# 传统方法





程序
```python
import numpy as np
import cv2
def replaceZeroes(data):
    min_nonzero = min(data[np.nonzero(data)])
    data[data == 0] = min_nonzero
    return data
def SSR(src_img, size):
    L_blur = cv2.GaussianBlur(src_img, (size, size), 0)
    img = replaceZeroes(src_img)
    L_blur = replaceZeroes(L_blur)
    dst_Img = cv2.log(img / 255.0)
    dst_Lblur = cv2.log(L_blur / 255.0)
    dst_IxL = cv2.multiply(dst_Img, dst_Lblur)
    log_R = cv2.subtract(dst_Img, dst_IxL)
    dst_R = cv2.normalize(log_R, None, 0, 255, cv2.NORM_MINMAX)
    log_uint8 = cv2.convertScaleAbs(dst_R)
    return log_uint8
# 使用照片修复的办法来去掉图片中的文字
def remove_text1(path):
    size = 3
    src_img = cv2.imread(path)
    # # 膨胀图片
    # src_img = cv2.dilate(src_img, kernel=np.ones((3, 3), dtype=np.uint8), iterations=2)
    # 腐蚀图片
    # src_img = cv2.erode(src_img, kernel=np.ones((3, 3), dtype=np.uint8), iterations=2)
    b_gray, g_gray, r_gray = cv2.split(src_img)
    b_gray = SSR(b_gray, size)
    g_gray = SSR(g_gray, size)
    r_gray = SSR(r_gray, size)
    ssrimg = cv2.merge([b_gray, g_gray, r_gray])
    canny_img = cv2.Canny(ssrimg, 200, 150)
    k = np.ones((3, 3), np.uint8)
    mask = cv2.morphologyEx(canny_img, cv2.MORPH_CLOSE, k)  # 闭运算
    # 对mask进行膨胀
    # mask = cv2.dilate(mask, kernel=np.ones((3, 3), dtype=np.uint8), iterations=6)
    dst = cv2.inpaint(src_img, mask, 3, cv2.INPAINT_TELEA)
    # dst = cv2.inpaint(dst, mask, 3, cv2.INPAINT_TELEA)
    # 膨胀
    dst = cv2.dilate(dst, kernel=np.ones((3, 3), dtype=np.uint8), iterations=5)
    return dst
# 使用mask直接填充颜色的办法来去掉图片中的文字
def remove_text2(path):
    size = 3
    src_img = cv2.imread(path)
    # 腐蚀图片
    # src_img = cv2.erode(src_img, kernel=np.ones((3, 3), dtype=np.uint8), iterations=2)
    b_gray, g_gray, r_gray = cv2.split(src_img)
    b_gray = SSR(b_gray, size)
    g_gray = SSR(g_gray, size)
    r_gray = SSR(r_gray, size)
    ssrimg = cv2.merge([b_gray, g_gray, r_gray])
    canny_img = cv2.Canny(ssrimg, 200, 150)
    k = np.ones((3, 3), np.uint8)
    mask = cv2.morphologyEx(canny_img, cv2.MORPH_CLOSE, k)  # 闭运算
    # 保存mask为图片
    cv2.imwrite("mask.png", mask)
    # mask等于1的区域，用白色填充
    # dst = cv2.bitwise_and(src_img, src_img, mask=mask)
    src_img[mask == 255] = (255, 255, 255)
    # 膨胀
    src_img = cv2.dilate(src_img, kernel=np.ones((3, 3), dtype=np.uint8), iterations=5)
    return src_img
# 移除背景色，增强文字
def remove_ground(path):
    size = 3
    src_img = cv2.imread(path)
    b_gray, g_gray, r_gray = cv2.split(src_img)
    b_gray = SSR(b_gray, size)
    g_gray = SSR(g_gray, size)
    r_gray = SSR(r_gray, size)
    ssrimg = cv2.merge([b_gray, g_gray, r_gray])
    canny_img = cv2.Canny(ssrimg, 200, 150)
    k = np.ones((3, 3), np.uint8)
    mask = cv2.morphologyEx(canny_img, cv2.MORPH_CLOSE, k)  # 闭运算
    # mask等于1的区域，用白色填充
    dst = cv2.bitwise_and(src_img, src_img, mask=mask)
    dst[mask == 0] = (255, 255, 255)
    return dst
if __name__ == '__main__':
    imgPath = "papertext.jpg"
    dst = remove_text2(imgPath)
    cv2.imwrite("result.jpg", dst)
```

