文字提取 python opencv3 区域改进

# coding:utf8
import sys

import cv2
import numpy as np
import matplotlib.pyplot as plt

def preprocess(gray):
    # 1. Sobel算子，x方向求梯度
    sobel = cv2.Sobel(gray, cv2.CV_8U, 1, 0, ksize = 3)
    # 2. 二值化
    ret, binary = cv2.threshold(sobel, 0, 255, cv2.THRESH_OTSU+cv2.THRESH_BINARY)
    
    # 3. 膨胀和腐蚀操作的核函数
    element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 9))
    element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (24, 6))
    
    # 4. 膨胀一次，让轮廓突出
    dilation = cv2.dilate(binary, element2, iterations = 1)
    
    # 5. 腐蚀一次，去掉细节，如表格线等。注意这里去掉的是竖直的线
    erosion = cv2.erode(dilation, element1, iterations = 1)
    
    # 6. 再次膨胀，让轮廓明显一些
    dilation2 = cv2.dilate(erosion, element2, iterations = 2)
    
    # 7. 存储中间图片
    #cv2.imwrite("binary.png", binary)
    #cv2.imwrite("dilation.png", dilation)
    #cv2.imwrite("erosion.png", erosion)
    #cv2.imwrite("dilation2.png", dilation2)
    
    return dilation2


def findTextRegion( org, img ):
    region = []
    
    # 1. 查找轮廓
    _,contours,hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    maxArea = 0
    maxContour = 0

    for i in range(len(contours)):
        cnt = contours[i]
        # 计算该轮廓的面积
        area = cv2.contourArea(cnt)
        if area > maxArea:
            maxArea = area
            maxContour = cnt

    maxRect = cv2.boundingRect(maxContour);
    x, y, w, h = cv2.boundingRect(maxContour)
    cv2.rectangle(org, (x, y), (x + w, y + h), (255, 255, 0), 2)

    #cv2.imshow("img", org)
    #cv2.waitKey(0)

    return x,y,w,h


def detect(img):
    # 1.  转化成灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
    # 2. 形态学变换的预处理，得到可以查找矩形的图片
    dilation = preprocess(gray)
    
    # 3. 查找和筛选文字区域
    x,y,w,h = findTextRegion(gray,dilation)

    cropImg = gray[y:y + h, x:x + w]
    cv2.imshow("img", cropImg)
    cv2.waitKey(0)


if __name__ == '__main__':
    # 读取文件
    imagePath = "/tmp/mypic/59.png" #sys.argv[1]
    img = cv2.imread(imagePath)
    detect(img)