uncomment debugging

app.py

@@ -21,30 +21,30 @@ def facial_emotion_recognition(img):
 
     img = resize(img, target_size)
     
-    # faces = face_detector.get(img)
-
-    # if len(faces) > 0:
-
-    #     highest_score_box = (0, 0, 0, 0)  # x, y, w, h
-    #     highest_score = 0
-
-    #     for face in faces:
-    #         if face['det_score'] > highest_score:
-    #             highest_score = face['det_score']
-    #             x1, y1, x2, y2 = face['bbox'].astype(int)
-    #             x_margin = int((x2 - x1) * face_margin)
-    #             y_margin = int((y2 - y1) * face_margin)
-    #             x = max(0, x1 - x_margin)
-    #             y = max(0, y1 - y_margin)
-    #             w = min(x2 + x_margin, img.shape[1]) - x
-    #             h = min(y2 + y_margin, img.shape[0]) - y
-    #             highest_score_box = (x, y, w, h)
-
-    #     x, y, w, h = highest_score_box
-    #     emotion, _ = hse_emo_model.predict_emotions(img[y:y+h, x:x+w], logits=True)
-
-    #     cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)
-    #     cv2.putText(img, emotion, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
+    faces = face_detector.get(img)
+
+    if len(faces) > 0:
+
+        highest_score_box = (0, 0, 0, 0)  # x, y, w, h
+        highest_score = 0
+
+        for face in faces:
+            if face['det_score'] > highest_score:
+                highest_score = face['det_score']
+                x1, y1, x2, y2 = face['bbox'].astype(int)
+                x_margin = int((x2 - x1) * face_margin)
+                y_margin = int((y2 - y1) * face_margin)
+                x = max(0, x1 - x_margin)
+                y = max(0, y1 - y_margin)
+                w = min(x2 + x_margin, img.shape[1]) - x
+                h = min(y2 + y_margin, img.shape[0]) - y
+                highest_score_box = (x, y, w, h)
+
+        x, y, w, h = highest_score_box
+        emotion, _ = hse_emo_model.predict_emotions(img[y:y+h, x:x+w], logits=True)
+
+        cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)
+        cv2.putText(img, emotion, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
 
     return img