原图：

![在这里插入图片描述](/static/img/images/04728.jpeg)

找到文字部分：

![在这里插入图片描述](/static/img/images/04729.png)

去除文字：

![在这里插入图片描述](/static/img/images/04730.jpeg)

保留清晰文字：

![在这里插入图片描述](/static/img/images/04731.jpeg)

# 深度学习

1、

Self-Supervised Text Erasing with Controllable Image Synthesis 即STE ： 论文[https://arxiv.org/abs/2204.12743](https://arxiv.org/abs/2204.12743)。

2、

EnsNet ： [https://blog.csdn.net/u013554402/article/details/104494261](https://blog.csdn.net/u013554402/article/details/104494261)。

3、

[https://paperswithcode.com/paper/mtrnet-one-stage-mask-based-scene-text-eraser](https://paperswithcode.com/paper/mtrnet-one-stage-mask-based-scene-text-eraser)。

4、

[http://www.cjig.cn/html/jig/2022/4/20220420.htm](http://www.cjig.cn/html/jig/2022/4/20220420.htm)





# 译文回填

SickZil-Machine用于去除文本： [https://github.com/KUR-creative/SickZil-Machine](https://github.com/KUR-creative/SickZil-Machine)

lama-cleaner技术：  [https://github.com/Sanster/lama-cleaner](https://github.com/Sanster/lama-cleaner)

【OpenCV】【深度学习】去除图片文字；只保留图片文字；译文回填

原因是png图片里面的profile ，好像又听说高版本的png在做什么。






解决方式很简单，重写一下这个图。


下面程序会读取 r"/ssd/xd/datasets/multilabelsTask/"下面的所有jpg和png图，多进程方式地重写一下图片，注意，如果opencv打不开这个图，这个图会被删除：
```python
import multiprocessing
import os
import cv2
import numpy as np
def listPathAllfiles(dirname):
    result = []
    for maindir, subdir, file_name_list in os.walk(dirname):
        for filename in file_name_list:
            apath = os.path.join(maindir, filename)
            result.append(apath)
    return result
def overwrite(files1):
    for fname in files1:
        try:
            if fname.endswith(".jpg"):
                img = cv2.imdecode(np.fromfile(fname, dtype=np.uint8), 1)  # img是矩阵
                cv2.imencode('.jpg', img)[1].tofile(fname)
            elif fname.endswith(".png"):
                img = cv2.imdecode(np.fromfile(fname, dtype=np.uint8), 1)  # img是矩阵
                cv2.imencode('.png', img)[1].tofile(fname)
        except:
            os.remove(fname)
            print(fname)
if __name__ == '__main__':
    root = r"/ssd/xd/datasets/multilabelsTask/"
    files = listPathAllfiles(root)
    p = multiprocessing.Pool()  # 创建一个包含2个进程的进程池
    # split files to several parts
    for i in range(0, len(files), 10000):
        p.apply_async(func=overwrite, args=(files[i:i + 1000],))  # 往池子里加一个异步执行的子进城
    p.close()  # 等子进程执行完毕后关闭进程池
    p.join()  # 主进程等待
```

【opencv】【python】libpng warning: iCCP: known incorrect sRGB profile 解决

截图目标，分析目标的颜色直方图：





```python
import cv2
from matplotlib import pyplot as plt
def show_img(path):
    img = cv2.imread(path)
    b, g, r = img[:, :, 0], img[:, :, 1], img[:, :, 2]
    hist_b = cv2.calcHist([b], [0], None, [256], [0, 256])
    hist_g = cv2.calcHist([g], [0], None, [256], [0, 256])
    hist_r = cv2.calcHist([r], [0], None, [256], [0, 256])
    plt.figure(figsize=(10, 5))
    plt.subplot(121)
    plt.plot(hist_r, color='r', label="r")
    plt.plot(hist_g, color='g', label="g")
    plt.plot(hist_b, color='b', label="b")
    plt.legend()
    img2 = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    h, s, v = img2[:, :, 0], img2[:, :, 1], img2[:, :, 2]
    hist_h = cv2.calcHist([h], [0], None, [256], [0, 256])
    hist_s = cv2.calcHist([s], [0], None, [256], [0, 256])
    hist_v = cv2.calcHist([v], [0], None, [256], [0, 256])
    plt.subplot(122)
    plt.plot(hist_h, color='r', label="h")
    plt.plot(hist_s, color='g', label="s")
    plt.plot(hist_v, color='b', label="v")
    plt.legend()
    plt.show()
    return hist_r, hist_g, hist_b, hist_h, hist_s, hist_v
r, g, b, h, s, v = show_img("lanse.png")
```

