最新检测程序

# -*- coding: utf-8 -*-import cv2 as cvimport numpy as npfrom matplotlib import pyplot as pltimport cv2## *针孔 zhenkong  Min<1 gate>=0.6  Min>1 gate>=0.8  12张# *起粒 qili 5  Min<1  Max<50 gate<=0.6   Min<1  Max>50 gate>=0.4  5张 选 0.4# *挂伤 guashang      Max<20   0.4<gate<=0.5   20<Max<40  0.1<gate<=0.4   Max>40 gate<=0.2# *挂流 gualiu  Min<1  Max<50  0.2<gate<=0.5   选0.4#C:/Users/Administrator/Desktop/漆膜数据集样本/qimoshujuji/困难的分割图片/gualiu/#guashang   qili   zhenkongimg = cv.imread(‘C:/Users/Administrator/Desktop/qimoshujujiance/qimoshujuji/hard_images/00.jpg‘,0)rows,cols = img.shapecrow,ccol = rows//2,cols//2im_copy_max=np.zeros([rows,cols],dtype=float)def max_filte(x,y,step,image_fiter_max3):    sum_s=[]    for k in range(-int(step/2),int(step/2)+1):        for m in range(-int(step/2),int(step/2)+1):            sum_s.append(image_fiter_max3[x+k][y+m])    sum_s.sort()    # if(max(sum_s)>140):    #     value=1    # else:    #       value=0    return max(sum_s)     #return sum_s[(int(step*step/2)+1)]def test(Step,image_fiter_max3):    for i in range(int(Step/2),img.shape[0]-int(Step/2)):        for j in range(int(Step/2),img.shape[1]-int(Step/2)):            im_copy_max[i][j]=max_filte(i,j,Step,image_fiter_max3)    return im_copy_maxf = np.fft.fft2(img)#F(u,v)频域变换fshift = np.fft.fftshift(f)#将低频移动到图像的中心fshift_ima=np.abs(fshift)#A(u,v)=|F(u,v)|求频幅#magnitude_spectrum = 20*np.log(np.abs(fshift))magnitude_spectrum = 20*np.log(np.abs(fshift))row_n,col_n=magnitude_spectrum.shapemagnitude_spectrum_max3=test(2,magnitude_spectrum)magnitude_spectrum_max3[0][:]=magnitude_spectrum[0][:]magnitude_spectrum_max3[rows-1][:]=magnitude_spectrum[rows-1][:]magnitude_spectrum_max3[:,0]=magnitude_spectrum[:,0]magnitude_spectrum_max3[:,cols-1]=magnitude_spectrum[:,cols-1]#拉伸小取对数L（u，v）=log(A(u,v))# laplacian_image=cv2.Laplacian(magnitude_spectrum_max3,cv2.CV_64F)#laplacian_image_max3=test(3,laplacian_image)#大津阈值分割法def OTSU_enhance(img_gray,th_begin=0,th_end=256,th_step=1):    assert img_gray.ndim == 2,"must input a gary_img"    max_g = 0    suitable_th = 0    for threshold in range(th_begin,th_end,th_step):        bin_img = img_gray > threshold        bin_img_inv = img_gray <= threshold        fore_pix = np.sum(bin_img)        back_pix = np.sum(bin_img_inv)        if 0 == fore_pix:            break        if 0 == back_pix:            continue        w0 = float(fore_pix) / img_gray.size        u0 = float(np.sum(img_gray * bin_img)) / fore_pix        w1 = float(back_pix) / img_gray.size        u1 = float(np.sum(img_gray * bin_img_inv)) / back_pix        # intra-class variance        g = w0 * w1 * (u0 - u1) * (u0 - u1)        if g > max_g:            max_g = g            suitable_th = threshold    return suitable_thth2 = OTSU_enhance(magnitude_spectrum_max3,th_step=1)MASK=np.zeros([rows,dtype=bool)def max_filte1(x,step):    sum_s=[]    for k in range(-int(step/2),int(step/2)+1):            