try YOLO

app.py

@@ -5,24 +5,40 @@ import os
 
 import torch
 import ultralytics
- 
+from ultralytics import YOLO
 
-model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5_0.65map_exp7_best.pt",
-                       force_reload=False) 
+
+# model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5_0.65map_exp7_best.pt",
+#                        force_reload=False) 
+
+model = YOLO("yolov5_0.65map_exp7_best.pt")
 model.conf = 0.20  # NMS confidence threshold
 
 path  = [['img/test-image.jpg'], ['img/test-image-2.jpg']]
 
+# def show_preds_image(image_path):
+#     image = cv2.imread(image_path)
+#     # outputs = model(source=image_path)
+#     # results = outputs[0].cpu().numpy()
+#     results = model(image_path)
+#     results.xyxy[0]  # img1 predictions (tensor)
+#     results.numpy().xyxy[0]  # img1 predictions (pandas)
+#     predictions = results.pred[0]
+#     for i, det in enumerate(results.boxes.xyxy):
+#         cv2.rectangle(
+#             image,
+#             (int(det[0]), int(det[1])),
+#             (int(det[2]), int(det[3])),
+#             color=(0, 0, 255),
+#             thickness=2,
+#             lineType=cv2.LINE_AA
+#         )
+#     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
 def show_preds_image(image_path):
     image = cv2.imread(image_path)
-    results = model(image_path)
-    results.xyxy[0]  # img1 predictions (tensor)
-    results.pandas().xyxy[0]  # img1 predictions (pandas)
-    predictions = results.pred[0]
-    boxes = predictions[:, :4] # x1, y1, x2, y2
-    scores = predictions[:, 4]
-    categories = predictions[:, 5]
-
+    outputs = model.predict(source=image_path)
+    results = outputs[0].cpu().numpy()
     for i, det in enumerate(results.boxes.xyxy):
         cv2.rectangle(
             image,