[opencv practice] this seal "artifact" is awesome, saving time and improving efficiency. It is powerful ~ (complete source code attached)

Preface

author ：“ Programmer pear ”

** The article brief introduction **： This article is mainly about opencv Small program for extracting seal text ！

** The source code of the article is available for free ： In order to thank everyone who pays attention to me, the project source code of each article is none

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Click here for the blue line font , If you need any source code, remember to say the title and name ！ I can also ！

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Text

This issue is about using opencv Extract seal , Many times we need electronic version of the chapter , So let's use the code today

extracted ！

Photoshop Although powerful , But how can Xiaobian not make , Yesterday, a little friend asked me if I could help , see ？PS though

But I won't , But I can write code ！ It's hard not to beat me ！ Arrange... Arrange ~

（ Special reminder ： All the friends who like designing and drawing , Remember never to help your boss or friends PS Fake public

Chapter , Criminal Law No 280 This article specifically states that , Forged certificate seal , It can be investigated for criminal responsibility , Don't do anything illegal .）

Tutorial delivery , The source code is in the blue font at the end of the text or above. Take it for free —— Come on ！

1） Effect display

Original picture ——

design sketch ——

 2） Source code display

import cv2
import numpy as np

class Seal:
    def __init__(self, img_path):
        """
         Initialize picture 
        :param img_path:  Original image path         """
        self.image = cv2.imread(img_path)
        self.img_shape = self.image.shape
        self.file_name = img_path.split('.')[0].split('\\')[-1]

    def unify_img_size(self):
        """
         Uniform picture size 
        :return: Return an unprocessed target image         """
        img_w = 650 if self.img_shape[1] > 600 else 400
        self.image = cv2.resize(self.image, (img_w, int(img_w * self.img_shape[0] / self.img_shape[1])), interpolation=cv2.IMREAD_COLOR)
        impng = cv2.cvtColor(self.image.copy(), cv2.COLOR_RGB2RGBA)
        return impng

    def img_binaryzation(self,hue_image, low_range, high_range, imgpng):

        th = cv2.inRange(hue_image, low_range, high_range)
        element = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1))
        th = cv2.dilate(th, element)
        index1 = th == 255
        print_img = np.zeros(imgpng.shape, np.uint8)
        print_img[:, :, :] = (255, 255, 255, 0)
        print_img[index1] = imgpng[index1]  # (0,0,255)
        return print_img

    def img_enhance(self):
        imgpng = self.unify_img_size()
        hue_image = cv2.cvtColor(self.image, cv2.COLOR_BGR2HSV)  #  Process image hue 
        low_range = np.array([130, 43, 46])  #  Set the lower boundary 
        high_range = np.array([180, 255, 255])  #  Set the upper boundary 
        print1 = self.img_binaryzation(hue_image, low_range, high_range, imgpng)
        low_range = np.array([0, 43, 46])
        high_range = np.array([9, 255, 255])
        print2 = self.img_binaryzation(hue_image, low_range, high_range, imgpng)
        imgreal = cv2.add(print2, print1)

        white_px = np.asarray([255, 255, 255, 255])
        (row, col, _) = imgreal.shape
        for r in range(row):
            for c in range(col):
                px = imgreal[r][c]
                if all(px == white_px):
                    imgreal[r][c] = imgpng[r][c]
        return imgreal

    def extension_img(self):
        """
         edge detection , Intercept and output the results 
        :return:
        """
        imgreal = self.img_enhance()
        #  Expand the picture to prevent interception 
        print4 = cv2.copyMakeBorder(imgreal, 50, 50, 50, 50, cv2.BORDER_CONSTANT, value=[255, 255, 255, 0])
        print2gray = cv2.cvtColor(print4, cv2.COLOR_RGBA2GRAY)
        _, grayfirst = cv2.threshold(print2gray, 254, 255, cv2.THRESH_BINARY_INV)

        element = cv2.getStructuringElement(cv2.MORPH_RECT, (22, 22))
        img6 = cv2.dilate(grayfirst, element)

        c_canny_img = cv2.Canny(img6, 10, 10)

        contours, hierarchy = cv2.findContours(c_canny_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        areas = []
        for i, cnt in enumerate(contours):
            x, y, w, h = cv2.boundingRect(cnt)
            area = w * h
            ars = [area, i]
            areas.append(ars)
        areas = sorted(areas, reverse=True)
        maxares = areas[:1]

        x, y, w, h = cv2.boundingRect(contours[maxares[0][1]])
        print5 = print4[y:(y + h), x:(x + w)]
        #  Height is less than width 
        if print5.shape[0] < print5.shape[1]:
            zh = int((print5.shape[1] - print5.shape[0]) / 2)
            print5 = cv2.copyMakeBorder(print5, zh, zh, 0, 0, cv2.BORDER_CONSTANT, value=[255, 255, 255, 0])
        else:
            zh = int((print5.shape[0] - print5.shape[1]) / 2)
            print5 = cv2.copyMakeBorder(print5, 0, 0, zh, zh, cv2.BORDER_CONSTANT, value=[255, 255, 255, 0])
        resultprint = cv2.resize(print5, (150, 150))

        cv2.imwrite(r'output\{}_result.png'.format(self.file_name), resultprint)


if __name__ == '__main__':
    s = Seal(r"src\2.jpg")
    s.extension_img()

summary

The effect is very good , Interested friends can try it ~

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