import cv2 img = cv2.imread("D:/photo/123456.jpg") cv2.imshow("img",img) cv2.waitKey(0) #灰度图 gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) cv2.imshow("gray",gray) cv2.waitKey(0) #二值化 ret,im_fixed=cv2.threshold(gray,50,100,cv2.THRESH_BINARY) cv2.imshow("im_fixed",im_fixed) cv2.waitKey(0) #反色 img2 = gray.copy() cv2.threshold(gray,80,255,0,gray) for i in range(0,gray.shape[0]): for j in range(0,gray.shape[1]): img2[i,j] = 255-gray[i,j] cv2.imshow("img2",img2) cv2.waitKey(0) #水平投影 img3 = img2.copy() #返回图像的高和宽 (h,w)=img3.shape #初始化一个跟图像高一样长度的数组，用于记录每一行的黑点个数 a=[0 for z in range(0,h)] for i in range(0,h): #遍历每一行 for j in range(0,w): #遍历每一列 if img3[i,j]==0: #判断该点是否为黑点，0代表黑点 a[i]+=1 #该行的计数器加一 img3[i,j]=255 #将其改为白点，即等于255 for i in range(0,h): #遍历每一行 for j in range(0,a[i]): #从该行应该变黑的最左边的点开始向最右边的点设置黑点 img3[i,j]=0 #设置黑点 cv2.imshow("shuiping",img3) cv2.waitKey(0) img4 = img2.copy() #垂直投影 #返回图像的高和宽 (h,w)=img4.shape #初始化一个跟图像宽一样长度的数组，用于记录每一列的黑点个数 a =[0 for z in range(0,w)] for i in range(0,w): #遍历每一列 for j in range(0,h): #遍历每一行 if img4[j,i]==0: #判断该点是否为黑点，0代表是黑点 a[i]+=1 #该列的计数器加1 img4[j,i]=255 #记录完后将其变为白色，即等于255 for i in range(0,w): #遍历每一列 for j in range(h-a[i],h): #从该列应该变黑的最顶部的开始向最底部设为黑点 img4[j,i]=0 #设为黑点 cv2.imshow("chuizhi",img4) cv2.waitKey(0) #画矩形框 image = img2.copy() cv2.rectangle(image, (10,10), (65,120),(0,0,255),1)#设置矩形的各参数依次是：图片，添加的文字，左上角坐标(整数)，字体，字体大小，颜色，字体粗细 cv2.rectangle(image, (75,10), (135,120),(0,0,255), 1) cv2.rectangle(image, (160,10), (220,120),(0,0,255), 1) cv2.rectangle(image, (225,10), (290,120),(0,0,255), 1) cv2.rectangle(image, (300,10), (355,120),(0,0,255), 1) cv2.rectangle(image, (360,10), (415,120),(0,0,255), 1) cv2.rectangle(image, (420,10), (480,120),(0,0,255), 1) cv2.imshow('image',image) cv2.waitKey(0)
import cv2 # 1、读取图像，并把图像转换为灰度图像并显示 img = cv2.imread("D:/photo/123456.jpg") # 读取图片 img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转换了灰度化 cv2.imshow('gray', img_gray) # 显示图片 cv2.waitKey(0) # 2、将灰度图像二值化，设定阈值是100 img_thre = img_gray cv2.threshold(img_gray, 100, 255, cv2.THRESH_BINARY_INV, img_thre) cv2.imshow('threshold', img_thre) cv2.waitKey(0) # 3、保存黑白图片 cv2.imwrite('thre_res.png', img_thre) # 4、分割字符 white = [] # 记录每一列的白色像素总和 black = [] # ..........黑色....... height = img_thre.shape[0] width = img_thre.shape[1] white_max = 0 black_max = 0 # 计算每一列的黑白色像素总和 for i in range(width): s = 0 # 这一列白色总数 t = 0 # 这一列黑色总数 for j in range(height): if img_thre[j][i] == 255: s += 1 if img_thre[j][i] == 0: t += 1 white_max = max(white_max, s) black_max = max(black_max, t) white.append(s) black.append(t) print(s) print(t) arg = False # False表示白底黑字；True表示黑底白字 if black_max > white_max: arg = True # 分割图像 def find_end(start_): end_ = start_+1 for m in range(start_+1, width-1): if (black[m] if arg else white[m]) > (0.95 * black_max if arg else 0.95 * white_max): # 0.95这个参数请多调整，对应下面的0.05 end_ = m break return end_ n = 1 start = 1 end = 2 while n < width-2: n += 1 if (white[n] if arg else black[n]) > (0.05 * white_max if arg else 0.05 * black_max): # 上面这些判断用来辨别是白底黑字还是黑底白字 # 0.05这个参数请多调整，对应上面的0.95 start = n end = find_end(start) n = end if end-start > 5: cj = img_thre[1:height, start:end] cv2.imshow('caijian', cj) cv2.waitKey(0)
结果截图：
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