Question

我需要准备文档图像以便进一步识别。我国有一条严格的规则，文件必须用黑色或灰色字体书写。签名和邮票也必须是蓝色的。因此，在当前步骤中，我需要通过删除非灰度色彩元素来删除标记和签名。这是文档片段的示例：我已编写代码将灰度级组件和非灰度级组件分别转换为灰度级：

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from skimage import io
from skimage.color import rgba2rgb, rgb2grey, colorconv
import matplotlib.pyplot as plt
def separate_color_regions(im):
    if im.shape[-1]==4:
        im=rgba2rgb(im)
    #representin each color chanel as float in range [0, 1.]
    im=colorconv._prepare_colorarray(im)

    #because we wont decompose black color instead of white
    im=1.-im

    #calculate gray component using the formula: grey=cos_a*|color|*(1, 1, 1)/sqrt(3), 
    #according to scalar multiplication formula cos_a*|color|=color*(1, 1, 1)/sqrt(3)
    #here sqrt(3) added since |(1, 1, 1)|=sqrt(3)
    gray=(im[..., 0]+im[..., 1]+im[..., 2])/3
    proj=im.copy()
    for i in range(3):
        proj[..., i]-=gray
    def norm(inp):
        """
        convert each color vector to its l2 norm
        """
        t=inp*inp
        return np.sqrt(np.sum(t, -1))
    def mask_nan(inp):
        where_are_NaNs = np.isnan(inp)
        inp[where_are_NaNs] = 0.
        return inp
    return 1.-mask_nan(norm(proj)/norm(im)), 1.-mask_nan((3.**0.5)*gray/norm(im))
im=io.imread('stamps.png')
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3)
ax1.imshow(im)
ax1.axis('off')
ax1.set_title('Original image')
nongray, gray=separate_color_regions(im)
ax2.imshow(nongray, cmap=plt.cm.gray, aspect='auto', interpolation='none')
ax2.axis('off')
ax2.set_title('Non-grey_components filter')
ax3.imshow(gray, cmap=plt.cm.gray, aspect='auto', interpolation='none')
ax3.axis('off')
ax3.set_title('Grey component filter')
fig.tight_layout()
plt.show()

以下是它的表现结果：您会发现它只能突出显示非灰度对象，但无法从中分离灰度对象并为图像带来许多噪点。如果我的案例有更好的解决方案，请告诉我。

Answer 1

使用手动设置的阈值解决此问题：

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from skimage import io
from skimage.color import rgba2rgb, rgb2grey, colorconv
import matplotlib.pyplot as plt
def separate_color_regions(im):
    if im.shape[-1]==4:
        im=rgba2rgb(im)
    #representin each color chanel as float in range [0, 1.]
    im=colorconv._prepare_colorarray(im)

    #because we wont decompose black color instead of white
    im=1.-im

    #calculate gray component using the formula: grey=cos_a*|color|*(1, 1, 1)/sqrt(3), 
    #according to scalar multiplication formula cos_a*|color|=color*(1, 1, 1)/sqrt(3)
    #here sqrt(3) added since |(1, 1, 1)|=sqrt(3)
    gray=(im[..., 0]+im[..., 1]+im[..., 2])/3
    proj=im.copy()
    for i in range(3):
        proj[..., i]-=gray
    def norm(inp):
        """
        convert each color vector to its l2 norm
        """
        t=inp*inp
        return np.sqrt(np.sum(t, -1))
    def mask_nan(inp):
        where_are_NaNs = np.isnan(inp)
        inp[where_are_NaNs] = 0.
        return inp
    return 1.-mask_nan(norm(proj)/norm(im)), 1.-mask_nan((3.**0.5)*gray/norm(im))
im=io.imread('stamps.png')
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3)
ax1.imshow(im)
ax1.axis('off')
ax1.set_title('Original image')
nongray, gray=separate_color_regions(im)
ax2.imshow(nongray, cmap=plt.cm.gray, aspect='auto', interpolation='none')
ax2.axis('off')
ax2.set_title('Non-grey_components filter')
mask=np.vectorize(lambda d: 0. if d>0.1 else 1.)
ax3.imshow(1.-(1.-rgb2grey(im))*mask(1.-nongray), cmap=plt.cm.gray, aspect='auto', interpolation='none')
ax3.axis('off')
ax3.set_title('Grey component filter')
fig.tight_layout()
plt.show()

从文档图像中删除非灰度色彩组件

1 个答案: