Update app.py

app.py

@@ -31,48 +31,48 @@ def expand_box(x, y, w, h, scale=1.5, img_shape=None):
 def predict(image):
     faces = detector.detect_faces(image)
     if not faces:
-        return "No faces detected", image
+        return "No face detected", image
 
+    output_image = image.copy()
     results = []
-    annotated = image.copy()
 
-    for i, face in enumerate(faces):
+    for idx, face in enumerate(faces):
         x, y, w, h = face['box']
-        x, y, w, h = max(0, x), max(0, y), w, h
-        x1, y1, x2, y2 = expand_box(x, y, w, h, scale=1.6, img_shape=image.shape)
-
-        face_crop = image[y1:y2, x1:x2]
-
-        # Preprocess for each model
-        xcp_img = cv2.resize(face_crop, (299, 299))
-        eff_img = cv2.resize(face_crop, (224, 224))
-
-        xcp_tensor = xcp_pre(xcp_img.astype(np.float32))[np.newaxis, ...]
-        eff_tensor = eff_pre(eff_img.astype(np.float32))[np.newaxis, ...]
-
-        xcp_pred = xcp_model.predict(xcp_tensor, verbose=0).flatten()[0]
-        eff_pred = eff_model.predict(eff_tensor, verbose=0).flatten()[0]
-        avg_pred = (xcp_pred + eff_pred) / 2
-        label = "Real" if avg_pred > 0.5 else "Fake"
-
-        results.append(
-            f"Face {i+1}: {label} (Avg: {avg_pred:.3f}, XCP: {xcp_pred:.3f}, EFF: {eff_pred:.3f})"
-        )
-
-        # Draw
-        color = (0, 255, 0) if label == "Real" else (255, 0, 0)
-        cv2.rectangle(annotated, (x1, y1), (x2, y2), color, 2)
-        cv2.putText(
-            annotated,
-            f"{label} ({avg_pred:.2f})",
-            (x1, y1 - 10),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            0.6,
-            color,
-            2,
-        )
-
-    return "\n".join(results), annotated
+
+        # Add 20% margin while staying inside bounds
+        margin = 0.2
+        img_h, img_w = image.shape[:2]
+        x = max(0, int(x - w * margin))
+        y = max(0, int(y - h * margin))
+        w = int(w * (1 + 2 * margin))
+        h = int(h * (1 + 2 * margin))
+        x2 = min(img_w, x + w)
+        y2 = min(img_h, y + h)
+
+        face_img = image[y:y2, x:x2]
+
+        # Resize + preprocess
+        face_xcp = cv2.resize(face_img, (299, 299))
+        face_eff = cv2.resize(face_img, (224, 224))
+        xcp_tensor = xcp_pre(face_xcp.astype(np.float32))[np.newaxis, ...]
+        eff_tensor = eff_pre(face_eff.astype(np.float32))[np.newaxis, ...]
+
+        # Predictions
+        pred_xcp = xcp_model.predict(xcp_tensor, verbose=0).flatten()[0]
+        pred_eff = eff_model.predict(eff_tensor, verbose=0).flatten()[0]
+        avg = (pred_xcp + pred_eff) / 2
+
+        label = "Real" if avg > 0.5 else "Fake"
+        color = (0, 255, 0) if label == "Real" else (0, 0, 255)
+
+        # Annotate image
+        cv2.rectangle(output_image, (x, y), (x2, y2), color, 2)
+        cv2.putText(output_image, f"{label} ({avg:.2f})", (x, y - 10),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
+
+        results.append(f"Face {idx+1}: {label} (Avg: {avg:.3f}, XCP: {pred_xcp:.3f}, EFF: {pred_eff:.3f})")
+
+    return "\n".join(results), output_image
 
 # Gradio Interface
 interface = gr.Interface(