dont know

app.py

@@ -5,70 +5,39 @@ 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)
-    outputs = model.predict(source=image_path)
-    results = outputs[0].cpu().numpy()
-    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):
-#     # perform inference
-#     image_path = path
-#     results = model(image_path, size=640)
-#     # Results
-#     results.print()
-
-#     results.xyxy[0]  # img1 predictions (tensor)
-#     results.pandas().xyxy[0]  # img1 predictions (pandas)
-
-#     # parse results
-#     predictions = results.pred[0]
-#     boxes = predictions[:, :4] # x1, y1, x2, y2
-#     scores = predictions[:, 4]
-#     categories = predictions[:, 5]
+    # outputs = model(source=image_path)
+    # results = outputs[0].cpu().numpy()
+    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]
+
+    # 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 results.show()
 
-#     return results.show()
 
-    
 
 inputs_image = [
     gr.components.Image(type="filepath", label="Input Image"),