问摄像机校准用cv2. cv2.findCirclesGrid网格未被检测到

EN

import math
import cv2
import numpy as np
import logging

image = 'PathToImage'

log = logging.getLogger(__name__)

im = cv2.imread(image, cv2.IMREAD_GRAYSCALE)

params = cv2.SimpleBlobDetector_Params()
params.minThreshold = 1
params.maxThreshold = 255

params.filterByArea = True
params.minArea = 50
params.maxArea = 300

params.filterByInertia = True
params.minInertiaRatio = 0.5

params.filterByCircularity = True
params.minCircularity = .8

params.minDistBetweenBlobs = 7


detector = cv2.SimpleBlobDetector_create(params)

# Keypoint class: pt->coordinates, size->diameter, angle->angle of the blob, response->response showing the confidence of the proposition, octave, class_id
keypoints = detector.detect(im)

# Generate image
im_with_keypoints = cv2.drawKeypoints(im, keypoints, np.array([]), (0, 0, 255),
                                      cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
imgResized = cv2.resize(im_with_keypoints, (665, 500))

# find circle centers and size
circle_number = 12
point_centres = []
point_locations = []

"""gathers an array of the centrepoints of circles detected"""
for keyPoint in keypoints:
    x = keyPoint.pt[0]
    y = keyPoint.pt[1]
    s = keyPoint.size
    log.info(f'{keyPoint.response=}')

    pt = [x, y, np.sqrt(s / math.pi)]
    pts = [[x, y]]
    point_centres.append(pt)
    point_locations.append(pts)

import cv2
import numpy as np
import glob
from find_circles import circle_centres
import logging

def main():
    log = logging.getLogger(__name__)
    logging.basicConfig(level = logging.INFO)

    CHECKERBOARD = (3, 4)
    SquareSize = 72

    # 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 = []


    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)
    objp = objp * SquareSize

    log.info(f'objPts\n {objp}')

    fnames = 'PathToImages'
    images = glob.glob(fnames)

    params = cv2.SimpleBlobDetector_Params()
    params.minThreshold = 1
    params.maxThreshold = 255

    # params.filterByConvexity = True
    # params.minConvexity = 0.4

    params.filterByArea = True
    params.minArea = 50
    params.maxArea = 300

    params.filterByInertia = True
    params.minInertiaRatio = 0.5

    params.filterByCircularity = True
    params.minCircularity = 0.8

    params.minDistBetweenBlobs = 7

    detector = cv2.SimpleBlobDetector_create(params)

    for fname in images:
        ret, centres = circle_centres(fname)

        img = cv2.imread(fname)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        ret, corners = cv2.findCirclesGrid(gray, CHECKERBOARD, None, flags=cv2.CALIB_CB_SYMMETRIC_GRID,blobDetector=detector)

        log.info(f'ret {ret}')

        if ret is True:
            imgCorners = cv2.drawChessboardCorners(img, CHECKERBOARD, corners, ret)
            resized = cv2.resize(imgCorners, (665, 500))
            cv2.imshow('Circular pattern', resized)
            cv2.waitKey()

if __name__ == "__main__":
    main()