我有以下几点:

     <data:polygon>
                            <data:point x="542" y="107"/>
                            <data:point x="562" y="102"/>
                            <data:point x="582" y="110"/>
                            <data:point x="598" y="142"/>
                            <data:point x="600" y="192"/>
                            <data:point x="601" y="225"/>
                            <data:point x="592" y="261"/>
                            <data:point x="572" y="263"/>
                            <data:point x="551" y="245"/>
                            <data:point x="526" y="220"/>
                            <data:point x="520" y="188"/>
                            <data:point x="518" y="152"/>
                            <data:point x="525" y="127"/>
                            <data:point x="542" y="107"/
 </data:polygon>

最佳答案

使用 cv2.fillConvexPoly 这样您就可以指定一个二维点数组并定义一个 mask ，该 mask 将这些点定义的形状填充为 mask 中的白色。在多边形中定义的点是凸的(因此名称 fillConvexPoly )的地方应该发出一些公平的警告。

然后我们可以将其转换为 bool 掩码，并使用它来索引您的图像以提取出您想要的像素。下面的代码生成一个名为 mask 的数组这将包含要从图像中保存的像素的 bool 掩码。另外，数组out将包含由多边形定义的所需提取的子图像。请注意，图像被初始化为完全黑暗，并且唯一要复制的像素是多边形定义的像素。

假设实际图像名为 img ，并假设您的 x和 y点表示图像中的水平和垂直坐标，你可以这样做:

import numpy as np
import cv2

pts = np.array([[542, 107], [562, 102], [582, 110], [598, 142], [600, 192], [601, 225], [592, 261], [572, 263], [551, 245], [526, 220], [520, 188], [518, 152], [525, 127], [524, 107]], dtype=np.int32)

mask = np.zeros((img.shape[0], img.shape[1]))

cv2.fillConvexPoly(mask, pts, 1)
mask = mask.astype(np.bool)

out = np.zeros_like(img)
out[mask] = img[mask]

cv2.imshow('Extracted Image', out)
cv2.waitKey(0)
cv2.destroyAllWindows()

cv2.imwrite('output.png', out)

img = 255*np.ones((300, 700, 3), dtype=np.uint8)

(meanx, meany) = pts.mean(axis=0)

(cenx, ceny) = (img.shape[1]/2, img.shape[0]/2)

(meanx, meany, cenx, ceny) = np.floor([meanx, meany, cenx, ceny]).astype(np.int32)

(offsetx, offsety) = (-meanx + cenx, -meany + ceny)

(mx, my) = np.meshgrid(np.arange(img.shape[1]), np.arange(img.shape[0]))
ox = (mx - offsetx).astype(np.float32)
oy = (my - offsety).astype(np.float32)
out_translate = cv2.remap(out, ox, oy, cv2.INTER_LINEAR)

topleft = pts.min(axis=0) + [offsetx, offsety]
bottomright = pts.max(axis=0) + [offsetx, offsety]
cv2.rectangle(out_translate, tuple(topleft), tuple(bottomright), color=(255,0,0))

# Import relevant modules
import numpy as np
import cv2

# Define points
pts = np.array([[542, 107], [562, 102], [582, 110], [598, 142], [600, 192], [601, 225], [592, 261], [572, 263], [551, 245], [526, 220], [520, 188], [518, 152], [525, 127], [524, 107]], dtype=np.int32)

### Define image here
img = 255*np.ones((300, 700, 3), dtype=np.uint8)

# Initialize mask
mask = np.zeros((img.shape[0], img.shape[1]))

# Create mask that defines the polygon of points
cv2.fillConvexPoly(mask, pts, 1)
mask = mask.astype(np.bool)

# Create output image (untranslated)
out = np.zeros_like(img)
out[mask] = img[mask]

# Find centroid of polygon
(meanx, meany) = pts.mean(axis=0)

# Find centre of image
(cenx, ceny) = (img.shape[1]/2, img.shape[0]/2)

# Make integer coordinates for each of the above
(meanx, meany, cenx, ceny) = np.floor([meanx, meany, cenx, ceny]).astype(np.int32)

# Calculate final offset to translate source pixels to centre of image
(offsetx, offsety) = (-meanx + cenx, -meany + ceny)

# Define remapping coordinates
(mx, my) = np.meshgrid(np.arange(img.shape[1]), np.arange(img.shape[0]))
ox = (mx - offsetx).astype(np.float32)
oy = (my - offsety).astype(np.float32)

# Translate the image to centre
out_translate = cv2.remap(out, ox, oy, cv2.INTER_LINEAR)

# Determine top left and bottom right of translated image
topleft = pts.min(axis=0) + [offsetx, offsety]
bottomright = pts.max(axis=0) + [offsetx, offsety]

# Draw rectangle
cv2.rectangle(out_translate, tuple(topleft), tuple(bottomright), color=(255,0,0))

# Show image, wait for user input, then save the image
cv2.imshow('Output Image', out_translate)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('output.png', out_translate)

关于python - 使用 OpenCV 从图像中提取多边形给定坐标，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/30901019/