44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
#retval, corners = cv2.findChessboardCorners(image,patternSize, flags)
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import cv2
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import numpy as np
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# Define the size of the chessboard
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chessboard_size = (21, 15)
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# Define the object points of the chessboard
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object_points = np.zeros((np.prod(chessboard_size), 3), dtype=np.float32)
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object_points[:, :2] = np.mgrid[0:chessboard_size[0], 0:chessboard_size[1]].T.reshape(-1, 2)
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# Create arrays to store the object points and image points from all the images
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object_points_array = []
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image_points_array = []
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# Load the images
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images = []
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images.append(cv2.imread("/home/ros/Bureau/ca_ur5/left.jpg"))
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images.append(cv2.imread("/home/ros/Bureau/ca_ur5/left2.jpg"))
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# Add more images as needed
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# Loop through each image and find the chessboard corners
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for image in images:
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# Find the chessboard corners
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found, corners = cv2.findChessboardCorners(gray, chessboard_size, None)
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# If the corners are found, add the object points and image points to the arrays
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if found:
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object_points_array.append(object_points)
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image_points_array.append(corners)
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# Calibrate the camera using the object points and image points
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ret, camera_matrix, distortion_coefficients, rotation_vectors, translation_vectors = cv2.calibrateCamera(
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object_points_array, image_points_array, gray.shape[::-1], None, None)
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# Print the camera matrix and distortion coefficients
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print("Camera matrix:")
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print(camera_matrix)
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print("Distortion coefficients:")
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print(distortion_coefficients)
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