cast(Sequence[MatLike], ch_corners), ch_ids)
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all_object_points.append(op)
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all_image_points.append(ip)
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if calibration is not None:
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mtx = cast(MatLike, ak.to_numpy(calibration["camera_matrix"]))
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dist = cast(
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MatLike, ak.to_numpy(calibration["distortion_coefficients"])
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)
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ret, rvec, tvec = cv2.solvePnP(op, ip, mtx, dist)
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if ret:
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img = cv2.drawFrameAxes(img, mtx, dist, rvec, tvec, 0.1)
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else:
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logger.warning(f"Failed to draw frame axes in {img_path}")
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else:
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logger.warning(f"Failed to detect Charuco board in {img_path}")
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continue
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if markers_corners is not None:
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aruco.drawDetectedMarkers(img, markers_corners, marker_ids)
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output_path = OUTPUT_FOLDER / (f"{img_path.stem}_output.jpg")
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logger.info(f"Saving to {output_path}")
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cv2.imwrite(str(output_path), img)
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# compute calibration
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if calibration is None and len(all_image_points) > 0:
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ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(
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all_object_points, all_image_points, (last_shape[0], last_shape[1]), None, None # type: ignore
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) # type: ignore
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logger.info(f"Camera matrix: {mtx}")
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logger.info(f"Distortion coefficients: {dist}")
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logger.info(f"Rotation vectors: {rvecs}")
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logger.info(f"Translation vectors: {tvecs}")
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parameters = {
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"camera_matrix": mtx,
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"distortion_coefficients": dist,
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"rotation_vectors": rvecs,
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"translation_vectors": tvecs,
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}
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ak.to_parquet([parameters], OUTPUT_FOLDER / "calibration.parquet")
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else:
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logger.warning(
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"no calibration data calculated; either no images or already calibrated"
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)
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if __name__ == "__main__":
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main()
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