Python实现多个圆和圆中圆的检测

Python检测多个圆 Python检测圆中圆 Python检测圆 2022-11-16 00:11:11 539人浏览安东尼

Python 官方文档：入门教程 => 点击学习

摘要

主要思想是先检测外边圆和圆心然后再外圆内检测小圆，计算小圆圆心与外圆圆心的距离判断是不是有问题或者可以计算两圆圆心的距离 # coding:utf-8 import math i

主要思想是先检测外边圆和圆心

然后再外圆内检测小圆，计算小圆圆心与外圆圆心的距离判断是不是有问题

或者可以计算两圆圆心的距离

# coding:utf-8
import math
import cv2
import numpy as np
import os
 
 
 
def findNeedlePoints(img):
    gray_src= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    minThreshValue = 50
    _, gray = cv2.threshold(gray_src, minThreshValue, 255, cv2.THRESH_BINARY)
    erosion_size = 3
    # element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * erosion_size + 1, 2 * erosion_size + 1),
    #                                     (erosion_size, erosion_size))
    element = cv2.getStructuringElement(cv2.MORPH_ERODE, (2 * erosion_size + 1, 2 * erosion_size + 1),
                                        (erosion_size, erosion_size))
    # MORPH_ELLIPSE 不同的测试一下
    erosion_gray = cv2.erode(gray, element, 3)
 
    cv2.imshow("erosion_gray", erosion_gray)
 
    paramsIn = cv2.SimpleBlobDetector_Params()
    paramsIn.filterByArea = True
 
    # 不同图片应该调节的参数
    paramsIn.minArea = 80
    paramsIn.maxArea = 1000
    paramsIn.minDistBetweenBlobs = 80
    paramsIn.filterByColor = True
    paramsIn.filterByConvexity = False
    paramsIn.minThreshold = 100*2
    paramsIn.maxThreshold = 1000
 
    # 图像取反
    needleGray = 255 - erosion_gray.copy()
    # 中值滤波和腐蚀去噪
    needleGray = cv2.medianBlur(needleGray, 3)
 
    # cv2.imshow('needleGray', needleGray)
 
 
    erosion_size = 2
    element = cv2.getStructuringElement(cv2.MORPH_RECT, (2 * erosion_size + 1, 2 * erosion_size + 1),
                                        (erosion_size, erosion_size))
    needlePoints = cv2.erode(needleGray, element, 1)
    cv2.imshow('needle=Points', needlePoints)
 
    detector2 = cv2.SimpleBlobDetector_create(paramsIn)
    needleKeypoints = detector2.detect(needlePoints)
    # OpenCV
    needle_keypoints = cv2.drawKeypoints(needlePoints, needleKeypoints, np.array([]), (255, 0, 0),
                                         cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
 
    allNeedlePoints = []
    if needleKeypoints is not None:
        for i in range(len(needleKeypoints)):
            allNeedlePoints.append(needleKeypoints[i].pt)
 
    color_img = cv2.cvtColor(needle_keypoints, cv2.COLOR_BGR2RGB)
    # needle_img = cv2.cvtColor(im_with_keypoints, cv2.COLOR_BGR2RGB)
 
    cv2.imshow('holeShow', color_img)
    # cv2.imshow('needleShow', needle_img)
 
    cv2.waiTKEy()
 
 
 
def innerHoughCicle(hsv_image, src_image, rect):
    # 霍夫变换圆检测
 
    gray_src = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2RGB)
    gray_src = cv2.cvtColor(gray_src, cv2.COLOR_RGB2GRAY)
    minThreshValue = 100
    _, gray = cv2.threshold(gray_src, minThreshValue, 255, cv2.THRESH_BINARY)
 
    kernel1 = np.ones((3, 3), dtype=np.uint8)
    kernel2 = np.ones((3, 3), dtype=np.uint8)
 
    gray = cv2.erode(gray, kernel2, 2)
    gray = cv2.dilate(gray, kernel1, 2)  # 1:迭代次数，也就是执行几次膨胀操作
 
    # cv2.namedWindow("gray", 2)
    # cv2.imshow("gray", gray)
    # cv2.waitKey()
    circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 2, 100, param1=100, param2=60, 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(hsv_image, (out_x, out_y), r, (0, 0, 255), 2)
            # # 画出圆的圆心
            cv2.circle(hsv_image, (out_x, out_y), 3, (0, 0, 255), -1)
 
