使用Python+OpenCV2进行图片中的文字分割（支持竖版） |
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使用Python+OpenCV2进行图片中的文字分割（支持竖版）
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扣字和分割
把图片中的文字，识别出来，并将每个字的图片抠出来； import cv2 import numpy as np HIOG = 50 VIOG = 3 Position = [] '''水平投影''' def getHProjection(image): hProjection = np.zeros(image.shape,np.uint8) # 获取图像大小 (h,w)=image.shape # 统计像素个数 h_ = [0]*h for y in range(h): for x in range(w): if image[y,x] == 255: h_[y]+=1 #绘制水平投影图像 for y in range(h): for x in range(h_[y]): hProjection[y,x] = 255 # cv2.imshow('hProjection2',cv2.resize(hProjection, None, fx=0.3, fy=0.5, interpolation=cv2.INTER_AREA)) # cv2.waitKey(0) return h_ def getVProjection(image): vProjection = np.zeros(image.shape,np.uint8); (h,w) = image.shape w_ = [0]*w for x in range(w): for y in range(h): if image[y,x] == 255: w_[x]+=1 for x in range(w): for y in range(h-w_[x],h): vProjection[y,x] = 255 # cv2.imshow('vProjection',cv2.resize(vProjection, None, fx=1, fy=0.1, interpolation=cv2.INTER_AREA)) # cv2.waitKey(0) return w_ def scan(vProjection, iog, pos = 0): start = 0 V_start = [] V_end = [] for i in range(len(vProjection)): if vProjection[i] > iog and start == 0: V_start.append(i) start = 1 if vProjection[i] len(V_end): V_end.append(w-5) # 分割行，分割之后再进行列分割并保存分割位置 for i in range(len(V_end)): #获取行图像 if V_end[i] - V_start[i] < 30: continue cropImg = img[0:h, V_start[i]:V_end[i]] # cv2.imshow('cropImg',cropImg) # cv2.waitKey(0) #对行图像进行垂直投影 H = getHProjection(cropImg) H_start, H_end = scan(H, VIOG, 40) if len(H_start) > len(H_end): H_end.append(h-5) for pos in range(len(H_start)): # 再进行一次列扫描 DcropImg = cropImg[H_start[pos]:H_end[pos], 0:w] d_h, d_w = DcropImg.shape # cv2.imshow("dcrop", DcropImg) sec_V = getVProjection(DcropImg) c1, c2 = scan(sec_V, 0) if len(c1) > len(c2): c2.append(d_w) x = 1 while x < len(c1): if c1[x] - c2[x-1] < 12: c2.pop(x-1) c1.pop(x) x -= 1 x += 1 # cv2.waitKey(0) if len(c1) == 1: Position.append([V_start[i],H_start[pos],V_end[i],H_end[pos]]) else: for x in range(len(c1)): Position.append([V_start[i]+c1[x], H_start[pos],V_start[i]+c2[x], H_end[pos]]) #根据确定的位置分割字符 number=0 for m in range(len(Position)): rectMin = (Position[m][0]-5,Position[m][1]-5) rectMax = (Position[m][2]+5,Position[m][3]+5) cv2.rectangle(origineImage,rectMin, rectMax, (0 ,0 ,255), 2) number=number+1 #start-crop CropImage(origineImage,'result/' + '%d.jpg' % number,rectMin,rectMax) # cv2.imshow('image',cv2.resize(origineImage, None, fx=0.6, fy=0.6, interpolation=cv2.INTER_AREA)) cv2.imshow('image',origineImage) cv2.imwrite('result/' + 'ResultImage.jpg' , origineImage) cv2.waitKey(0) ############################# rawPicPath = r"H:\TEMP\TEXT_PROCCESS\TEST05.jpg" DOIT(rawPicPath) #############################原图片：
分割后文件夹：
可见此时文件都还是数字为文件名称，那么接下来要利用OCR自动给每个文字图片文件命名 我们使用UMIOCR ， UMI-OCR的安装建议去GITHUB上查，windows上部署还是很方便的； 这里使用本机安装好的UMI-OCR 的 API地址 http://127.0.0.1:1224/api/ocr 先定义API调用方法 ############################################ import base64 import requests import json #API访问使用 def UMI_OCR_OPT(url,img_path): # url = "http://127.0.0.1:1224/api/ocr" # img_path= './result/123.jpg' with open(img_path,'rb') as f: image_base64 = base64.b64encode(f.read()) image_base64 =str(image_base64,'utf-8') data = { "base64": image_base64, # 可选参数 # Paddle引擎模式 # "options": { # "ocr.language": "models/config_chinese.txt", # "ocr.cls": False, # "ocr.limit_side_len": 960, # "tbpu.parser": "MergeLine", # } # Rapid引擎模式 # "options": { # "ocr.language": "简体中文", # "ocr.angle": False, # "ocr.maxSideLen": 1024, # "tbpu.parser": "MergeLine", # } } headers = {"Content-Type": "application/json"} data_str = json.dumps(data) response = requests.post(url, data=data_str, headers=headers) if response.status_code == 200: res_dict = json.loads(response.text) #检测失败与否 if(str(res_dict).find('No text found in image')!=-1): # print("返回失败内容\n", res_dict) return '' print("返回值字典\n", res_dict) resText = res_dict['data'][0]['text'] return resText return '' 开始批量调用检测 import easyocr import shutil error ='' #api地址 uni_orc_url="http://127.0.0.1:1224/api/ocr" #将所有字体 for i in range(1,285): filePath = "./result/"+str(i)+".jpg" # result = reader.readtext(filePath, detail = 0) result = UMI_OCR_OPT(uni_orc_url,filePath) if(len(result)==0 or result == ''): error+= filePath +'\n' continue print(result) destPath = "./resultX/"+result[0]+".jpg" print('sour :: '+ filePath) print('dest :: '+ destPath) shutil.copyfile(filePath, destPath) print('error:\n'+error)然后将能够识别出来的所有文字图片都复制并重命名为该字 效果如下：
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