python - 无法均衡图像

Question

我正在使用 Python 中的 skimage 库来增加图像的对比度。 我的图像采用 RGB 格式，位于名为 X_train 的列表中，其图像形状为:(32x32x3)。

首先，我将其转换为 [0, 1]，然后从 RGB 转换为 HSV，然后使用库中的方法:

X_train = X_train/256
X_train_hsv = matplotlib.colors.rgb_to_hsv(X_train)
X_train_eq = skimage.exposure.equalize_adapthist(X_train_hsv, kernel_size=None,
                                                 clip_limit=0.01, nbins=256, )

问题是我收到此错误:

/home/carnd/anaconda3/envs/carnd-term1/lib/python3.5/site-packages/skimage/exposure/_adapthist.py in interpolate(image, xslice, yslice, mapLU, mapRU, mapLB, mapRB, lut)

        333                  int(xslice[0]):int(xslice[-1] + 1)]
        334     im_slice = lut[view]
    --> 335     new = ((y_inv_coef * (x_inv_coef * mapLU[im_slice]
        336                           + x_coef * mapRU[im_slice])
        337             + y_coef * (x_inv_coef * mapLB[im_slice]

ValueError: operands could not be broadcast together with shapes (2175,2) (2175,2,32,3)

有人知道我可能犯了什么错误吗？

Answer 1

根据scikit-image documentation ，您不需要将图像重新缩放为 0..1 并将其从 RGB 转换为 HSV:

Notes

• For color images, the following steps are performed:

  • The image is converted to HSV color space
  • The CLAHE algorithm is run on the V (Value) channel
  • The image is converted back to RGB space and returned

• For RGBA images, the original alpha channel is removed.

因此，当您使用关键字参数 kernel_size、clip_limit 和 nbins 的默认值时，您可以简单地编写:

X_train_eq = skimage.exposure.equalize_adapthist(X_train)