Camera Calibration Using a Circle Grid

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Preparation

By reading two of our previous blogs Camera Calibration Using a Chessboard and Camera Posture Estimation Using Circle Grid Pattern, it wouldn’t be hard to replace the chessboard by a circle grid to calculate camera calibration parameters.

Coding

Our code can be found at OpenCV Examples.

First of all

As mentioned in Camera Calibration Using a Chessboard, for intrinsic parameters estimation, namely, camera calibration, there is NO need to measure the circle unit size. And the circle gird is to be adopted is exactly the same one as used in Camera Posture Estimation Using Circle Grid Pattern:

asymmetric_circle_grid

Secondly

Required packages need to be imported.

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import numpy as np
import cv2
import yam

Thirdly

Some initialization work need to be done, including: 1) define the termination criteria when refine the corner sub-pixel later on; 2) blob detection parameters; 3) object points coordinators initialization.

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# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)

########################################Blob Detector##############################################

# Setup SimpleBlobDetector parameters.
blobParams = cv2.SimpleBlobDetector_Params()

# Change thresholds
blobParams.minThreshold = 8
blobParams.maxThreshold = 255

# Filter by Area.
blobParams.filterByArea = True
blobParams.minArea = 64     # minArea may be adjusted to suit for your experiment
blobParams.maxArea = 2500   # maxArea may be adjusted to suit for your experiment

# Filter by Circularity
blobParams.filterByCircularity = True
blobParams.minCircularity = 0.1

# Filter by Convexity
blobParams.filterByConvexity = True
blobParams.minConvexity = 0.87

# Filter by Inertia
blobParams.filterByInertia = True
blobParams.minInertiaRatio = 0.01

# Create a detector with the parameters
blobDetector = cv2.SimpleBlobDetector_create(blobParams)

###################################################################################################

###################################################################################################

# Original blob coordinates, supposing all blobs are of z-coordinates 0
# And, the distance between every two neighbour blob circle centers is 72 centimetres
# In fact, any number can be used to replace 72.
# Namely, the real size of the circle is pointless while calculating camera calibration parameters.
objp = np.zeros((44, 3), np.float32)
objp[0]  = (0  , 0  , 0)
objp[1]  = (0  , 72 , 0)
objp[2]  = (0  , 144, 0)
objp[3]  = (0  , 216, 0)
objp[4]  = (36 , 36 , 0)
objp[5]  = (36 , 108, 0)
objp[6]  = (36 , 180, 0)
objp[7]  = (36 , 252, 0)
objp[8]  = (72 , 0  , 0)
objp[9]  = (72 , 72 , 0)
objp[10] = (72 , 144, 0)
objp[11] = (72 , 216, 0)
objp[12] = (108, 36,  0)
objp[13] = (108, 108, 0)
objp[14] = (108, 180, 0)
objp[15] = (108, 252, 0)
objp[16] = (144, 0  , 0)
objp[17] = (144, 72 , 0)
objp[18] = (144, 144, 0)
objp[19] = (144, 216, 0)
objp[20] = (180, 36 , 0)
objp[21] = (180, 108, 0)
objp[22] = (180, 180, 0)
objp[23] = (180, 252, 0)
objp[24] = (216, 0  , 0)
objp[25] = (216, 72 , 0)
objp[26] = (216, 144, 0)
objp[27] = (216, 216, 0)
objp[28] = (252, 36 , 0)
objp[29] = (252, 108, 0)
objp[30] = (252, 180, 0)
objp[31] = (252, 252, 0)
objp[32] = (288, 0  , 0)
objp[33] = (288, 72 , 0)
objp[34] = (288, 144, 0)
objp[35] = (288, 216, 0)
objp[36] = (324, 36 , 0)
objp[37] = (324, 108, 0)
objp[38] = (324, 180, 0)
objp[39] = (324, 252, 0)
objp[40] = (360, 0  , 0)
objp[41] = (360, 72 , 0)
objp[42] = (360, 144, 0)
objp[43] = (360, 216, 0)
###################################################################################################

# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.

Fourthly

After localizing 10 frames (10 can be changed to any positive integer as you wish) of a grid of 2D circles, camera matrix and distortion coefficients can be calculated by invoking the function calibrateCamera. Here, we are testing on a real-time camera stream. Similar to Camera Posture Estimation Using Circle Grid Pattern, the trick is to do blobDetector.detect() and draw the detected blobs using cv2.drawKeypoints() before invoking cv2.findCirclesGrid(), so that the entire grid is easier to be found.

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cap = cv2.VideoCapture(0)
found = 0
while(found < 10):  # Here, 10 can be changed to whatever number you like to choose
    ret, img = cap.read() # Capture frame-by-frame
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    keypoints = blobDetector.detect(gray) # Detect blobs.

    # Draw detected blobs as red circles. This helps cv2.findCirclesGrid() .
    im_with_keypoints = cv2.drawKeypoints(img, keypoints, np.array([]), (0,255,0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
    im_with_keypoints_gray = cv2.cvtColor(im_with_keypoints, cv2.COLOR_BGR2GRAY)
    ret, corners = cv2.findCirclesGrid(im_with_keypoints, (4,11), None, flags = cv2.CALIB_CB_ASYMMETRIC_GRID)   # Find the circle grid

    if ret == True:
        objpoints.append(objp)  # Certainly, every loop objp is the same, in 3D.

        corners2 = cv2.cornerSubPix(im_with_keypoints_gray, corners, (11,11), (-1,-1), criteria)    # Refines the corner locations.
        imgpoints.append(corners2)

        # Draw and display the corners.
        im_with_keypoints = cv2.drawChessboardCorners(img, (4,11), corners2, ret)
        found += 1

    cv2.imshow("img", im_with_keypoints) # display
    cv2.waitKey(2)

# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()

ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)

Finally

Write the calculated calibration parameters into a yaml file. Here, it is a bit tricky. Please bear in mind that you MUST call function tolist() to transform a numpy array to a list.

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# It's very important to transform the matrix to list.
data = {'camera_matrix': np.asarray(mtx).tolist(), 'dist_coeff': np.asarray(dist).tolist()}
with open("calibration.yaml", "w") as f:
    yaml.dump(data, f)

Additionally

You may use the following piece of code to load the calibration parameters from file “calibration.yaml”.

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with open('calibration.yaml') as f:
    loadeddict = yaml.load(f)
mtxloaded = loadeddict.get('camera_matrix')
distloaded = loadeddict.get('dist_coeff')