复习opencv：螺丝螺纹缺陷检测
简述
今天收到了一个检测螺牙缺陷的问题，当复习opencv练个手，记录一下基础知识。这里的代码是检测弯曲的，其他缺陷用yolo处理。东家给的图片有的是有干扰的(红框标识)，所以要求一下最大凸包。
里面很多知识是复习用，最终代码在最后一行，给的101张图片，有2个弯曲度超过0.25，来到了0.33以上有个小技巧可以提高弯直的区分度，这里就不介绍了，需要的私信。 去噪
椒盐噪声
import cv2 img = cv2.imread('image.jpg') median = cv2.medianBlur(img, 5) cv2.imshow('Median filter', median) cv2.waitKey(0) cv2.destroyAllWindows() 1234567
高斯噪声
import cv2 img = cv2.imread('image.jpg') gaussian = cv2.GaussianBlur(img, (5,5), 0) cv2.imshow('Gaussian filter', gaussian) cv2.waitKey(0) cv2.destroyAllWindows() 1234567
小波变换
import cv2 import pywt img = cv2.imread('image.jpg', 0) coeffs = pywt.dwt2(img, 'haar') cA, (cH, cV, cD) = coeffs cv2.imshow('Wavelet denoising', pywt.idwt2((cA, (None, None, None)), 'haar')) cv2.waitKey(0) cv2.destroyAllWindows() 123456789
引导滤波
import numpy as np import cv2 main_path="D:/Handletiling/NG_org_20230602103406819.bmp" def guideFilter(I, p, winSize, eps): mean_I = cv2.blur(I, winSize) mean_p = cv2.blur(p, winSize) mean_II = cv2.blur(I * I, winSize) mean_Ip = cv2.blur(I * p, winSize) var_I = mean_II - mean_I * mean_I cov_Ip = mean_Ip - mean_I * mean_p a = cov_Ip / (var_I + eps) b = mean_p - a * mean_I mean_a = cv2.blur(a, winSize) mean_b = cv2.blur(b, winSize) q = mean_a * I + mean_b return q if __name__ == '__main__': eps = 0.01 winSize = (5,5) image = cv2.imread(main_path, cv2.IMREAD_ANYCOLOR) image = cv2.resize(image, None,fx=0.7, fy=0.7, interpolation=cv2.INTER_CUBIC) I = image/255.0 #将图像归一化 p =I guideFilter_img = guideFilter(I, p, winSize, eps) # 保存导向滤波结果 guideFilter_img = guideFilter_img * 255 guideFilter_img [guideFilter_img > 255] = 255 guideFilter_img = np.round(guideFilter_img ) guideFilter_img = guideFilter_img.astype(np.uint8) cv2.imshow("image",image) cv2.imshow("winSize_5", guideFilter_img ) cv2.waitKey(0) cv2.destroyAllWindows()
123456789101112131415161718192021222324252627282930313233343536373839404142434445求最大凸包
import cv2 import os import numpy as np from skimage.measure import label main_path="D:Handletilingluoya/NG/NG_org_20230602103407364.bmp" ##method1 --opencv def get_lagrest_connect_component1(img): # rgb->gray img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # gaussian filter img_gray = cv2.GaussianBlur(img_gray, (5, 5), 0) # binary exp-threshold=0 # https://blog.csdn.net/weixin_42272768/article/details/110746790 _, img_gray = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY_INV) # ret==threshold # find contour contours, _ = cv2.findContours(img_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # cv2.drawContours(img_gray, contours, -1, 255, 3) # find the area_max region area = [] for i in range(len(contours)): area.append(cv2.contourArea(contours[i])) if len(area) >= 1: max_idx = np.argmax(area) max_contour_area = area[max_idx] for k in range(len(contours)): if k != max_idx: cv2.fillPoly(img_gray, [contours[k]], 0) else: max_contour_area = 0 return max_contour_area, img_gray if __name__ == '__main__': img = cv2.imread(main_path) max_area, img_gray = get_lagrest_connect_component1(img) print(max_area) cv2.imwrite('img_gray.jpg', img_gray)
123456789101112131415161718192021222324252627282930313233343536373839404142判断曲直
1.查找轮廓,提取凸包
2.获得点集
3.计算导数方差
4.比较阈值
def calccurl(str,thresh): img = cv2.imread(str) # cv2.imshow('src',img) #提取凸包 max_area, img_gray =get_lagrest_connect_component1(img) gray = img_gray #阈值处理 ret,binary = cv2.threshold(gray,127,255,0) #查找轮廓,提取凸包 contours,hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) hull = cv2.convexHull(contours[0]) cv2.polylines(img_gray,[hull],True,(255,255,0),2) min_x=min(hull[:,0,0]) max_x=max(hull[:,0,0]) topList=[] bottomList=[] thresh_left=220 thresh_right=30 count=hull.shape[0] isthesame=False for i in range(hull.shape[0]): point = tuple(hull[i][0]) # # cv2.circle(img, point, 1, (0, 255, 0) , 12) if point[0]<(max_x-thresh_right) and point[0]>(min_x+thresh_left): length=binary.shape[0] x1 = np.linspace(start=0, stop=length-1, num=length) temp=x1 * (binary[:, point[0]] / 255) n = np.sum(temp > 0) index=temp.sum()/(n*1.) # if i in[0,count-1]: # if not isthesame: # isthesame=True # else: # continue if point[1]>index: bottomList.append(point) else: topList.append(point) space = np.linspace(start=min_x+thresh_left, stop=max_x-thresh_right, num=15) space= np.ceil(space) length = img_gray.shape[0] x1 = np.linspace(start=0, stop=length - 1, num=length) tempM = (img_gray / 255.)