96e5c12dc5a3a082cbd52fb8164da31318e0201e,skimage/feature/_hog.py,,hog,#Any#Any#Any#Any#Any#Any#Any#Any#Any#,53

Before Change

        image = image.astype("float")

    if len(image.shape) == 3:
        channel_gradients = []
        gradient = np.zeros(image.shape)

        for dimension in range(image.shape[2]):
            gx, gy = compute_image_gradients(image[:, :, dimension])
            gradient[:, :, dimension] = np.hypot(gx, gy)
            channel_gradients.append((gx, gy))

        gx = np.empty(image.shape[0:2], dtype=np.double)
        gy = np.empty(image.shape[0:2], dtype=np.double)

        max_index_array = gradient.argmax(axis = 2)

        for row_index, row in enumerate(max_index_array):
            for col_index, value in enumerate(row):
                gx[row_index][col_index] = channel_gradients[value][0][row_index][col_index]
                gy[row_index][col_index] = channel_gradients[value][1][row_index][col_index]
    else:
        gx, gy = compute_image_gradients(image)

    

After Change

            gradient_magnitude[:, :, dimension] = np.hypot(x_gradient[:, :, dimension], y_gradient[:, :, dimension])

        max_index_array = gradient_magnitude.argmax(axis=2)
        rr, cc = np.meshgrid(np.arange(image.shape[0]), np.arange(image.shape[1]))

        gx = x_gradient[rr, cc, max_index_array]
        gy = y_gradient[rr, cc, max_index_array]
    else:

Instances