Update pages/2_Player_Comparison_Throw_Image.py

pages/2_Player_Comparison_Throw_Image.py

@@ -29,46 +29,34 @@ def get_target_shape(expected_features):
     return None
 
 # Predict player name from image
-def detect_and_predict_face(image_file, model):
-    import cv2
-    import numpy as np
-    from PIL import Image
-
+def detect_and_predict_face(image_file):
     image = Image.open(image_file).convert("RGB")
     img_np = np.array(image)
     gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY)
 
     faces = face_cascade.detectMultiScale(gray, 1.3, 5)
     if len(faces) == 0:
-        return None, "⚠️ No face detected!"
+        return None, "No face detected!"
 
     x, y, w, h = faces[0]
     face = gray[y:y+h, x:x+w]
 
-    expected_features = model.named_steps['scaler'].n_features_in_
-
-    # Resize to shape close to the expected features
-    resized_face = cv2.resize(face, (113, 109))  # width x height
-
+    # Resize to 64x64 (what model expects)
+    resized_face = cv2.resize(face, (64, 64))  # width=64, height=64
     flattened = resized_face.flatten().reshape(1, -1)
 
-    # 🔧 Fix: if extra pixels due to rounding, trim them
+    # Force to 4096 features
+    expected_features = 64 * 64
     if flattened.shape[1] > expected_features:
         flattened = flattened[:, :expected_features]
     elif flattened.shape[1] < expected_features:
-        return None, f"❌ Still too small after resizing. Got {flattened.shape[1]}"
-
-    # Final sanity check
-    if flattened.shape[1] != expected_features:
-        return None, f"❌ Mismatch in feature count. Got {flattened.shape[1]}, expected {expected_features}"
-
-    pred_label = model.predict(flattened)[0]
-    return pred_label, None
-
-
-
-
+        # pad with zeros
+        padding = expected_features - flattened.shape[1]
+        flattened = np.pad(flattened, ((0, 0), (0, padding)), mode='constant')
 
+    # Predict
+    pred_name = model.predict(flattened)[0]
+    return pred_name, None