Python+OpenCV实现单个圆形孔和针检测
天人合一peng 人气:0如果中间红色区域是针则可以用下面的代码检测,其阈值和斑点检测的参数根据图像像素值做相应修改
检测的主要思路是先通过找到外面的大圆,再通过圆心定位出一个ROI区域,在ROI区域中检测中心的检测对象
import os import cv2 import numpy as np import math # 检测针脚位置 def needelCenter_detect(img): params = cv2.SimpleBlobDetector_Params() # Setup SimpleBlobDetector parameters. # print('params') # print(params) # print(type(params)) # Filter by Area. params.filterByArea = True params.minArea = 100 params.maxArea = 10e3 params.minDistBetweenBlobs = 50 # 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 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Detect blobs. minThreshValue = 110 _, 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) # 找到距离原点(0,0)最近和最远的点 detector = cv2.SimpleBlobDetector_create(params) keypoints = detector.detect(gray) # print(len(keypoints)) # print(keypoints[0].pt[0]) # 如果这儿没检测到可能会出错 if len(keypoints) == 0: print("没有检测到针角坐标,可能需要调整针角斑点检测参数") return keypoints else: print(len(keypoints)) im_with_keypoints = cv2.drawKeypoints(gray, keypoints, np.array([]), (255, 0, 0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) # if keypoints is not None: color_img = cv2.cvtColor(im_with_keypoints, cv2.COLOR_BGR2RGB) # 画出圆的圆心 cv2.circle(color_img, (int(keypoints[0].pt[0]), int(keypoints[0].pt[1])), 5, (0, 255, 0), -1) cv2.imshow("color_img",color_img) # cv2.waitKey() return keypoints # 检测连接器圆形位置 def circle_detect(image): # 灰度化 circle_img = image.copy() gray = cv2.cvtColor(circle_img, cv2.COLOR_BGR2GRAY) # 输出图像大小,方便根据图像大小调节minRadius和maxRadius # print(image.shape) # 进行中值滤波 img = cv2.medianBlur(gray, 3) # 针角圆心坐标 out_x = 0 out_y = 0 # 霍夫变换圆检测 circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 10e10, param1=100, param2=30, minRadius=10, maxRadius=100) # 如果没检测到会报错 # 这种判断方式过于简单 if circles is None: print("没有检测到连接器外圆") else: for circle in circles[0]: # 圆的基本信息 # print(circle[2]) # 坐标行列-圆心坐标 out_x = int(circle[0]) out_y = int(circle[1]) # 半径 r = int(circle[2]) # 在原图用指定颜色标记出圆的边界 cv2.circle(circle_img, (out_x, out_y), r, (0, 0, 255), 2) # # 画出圆的圆心 cv2.circle(circle_img, (out_x, out_y), 3, (0, 255, 255), -1) # 记录外圆坐标 out_xpoint = out_x out_ypoint = out_y # 只框出单个针角的位置区域 step_center = 30 step_rect = 60 out_x -= step_center out_y -= step_center needleRect = image[out_y: out_y + step_rect, out_x: out_x + step_rect] # cv2.imshow("needleRect", needleRect) # 根据检测到的圆形连接器中心找针角位置 centerPoint = needelCenter_detect(needleRect) if len(centerPoint) == 0: print("调整位置") else: # 将针角的坐标原还至原图 in_x = int(centerPoint[0].pt[0]) in_y = int(centerPoint[0].pt[1]) in_x += out_x in_y += out_y # 画出针角的圆心 cv2.circle(circle_img, (in_x, in_y), 3, (0, 255, 0), -1) # 计算两者的距离 # 假设通过标定其一个像素代表0.0056mm DPI = 0.00568 dis = math.sqrt(math.pow(out_xpoint - in_x,2) + math.pow(out_ypoint - in_y,2)) print("两者相互之间的距离为(mm):", dis*DPI) cv2.imshow("image",circle_img) cv2.waitKey(1) if __name__ == "__main__": # # 测试0 如果是小图 需要将检测程序中的cv2.waitKey(1)修改为cv2.waitKey()不然看到图片 # image = cv2.imread("images/CircleLinker/CLinker01.jpg") # # cv2.imshow("show",image) # # cv2.waitKey() # roi = image # circle_detect(roi) # 测试1 从原图中换到连接器位置 image = cv2.imread("SingleImages/src/single.jpg") # cv2.imshow("show",image) # cv2.waitKey() # 如何准确找到圆形连接器 ---》用yolo训练后能准备找到 roi = image[1800:2300, 1800:2300 ] # cv2.imshow("show",roi) # cv2.waitKey() circle_detect(roi) # # 测试2 如果是小图 需要将检测程序中的cv2.waitKey(1)修改为cv2.waitKey()不然看到图片 # image = cv2.imread("SingleImages/single04.jpg") # # cv2.imshow("show",image) # # cv2.waitKey() # roi = image # circle_detect(roi) # # 测试3 检测文件夹下所有图片 # path = r"D:\BUFFER\Pycharm\ZhenJiaoDect\SingleImages" # for filename in os.listdir(path): # listdir的参数是文件夹的路径 # filenames = path + '\\' + filename # # print(filenames) # img_orig = cv2.imread(filenames, 1) # print(filenames) # # if img_orig is None: # print("Warning: No Pictures") # else: # circle_detect(img_orig) # # # 测试4 打开相机测试 # # 需要将检测程序中的cv2.waitKey()修改为cv2.waitKey(1) # # 否则看到不视频实时检测结果 # capture = cv2.VideoCapture(0) # # while (True): # # 获取一帧 # ret, frame = capture.read() # circle_detect(frame) # # # cv2.imshow('frame', frame) # # if cv2.waitKey(1) == ord('q'): # break
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