 
 
 
        cv2.namedWindow("hsv_circle", 2)
        cv2.imshow("hsv_circle",hsv_image)
        cv2.waitKey()
 
def outHoughCicle(hsv_image, src_image, rect):
    # 霍夫变换圆检测
 
    gray_src = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2RGB)
    gray_src = cv2.cvtColor(gray_src, cv2.COLOR_RGB2GRAY)
    minThreshValue = 50
    _, gray = cv2.threshold(gray_src, minThreshValue, 255, cv2.THRESH_BINARY)
 
    kernel1 = np.ones((3, 3), dtype=np.uint8)
    kernel2 = np.ones((3, 3), dtype=np.uint8)
 
    gray = cv2.erode(gray, kernel2, 2)
    gray = cv2.dilate(gray, kernel1, 2)  # 1:迭代次数，也就是执行几次膨胀操作
 
    cv2.namedWindow("gray", 2)
    cv2.imshow("gray", gray)
    cv2.waitKey()
    circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 10e10, param1=100, param2=60, minRadius=500, maxRadius=10000)
    # 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(hsv_image, (out_x, out_y), r, (0, 0, 255), 2)
            # # 画出圆的圆心
            cv2.circle(hsv_image, (out_x, out_y), 3, (0, 0, 255), -1)
 
            # 画在原图上
            cv2.circle(src_image, (out_x + rect[0], out_y + rect[1]), r, (0, 0, 255), 2)
            # # 画出圆的圆心
            cv2.circle(src_image, (out_x + rect[0], out_y+ rect[1]), 3, (0, 0, 255), -1)
 
 
        cv2.namedWindow("hsv_circle", 2)
        cv2.imshow("hsv_circle",hsv_image)
 
        cv2.namedWindow("src_image", 2)
        cv2.imshow("src_image",src_image)
        cv2.waitKey()
 
 
 
# 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 = 100
    _, gray = cv2.threshold(gray, minThreshValue, 255, cv2.THRESH_BINARY)
 
    erosion_size = 1
    # element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * erosion_size + 1, 2 * erosion_size + 1),
    #                                     (erosion_size, erosion_size))
    element = cv2.getStructuringElement(cv2.MORPH_ERODE, (2 * erosion_size + 1, 2 * erosion_size + 1),
                                        (erosion_size, erosion_size))
    dilate_gray = cv2.dilate(gray, element, 1)
 
    # cv2.namedWindow("gray", 2)
    # cv2.imshow("gray",dilate_gray)
    # cv2.waitKey()
 
    detector = cv2.SimpleBlobDetector_create(params)
    keypoints = detector.detect(dilate_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")
        print(keypoints)
        return keypoints
 
    else:
        print("检测到孔的数量", len(keypoints))
        # im_with_keypoints = cv2.drawKeypoints(img, keypoints, np.array([]), (255, 0, 0),
        #                                       cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
        #
        # color_img = cv2.cvtColor(im_with_keypoints, cv2.COLOR_BGR2RGB)
        # 画出圆的圆心
        # for kp in keypoints:
        #     cv2.circle(img, (int(kp.pt[0]), int(kp.pt[1])), 3, (0, 0, 255), -1)
        #
        # cv2.namedWindow("color_img", 2)
        # cv2.imshow("color_img",img)
        # # cv2.waitKey()
 
        return keypoints
 
 
# 检测外部区域针或孔的位置
def out_circle_detect(rect_hole_info, src):
    # 灰度化
    circle_img = rect_hole_info
 
    gray = cv2.cvtColor(circle_img, cv2.COLOR_HSV2RGB)
    gray = cv2.cvtColor(gray, cv2.COLOR_RGB2GRAY)
 
    # 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)
 
    erosion_size = 3
    # element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * erosion_size + 1, 2 * erosion_size + 1),
    #                                     (erosion_size, erosion_size))
    element = cv2.getStructuringElement(cv2.MORPH_ERODE, (2 * erosion_size + 1, 2 * erosion_size + 1),
                                        (erosion_size, erosion_size))
    dilate_gray = cv2.dilate(img, element, 1)
    # cv2.namedWindow("dilate_gray", 2)
    # cv2.imshow("dilate_gray", dilate_gray)
    # cv2.waitKey()
 
    # 针角圆心坐标
    out_x, out_y, r = 0, 0, 0
 
    # 霍夫变换检测最大圆
    circles = cv2.HoughCircles(dilate_gray, cv2.HOUGH_GRADIENT, 1, 1000, param1=100, param2=30, minRadius=500, maxRadius=1000)
    # 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")
        return 0, 0, 0
    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])
            # 将检测到的坐标保存
            # 半径
            r = int(circle[2])
            # print(r)
            # # # 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), 5, (0, 0, 255), -1)
 
        cv2.namedWindow("circle_imgs", 2)
        cv2.imshow("circle_imgs", circle_img)
        cv2.waitKey()
 
        return out_x, out_y, r
 
 
 
