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我尝试使用SSIM提取两个图像之间的差异以仅获取地板区域（image_a是原始图像，而image_b已绘制地板）。

预期的输出是阈值掩码。

我遇到的问题是 ssim 差异的阈值在我的情况下不起作用（示例如下所示）。

有人可以提供更好的阈值技术或理论吗？

from skimage.measure import compare_ssim
import cv2
...

image_a = cv2.imread(first)
image_b = cv2.imread(second)

gray_a = cv2.cvtColor(image_a, cv2.COLOR_BGR2GRAY)
gray_b = cv2.cvtColor(image_b, cv2.COLOR_BGR2GRAY)

_, diff = compare_ssim(gray_a, gray_b, full=True, gaussian_weights=True)
diff = (diff * 255).astype("uint8")

thresh = cv2.threshold(diff, 0, 255,
                       cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]

contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]

contour_sizes = [(cv2.contourArea(contour), contour) for contour in contours]


if len(contour_sizes) > 0:
    largest_contour = max(contour_sizes, key=lambda x: x[0])[1]
    x, y, w, h = cv2.boundingRect(largest_contour)
    cv2.rectangle(image_a, (x, y), (x + w, y + h), (36, 255, 12), 2)
    cv2.rectangle(image_b, (x, y), (x + w, y + h), (36, 255, 12), 2)

cv2.imwrite('image_a.jpg', image_a)
cv2.imwrite('image_b.jpg',image_b)
cv2.imwrite('thresh.jpg', thresh)

检测到最大轮廓 的 image_a 检测到最大轮廓的 image_b阈值在此处输入图像描述 在此处输入图像描述 在此处输入图像描述

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1 回答 1

3

通过对给定图像之间差异的平均值进行阈值化可以获得更好的结果。

def get_mask(img1, img2, thresh):
    if img1.shape != img2.shape:
        return
    diff = cv2.absdiff(img1, img2)
    diff = np.mean(diff, axis=2)
    diff[diff <= thresh] = 0
    diff[diff > thresh] = 255
    mask = np.dstack([diff] * 3)
    return mask

thresh_morph

伪影可能会出现在生成的蒙版中，并且可以通过应用Morphological Transformations来减少。

于 2019-10-07T05:27:01.213 回答