Update app.py

app.py

@@ -79,27 +79,27 @@ def detect_objects_in_image(image):
     print("passed4")
     mask = pred[:, 4] > conf_thres
     pred = pred[mask]
-
+    print("passed5")
     if len(pred) == 0:
         return Image.fromarray(np.array(image)), None  # Return only image and None for graph
-
+    print("passed6")
     boxes, scores, class_probs = pred[:, :4], pred[:, 4], pred[:, 5:]
     class_ids = np.argmax(class_probs, axis=1)
-
+    print("passed7")
     boxes[:, 0] = boxes[:, 0] - (boxes[:, 2] / 2)
     boxes[:, 1] = boxes[:, 1] - (boxes[:, 3] / 2)
     boxes[:, 2] = boxes[:, 0] + boxes[:, 2]
     boxes[:, 3] = boxes[:, 1] + boxes[:, 3]
-
+    print("passed8")
     boxes[:, [0, 2]] *= orig_w / 640
     boxes[:, [1, 3]] *= orig_h / 640
     boxes = np.clip(boxes, 0, [orig_w, orig_h, orig_w, orig_h])
-
+    print("passed9")
     indices = cv2.dnn.NMSBoxes(boxes.tolist(), scores.tolist(), conf_thres, 0.5)
-
+    print("passed10")
     object_counts = {name: 0 for name in OBJECT_NAMES}
     img_array = np.array(image)
-
+    print("passed11")
     if len(indices) > 0:
         for i in indices.flatten():
             x1, y1, x2, y2 = map(int, boxes[i])