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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):
# Convert the color frequency list to a set of unique colors
unique_colors = set([color for _, color in color_frequency_list])
# Create a mask for the image with True where the color is in the unique colors set
mask = np.any(np.all(image.reshape(-1, 1, 3) == np.array(list(unique_colors)), axis=2), axis=1).reshape(image.shape[:2])
# Create a new image with all pixels set to white
new_image = np.full_like(image, 255)
# Copy the pixels from the original image that have a color in the color frequency list
new_image[mask] = image[mask]
return new_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
def color_distance(color1, color2):
return sum((a - b) ** 2 for a, b in zip(color1, color2)) ** 0.5
def replace_color(image, old_color, new_color):
pixels = image.load()
width, height = image.size
for x in range(width):
for y in range(height):
if pixels[x, y] == old_color:
pixels[x, y] = new_color
def opaque_color_on_white(color, a):
r, g, b = color
opaque_red = int((1 - a) * 255 + a * r)
opaque_green = int((1 - a) * 255 + a * g)
opaque_blue = int((1 - a) * 255 + a * b)
return (opaque_red, opaque_green, opaque_blue) |