95 lines
2.8 KiB
Python
95 lines
2.8 KiB
Python
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'''
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Find camera intrinsicts:
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camera matrix and distortion coefficients
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Largely a copy from https://longervision.github.io/2017/03/16/ComputerVision/OpenCV/opencv-internal-calibration-chessboard/
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Usage:
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1. Set dataset variable to point to a directory containing chessboard.mp4
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2. make sure CHECKERBOARD has the nr of corners in the printed board used. Use (6,9) for https://github.com/opencv/opencv/blob/4.x/doc/pattern.png
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3. Scripts creates a `calibration.json` in the dataset folder
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'''
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import numpy as np
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import cv2
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import json
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import tqdm
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import math
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dataset = 'hof2'
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# set needed detections. Use math.inf to scan the whole video
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needed_detections = math.inf # 20
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# Defining the dimensions of checkerboard
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CHECKERBOARD = (6,9)
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# termination criteria
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criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
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# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
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objp = np.zeros((CHECKERBOARD[0] * CHECKERBOARD[1],3), np.float32)
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objp[:,:2] = np.mgrid[0:CHECKERBOARD[0],0:CHECKERBOARD[1]].T.reshape(-1,2)
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# Arrays to store object points and image points from all the images.
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objpoints = [] # 3d point in real world space
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imgpoints = [] # 2d points in image plane.
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cap = cv2.VideoCapture(dataset / "chessboard.mp4")
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frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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dim = {
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'width': frame_width,
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'height': frame_height,
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}
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found = 0
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p = tqdm.tqdm()
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p2 = tqdm.tqdm(total=needed_detections)
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while ((found < needed_detections) if math.isfinite(needed_detections) else True):
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ret, img = cap.read() # Capture frame-by-frame
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if not ret:
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break
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p.update()
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# Find the chess board corners
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ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD,None)
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# If found, add object points, image points (after refining them)
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if ret == True:
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objpoints.append(objp) # Certainly, every loop objp is the same, in 3D.
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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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p2.update()
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p2.n
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# Draw and display the corners
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img = cv2.drawChessboardCorners(img, CHECKERBOARD, corners2, ret)
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found += 1
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cv2.imshow('img', img)
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cv2.waitKey(1)
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# When everything done, release the capture
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cap.release()
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cv2.destroyAllWindows()
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print(f"Calculating matrixes with {found} detections")
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ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
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# It's very important to transform the matrix to list.
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data = {'dim': dim, 'camera_matrix': np.asarray(mtx).tolist(), 'dist_coeff': np.asarray(dist).tolist()}
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fn = dataset + "/calibration.json"
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print(f"write to {fn}")
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with open(fn, "w") as f:
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json.dump(data, f)
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