 
# 检测内部区域针或孔的位置
def inner_circle_detect(rect_hole_info, src):
    # 灰度化
    circle_img = rect_hole_info
 
    gray = cv2.cvtColor(circle_img, cv2.COLOR_HSV2RGB)
    gray = cv2.cvtColor(gray, cv2.COLOR_RGB2GRAY)
 
    # 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)
 
    erosion_size = 3
    # element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * erosion_size + 1, 2 * erosion_size + 1),
    #                                     (erosion_size, erosion_size))
    element = cv2.getStructuringElement(cv2.MORPH_ERODE, (2 * erosion_size + 1, 2 * erosion_size + 1),
                                        (erosion_size, erosion_size))
    dilate_gray = cv2.dilate(img, element, 1)
    # cv2.namedWindow("dilate_gray", 2)
    # cv2.imshow("dilate_gray", dilate_gray)
    # cv2.waitKey()
 
 
    # 针角圆心坐标
    out_x_p = []
    out_y_p = []
    rudis = []
 
    # 霍夫变换检测最大圆
    circles = cv2.HoughCircles(dilate_gray, cv2.HOUGH_GRADIENT, 1, 100, param1=100, param2=30, minRadius=20, 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")
        return out_x_p, out_y_p
 
    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])
            # 将检测到的坐标保存
            out_x_p.append(out_x)
            out_y_p.append(out_y)
            # 半径
            r = int(circle[2])
            rudis.append(r)
            # print(r)
            # # # 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), 5, (0, 0, 255), -1)
 
        cv2.namedWindow("circle_img", 2)
        cv2.imshow("circle_img",circle_img)
        cv2.waitKey()
 
        # 记录外圆坐标
 
        out_xpoints = out_x_p.copy()
        out_ypoints = out_y_p.copy()
        out_rudis = rudis.copy()
        # print("out_xpoints",out_xpoints)
        # print("out_ypoints",out_ypoints)
 
        # 只框出单个针角的位置区域
        step_center = 25
        step_rect = 50
 
        # 遍历所有的孔的位置
        # 记录孔的位置
        in_x_p = []
        in_y_p = []
        for i in range(0, len(out_xpoints)):
            out_x_begin = out_xpoints[i] - step_center
            out_y_begin = out_ypoints[i] - step_center
            needleRect = circle_img[out_y_begin: out_y_begin + step_rect, out_x_begin: out_x_begin + step_rect]
            # cv2.namedWindow("needleRect", 2)
            # cv2.imshow("needleRect", needleRect)
            # cv2.waitKey()
 
            # 根据检测到的圆形连接器中心找针角位置
            centerPoint = needelCenter_detect(needleRect)
            # print(len(centerPoint))
 
            if len(centerPoint) == 0:
                out_x_p.remove(out_xpoints[i])
                out_y_p.remove(out_ypoints[i])
                rudis.remove(out_rudis[i])
                print("调整位置")
            else:
                for cp in centerPoint:
                        # 将针角的坐标原还至原图
                    in_x = int(cp.pt[0])
                    in_y = int(cp.pt[1])
                    in_x += out_x_begin
                    in_y += out_y_begin
 
                    in_x_p.append(in_x)
                    in_y_p.append(in_y)
 
                # # # 画出中心孔的圆心
                #     cv2.circle(circle_img, (in_x, in_y), 4, (0, 255, 0), -1)
                #     # 画出外孔的圆心
                #     cv2.circle(circle_img, (out_xpoints[i], out_ypoints[i]), 4, (0, 0, 255), -1)
 
                # # 计算两者的距离
                #     # 假设通过标定其一个像素代表0.0056mm
                #     DPI = 0.0198
                #     dis = math.sqrt(math.pow(out_xpoints[i] - in_x,2) + math.pow(out_ypoints[i] - in_y,2))
                #     print("两者相互之间的距离为（mm）:", dis*DPI)
        return in_x_p,in_y_p
 
                # cv2.namedWindow("image", 2)
                # cv2.imshow("image",circle_img)
                # cv2.waitKey()
        # if len(out_x_p) == 0:
        #     print("没检测到，需要调整位置")
        # else:
        #     for j in range(0,len(out_x_p)):
        #         # 画出外孔的圆心
        #         cv2.circle(circle_img, (out_x_p[j], out_y_p[j]), rudis[j], (0, 0, 255), 3)
        #         cv2.circle(circle_img, (out_x_p[j], out_y_p[j]), 3, (0, 0, 255), -1)
        #
        #         # cv2.circle(circle_img, (in_x_p[j], in_y_p[j]), 3, (0, 255, 0), -1)
        #
        #     cv2.namedWindow("image", 2)
        #     cv2.imshow("image",circle_img)
        #     cv2.waitKey()
 
