opencv项目--文档扫描

scan_file.py 

import numpy as np

import argparse

import cv2



# 设置参数

ap = argparse.ArgumentParser()

ap.add_argument("-i", "--image", required=True,

                help="Path to the image to be scanned")

args = vars(ap.parse_args())

print(args)




# 绘图展示

def cv_show(name, img):

    cv2.imshow(name, img)

    cv2.waitKey(0)

    cv2.destroyAllWindows()




# 读取图片数据

image = cv2.imread(args['image'])

# cv_show('image',image)

print(image.shape)

# 将要对图像进行大小变化，先保存一下变化率

ratio = image.shape[0] / 500.0

# 获得原始图像

orig = image.copy()




# 按比例变化图像大小

def resize(image, width=None, height=None, inter=cv2.INTER_AREA):

    dim = None

    (h, w) = image.shape[:2]

    if width is None and height is None:

        return image

    if width is None:

        r = height / float(h)

        dim = (int(w * r), height)

        print('width is None', dim)



    else:

        r = width / float(w)

        dim = (width, int(h * r))

        print('height is None', dim)

    resized = cv2.resize(image, dim, interpolation=inter)

    return resized




image = resize(orig, height=500)

print(image.shape)

# cv_show('image',image)



# 对图像进行预处理操作

# 转灰度图

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

cv_show('gray', gray)

# 去噪操作

gray = cv2.GaussianBlur(gray, (5, 5), 0)

# cv_show('gray',gray)



# 进行Canny边缘检测

edged = cv2.Canny(gray, 75, 200)

print(edged.shape)

# cv_show('Canny_edged',edged)



# 展示预处理结果

print("STEP 1: 边缘检测")

cv_show('image', image)

cv_show('Canny_edged', edged)



# 再进行轮廓检测

# RETR_LIST：检索所有的轮廓，并将其保存到一条链表当中

# CHAIN_APPROX_SIMPLE:压缩水平的、垂直的和斜的部分，也就是，函数只保留他们的终点部分。

cnts, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

draw_img = image.copy()

res = cv2.drawContours(draw_img, cnts, -1, (0, 0, 255), 2)

cv_show('find_Cnts', draw_img)



# 根据轮廓面积排序选择最大的五个轮廓

cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]

draw_img = image.copy()

res = cv2.drawContours(draw_img, cnts, -1, (0, 0, 255), 2)

cv_show('find_Cnts', res)



# 遍历这五个最大的轮廓

for c in cnts:

    # 计算轮廓近似

    # 由于有些轮廓比较粗糙或者太详细，可以改变近似算法的阈值来调整

    peri = cv2.arcLength(c, True)

    # c:输入的点集

    # epsilon：从原始轮廓到近似轮廓的最大距离，它是一个准确度参数

    # True ： 表示封闭

    approx = cv2.approxPolyDP(c, 0.02 * peri, True)



    # 有4个点的时候就拿出来

    if len(approx) == 4:

        screenCnt = approx

        break

# 展示结果

print("STEP 2: 获取轮廓")

cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)

cv_show('Get_Cnts', image)




# 透视变换

def order_points(pts):

    # 一共4个坐标点

    rect = np.zeros((4, 2), dtype="float32")



    # 按顺序找到对应坐标0123分别是 左上，右上，右下，左下

    # 计算左上，右下

    s = pts.sum(axis=1)




    rect[0] = pts[np.argmin(s)]

    rect[2] = pts[np.argmax(s)]



    # 计算右上和左下

    diff = np.diff(pts, axis=1)

    rect[1] = pts[np.argmin(diff)]

    rect[3] = pts[np.argmax(diff)]



    return rect




def four_point_transform(image, pts):

    # 获取输入坐标点

    rect = order_points(pts)

    (tl, tr, br, bl) = rect



    # 计算输入的w和h值

    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))

    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))

    maxWidth = max(int(widthA), int(widthB))



    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))

    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))

    maxHeight = max(int(heightA), int(heightB))



    # 变换后对应坐标位置

    dst = np.array([

        [0, 0],

        [maxWidth - 1, 0],

        [maxWidth - 1, maxHeight - 1],

        [0, maxHeight - 1]], dtype="float32")



    # 计算变换矩阵

    # 只能是矩形形状较好

    M = cv2.getPerspectiveTransform(rect, dst)

    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))



    # 返回变换后结果

    return warped




warped =four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)



# cv_show("warped",warped)



# 二值处理

warped = cv2.cvtColor(warped,cv2.COLOR_BGR2GRAY)

ref = cv2.threshold(warped,100,255,cv2.THRESH_BINARY)[1]

# cv_show('Binary_warped',warped)



# 保存图像

cv2.imwrite('scan.jpg',ref)



# 展示结果

print("STEP 3: 变换")

cv_show("Original", resize(orig, height = 650))

cv_show("Scanned", resize(ref, height = 650))

test.py

# https://digi.bib.uni-mannheim.de/tesseract/
# 配置环境变量如E:\Program Files (x86)\Tesseract-OCR
#  -v进行测试
# tesseract XXX.png 得到结果 
# pip install pytesseract
# anaconda lib site-packges pytesseract pytesseract.py
# tesseract_cmd 修改为绝对路径即可
from PIL import Image
import pytesseract
import cv2
import os

preprocess = 'blur' #thresh

image = cv2.imread('scan.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

if preprocess == "thresh":
    gray = cv2.threshold(gray, 0, 255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]

if preprocess == "blur":
    gray = cv2.medianBlur(gray, 3)
    
# filename = "{}.png".format(os.getpid())
filename = "test.png"
cv2.imwrite(filename, gray)
    
text = pytesseract.image_to_string(Image.open(filename))
print(text)
os.remove(filename)

cv2.imshow("Image", image)
cv2.imshow("Output", gray)
cv2.waitKey(0)                                   

 记得安装谷歌的tesseract-ocr-setup-4.00.00dev.exe