enhancement: use scikit-learn to get the most distinct color for the …

…embed

requirements.txt

@@ -5,4 +5,5 @@ numpy==1.26.4
 Pillow==10.3.0
 pytz==2024.1
 Requests==2.31.0
-psnawp_api==1.3.3
+psnawp_api==1.3.3
+scikit-learn==1.4.2

utils/image_utils.py

@@ -1,37 +1,46 @@
-import requests
 from PIL import Image
+import requests
 from io import BytesIO
-from colorthief import ColorThief
 import numpy as np
+from sklearn.cluster import KMeans
 
-def get_discord_color(image_url, border_percentage=0.2):
+def get_discord_color(image_url, num_colors=5, crop_percentage=0.5):
     """
-    Get the most common color from an image for Discord usage.
+    Get the most distinct color from the center of an image for Discord usage.
 
     Args:
         image_url (str): The URL of the image to analyze.
-        border_percentage (float): The percentage of border to exclude when cropping the image (default is 0.2).
+        num_colors (int): Number of dominant colors to extract (default is 5).
+        crop_percentage (float): Percentage of the image to keep in the center (default is 0.5).
 
     Returns:
-        int: The most common color in hexadecimal format.
+        int: The most distinct color in hexadecimal format.
 
     Raises:
         (Exception): If there is an issue with fetching or processing the image.
     """
     response = requests.get(image_url)
-    img = Image.open(BytesIO(response.content)).convert('RGB')
-
-    # Crop the image to exclude borders
+    img = Image.open(BytesIO(response.content))
+    
+    # Calculate the crop dimensions
     width, height = img.size
-    img = img.crop((width * border_percentage, height * border_percentage, width * (1 - border_percentage), height * (1 - border_percentage)))
+    crop_width = int(width * crop_percentage)
+    crop_height = int(height * crop_percentage)
+    left = (width - crop_width) // 2
+    top = (height - crop_height) // 2
+    right = left + crop_width
+    bottom = top + crop_height
+
+    img = img.crop((left, top, right, bottom))
+
+    img = img.resize((img.width // 2, img.height // 2))  # Resize for faster processing
 
-    color_thief = ColorThief(BytesIO(response.content))
-    palette = color_thief.get_palette(color_count=4, quality=4)
+    img_array = np.array(img)
+    img_flattened = img_array.reshape(-1, 3)
 
-    img_arr = np.array(img)
-    color_counts = {tuple(color): np.sum(np.all(img_arr == color, axis=-1)) for color in palette}
+    kmeans = KMeans(n_clusters=num_colors)
+    kmeans.fit(img_flattened)
 
-    # Find the most common color
-    most_common_color = max(color_counts, key=color_counts.get)
+    dominant_color = kmeans.cluster_centers_[np.argmax(np.bincount(kmeans.labels_))]
 
-    return int('0x{:02x}{:02x}{:02x}'.format(*most_common_color), 16)
+    return int('0x{:02x}{:02x}{:02x}'.format(*dominant_color.astype(int)), 16)