如何找到边缘附近的黑色区域

Question

我有一张像下面这样的图片。它在图像的顶部和右侧有黑色边框/区域。我希望能够找到这些区域，如第二张图片所示。请注意，这些区域应始终是直的（即矩形）。我希望能够使用“使用不使用 photoshop 的代码进行图像处理”（例如 matlab、c# 或 opencv）来做到这一点。

我对“图像处理”非常陌生。我试图找到所有具有 (0,0,0) rgb 值的点。但是因为在噪声部分（以及图像中的任何其他地方）中有很多这样的黑色值。我的结果区域也包含这些不需要的区域....

--------- 编辑 --------------- 感谢所有 cmets/答案。但是，我有很多这样的图像。其中一些是旋转的，这有点难以处理。我刚刚上传了一张，如下图。

Answer 1

color_img = imread('0k4Kh.jpg');
img = rgb2gray(color_img);
[x, y] = size(img);

for i = 1:x
    if length(find(img(i, :))) ~= 0
        lastmarginalrow = i-1;
        break;
    end
end

for ii = y:-1:1
    if length(find(img(:, ii))) ~= 0
        lastmarginalcol = ii-1;
        break;
    end
end

figure;
fig = imshow(color_img);
h = impoly(gca, [0,x; lastmarginalcol,x; lastmarginalcol,lastmarginalrow; 0,lastmarginalrow]);
api = iptgetapi(h);
api.setColor('red');
saveas(fig, 'test.jpg');
close all;

这是 MATLAB 中的实现。找到 zeros 列和 zeros 行并使用它们绘制边框。

适用于旋转图像（也适用于非旋转图像）

color_img = imread('N6vK9.png');
img = rgb2gray(color_img);
[x, y] = size(img);

verts = [];
% traversing through all columns
for i = 1:y
    % find all non-zero pixels in each column
    nonzeros = find(img(:,i));
    % if all pixels are black in a column, below if condition will skip
    if length(nonzeros) ~= 0
        % if there is atleast one non-zero pixel, not that co-oridinate/positions in matrix by appending
        verts = [i, nonzeros(1); verts];
    end
end

figure;
fig = imshow(color_img);
% polygon based on first and last vertix/co-ordinate of found non-zero co-ordinates
% Assumed that it was slanted straight line, used last and first co-ordinate. If it is curvy border, anyways we have all veritices/co-ordinates of first non-zero pixel in all columns.
h = impoly(gca, [verts(1,:); verts(length(verts), :); 1,x; verts(1),x]);
api = iptgetapi(h);
api.setColor('red');
saveas(fig, 'test.jpg');
close all;

Answer 2

使用 Python2.7 + OpenCV3。这个想法是只保留非零的行和列。代码如下。

import cv2
import numpy as np
#Read in the image
arr = np.array(cv2.imread('image.jpg'))
#Convert to grayscale
gray = np.sum(arr, axis=2)
print gray.shape #(496, 1536)
filter_row = np.sum(gray,axis=1)!=0
# Assuming first few values are all False, find index of first True, and set all values True after that 
filter_row[list(filter_row).index(True):,] = True
# Keep only non-zero rows
horiz = gray[filter_row,:]

filter_column = np.sum(gray,axis=0)!=0
# Assuming first few values are all False, find index of first False, and set all values True before that 
filter_column[:list(filter_column).index(False),] = True
# Keep only non-zero columns
vert = horiz[:,filter_column]
print vert.shape #(472, 1528)
bordered = cv2.rectangle(cv2.imread('image.jpg'), (0, gray.shape[0]-vert.shape[0]), (vert.shape[1],gray.shape[0] ), (255,0,0), 2)
cv2.imwrite(bordered,'result.jpg')