Python+OpenCV实现图片中的圆形检测_Python

Python+OpenCV实现图片中的圆形检测

2022-11-24 10:09天人合一peng Python

这篇文章主要介绍了如何利用Python+OpenCV实现检测图片中的圆形，文中的示例代码讲解详细，感兴趣的小伙伴快跟随小编一起学习一下

效果展示

Python+OpenCV实现图片中的圆形检测

中心的三个没检测到

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									import cv2

									import numpy as np

									import matplotlib.pyplot as plt

									w = 20

									h = 5

									params = cv2.SimpleBlobDetector_Params()

									# Setup SimpleBlobDetector parameters.

									print('params')

									print(params)

									print(type(params))

									# Filter by Area.

									params.filterByArea = True

									params.minArea = 10e1

									params.maxArea = 10e3

									params.minDistBetweenBlobs = 25

									# params.filterByColor = True

									params.filterByConvexity = False

									# tweak these as you see fit

									# Filter by Circularity

									# params.filterByCircularity = False

									# params.minCircularity = 0.2

									# params.blobColor = 0

									# # # Filter by Convexity

									# params.filterByConvexity = True

									# params.minConvexity = 0.87

									# Filter by Inertia

									# params.filterByInertia = True

									# params.filterByInertia = False

									# params.minInertiaRatio = 0.01

									# img = cv2.imread("circles/circels.jpg",1)

									img = cv2.imread("circles/Snap_001.jpg",1)

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

									# Detect blobs.

									# image = cv2.resize(gray_img, (int(img.shape[1]/4),int(img.shape[0]/4)), 1, 1, cv2.INTER_LINEAR)

									# image = cv2.resize(gray_img, dsize=None, fx=0.25, fy=0.25, interpolation=cv2.INTER_LINEAR)

									minThreshValue = 120

									_, gray = cv2.threshold(gray, minThreshValue, 255, cv2.THRESH_BINARY)

									gray = cv2.resize(gray, dsize=None, fx=2, fy=2, interpolation=cv2.INTER_LINEAR)

									# plt.imshow(gray)

									# cv2.imshow("gray",gray)

									detector = cv2.SimpleBlobDetector_create(params)

									keypoints = detector.detect(gray)

									print(len(keypoints))

									fig = plt.figure()

									# im_with_keypoints = cv2.drawKeypoints(gray, keypoints, np.array([]), (0, 0, 255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

									im_with_keypoints = cv2.drawKeypoints(gray, keypoints, np.array([]), (0, 0, 255),  cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

									plt.imshow(cv2.cvtColor(im_with_keypoints, cv2.COLOR_BGR2RGB),interpolation='bicubic')

									fname = "key points"

									titlestr = '%s found %d keypoints' % (fname, len(keypoints))

									plt.title(titlestr)

									plt.show()

									# cv2.imshow("graykey",gray)

									# cv2.waitKey()

									fig.canvas.set_window_title(titlestr)

									ret, corners = cv2.findCirclesGrid(gray, (w, h), flags=(cv2.CALIB_CB_SYMMETRIC_GRID + cv2.CALIB_CB_CLUSTERING ), blobDetector=detector )

									if corners is not None:

									    cv2.drawChessboardCorners(img, (w, h), corners, corners is not None)

									    print("find blob")

									# # cv2.imshow('findCorners', img)

									# cv2.waitKey()

									    plt.imshow(img)

									plt.show()