utils.py

# import cv2
import numpy as np
# import matplotlib.pyplot as plt
from skimage.color import rgb2lab, lab2rgb, rgb2hsv, hsv2rgb
from PIL import Image


def stack_list(x):
    stack = []
    for id, val in enumerate(x):
        if np.size(val) != 0:
            if stack == []:
                stack = val
            else:
                stack = np.vstack([stack, val])
    return stack   

def rgb_to_hex(r, g, b):
    return '#{:02x}{:02x}{:02x}'.format(r, g, b)

def hex_to_rgb(hex):
    # print(hex)
    rgb = []
    for i in (1, 3, 5):
        decimal = int(hex[i:i+2], 16)
        rgb.append(decimal)
    return tuple(rgb)


def image_resize(img, c_w, c_h):
    # img : PIL Image
    if type(img) is np.ndarray:
        img = Image.fromarray(img)
    h, w = img.size
    h_factor = c_h / w
    w_factor = c_w / h
    # factor = h_factor
    factor = np.minimum(h_factor, w_factor)
    # print(w*factor, h*factor)
    img = img.resize((np.round(h*factor).astype(np.int64), 
                     np.round(w*factor).astype(np.int64)),
                     Image.BILINEAR)
    return img

def get_palette(num_cls):
    """ Returns the color map for visualizing the segmentation mask.
    Args:
        num_cls: Number of classes
    Returns:
        The color map
    """
    n = num_cls
    palette = [0] * (n * 3)
    for j in range(0, n):
        lab = j
        palette[j * 3 + 0] = 0
        palette[j * 3 + 1] = 0
        palette[j * 3 + 2] = 0
        i = 0
        while lab:
            palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
            palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
            palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
            i += 1
            lab >>= 3
    return palette


def visualize_palette(palette_lab, patch_size=20):
    # print(palette_lab)
    if palette_lab is None:
        return np.ones((patch_size, patch_size, 3)) * [1.,1.,1.]
    palette_lab = np.expand_dims(palette_lab, axis=0)
    # palette_lab = np.sort(palette_lab, axis=1)

    # # lab transfer by lookuptable
    # # cluster_cen_rgb = lab2rgb_lut(cluster_cen_lab)
    palette_rgb = lab2rgb(palette_lab)
    palette_rgb = np.squeeze(palette_rgb, axis=0)

    for id in range(np.size(palette_rgb, 0)):
        rgb = np.expand_dims(palette_rgb[id,:], axis=(0, 1))
        if id==0:
            img_palette = np.ones((patch_size, patch_size, 3)) * rgb
        else:
            img_palette = np.append(img_palette, np.ones((patch_size, patch_size, 3)) * rgb, axis=1)
    
    return img_palette


def visualize_palette_rgb(palette_rgb, patch_size=20):
    # print(palette_lab)
    if palette_rgb == 0:
        return np.ones((patch_size, patch_size, 3)) * [1.,1.,1.]


    for id in range(np.size(palette_rgb, 0)):
        rgb = np.expand_dims(palette_rgb[id,:], axis=(0, 1))
        if id==0:
            img_palette = np.ones((patch_size, patch_size, 3)) * rgb
        else:
            img_palette = np.append(img_palette, np.ones((patch_size, patch_size, 3)) * rgb, axis=1)
    
    return img_palette



# def vis_consistency(img_rgb_all, img_rgb_out_all, label_colored_all, c_center, L_idx):



def color_difference(img1, img2):
    h, w, c = img1.shape
    img1_lab = rgb2lab(img1)
    img2_lab = rgb2lab(img2)

    diff=img1_lab-img2_lab
    
    dE = np.sqrt(diff[:,:,0]**2 + diff[:,:,1]**2 + diff[:,:,2]**2)
    # dE = np.sqrt(diff[:,:,0]**2 + diff[:,:,0]**2)
    dE = np.sum(dE)/(h*w)

    return dE