檢測矩形並將圖像裁剪至其大小

Question

我是圖像處理新手，並開始學習scikit-image。我試圖檢測一個矩形的角落，然後裁剪整個圖像。但我迷失在大量的分割和檢測算法中，不知道我需要哪一個以及如何去做。

此代碼生成示例圖像。我想把它裁剪成綠色的矩形。我需要做什麼？

從matplotlib進口pyplot作爲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() 

的任務是檢測矩形（聚陣列）的邊緣和圖像裁剪到它。

我試過哈里斯角點檢測，Canny邊緣檢測等等，但我完全搞不懂。這似乎是一個簡單的任務，但我沒有得到它。

這對OpenCV會更容易嗎？請幫助！

Answer 1

如果您使用輪廓尋找方法，OpenCV是目前最好的選擇。如果您可以用其他方式識別對象，則使用skimage可以很容易地識別座標並裁剪圖像。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()