opencv处理验证码python代码

# -*- coding: utf-8 -*-

# @Time : 2019-02-11 09:39

# @Author : cxa

# @File : bgr2gry.py

# @Software: PyCharm

import cv2

import pathlib

import numpy as np

import time

import os

file_path = pathlib.Path.cwd().joinpath("picture/1.png")

char_path = pathlib.Path.cwd().joinpath("char_path")

def get_rect_box(contours):

    ws = []

    valid_contours = []

    for contour in contours:

        x, y, w, h = cv2.boundingRect(contour)

        if w < 7:

            continue

        valid_contours.append(contour)

        ws.append(w)

    w_min = min(ws)

    w_max = max(ws)

    result = []

    if len(valid_contours) == 4:

        for contour in valid_contours:

            x, y, w, h = cv2.boundingRect(contour)

            box = np.int0([[x, y], [x + w, y], [x + w, y + h], [x, y + h]])

            result.append(box)

    elif len(valid_contours) == 3:

        for contour in valid_contours:

            x, y, w, h = cv2.boundingRect(contour)

            if w == w_max:

                box_left = np.int0([[x, y], [x + w / 2, y], [x + w / 2, y + h], [x, y + h]])

                box_right = np.int0([[x + w / 2, y], [x + w, y], [x + w, y + h], [x + w / 2, y + h]])

                result.append(box_left)

                result.append(box_right)

            else:

                box = np.int0([[x, y], [x + w, y], [x + w, y + h], [x, y + h]])

                result.append(box)

    elif len(valid_contours) == 2:

        for contour in valid_contours:

            x, y, w, h = cv2.boundingRect(contour)

            if w == w_max and w_max >= w_min * 2:

                box_left = np.int0([[x, y], [x + w / 3, y], [x + w / 3, y + h], [x, y + h]])

                box_mid = np.int0([[x + w / 3, y], [x + w * 2 / 3, y], [x + w * 2 / 3, y + h], [x + w / 3, y + h]])

                box_right = np.int0([[x + w * 2 / 3, y], [x + w, y], [x + w, y + h], [x + w * 2 / 3, y + h]])

                result.append(box_left)

                result.append(box_mid)

                result.append(box_right)

            elif w_max < w_min * 2:

                box_left = np.int0([[x, y], [x + w / 2, y], [x + w / 2, y + h], [x, y + h]])

                box_right = np.int0([[x + w / 2, y], [x + w, y], [x + w, y + h], [x + w / 2, y + h]])

                result.append(box_left)

                result.append(box_right)

            else:

                box = np.int0([[x, y], [x + w, y], [x + w, y + h], [x, y + h]])

                result.append(box)

    elif len(valid_contours) == 1:

        contour = valid_contours[0]

        x, y, w, h = cv2.boundingRect(contour)

        box0 = np.int0([[x, y], [x + w / 4, y], [x + w / 4, y + h], [x, y + h]])

        box1 = np.int0([[x + w / 4, y], [x + w * 2 / 4, y], [x + w * 2 / 4, y + h], [x + w / 4, y + h]])

        box2 = np.int0([[x + w * 2 / 4, y], [x + w * 3 / 4, y], [x + w * 3 / 4, y + h], [x + w * 2 / 4, y + h]])

        box3 = np.int0([[x + w * 3 / 4, y], [x + w, y], [x + w, y + h], [x + w * 3 / 4, y + h]])

        result.extend([box0, box1, box2, box3])

    elif len(valid_contours) > 4:

        for contour in valid_contours:

            x, y, w, h = cv2.boundingRect(contour)

            box = np.int0([[x, y], [x + w, y], [x + w, y + h], [x, y + h]])

            result.append(box)

    result = sorted(result, key=lambda x: x[0][0])

    return result

# 干扰线降噪

def interference_line(img, img_name):

    h, w = img.shape[:2]

    # ！！！opencv矩阵点是反的

    # img[1,2] 1:图片的高度，2：图片的宽度

    for y in range(1, w - 1):

        for x in range(1, h - 1):

            count = 0

            if img[x, y - 1] > 245:

                count = count + 1

            if img[x, y + 1] > 245:

                count = count + 1

            if img[x - 1, y] > 245:

                count = count + 1

            if img[x + 1, y] > 245:

                count = count + 1

            if count > 2:

                img[x, y] = 255

    cv2.imwrite(str(img_name), img)

    return img

im = cv2.imread(str(file_path))

im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)  # 将图片转成灰度图

# 将图片做二值化处理

ret, im_inv = cv2.threshold(im_gray, 127, 255, cv2.THRESH_BINARY_INV)

# 高斯模糊对图片进行降噪

kernel = 1 / 16 * np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]])

im_blur = cv2.filter2D(im_inv, -1, kernel)

# 再做一轮二值化处理

ret, binary = cv2.threshold(im_blur, 127, 255, cv2.THRESH_BINARY)

f_path2 = pathlib.Path.cwd().joinpath("picture/2.png")

# 去干扰线

last_im = interference_line(binary, f_path2)

# 识别

# 切割图片

# contours, hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# boxes=get_rect_box(contours)

# for box in boxes:

#         cv2.drawContours(im, [box], 0, (0,0,255),2)

#         roi = binary[box[0][1]:box[3][1], box[0][0]:box[1][0]]

#         roistd = cv2.resize(roi, (30, 30))

#         timestamp = int(time.time() * 1e6)

#         filename = "{}.jpg".format(timestamp)

#         filepath = os.path.join(char_path, filename)

#         cv2.imwrite(filepath, roistd)

# cv2.drawContours(im, contours, -1, (0, 0, 255), 3)

# cv2.imshow('IMG', im)

# cv2.waitKey(0)

# cv2.destroyAllWindows()
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opencv处理验证码python代码

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