import numpy as np
import cv2
from PIL import Image
from skimage.color import rgb2lab
from skimage.color import lab2rgb
from sklearn.cluster import KMeans

    
def count_high_freq_colors(image):
    im = image.getcolors(maxcolors=1024*1024)
    sorted_colors = sorted(im, key=lambda x: x[0], reverse=True)

    freqs = [c[0] for c in sorted_colors]
    mean_freq = sum(freqs) / len(freqs)

    high_freq_colors = [c for c in sorted_colors if c[0] > max(2, mean_freq*1.25)]
    return high_freq_colors

def get_high_freq_colors(image, similarity_threshold=30):
    image_copy = image.copy()
    high_freq_colors = count_high_freq_colors(image)
    # Check for similar colors and replace the lower frequency color with the higher frequency color in the image
    for i, (freq1, color1) in enumerate(high_freq_colors):
        for j, (freq2, color2) in enumerate(high_freq_colors):      
            if (color_distance(color1, color2) < similarity_threshold) or (color_distance(color1, opaque_color_on_white(color2, 0.5)) < 5):
              if(freq2 > freq1):
                replace_color(image_copy, color1, color2)
            
    high_freq_colors = count_high_freq_colors(image_copy)
    print(high_freq_colors)
    return [high_freq_colors, image_copy]
    
def color_quantization(image, color_frequency_list):
    # Extract the color values from the frequency list
    color_values = [color for _, color in color_frequency_list]

    # Replace the colors that are not in the frequency list with white
    mask = np.ones(image.shape[:2], dtype=bool)
    for color in color_values:
        color_mask = np.all(image == color, axis=2)
        mask = np.logical_and(mask, np.logical_not(color_mask))
    image[mask] = (255, 255, 255)
    
    return image

def create_binary_matrix(img_arr, target_color):
    # Create mask of pixels with target color
    mask = np.all(img_arr == target_color, axis=-1)
    
    # Convert mask to binary matrix
    binary_matrix = mask.astype(int)
    from datetime import datetime
    binary_file_name = f'mask-{datetime.now().timestamp()}.png'
    cv2.imwrite(binary_file_name, binary_matrix * 255)
    
    #binary_matrix = torch.from_numpy(binary_matrix).unsqueeze(0).unsqueeze(0)
    return binary_file_name