Sync from GitHub Actions

autocatalog/data/preprocessing.py

@@ -7,54 +7,72 @@ COLOR_FEATURE_DIM = 37
 def extract_color_features(image, image_size=COLOR_IMAGE_SIZE):
     image = image.convert("RGB").resize((image_size, image_size))
     margin = int(image_size * 0.10)
-    
-    image = image.crop((
-     margin, margin, image_size - margin, image_size - margin   
-    ))
-    
+    image = image.crop(
+        (
+            margin,
+            margin,
+            image_size - margin,
+            image_size - margin,
+        )
+    )
+
     rgb = np.asarray(image, dtype=np.float32) / 255.0
     hsv = np.asarray(image.convert("HSV"), dtype=np.float32) / 255.0
-    
-    rgb_flat = rgb.resize(-1, 3)
-    hsv_flat = hsv.resize(-1, 3)
-    
+
+    rgb_flat = rgb.reshape(-1, 3)
+    hsv_flat = hsv.reshape(-1, 3)
+
     saturation = hsv_flat[:, 1]
     value = hsv_flat[:, 2]
     foreground_mask = (saturation > 0.08) | (value < 0.92)
-    
+
     if foreground_mask.sum() < 256:
         foreground_mask = np.ones(len(hsv_flat), dtype=bool)
-    
+
     selected_rgb = rgb_flat[foreground_mask]
     selected_hsv = hsv_flat[foreground_mask]
-    
-    hue_hist, _ = np.histogram(selected_hsv[:, 0], bins=12, range=(0.0, 1.0))
-    
-    saturation_hist, _ = np.histogram(selected_hsv[:, 2], bins=8, range=(0.0, 1.0))
-    
-    value_hist, _ = np.histogram(selected_hsv[:, 2], bins=8, range=(0.0, 1.0))
-    
+
+    hue_hist, _ = np.histogram(
+        selected_hsv[:, 0],
+        bins=12,
+        range=(0.0, 1.0),
+    )
+
+    saturation_hist, _ = np.histogram(
+        selected_hsv[:, 1],
+        bins=8,
+        range=(0.0, 1.0),
+    )
+
+    value_hist, _ = np.histogram(
+        selected_hsv[:, 2],
+        bins=8,
+        range=(0.0, 1.0),
+    )
+
     hue_hist = hue_hist.astype(np.float32)
     saturation_hist = saturation_hist.astype(np.float32)
     value_hist = value_hist.astype(np.float32)
-    
+
     hue_hist /= max(hue_hist.sum(), 1.0)
     saturation_hist /= max(saturation_hist.sum(), 1.0)
     value_hist /= max(value_hist.sum(), 1.0)
-    
+
     rgb_mean = selected_rgb.mean(axis=0).astype(np.float32)
     rgb_std = selected_rgb.std(axis=0).astype(np.float32)
     rgb_median = np.median(selected_rgb, axis=0).astype(np.float32)
-    
-    features = np.concatenate([
-        hue_hist,
-        saturation_hist,
-        value_hist,
-        rgb_mean,
-        rgb_std,
-        rgb_median
-    ]).astype(np.float32)
-    
+
+    features = np.concatenate(
+        [
+            hue_hist,
+            saturation_hist,
+            value_hist,
+            rgb_mean,
+            rgb_std,
+            rgb_median,
+        ]
+    ).astype(np.float32)
+
     if features.shape[0] != COLOR_FEATURE_DIM:
         raise ValueError(
             f"Expected {COLOR_FEATURE_DIM} color features, "