152 lines
4.8 KiB
Python
152 lines
4.8 KiB
Python
# -*- coding: UTF-8 -*-
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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import pytesseract
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from PIL import Image
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debug = 1
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def preprocess(gray):
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ret, binary = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY_INV)
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ele = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 10))
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dilation = cv2.dilate(binary, ele, iterations=1)
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cv2.imwrite("binary.png", binary)
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cv2.imwrite("dilation.png", dilation)
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return dilation
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def findTextRegion(img):
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region = []
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# 1. 查找轮廓
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#image, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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# 2. 筛选那些面积小的
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for i in range(len(contours)):
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cnt = contours[i]
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# 计算该轮廓的面积
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area = cv2.contourArea(cnt)
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# 面积小的都筛选掉
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if (area < 300):
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continue
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# 轮廓近似，作用很小
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epsilon = 0.001 * cv2.arcLength(cnt, True)
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approx = cv2.approxPolyDP(cnt, epsilon, True)
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# 找到最小的矩形，该矩形可能有方向
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rect = cv2.minAreaRect(cnt)
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if debug:
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print("rect is: ", rect)
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# box是四个点的坐标
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box = cv2.boxPoints(rect)
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box = np.int0(box)
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# 计算高和宽
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height = abs(box[0][1] - box[2][1])
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width = abs(box[0][0] - box[2][0])
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# 筛选那些太细的矩形，留下扁的
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if (height > width * 1.2):
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continue
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# 太扁的也不要
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if (height * 18 < width):
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continue
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if (width > img.shape[1] / 2 and height > img.shape[0] / 20):
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region.append(box)
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return region
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def grayImg(img):
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# 转化为灰度图
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gray = cv2.resize(img, (img.shape[1] * 3, img.shape[0] * 3), interpolation=cv2.INTER_CUBIC)
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gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY)
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retval, gray = cv2.threshold(gray, 120, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)
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return gray
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def detect(img):
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# fastNlMeansDenoisingColored(InputArray src, OutputArray dst, float h=3, float hColor=3, int templateWindowSize=7, int searchWindowSize=21 )
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gray = cv2.fastNlMeansDenoisingColored(img, None, 10, 3, 3, 3)
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coefficients = [0, 1, 1]
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m = np.array(coefficients).reshape((1, 3))
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gray = cv2.transform(gray, m)
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if debug:
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cv2.imwrite("gray.png", gray)
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# 2. 形态学变换的预处理，得到可以查找矩形的图片
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dilation = preprocess(gray)
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# 3. 查找和筛选文字区域
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region = findTextRegion(dilation)
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# 4. 用绿线画出这些找到的轮廓
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ii = 0
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for box in region:
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h = abs(box[0][1] - box[2][1])
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w = abs(box[0][0] - box[2][0])
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Xs = [i[0] for i in box]
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Ys = [i[1] for i in box]
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x1 = min(Xs)
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y1 = min(Ys)
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cv2.drawContours(img, [box], 0, (0, 255, 0), 2)
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if w > 0 and h > 0 and x1 < gray.shape[1] / 2:
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idImg = grayImg(img[y1:y1 + h, x1:x1 + w])
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cv2.imwrite(str(ii) + ".png", idImg)
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break
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ii += 1
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if debug:
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# 带轮廓的图片
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cv2.imwrite("contours.png", img)
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return idImg
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def crop_image(img, tol=0):
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mask = img < tol
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return img[np.ix_(mask.any(1), mask.any(0))]
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def ocrIdCard(imgPath, realId=""):
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import os
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# 获取 TESSDATA_PREFIX 环境变量的值
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tessdata_prefix = os.environ.get('TESSDATA_PREFIX')
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# 打印出 TESSDATA_PREFIX 环境变量的值
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print("TESSDATA_PREFIX:", tessdata_prefix)
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img = cv2.imread(imgPath, cv2.IMREAD_COLOR)
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img = cv2.resize(img, (428, 270), interpolation=cv2.INTER_CUBIC)
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idImg = detect(img)
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image = Image.fromarray(idImg)
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#tessdata_dir_config = '-c tessedit_char_whitelist=0123456789X --tessdata-dir "./"'
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print("checking")
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print(realId)
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result = pytesseract.image_to_string(img, lang='chi_sim' )
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#result = pytesseract.image_to_string(image, lang='chi_sim' )#ocrb,config=tessdata_dir_config,chi_sim
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print(result)
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# print(pytesseract.image_to_string(image, lang='eng', config=tessdata_dir_config))
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if debug:
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f, axarr = plt.subplots(2, 3)
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axarr[0, 0].imshow(cv2.imread(imgPath))
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axarr[0, 1].imshow(cv2.imread("gray.png"))
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axarr[0, 2].imshow(cv2.imread("binary.png"))
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axarr[1, 0].imshow(cv2.imread("dilation.png"))
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axarr[1, 1].imshow(cv2.imread("contours.png"))
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axarr[1, 2].set_title("exp:" + realId + "\nocr:" + result)
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axarr[1, 2].imshow(cv2.imread("0.png"))
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plt.show()
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ocrIdCard("/Users/wangvivi/Desktop/Code/ocrtest/images/id_card.JPG", "11204416541220243X")
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# ocrIdCard("test2.png", "430523197603204314")
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# ocrIdCard("test3.png", "37030519820727311X")
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# ocrIdCard("test4.png", "431023199205297212")
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# ocrIdCard("test0.png", "445281198606095334")
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