python批量去除图片文字水印源码，愿共同学习交流

yifukang · 发表于 2023-8-13 21:37:42

1#!/usr/bin/env python

# -*- coding:utf-8 -*-
# 需要安装的库
# pip install paddlepaddle -i https://mirrors.aliyun.com/pypi/simple/
# pip install paddleocr -i https://mirrors.aliyun.com/pypi/simple/
# pip install cv2 -i https://mirrors.aliyun.com/pypi/simple/
# pip install numpy -i https://mirrors.aliyun.com/pypi/simple/
# pip install Pillow -i https://mirrors.aliyun.com/pypi/simple/

import os
import cv2
import numpy as np
from PIL import Image
from paddleocr import PaddleOCR, draw_ocr

class DeleteImageWatermark:
def __init__(self):
      pass

def distinguish_string(self, img_path, lang='ch'):
      """
      得到文字识别结果列表
      img_path: 图片路径
      lang: 默认为识别中文
      return: 返回所有被识别到的文字文本框坐标、文字内容和置信度
      如：[
         [[[1415.0, 977.0], [1482.0, 977.0], [1482.0, 1001.0], [1415.0, 1001.0]], ('小红书', 0.868567168712616)],
         [[[1441.0, 1001.0], [1493.0, 1001.0], [1493.0, 1024.0], [1441.0, 1024.0]], ('小红书', 0.9620211124420166)]
      ]
      """
      orc = PaddleOCR(use_angle_cls=True, lang=lang)
      result = orc.ocr(img_path, cls=True)
      return result

def save_distinguish_result(self, result, img_path, save_path):
      """
      将识别文字的结果输出图片
      """
      image = Image.open(img_path).convert('RGB')
      boxes = [line[0] for line in result]
      txts = [line[1][0] for line in result]
      scores = [line[1][1] for line in result]
      im_show = draw_ocr(image, boxes, txts, scores, font_path='./fonts/simfang.ttf')
      im_show = Image.fromarray(im_show)
      im_show.save(save_path)

def delete_watermark(self, result_list, kw_list, img_path, delete_path):
      """
      将符合目标的水印，模糊化处理
      """
      # 获取所有符合目标的文本框位置
      text_axes_list = []
      for line in result_list:
         for kw in kw_list:
            if kw in line[1][0]:
                  min_width = int(min(line[0][0][0], line[0][3][0]))
                  max_width = int(max(line[0][1][0], line[0][2][0]))
                  min_hight = int(min(line[0][0][1], line[0][1][1]))
                  max_hight = int(max(line[0][2][1], line[0][3][1]))
                  text_axes_list.append([min_width, min_hight, max_width, max_hight])
                  break
      # 去除水印
      delt = 10  # 文本框范围扩大
      img = cv2.imread(img_path, 1)
      tmp_delete_path = delete_path.split('.')[0] + '_test.' + delete_path.split('.')[1]  # 临时图片地址
      cv2.imwrite(tmp_delete_path, img)
      for text_axes in text_axes_list:
         img = cv2.imread(tmp_delete_path, 1)
         hight, width = img.shape[0:2]
         # 截取图片
         min_width = text_axes[0] - delt if text_axes[0] - delt >= 0 else 0
         min_hight = text_axes[1] - delt if text_axes[1] - delt >= 0 else 0
         max_width = text_axes[2] + delt if text_axes[2] + delt <= width else width
         max_hight = text_axes[3] + delt if text_axes[3] + delt <= hight else hight
         cropped = img[min_hight:max_hight, min_width:max_width]  # 裁剪坐标为[y0:y1, x0:x1]
         cv2.imwrite(delete_path, cropped)  # 保存截取的图片
         imgSY = cv2.imread(delete_path, 1)
         # 图片二值化处理，把[200,200,200]-[250,250,250]以外的颜色变成0
         start_rgb = 200
         thresh = cv2.inRange(imgSY, np.array([start_rgb, start_rgb, start_rgb]), np.array([250, 250, 250]))
         # 创建形状和尺寸的结构元素
         kernel = np.ones((3, 3), np.uint8)  # 设置卷积核3*3全是1；将当前的数组作为图像类型来进⾏各种操作，就要转换到uint8类型
         # 扩展待修复区域
         hi_mask = cv2.dilate(thresh, kernel, iterations=10)  # 膨胀操作，白色区域增大，iterations迭代次数
         specular = cv2.inpaint(imgSY, hi_mask, 5, flags=cv2.INPAINT_TELEA)
         # imgSY：输入8位1通道或3通道图像。
         # hi_mask：修复掩码，8位1通道图像。非零像素表示需要修复的区域。
         # specular：输出与imgSY具有相同大小和类型的图像。
         # 5：算法考虑的每个点的圆形邻域的半径。
         # flags：NPAINT_NS基于Navier-Stokes的方法、Alexandru Telea的INPAINT_TELEA方法
         cv2.imwrite(delete_path, specular)
         # 覆盖图片
         imgSY = Image.open(delete_path)
         img = Image.open(tmp_delete_path)
         img.paste(imgSY, (min_width, min_hight, max_width, max_hight))
         img.save(tmp_delete_path)
      os.remove(delete_path)
      os.rename(tmp_delete_path, delete_path)

def has_kw(self, result_list, kw_list):
      """
      图片是否包含目标水印，返回匹配到的文字列表
      """
      result_str_list = []
      for line in result_list:
         for kw in kw_list:
            if kw in line[1][0]:
                  result_str_list.append(line[1][0])
                  break
      return result_str_list

def main(kw_list, img_path, result_path):
"""
kw_list: 需要识别的文字列表
img_path: 输入的图片地址
result_path: 输出去水印的结果图片地址
"""
d = DeleteImageWatermark()
# 识别文字
result = d.distinguish_string(img_path)
for line in result:
      print(line)  # 打印识别结果：识别到的文字文本框坐标、文字内容和置信度

# 显示文字识别结果
d.save_distinguish_result(result, img_path, os.path.dirname(__file__) + '/test_01.jpg')

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