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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 color_quantization(image, n_colors):
# Convert image to LAB color space
lab_image = rgb2lab(image)
# Reshape image to 2D array of pixels
pixels = lab_image.reshape(-1, 3)
# Perform K-means clustering
kmeans = KMeans(n_clusters=n_colors, random_state=0).fit(pixels)
# Replace each pixel with the closest color
labels = kmeans.predict(pixels)
colors = kmeans.cluster_centers_
quantized_pixels = colors[labels]
# Convert quantized image back to RGB color space
quantized_lab_image = quantized_pixels.reshape(lab_image.shape)
quantized_rgb_image = lab2rgb(quantized_lab_image)
return (quantized_rgb_image * 255).astype(np.uint8)
def get_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)] # Ignore colors that occur very few times (less than 2) or less than half the average frequency
return high_freq_colors
def color_quantization_old(image, n_colors):
# Get color histogram
hist, _ = np.histogramdd(image.reshape(-1, 3), bins=(256, 256, 256), range=((0, 256), (0, 256), (0, 256)))
# Get most frequent colors
colors = np.argwhere(hist > 0)
colors = colors[np.argsort(hist[colors[:, 0], colors[:, 1], colors[:, 2]])[::-1]]
colors = colors[:n_colors]
# Replace each pixel with the closest color
dists = np.sum((image.reshape(-1, 1, 3) - colors.reshape(1, -1, 3))**2, axis=2)
labels = np.argmin(dists, axis=1)
return colors[labels].reshape((image.shape[0], image.shape[1], 3)).astype(np.uint8)
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 |