opencv-python提取二维码（二）

opencv-python提取二维码（二）

今天尝试了一下博客​

发现报了各种错误，然后我把解决过程记录下来，我安装了下面的库：

pip install opencv-python-headless

opencv相关的版本为：

opencv-contrib-python 3.4.2.16opencv-python 4.2.0.32opencv-python-headless 4.2.0.34

代码：

# -*- coding=utf-8 -*-import osimport cv2import numpy as npimport copyimg_path='image'img_result='results'def reshape_image(image): '''归一化图片尺寸：短边400，长边不超过800，短边400，长边超过800以长边800为主''' width,height=image.shape[1],image.shape[0] min_len=width scale=width*1.0/400 new_width=400 new_height=int(height/scale) if new_height>800: new_height=800 scale=height*1.0/800 new_width=int(width/scale) out=cv2.resize(image,(new_width,new_height)) return outdef detecte(image): '''提取所有轮廓''' gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) _,gray=cv2.threshold(gray,0,255,cv2.THRESH_OTSU+cv2.THRESH_BINARY_INV) contours,hierachy=cv2.findContours(gray,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) return contours,hierachydef compute_1(contours,i,j): '''最外面的轮廓和子轮廓的比例''' area1 = cv2.contourArea(contours[i]) area2 = cv2.contourArea(contours[j]) if area2==0: return False ratio = area1 * 1.0 / area2 if abs(ratio - 49.0 / 25): return True return Falsedef compute_2(contours,i,j): '''子轮廓和子子轮廓的比例''' area1 = cv2.contourArea(contours[i]) area2 = cv2.contourArea(contours[j]) if area2==0: return False ratio = area1 * 1.0 / area2 if abs(ratio - 25.0 / 9): return True return Falsedef compute_center(contours,i): '''计算轮廓中心点''' M=cv2.moments(contours[i]) cx = int(M['m10'] / M['m00']) cy = int(M['m01'] / M['m00']) return cx,cydef detect_contours(vec): '''判断这个轮廓和它的子轮廓以及子子轮廓的中心的间距是否足够小''' distance_1=np.sqrt((vec[0]-vec[2])**2+(vec[1]-vec[3])**2) distance_2=np.sqrt((vec[0]-vec[4])**2+(vec[1]-vec[5])**2) distance_3=np.sqrt((vec[2]-vec[4])**2+(vec[3]-vec[5])**2) if sum((distance_1,distance_2,distance_3))/3<3: return True return Falsedef juge_angle(rec): '''判断寻找是否有三个点可以围成等腰直角三角形''' if len(rec)<3: return -1,-1,-1 for i in range(len(rec)): for j in range(i+1,len(rec)): for k in range(j+1,len(rec)): distance_1 = np.sqrt((rec[i][0] - rec[j][0]) ** 2 + (rec[i][1] - rec[j][1]) ** 2) distance_2 = np.sqrt((rec[i][0] - rec[k][0]) ** 2 + (rec[i][1] - rec[k][1]) ** 2) distance_3 = np.sqrt((rec[j][0] - rec[k][0]) ** 2 + (rec[j][1] - rec[k][1]) ** 2) if abs(distance_1-distance_2)<5: if abs(np.sqrt(np.square(distance_1)+np.square(distance_2))-distance_3)<5: return i,j,k elif abs(distance_1-distance_3)<5: if abs(np.sqrt(np.square(distance_1)+np.square(distance_3))-distance_2)<5: return i,j,k elif abs(distance_2-distance_3)<5: if abs(np.sqrt(np.square(distance_2)+np.square(distance_3))-distance_1)<5: return i,j,k return -1,-1,-1def find(image,image_name,contours,hierachy,root=0): '''找到符合要求的轮廓''' rec=[] for i in range(len(hierachy)): child = hierachy[i][2] child_child=hierachy[child][2] if child!=-1 and hierachy[child][2]!=-1: if compute_1(contours, i, child) and compute_2(contours,child,child_child): cx1,cy1=compute_center(contours,i) cx2,cy2=compute_center(contours,child) cx3,cy3=compute_center(contours,child_child) if detect_contours([cx1,cy1,cx2,cy2,cx3,cy3]): rec.append([cx1,cy1,cx2,cy2,cx3,cy3,i,child,child_child]) '''计算得到所有在比例上符合要求的轮廓中心点''' i,j,k=juge_angle(rec) # print(i,j,k) if i==-1 or j== -1 or k==-1: return ts = np.concatenate((contours[rec[i][6]], contours[rec[j][6]], contours[rec[k][6]])) rect = cv2.minAreaRect(ts) box = cv2.boxPoints(rect) box = np.int0(box) result=copy.deepcopy(image) cv2.drawContours(result, [box], 0, (0, 0, 255), 2) cv2.drawContours(image,contours,rec[i][6],(255,0,0),2) cv2.drawContours(image,contours,rec[j][6],(255,0,0),2) cv2.drawContours(image,contours,rec[k][6],(255,0,0),2) cv2.imshow('img',image) cv2.waitKey(0) cv2.imshow('img',result) cv2.waitKey(0) path=os.path.join(img_result,image_name) cv2.imwrite(path,result) returnif __name__ == '__main__': files=os.listdir(img_path) files=[item for item in files if(item.split('.')[-1]!='DS_Store')] for file in files: print(file) image=cv2.imread(os.path.join(img_path,file)) image=reshape_image(image) # cv2.imshow('img', image) # cv2.waitKey(0) contours,hierachy=detecte(image) find(image,file,contours,np.squeeze(hierachy))

自己建一个image文件夹，和results文件夹，然后运行就可以了。

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