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app/services/puffy_eyes_service.py

@@ -1,5 +1,4 @@
 from ultralytics import YOLO
-import logging
 from PIL import Image, ImageDraw
 import io
 import base64
@@ -8,40 +7,39 @@ import base64
 MODEL_PATH = "app/models/puffy_eyes_model.pt"
 model = YOLO(MODEL_PATH)
 
-# Set confidence threshold
-CONFIDENCE_THRESHOLD = 0.25
-MAX_SHOW = 2  # Only allow 2 detections
+CONFIDENCE_THRESHOLD = 0.3
 
 def predict_puffy_eyes(image: Image):
-    """Run YOLO model, only return if exactly 2 detections."""
-    
-    # Run YOLO detection
+    """Return exactly 2 highest-confidence puffy eye detections with annotated image (if available)."""
+
     results = model(image, conf=CONFIDENCE_THRESHOLD)
-    all_dets = []
-    
+    detections = []
+
     for result in results:
         for box in result.boxes:
             conf = float(box.conf)
             if conf >= CONFIDENCE_THRESHOLD:
                 x1, y1, x2, y2 = [float(c) for c in box.xyxy[0]]
-                all_dets.append({"confidence": conf, "bbox": [x1, y1, x2, y2]})
-
-    # Only continue if exactly 2 detections
-    if len(all_dets) != 2:
-     logging.info(f"[Puffy Eyes] Skipping: found {len(all_dets)} detections (need exactly 2)")
-     return {
-        "detections": [],
-        "labeled_image": None,
-        "max_confidence": None,
-        "message": f"Expected 2 puffy eye detections, but found {len(all_dets)}"
+                detections.append({
+                    "confidence": conf,
+                    "bbox": [x1, y1, x2, y2]
+                })
+
+    # Sort and keep only top 2
+    detections.sort(key=lambda d: d["confidence"], reverse=True)
+    top_two = detections[:2]
+
+    # If fewer than 2 detections, return empty
+    if len(top_two) < 2:
+        return {
+            "detections": [],
+            "labeled_image": None,
+            "message": "Fewer than 2 puffy eyes detected"
         }
 
-    # Sort by confidence
-    all_dets.sort(key=lambda d: d["confidence"], reverse=True)
-
-    # Draw the two boxes
+    # Draw boxes on image
     draw = ImageDraw.Draw(image)
-    for det in all_dets:
+    for det in top_two:
         x1, y1, x2, y2 = det["bbox"]
         draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
 
@@ -51,6 +49,6 @@ def predict_puffy_eyes(image: Image):
     encoded_image = base64.b64encode(buf.getvalue()).decode("utf-8")
 
     return {
-        "detections": all_dets,
+        "detections": top_two,
         "labeled_image": encoded_image
     }

app/services/verify_and_crop_face_service.py

@@ -31,12 +31,21 @@ async def verify_and_crop_face_service(file: UploadFile) -> str:
     if len(faces) == 0:
         print("❌ [Error] No face detected.")
         raise HTTPException(status_code=400, detail="No face detected. Upload a clear front-facing photo.")
-    if len(faces) > 1:
-        print("❌ [Error] Multiple faces detected.")
-        raise HTTPException(status_code=400, detail="Multiple faces detected. Upload only one face.")
 
-    (x, y, w, h) = faces[0]
-    print(f"📍 [Face] Detected box: x={x}, y={y}, w={w}, h={h}")
+    # Select the most centered face
+    image_center = (img.shape[1] / 2, img.shape[0] / 2)
+    print(f"🎯 [Image Center] x={image_center[0]}, y={image_center[1]}")
+
+    def center_distance(face):
+        x, y, w, h = face
+        face_center = (x + w / 2, y + h / 2)
+        dx = face_center[0] - image_center[0]
+        dy = face_center[1] - image_center[1]
+        return dx**2 + dy**2
+
+    best_face = sorted(faces, key=center_distance)[0]
+    (x, y, w, h) = best_face
+    print(f"📍 [Selected Face] x={x}, y={y}, w={w}, h={h}")
 
     # Expand the crop with margin
     margin = 0.5  # 50% margin