Update pages/2_Player_Comparison_Throw_Image.py

pages/2_Player_Comparison_Throw_Image.py

@@ -29,28 +29,34 @@ def get_target_shape(expected_features):
     return None
 
 # Predict player name from image
-def detect_and_predict_face(image_file):
+def detect_and_predict_face(image_file, model):
+    import cv2
+    import numpy as np
+    from PIL import Image
+
+    # Load image and convert to grayscale
     image = Image.open(image_file).convert("RGB")
     img_np = np.array(image)
     gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY)
 
+    # Detect face
     faces = face_cascade.detectMultiScale(gray, 1.3, 5)
     if len(faces) == 0:
-        return None, "No face detected!"
+        return None, "⚠️ No face detected!"
 
+    # Crop face
     x, y, w, h = faces[0]
     face = gray[y:y+h, x:x+w]
 
-    # Resize to 64x64 (what model expects)
-    resized_face = cv2.resize(face, (64, 64))  # width=64, height=64
+    # Resize to match training dimensions: 113x109
+    resized_face = cv2.resize(face, (113, 109))  # width x height = 12289
     flattened = resized_face.flatten().reshape(1, -1)
 
-    # Force to 4096 features
-    expected_features = 64 * 64
+    # Ensure shape matches exactly
+    expected_features = 12289
     if flattened.shape[1] > expected_features:
         flattened = flattened[:, :expected_features]
     elif flattened.shape[1] < expected_features:
-        # pad with zeros
         padding = expected_features - flattened.shape[1]
         flattened = np.pad(flattened, ((0, 0), (0, padding)), mode='constant')
 
@@ -61,6 +67,7 @@ def detect_and_predict_face(image_file):
 
 
 
+
 # Get player details from the dataset
 def get_player_details(df, player_name):
     return df[df['Player'] == player_name].iloc[0]