 
 
def j599_4_holes_dectWX(imagePath, templatePath):
    # templatePath需要用户手动框获取ROI
    img = cv2.imread(imagePath)
    img_roi = cv2.imread(templatePath)
 
    if img_roi is  None:
        print("no image")
 
    # HSV二值化
    img_roi = cv2.medianBlur(img_roi, 5)  # 中值滤波
    outx, outy, outR = out_circle_detect(img_roi, img)
    print(outx, outy, outR )
    inx, iny = inner_circle_detect(img_roi, img)
    if len(inx) == 0 or outx == 0:
        print("没检测到位置")
        return "没检测到对象", -1
    else:
        cv2.circle(img_roi, (outx, outy), outR, (0, 0, 255), 3)
 
        is_ok = []
 
        for k in range(0, len(inx)):
            # 计算两者的距离
            # 假设通过标定其一个像素代表0.0056mm
            # 两者相互之间的距离为（mm）: 9.311053946788194
            # 两者相互之间的距离为（mm）: 9.163550379629067
            # 两者相互之间的距离为（mm）: 8.95984457900917
            # 两者相互之间的距离为（mm）: 8.977940966613671
            # 平均值为 9.103 所以其阈值为9.103 + 0.5
            DPI = 0.0198
            dis = math.sqrt(math.pow(outx - inx[k], 2) + math.pow(outy - iny[k], 2))
            dis *= DPI
            # print("两者相互之间的距离为（mm）:", dis)
            if dis < 9.603:
                cv2.circle(img_roi, (inx[k], iny[k]), 8, (0, 255, 0), -1)
                # print("没有插针歪斜，产品合格")
                is_ok.append(1)
            else:
                cv2.circle(img_roi, (inx[k], iny[k]), 20, (0, 0, 255), 3)
                # print("有插针歪斜，不合格")
                is_ok.append(0)
 
        # cv2.namedWindow("image", 2)
        # cv2.imshow("image",img_roi)
        # cv2.waitKey()
 
        isExists = os.path.exists("./runs/J599/")
        if not isExists:
            os.makedirs("./runs/J599/")
        cv2.imwrite("./runs/J599/result.jpg", img_roi)
 
        if 0 in is_ok:
            print("有插针歪斜，不合格")
            return "有插针歪斜，不合格"
        else:
            print("没有插针歪斜，产品合格")
            return "没有插针歪斜，产品合格"
 
 
 
 
if __name__ == "__main__":
    reslut = j599_4_holes_dectWX("images/Final/E_0_8.jpg","J599-4holes_template.jpg")
    print(reslut)
#
#     # # # 4holes
#     img = cv2.imread("images/Final/E_0_8.jpg", 1)
#     # img_roi = img[973:2027, 1713:2751]
#     # img_roi = img[852:2224, 1515:2940]
#     img_roi = img[842:2234, 1480:2950]
#     cv2.imwrite("J599-4holes_template.jpg",img_roi)
#
#     # cv2.namedWindow("img_roi",2)
#     # cv2.imshow("img_roi", img_roi)
#     # cv2.waitKey()
 
#     if img_roi is  None:
#         print("no image")
#     else:
#         # HSV二值化
#         img_roi = cv2.medianBlur(img_roi, 5)  # 中值滤波
#         outx, outy, outR = out_circle_detect(img_roi, img)
#         print(outx, outy, outR )
#         inx, iny = inner_circle_detect(img_roi, img)
#         if len(inx) == 0 or outx == 0:
#             print("没检测到位置")
#         else:
#             cv2.circle(img_roi, (outx, outy), outR, (0, 0, 255), 3)
#
#             for k in range(0, len(inx)):
#                 # 计算两者的距离
#                 # 假设通过标定其一个像素代表0.0056mm
#                 # 两者相互之间的距离为（mm）: 9.311053946788194
#                 # 两者相互之间的距离为（mm）: 9.163550379629067
#                 # 两者相互之间的距离为（mm）: 8.95984457900917
#                 # 两者相互之间的距离为（mm）: 8.977940966613671
#                 # 平均值为 9.103 所以其阈值为9.103 + 0.5
#                 DPI = 0.0198
#                 dis = math.sqrt(math.pow(outx - inx[k], 2) + math.pow(outy - iny[k], 2))
#                 dis *= DPI
#                 # print("两者相互之间的距离为（mm）:", dis)
#                 if dis > 9.603:
#                     cv2.circle(img_roi, (inx[k], iny[k]), 20, (0, 0, 255), 3)
#                     print("有插针歪斜，不合格")
#                 else:
#                     cv2.circle(img_roi, (inx[k], iny[k]), 8, (0, 255, 0), -1)
#                     print("没有插针歪斜，产品合格")
#
#             cv2.namedWindow("image", 2)
#             cv2.imshow("image",img_roi)
#             cv2.waitKey()