Update utils/detector.py

utils/detector.py

@@ -1,59 +1,30 @@
-
-from io import BytesIO
 from PIL import Image
+from io import BytesIO
 from models.vision import VisionModel
 from utils.bg_removal import remove_background
 
 vision = VisionModel()
-FASHION_LABELS = {
-    "shirt", "t-shirt", "blouse", "tank top", "sweater", "hoodie", "jacket",
-    "coat", "overcoat", "raincoat", "windbreaker", "cardigan", "blazer",
-    "pants", "jeans", "shorts", "leggings", "tights", "skirt", "dress",
-    "suit", "jumpsuit", "romper", "vest", "sports bra", "tracksuit",
-    "belt", "tie", "scarf", "hat", "cap", "gloves", "socks",
-    "shoe", "sneakers", "boots", "sandals", "heels",
-    "watch", "necklace", "bracelet", "earrings", "ring",
-    "backpack", "handbag", "purse", "wallet"
-}
 
 def detect_clothing(image_input, do_bg_remove: bool = False):
-    # 1) Load into a PIL.Image if it's a filepath
+    # 1) If filepath, load; else assume PIL.Image
     if isinstance(image_input, str):
-        img = Image.open(image_input)
+        img = Image.open(image_input).convert("RGB")
     else:
         img = image_input
 
-    # 2) Optionally remove background (works on bytes)
+    # 2) Optional background removal
     if do_bg_remove:
         buf = BytesIO()
-        img.convert("RGB").save(buf, format="JPEG")
+        img.save(buf, format="JPEG")
         img_bytes = buf.getvalue()
         img = remove_background(img_bytes)
     else:
-        # ensure you drop any alpha channel
         img = img.convert("RGB")
 
     # 3) Run detection
-    raw_detections = vision.detect(img)
-
-    # 4) Filter and deduplicate
-    filtered = {}
-    for det in raw_detections:
-        label = det["label"].lower()
-        if label in FASHION_LABELS:
-            # Only keep the first or highest score if multiple detected
-            if label not in filtered or det["score"] > filtered[label]["score"]:
-                filtered[label] = {
-                    "label": label,
-                    "score": det["score"],
-                    "box": det.get("box", [])
-                }
-
-    # 5) Return dict or fallback if empty
-    if not filtered:
-        return {"outfit": {"label": "outfit", "score": 1.0, "box": []}}
-
-    return filtered
-
+    detections = vision.detect(img)
 
+    if not detections:
+        detections = [{"label": "outfit", "score": 1.0, "box": []}]
 
+    return detections