sum_s.append(magnitude_spectrum_max3[x+k][y+m])    sum_s.sort()    if(max(sum_s)>th2*2):        value=0    else:          value=1    return value   # return sum_s[(int(step*step/2)+1)]def test1(Step):    for i in range(int(Step/2),img.shape[1]-int(Step/2)):            MASK[i][j]=max_filte1(i,Step)test1(2)# plt.subplot(231),plt.imshow(img,cmap = ‘gray‘)# plt.title(‘img‘),plt.xticks([]),plt.yticks([])# plt.subplot(232),plt.imshow(fshift_ima,cmap = ‘gray‘)# plt.title(‘fshift_ima‘),plt.yticks([])# plt.subplot(233),plt.imshow(magnitude_spectrum,cmap = ‘gray‘)# plt.title(‘magnitude_spectrum‘),plt.yticks([])## ## plt.subplot(234),plt.imshow(magnitude_spectrum_max3,cmap = ‘gray‘)# plt.title(‘magnitude_spectrum_max3‘),plt.yticks([])### # plt.subplot(235),plt.imshow(laplacian_image,cmap = ‘gray‘)# # plt.title(‘laplacian_image3‘),plt.yticks([])### plt.subplot(236),plt.imshow(MASK,cmap = ‘gray‘)# plt.title(‘MASK‘),plt.yticks([])## plt.show()img_bad = cv.imread(‘C:/Users/Administrator/Desktop/qimoshujujiance/qimoshujuji/hard_images/00.jpg‘,colsimg_bad = cv2.resize(img_bad,(cols,rows),interpolation=cv2.INTER_CUBIC)#im_copy_max=im_copy_max.reshape((rows,cols))#进行傅里叶变换f = np.fft.fft2(img_bad)#平移中心fshift1 = np.fft.fftshift(f)#进行收缩变换magnitude_spectrum1 = 20*np.log(np.abs(fshift1))fshift=fshift1*MASK#平移逆变换f_ishift = np.fft.ifftshift(fshift)#傅里叶反变换img_back = np.fft.ifft2(f_ishift)# 取绝对值img_back = np.abs(img_back)fshift_image=np.abs(fshift)#%求最大灰度值bad_Max = max(np.max(img_back,axis=0))#%求最小灰度值bad_Min =min(np.min(img_back,axis=0))ret,thresh1=cv2.threshold(img_back,bad_Max*0.3,255,cv2.THRESH_BINARY)# plt.subplot(321),plt.imshow(img_bad,cmap = ‘gray‘)# plt.title(‘Input img_bad‘),plt.yticks([])## plt.subplot(322),plt.imshow(magnitude_spectrum1,cmap = ‘gray‘)# plt.title(‘magnitude_spectrum1‘),plt.yticks([])#### plt.subplot(323),plt.imshow(np.abs(fshift),cmap = ‘gray‘)# plt.title(‘np.abs(fshift)‘),plt.yticks([])## plt.subplot(324),plt.imshow(img_back,cmap = ‘gray‘)# plt.title(‘img_back)‘),plt.yticks([])## plt.subplot(325),plt.imshow(thresh1,cmap = ‘gray‘)# plt.title(‘thresh1)‘),plt.yticks([])## plt.show()#求最小外接矩形image = np.array(image,np.uint8)img_back2 = np.array(img_back,np.uint8)ret2,thresh2=cv2.threshold(img_back2,cv2.THRESH_BINARY )image2,contours2,hier2=cv2.findContours(thresh2,2,1)for c in contours2:  # find bounding box coordinates  x,w,h = cv2.boundingRect(c)  cv2.rectangle(img,(x,y),(x+w,y+h),(0,0),2)  # find minimum area  rect = cv2.minAreaRect(c)  # calculate coordinates of the minimum area rectangle  box = cv2.boxPoints(rect)  # normalize coordinates to integers  box = np.int0(box)  # draw contours  cv2.drawContours(img,[box],255),3)  # calculate center and radius of minimum enclosing circle  (x,radius = cv2.minEnclosingCircle(c)  # cast to integers  center = (int(x),int(y))  radius = int(radius)*2  # draw the circle  img = cv2.circle(img,center,radius,2)cv2.drawContours(img,-1,(255,1)cv2.imshow("contours",img)cv2.waitKey()