身份证识别

安装环境

tar leptonica-1.77.0 ./configure make && make install centos yum install epel-release yum install tesseract-devel leptonica-develdebian apt-get install libtesseract-dev libtesseract3 tesseract-ocr

pip install opencv-python pytesseract tesseract tesseract-ocr

代码

import re import cv2 import pytesseract from PIL import Image # 身份证号 r = r'^([1-9]\d{5}[12]\d{3}(0[1-9]|1[012])(0[1-9]|[12][0-9]|3[01])\d{3}[0-9xX])$' # 根据比例和偏移算出号码位置 # 一般面部比例 def CalcIdRectByFaceRect_normal(x, y, w, h): scale = float(w) / 95 x1 = int(x + ((0 - 159)) * scale) y1 = int(y + (0 + (149)) * scale) x2 = int(x + (0 - 159 + (275)) * scale) y2 = int(y + (0 + (149) + (45)) * scale) return (x1, y1, x2, y2) # 较大面部比例 def CalcIdRectByFaceRect_big(x, y, w, h): scale = float(w) / 95 x1 = int(x + ((0 - 159) + 10) * scale) y1 = int(y + (0 + (149 - 3)) * scale) x2 = int(x + (0 - 159 + (275 - 10)) * scale) y2 = int(y + (0 + (149 - 3) + (45 - 10)) * scale) return (x1, y1, x2, y2) # 较小面部比例 def CalcIdRectByFaceRect_small(x, y, w, h): scale = float(w) / 95 x1 = int(x + ((0 - 159) - 10) * scale) y1 = int(y + (0 + (149 + 3)) * scale) x2 = int(x + (0 - 159 + (275 + 10)) * scale) y2 = int(y + (0 + (149 + 5) + (45 + 10)) * scale) return (x1, y1, x2, y2) # 二值化算法 def binarizing(img, threshold): pixdata = img.load() w, h = img.size for y in range(h): for x in range(w): if pixdata[x, y] < threshold: pixdata[x, y] = 0 else: pixdata[x, y] = 255 return img # 去除干扰线算法 def depoint(img): # input: gray image pixdata = img.load() w, h = img.size for y in range(1, h - 1): for x in range(1, w - 1): count = 0 if pixdata[x, y - 1] > 245: count = count + 1 if pixdata[x, y + 1] > 245: count = count + 1 if pixdata[x - 1, y] > 245: count = count + 1 if pixdata[x + 1, y] > 245: count = count + 1 if count > 2: pixdata[x, y] = 255 return img # 通过头像的位置 身份证号码识别 def identity_OCR_byFaceRect(oImg, faceRect): (x, y, w, h) = faceRect iw, ih = oImg.size # 将身份证放大3倍 largeImg = oImg.resize((iw * 3, ih * 3), Image.ANTIALIAS) # largeImg.save('1_large.png') (x1, y1, x2, y2) = CalcIdRectByFaceRect_normal(x, y, w, h) region = (x1 * 3, y1 * 3, x2 * 3, y2 * 3) code = GetRegionString(largeImg, region) if not re.match(r, code): (x1, y1, x2, y2) = CalcIdRectByFaceRect_small(x, y, w, h) region = (x1 * 3, y1 * 3, x2 * 3, y2 * 3) code = GetRegionString(largeImg, region) if not re.match(r, code): (x1, y1, x2, y2) = CalcIdRectByFaceRect_big(x, y, w, h) region = (x1 * 3, y1 * 3, x2 * 3, y2 * 3) code = GetRegionString(largeImg, region) if not re.match(r, code): code = 'NONE' return code, (x1, y1, x2, y2) def GetRegionString(img, region): # 裁切身份证号码图片 cropImg = img.crop(region) cropImg.save('/tmp/2_crop.png') # 转化为灰度图 grayImg = cropImg.convert('L') # grayImg.save('3_grey.png') # 把图片变成二值图像。 bImg = binarizing(grayImg, 100) # bImg.save('4_bin.png') dImg = depoint(bImg) # dImg.save('5_depoint.png') code = pytesseract.image_to_string(dImg) code = PostProc(code) return code # 号码后处理 def PostProc(s): res = s res = res.replace(" ", "") res = res.replace("O", "0") res = res.replace("U", "0") res = res.replace("D", "0") res = res.replace("Z", "2") res = res.replace("S", "5") res = res.replace("s", "5") res = res.replace("o", "6") res = res.replace("f", "7") res = res.replace("H", "11") return res # 检测身份证 def DetectFacesAndIDs(pic_path): frame = cv2.imread(pic_path) oImg = Image.open(pic_path) ih, iw = frame.shape[:2] # 人脸识别分类器 classfier = cv2.CascadeClassifier("/usr/local/lib/python3.6/site-packages/cv2/data/haarcascade_frontalface_alt.xml") # 识别出人脸后要画的边框的颜色，RGB格式 color = (0, 255, 0) color2 = (255, 0, 0) # 将当前帧转换成灰度图像 gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 人脸检测，1.2和2分别为图片缩放比例和需要检测的有效点数 faceRects = classfier.detectMultiScale( gray, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32)) if len(faceRects) > 0: # 大于0则检测到人脸 for faceRect in faceRects: # 单独框出每一张人脸 x, y, w, h = faceRect cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) code, (x1, y1, x2, y2) = identity_OCR_byFaceRect(oImg, faceRect) cv2.rectangle(frame, (x1, y1), (x2, y2), color2, 2) #cv2.imwrite("%s.iddet.png" % pic_path, frame) return code return 'NONE' if __name__ == '__main__': DetectFacesAndIDs('1.jpg')