nathforge · November 1, 2010 15:22 · Nov 1, 2010 · Nov 1, 2010
diff --git a/gistfile1.py b/gistfile1.py
@@ -1,28 +1,36 @@
+import colorsys
+
 def get_dominant_color(image):
     """
-    Find an image's dominant color.
+    Find a PIL image's dominant color, returning an (r, g, b) tuple.
     """
 
-    image = image.convert('RGB')
+    image = image.convert('RGBA')
 
-    # Shrink the image so we don't spend too long analysing color frequencies.
+    # Shrink the image, so we don't spend too long analysing color
+    # frequencies. We're not interpolating so should be quick.
     image.thumbnail((200, 200))
 
     max_score = None
     dominant_color = None
 
-    for count, (r, g, b) in image.getcolors(image.size[0] * image.size[1]):
+    for count, (r, g, b, a) in image.getcolors(image.size[0] * image.size[1]):
+        # Skip 100% transparent pixels
+        if a == 0:
+            continue
+
         # Get color saturation, 0-1
         saturation = colorsys.rgb_to_hsv(r / 255.0, g / 255.0, b / 255.0)[1]
 
-        # Calculate luminance - integer YUV conversion from http://en.wikipedia.org/wiki/YUV
-        luminance = min(abs(r * 2104 + g * 4130 + b * 802 + 4096 + 131072) >> 13, 235)
+        # Calculate luminance - integer YUV conversion from
+        # http://en.wikipedia.org/wiki/YUV
+        y = min(abs(r * 2104 + g * 4130 + b * 802 + 4096 + 131072) >> 13, 235)
 
         # Rescale luminance from 16-235 to 0-1
-        luminance = (luminance - 16.0) / (235 - 16)
+        y = (y - 16.0) / (235 - 16)
 
-        # Ignore extremely bright colors
-        if luminance > 0.9:
+        # Ignore the brightest colors
+        if y > 0.9:
             continue
 
         # Calculate the score, preferring highly saturated colors.

diff --git a/gistfile1.py b/gistfile1.py
@@ -0,0 +1,38 @@
+def get_dominant_color(image):
+    """
+    Find an image's dominant color.
+    """
+
+    image = image.convert('RGB')
+
+    # Shrink the image so we don't spend too long analysing color frequencies.
+    image.thumbnail((200, 200))
+
+    max_score = None
+    dominant_color = None
+
+    for count, (r, g, b) in image.getcolors(image.size[0] * image.size[1]):
+        # Get color saturation, 0-1
+        saturation = colorsys.rgb_to_hsv(r / 255.0, g / 255.0, b / 255.0)[1]
+
+        # Calculate luminance - integer YUV conversion from http://en.wikipedia.org/wiki/YUV
+        luminance = min(abs(r * 2104 + g * 4130 + b * 802 + 4096 + 131072) >> 13, 235)
+
+        # Rescale luminance from 16-235 to 0-1
+        luminance = (luminance - 16.0) / (235 - 16)
+
+        # Ignore extremely bright colors
+        if luminance > 0.9:
+            continue
+
+        # Calculate the score, preferring highly saturated colors.
+        # Add 0.1 to the saturation so we don't completely ignore grayscale
+        # colors by multiplying the count by zero, but still give them a low
+        # weight.
+        score = (saturation + 0.1) * count
+
+        if score > max_score:
+            max_score = score
+            dominant_color = (r, g, b)
+
+    return dominant_color