* x1[:,None] if len(topList) >len(bottomList): topList.clear() for x in space: temp=tempM[:, int(x)] temp = temp[temp != 0] topList.append([x,temp.min()]) line = np.array(topList, dtype=float) else: bottomList.clear() for x in space: temp = tempM[:, int(x)] temp = temp[temp != 0] bottomList.append([x, temp.max()]) line = np.array(bottomList, dtype=float) if line.size > 0: #method1 slopend=(line[-1,1]-line[0,1])/(line[-1,0]-line[0,0]) slop=(line[1:,1]-line[:-1,1])/((line[1:,0]-line[:-1,0])+0.0001) dis=slop-slopend std = np.std(dis) if std>thresh: return 0,std return 1,0 # method2 # std= np.std(slop) if __name__ == '__main__': filesPath = os.listdir(main_path) threshstd=0.025 files = tqdm(filesPath) for file in files: absolute_file_path = os.path.join(main_path, file) if '.bmp' in absolute_file_path.lower(): result,std=calccurl(absolute_file_path,threshstd) if result: print(absolute_file_path+":", std)
12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788
结果 -------------------- std: 0.037498851806574245 123
全部代码
import cv2 import numpy as np import os from tqdm import tqdm import shutil #需要检测弯曲的图片的文件夹地址 main_path="D:Handletilingsrc" def get_lagrest_connect_component1(img): img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) img_gray = cv2.GaussianBlur(img_gray, (5, 5), 0) _, img_gray = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY_INV) # ret==threshold contours, _ = cv2.findContours(img_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) area = [] for i in range(len(contours)): area.append(cv2.contourArea(contours[i])) if len(area) >= 1: max_idx = np.argmax(area) max_contour_area = area[max_idx] for k in range(len(contours)): if k != max_idx: cv2.fillPoly(img_gray, [contours[k]], 0) else: max_contour_area = 0 return max_contour_area, img_gray def calccurl(str,thresh): img = cv2.imread(str) #提取凸包 max_area, img_gray =get_lagrest_connect_component1(img) gray = img_gray #阈值处理 ret,binary = cv2.threshold(gray,127,255,0) #查找轮廓 contours,hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) hull = cv2.convexHull(contours[0]) cv2.polylines(img_gray,[hull],True,(255,255,0),2) min_x=min(hull[:,0,0]) max_x=max(hull[:,0,0]) topList=[] bottomList=[] thresh_left=220 thresh_right=30 count=hull.shape[0] isthesame=False for i in range(hull.shape[0]): point = tuple(hull[i][0]) # # cv2.circle(img, point, 1, (0, 255, 0) , 12) if point[0]<(max_x-thresh_right) and point[0]>(min_x+thresh_left): length=binary.shape[0] x1 = np.linspace(start=0, stop=length-1, num=length) temp=x1 * (binary[:, point[0]] / 255) n = np.sum(temp > 0) index=temp.sum()/(n*1.) # if i in[0,count-1]: # if not isthesame: # isthesame=True # else: # continue if point[1]>index: bottomList.append(point) else: topList.append(point) space = np.linspace(start=min_x+thresh_left, stop=max_x-thresh_right, num=15) space= np.ceil(space) length = img_gray.shape[0] x1 = np.linspace(start=0, stop=length - 1, num=length) tempM = (img_gray / 255.)* x1[:,None] if len(topList) >len(bottomList): topList.clear() for x in space: temp=tempM[:, int(x)] temp = temp[temp != 0] topList.append([x,temp.min()]) line = np.array(topList, dtype=float) else: bottomList.clear() for x in space: temp = tempM[:, int(x)] temp = temp[temp != 0] bottomList.append([x, temp.max()]) line = np.array(bottomList, dtype=float) if line.size > 0: #method1 slopend=(line[-1,1]-line[0,1])/(line[-1,0]-line[0,0]) slop=(line[1:,1]-line[:-1,1])/((line[1:,0]-line[:-1,0])+0.0001) dis=slop-slopend std = np.std(dis) if std>thresh: return 0,std return 1,0 # method2 # std= np.std(slop) if __name__ == '__main__': filesPath = os.listdir(main_path) threshstd=0.025 files = tqdm(filesPath) for file in files: absolute_file_path = os.path.join(main_path, file) if '.bmp' in absolute_file_path.lower(): result,std=calccurl(absolute_file_path,threshstd) if not result: print(absolute_file_path+"-图片弯曲:", std)
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126#结果 [00:00<00:01, 44.18it/s]D:HandletilingsrcNG_org_20230602103409544.bmp-图片弯曲: 0.0334415572613885 [00:00<00:01, 44.24it/s]D:HandletilingsrcNG_org_20230602103410530.bmp-图片弯曲: 0.037498851806574245 123
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