Python+OpenCV实现单个圆形孔和针检测，

Python+OpenCV实现单个圆形孔和针检测，

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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