Fix next_level image quantization to preserve dark tones using fixed thresholds

scripts/process_next_level.py

@@ -1,34 +1,20 @@
 from PIL import Image
 import numpy as np
-from scipy.cluster.vq import kmeans, vq
 
 # Open original and convert to grayscale
 img = Image.open('assets/next_level_original.png').convert('L')
-pixels = np.array(img, dtype=float)
+pixels = np.array(img, dtype=np.uint8)
 
-# Flatten for clustering
-flat_pixels = pixels.flatten()
+# Fixed thresholds for Game Boy colors
+quantized_pixels = np.zeros_like(pixels)
 
-# Find 4 clusters
-centroids, _ = kmeans(flat_pixels, 4)
-
-# Sort centroids by brightness (dark to light)
-sorted_centroids = np.sort(centroids)
-
-# Map centroids to GB colors
-# GB palette: 0=Black, 1=Dark Gray, 2=Light Gray, 3=White
-gb_colors = np.array([0, 85, 170, 255])
-
-# Find the closest centroid for each pixel
-# Assign the corresponding GB color
-quantized_pixels = np.zeros_like(flat_pixels, dtype=np.uint8)
-for i, p in enumerate(flat_pixels):
-    dist = np.abs(sorted_centroids - p)
-    closest_idx = np.argmin(dist)
-    quantized_pixels[i] = gb_colors[closest_idx]
+# Map based on standard thresholds to preserve darkness
+quantized_pixels[pixels < 40] = 0           # Black
+quantized_pixels[(pixels >= 40) & (pixels < 128)] = 85   # Dark Gray
+quantized_pixels[(pixels >= 128) & (pixels < 200)] = 170 # Light Gray
+quantized_pixels[pixels >= 200] = 255       # White
 
 # Reshape back to image
-quantized_img_data = quantized_pixels.reshape(pixels.shape)
-new_img = Image.fromarray(quantized_img_data, mode='L').convert('RGB')
+new_img = Image.fromarray(quantized_pixels, mode='L').convert('RGB')
 new_img.save('assets/next_level.png')
-print("Processed next_level.png with k-means clustering!")
+print("Processed next_level.png with fixed thresholds!")