![在这里插入图片描述](/static/img/images/00262.png)


```
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread("1111.jpg")
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
lower_red = np.array([115, 0, 160])
upper_red = np.array([140, 30, 190])
mask = cv2.inRange(hsv, lower_red, upper_red)
res = cv2.bitwise_and(img, img, mask=mask)
# cv2.imshow('img', img)
cv2.imshow('mask', mask)
cv2.imshow('res', res)
cv2.waitKey(0)
cv2.destroyAllWindows()
```

Opencv 颜色过滤 Python mask 选取颜色 颜色直方图

# 边缘检测





![在这里插入图片描述](/static/img/images/00263.jpeg)

拉普拉斯边缘分割：

![在这里插入图片描述](/static/img/images/00264.png)

sobel算子：

![在这里插入图片描述](/static/img/images/00265.png)

canny算子：

![在这里插入图片描述](/static/img/images/00266.png)

加了高斯模糊再用canny算子：

![在这里插入图片描述](/static/img/images/00267.png)






参考：

水文标尺识别opencv：[https://blog.csdn.net/sinat_28371057/article/details/109968240](https://blog.csdn.net/sinat_28371057/article/details/109968240)

边缘 图像分割： [https://www.jianshu.com/p/fb48de8120fd](https://www.jianshu.com/p/fb48de8120fd)

图像分割：[https://zhuanlan.zhihu.com/p/70758906](https://zhuanlan.zhihu.com/p/70758906)

