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# 导入工具包
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import numpy as np
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import argparse
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import cv2
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import pytesseract
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import os
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from PIL import Image
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# 设置参数
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ap = argparse.ArgumentParser()
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ap.add_argument("-i", "--image", required = True,
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help = "Path to the image to be scanned")
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args = vars(ap.parse_args())
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def order_points(pts):
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# 一共4个坐标点
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rect = np.zeros((4, 2), dtype = "float32")
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# 按顺序找到对应坐标0123分别是 左上，右上，右下，左下
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# 计算左上，右下
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s = pts.sum(axis = 1)
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rect[0] = pts[np.argmin(s)]
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rect[2] = pts[np.argmax(s)]
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# 计算右上和左下
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diff = np.diff(pts, axis = 1)
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rect[1] = pts[np.argmin(diff)]
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rect[3] = pts[np.argmax(diff)]
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return rect
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def four_point_transform(image, pts):
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# 获取输入坐标点
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rect = order_points(pts)
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(tl, tr, br, bl) = rect
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# 计算输入的w和h值
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widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
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widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
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maxWidth = max(int(widthA), int(widthB))
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heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
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heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
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maxHeight = max(int(heightA), int(heightB))
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# 变换后对应坐标位置
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dst = np.array([
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[0, 0],
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[maxWidth - 1, 0],
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[maxWidth - 1, maxHeight - 1],
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[0, maxHeight - 1]], dtype = "float32")
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# 计算变换矩阵
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M = cv2.getPerspectiveTransform(rect, dst)
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warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
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# 返回变换后结果
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return warped
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def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
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dim = None
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(h, w) = image.shape[:2]
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if width is None and height is None:
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return image
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if width is None:
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r = height / float(h)
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dim = (int(w * r), height)
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else:
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r = width / float(w)
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dim = (width, int(h * r))
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resized = cv2.resize(image, dim, interpolation=inter)
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return resized
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# 读取输入
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image = cv2.imread(args["image"])
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#坐标也会相同变化
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ratio = image.shape[0] / 500.0
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orig = image.copy()
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image = resize(orig, height = 500)
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# 预处理
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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gray = cv2.GaussianBlur(gray, (5, 5), 0)
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edged = cv2.Canny(gray, 75, 200)
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# 展示预处理结果
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print("STEP 1: 边缘检测")
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cv2.imshow("Image", image)
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cv2.imshow("Edged", edged)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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# 轮廓检测
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cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0]
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cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]
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# 遍历轮廓
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for c in cnts:
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# 计算轮廓近似
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peri = cv2.arcLength(c, True)
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# C表示输入的点集
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# epsilon表示从原始轮廓到近似轮廓的最大距离，它是一个准确度参数
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# True表示封闭的
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approx = cv2.approxPolyDP(c, 0.05 * peri, True)
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# 4个点的时候就拿出来
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if len(approx) == 4:
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screenCnt = approx
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break
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# 展示结果
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print("STEP 2: 获取轮廓")
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cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
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cv2.imshow("Outline", image)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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# 透视变换
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warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
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# 二值处理
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warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
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ref = cv2.threshold(warped, 100, 255, cv2.THRESH_BINARY)[1]
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cv2.imwrite('scan.jpg', ref)
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# 展示结果
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print("STEP 3: 变换")
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cv2.imshow("Original", resize(orig, height = 650))
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cv2.imshow("Scanned", resize(ref, height = 650))
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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'''
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OCR扫描
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'''
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preprocess = "blur"
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if preprocess == "thresh":
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gray = cv2.threshold(ref, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
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if preprocess == "blur":
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gray = cv2.medianBlur(ref, 3)
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cv2.imshow("Detect", gray)
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filename = "{}.png".format(os.getpid())
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cv2.imwrite(filename, gray)
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text = pytesseract.image_to_string(Image.open(filename))
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os.remove(filename)
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encodings = ['utf-8', 'latin1', 'iso-8859-1', 'cp1252', 'gbk', 'big5']
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for encoding in encodings:
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try:
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with open("out.txt", 'w', encoding=encoding, errors="replace") as file:
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file.write(text)
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break
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except UnicodeDecodeError:
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continue
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file.close()
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print("text is written to out.txt")
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# Wait for pressing any key
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cv2.waitKey(0)
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cv2.destroyAllWindows() |
