Photo

calib.py

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+#retval, corners = cv2.findChessboardCorners(image,patternSize, flags)
+
+import cv2
+import numpy as np
+
+# Define the size of the chessboard
+chessboard_size = (22, 16)
+
+# Define the object points of the chessboard
+object_points = np.zeros((np.prod(chessboard_size), 3), dtype=np.float32)
+object_points[:, :2] = np.mgrid[0:chessboard_size[0], 0:chessboard_size[1]].T.reshape(-1, 2)
+
+# Create arrays to store the object points and image points from all the images
+object_points_array = []
+image_points_array = []
+
+# Load the images
+images = []
+images.append(cv2.imread("left.jpg"))
+images.append(cv2.imread("right.jpg"))
+# Add more images as needed
+
+# Loop through each image and find the chessboard corners
+for image in images:
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Find the chessboard corners
+    found, corners = cv2.findChessboardCorners(gray, chessboard_size, None)
+
+    # If the corners are found, add the object points and image points to the arrays
+    if found:
+        object_points_array.append(object_points)
+        image_points_array.append(corners)
+
+# Calibrate the camera using the object points and image points
+ret, camera_matrix, distortion_coefficients, rotation_vectors, translation_vectors = cv2.calibrateCamera(
+    object_points_array, image_points_array, gray.shape[::-1], None, None)
+
+# Print the camera matrix and distortion coefficients
+print("Camera matrix:")
+print(camera_matrix)
+print("Distortion coefficients:")
+print(distortion_coefficients)

test2.py

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+import cv2
+import numpy as np
+import os
+import glob
+
+# Defining the dimensions of checkerboard
+CHECKERBOARD = (16,22)
+criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
+
+# Creating vector to store vectors of 3D points for each checkerboard image
+objpoints = []
+# Creating vector to store vectors of 2D points for each checkerboard image
+imgpoints = [] 
+
+
+# Defining the world coordinates for 3D points
+objp = np.zeros((1, CHECKERBOARD[0] * CHECKERBOARD[1], 3), np.float32)
+objp[0,:,:2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2)
+prev_img_shape = None
+
+
+h = 480
+w = 640
+
+# Extracting path of individual image stored in a given directory
+images = glob.glob('/home/ros/Bureau/ca_ur5/*.jpg')
+for fname in images:
+	img = cv2.imread(fname)
+	h,w = img.shape[:2]
+	gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+	print(gray)
+	# Find the chess board corners
+	# If desired number of corners are found in the image then ret = true
+	ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD, cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK + cv2.CALIB_CB_NORMALIZE_IMAGE)
+	"""
+	If desired number of corner are detected,
+	we refine the pixel coordinates and display 
+	them on the images of checker board
+	"""
+	if ret == True:
+		objpoints.append(objp)
+		# refining pixel coordinates for given 2d points.
+		corners2 = cv2.cornerSubPix(gray, corners, (11,11),(-1,-1), criteria)
+		imgpoints.append(corners2)
+
+		# Draw and display the corners
+		img = cv2.drawChessboardCorners(img,CHECKERBOARD,corners2,ret)
+
+	cv2.imshow('img',img)
+	cv2.waitKey(0)
+
+cv2.destroyAllWindows()
+
+#h,w = img.shape[:2]
+
+"""
+Performing camera calibration by 
+passing the value of known 3D points (objpoints)
+and corresponding pixel coordinates of the 
+detected corners (imgpoints)
+"""
+ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, (w,h), None, None)
+
+print("Camera matrix : \n")
+print(mtx)
+print("dist : \n")
+print(dist)
+print("rvecs : \n")
+print(rvecs)
+print("tvecs : \n")
+print(tvecs)