python识别文字 opencv_OPENCV+PYTHON 文字识别（重点图像透视变换）

# Author：Winter Liu is coming!

import cv2 as cv

import numpy as np

import pytesseract

# 预处理，高斯滤波（用处不大），4次开操作

# 过滤轮廓唯一

def contour_demo(img):

gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

gray = cv.GaussianBlur(gray, (5, 5), 1)

ref, thresh = cv.threshold(gray, 127, 255, cv.THRESH_BINARY)

kernel = np.ones((9, 9), np.uint8)

thresh = cv.morphologyEx(thresh, cv.MORPH_OPEN, kernel, iterations=4)

contours, hierachy = cv.findContours(thresh, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)

print(len(contours))

return contours

def capture(img):

contours = contour_demo(img)

# 轮廓唯一，以后可以扩展

contour = contours[0]

# 求周长，可在后面的转换中使用周长和比例

print(cv.arcLength(contour,True))

img_copy = img.copy()

# 使用approxPolyDP，将轮廓转换为直线，22为精度（越高越低），TRUE为闭合

approx = cv.approxPolyDP(contour, 22, True)

# print(approx.shape)

# print(approx)

# cv.drawContours(img_copy, [approx], -1, (255, 0, 0), 15)

n = []

# 生产四个角的坐标点

for x, y in zip(approx[:, 0, 0], approx[:, 0, 1]):

n.append((x, y))

p1 = np.array(n, dtype=np.float32)

# 对应点

p2 = np.array([(0, 0), (0, 1500), (1000, 1500), (1000, 0)], dtype=np.float32)

M = cv.getPerspectiveTransform(p1, p2) # 变换矩阵

# 使用透视变换

result = cv.warpPerspective(img_copy, M, (0, 0))

# 重新截取

result = result[:1501, :1001]

cv.imwrite(r"C:\PycharmProjects\OpenCV\pic\ocr.png", result)

return result

# 图像识别代码，需要预先下载安装开源工具包 pytesseract，配置环境变量

# pip install pytesseract

# 修改“C:\Python\Python37\Lib\site-packages\pytesseract\pytesseract.py”中“cmd”为绝对路径

def ocr_img(img):

gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

# 图像清晰度越高结果越精确，时间更长

text = pytesseract.image_to_string(gray)

print(text)

src = cv.imread(r"C:\PycharmProjects\OpenCV\pic\page.jpg")

res = capture(src)

ocr_img(res)

cv.waitKey(0)

cv.destroyAllWindows()

原文链接:https://blog.csdn.net/huang714/article/details/105633165