检测矩形并将图像裁剪为其大小

Question

我是图像处理的新手，并开始学习scikit-image。我试图检测矩形的角落，然后将整个图像裁剪为它。但是我在大量的分割和检测算法中迷失了，并且不知道我需要哪一个以及如何去做。

此代码生成示例图像。我想把它裁剪成绿色矩形。我需要做什么？

从matplotlib导入pyplot作为pyplot

import numpy as np
import skimage
from skimage import draw

img = np.zeros((500, 500, 3), dtype=np.double)

poly = np.array([[25, 25],
                 [25, 75],
                 [75, 75],
                 [75, 25]])

rr, cc = draw.polygon(poly[:, 0], poly[:, 1], img.shape)

img[rr, cc, 1] = 1
plt.imshow(img)
plt.show()

任务是检测矩形（多边形阵列）的边缘并将图像裁剪到它。

我尝试过哈里斯角检测，精确边缘检测等等，但我完全糊涂了。这似乎是一项简单的任务，但我没有得到它。

使用OpenCV会更容易吗？请帮忙！

Answer 1

如果您与轮廓寻找方法联系在一起，OpenCV是目前最好的选择。如果您可以通过其他方式识别对象，则可以很容易地识别坐标并裁剪图像。例如：

import numpy as np
import skimage
from skimage import draw, measure
import matplotlib.pyplot as plt

# --- Generate test data ---
img = np.zeros((500, 500, 3), dtype=np.double)

poly = np.array([[25, 25],
                 [25, 75],
                 [75, 75],
                 [75, 25]])

rr, cc = draw.polygon(poly[:, 0], poly[:, 1], img.shape)

img[rr, cc, 1] = 1
# --- End generate test data ---

# Generate a mask with all objects close to green
green = (0, 1, 0)
mask = np.sum((img - green)**2, axis=2) < 0.1

# Label the different objects in the image
label_img = measure.label(mask)

# Find all objects
regions = measure.regionprops(label_img)

# Find the first object (since we only have one, that's the rectangle!)
r = regions[0]

# We'll use the `bbox` property to select the bounding box
# # For a full list of properties, take a look at the docstring of
# measure.regionprops

print('The bounding box is {}'.format(r.bbox))

r0, c0, r1, c1 = r.bbox
img_crop = img[r0:r1, c0:c1]

f, (ax0, ax1) = plt.subplots(1, 2)
ax0.imshow(img)
ax1.imshow(img_crop)
plt.show()