水文标尺识别 深度学习 边缘检测 图像分割

修改video_path = 'test1.mp4' 这一句即可，源码地址：https://github.com/hongcyu/char_painting





```python
import cv2
from PIL import Image, ImageFont, ImageDraw
import os
from cv2 import VideoWriter, VideoWriter_fourcc, imread, resize
# =========================
# coding:UTF-8
# 视频转字符画含音频version-1
# 参考1：https://blog.csdn.net/mp624183768/article/details/81161260
# 参考2：https://blog.csdn.net/qq_42820064/article/details/90958577
# 参考3：https://blog.csdn.net/zj360202/article/details/79026891
# =========================
def get_char(r, g, b, alpha=256):
    ascii_char = list(r"#TONGHUIgogoggoWGPZ*@$C&998798213123123128?3345462I1>;\';][.../]'\]!:-;. ")
    # ascii_char = list("#RMNHQODBWGPZ*@$C&98?32I1>!:-;. ")
    # ascii_char = list("$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\|()1{}[]?-_+~<>i!lI;:oa+>!:+. ")
    if alpha == 0:
        return ''
    length = len(ascii_char)
    gray = int(0.2126 * r + 0.7152 * g + 0.0722 * b)
    unit = (256.0 + 1) / len(ascii_char)
    return ascii_char[int(gray / unit)]
# 将视频转换为图片 并进行计数，返回总共生成了多少张图片！
def video_to_pic(vp):
    # vp = cv2.VideoCapture(video_path)
    number = 0
    if vp.isOpened():
        r, frame = vp.read()
        if not os.path.exists('cache_pic'):
            os.mkdir('cache_pic')
        os.chdir('cache_pic')
    else:
        r = False
    while r:
        number += 1
        cv2.imwrite(str(number) + '.jpg', frame)
        r, frame = vp.read()
    print('\n由视频一共生成了{}张图片！'.format(number))
    os.chdir("..")
    return number
def img_to_char(image_path, raw_width, raw_height, task):
    width = int(raw_width / 6)
    height = int(raw_height / 15)
    im = Image.open(image_path).convert('RGB')  # 必须以RGB模式打开
    im = im.resize((width, height), Image.NEAREST)
    txt = ''
    color = []
    for i in range(height):
        for j in range(width):
            pixel = im.getpixel((j, i))
            color.append((pixel[0], pixel[1], pixel[2]))  # 将颜色加入进行索引
            if len(pixel) == 4:
                txt += get_char(pixel[0], pixel[1], pixel[2], pixel[3])
            else:
                txt += get_char(pixel[0], pixel[1], pixel[2])
        txt += '\n'
        color.append((255, 255, 255))
    im_txt = Image.new("RGB", (raw_width, raw_height), (255, 255, 255))
    dr = ImageDraw.Draw(im_txt)
    # font = ImageFont.truetype('consola.ttf', 10, encoding='unic') #改为这个字体会让图片比例改变
    font = ImageFont.load_default().font
    x, y = 0, 0
    font_w, font_h = font.getsize(txt[1])
    font_h *= 1.37  # 调整字体大小
    for i in range(len(txt)):
        if (txt[i] == '\n'):
            x += font_h
            y = -font_w
        dr.text((y, x), txt[i], fill=color[i])
        y += font_w
    os.chdir('cache_char')
    im_txt.save(str(task) + '.jpg')
    os.chdir("..")
    return 0
def star_to_char(number, save_pic_path):
    if not os.path.exists('cache_char'):
        os.mkdir('cache_char')
    img_path_list = [save_pic_path + r'/{}.jpg'.format(i) for i in range(1, number + 1)]  # 生成目标图片文件的路径列表
    task = 0
    for image_path in img_path_list:
        img_width, img_height = Image.open(image_path).size  # 获取图片的分辨率
        task += 1
        img_to_char(image_path, img_width, img_height, task)
        print('{}/{} is finished.'.format(task, number))
    print('=======================')
    print('All image was finished!')
    print('=======================')
    return 0
def process_bar(percent, start_str='', end_str='', total_length=0):
    # 进度条
    bar = ''.join("■ " * int(percent * total_length)) + ''
    bar = '\r' + start_str + bar.ljust(total_length) + ' {:0>4.1f}%|'.format(percent * 100) + end_str
    print(bar, end='', flush=True)
def jpg_to_video(char_image_path, FPS):
    video_fourcc = VideoWriter_fourcc(*"MP42")  # 设置视频编码器,这里使用使用MP42编码器,可以生成更小的视频文件
    char_img_path_list = [char_image_path + r'/{}.jpg'.format(i) for i in range(1, number + 1)]  # 生成目标字符图片文件的路径列表
    char_img_test = Image.open(char_img_path_list[1]).size  # 获取图片的分辨率
    if not os.path.exists('video'):
        os.mkdir('video')
    video_writter = VideoWriter('video/new_char_video.avi', video_fourcc, FPS, char_img_test)
    sum = len(char_img_path_list)
    count = 0
    for image_path in char_img_path_list:
        img = cv2.imread(image_path)
        video_writter.write(img)
        end_str = '100%'
        count = count + 1
        process_bar(count / sum, start_str='', end_str=end_str, total_length=15)
    video_writter.release()
    print('\n')
    print('=======================')
    print('The video is finished!')
    print('=======================')
if __name__ == '__main__':
    video_path = 'test1.mp4'
    save_pic_path = 'cache_pic'
    save_charpic_path = 'cache_char'
    vp = cv2.VideoCapture(video_path)
    number = video_to_pic(vp)
    FPS = vp.get(cv2.CAP_PROP_FPS)
    star_to_char(number, save_pic_path)
    vp.release()
    jpg_to_video(save_charpic_path, FPS)
```

还是有人不会用，下面具体一点。前提是你会python，你需要有python环境。

安装程序的依赖包：
```
pip install opencv-python pillow
```

把代码弄进去，同级目录下加入一个test1.mp4，然后直接运行代码。

![在这里插入图片描述](/static/img/images/00311.png)

就能得到效果视频了：

![在这里插入图片描述](/static/img/images/00312.gif)

Python 井川里予 当程序员看多了 程序 生成 字符画

我是一个专注技术分享的博主,尤其爱搞深度学习和单片机电子。你现在看到的是我的个人博客网站。