Update app.py

app.py

@@ -18,7 +18,7 @@ from yolov5.models.experimental import attempt_load
 from yolov5.utils.general import non_max_suppression
 from yolov5.utils.augmentations import letterbox
 
-'''
+#'''
 # Example URLs for downloading images
 file_urls = [
     "https://www.dropbox.com/scl/fi/n3bs5xnl2kanqmwv483k3/1_jpg.rf.4a59a63d0a7339d280dd18ef3c2e675a.jpg?rlkey=4n9dnls1byb4wm54ycxzx3ovi&st=ue5xv8yx&dl=0",
@@ -40,7 +40,7 @@ def download_file(url, save_name):
 # Download images
 for i, url in enumerate(file_urls):
     download_file(url, f"image_{i}.jpg")
-'''
+
 
 # Load YOLOv5 model (placeholder)
 model_path = "best.pt"  # Path to your YOLOv5 model
@@ -48,11 +48,11 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # Use GPU
 model = attempt_load(model_path, device=device)  # Placeholder for model loading
 model.eval()  # Set the model to evaluation mode
 
-#def preprocess_image(image_path):
-def preprocess_image(image):
-    #img0 = cv2.imread(image_path)
-    print("in preprocess-0 image.shape:",image.shape)
-    img0 = letterbox(image, 640, stride=32, auto=True)[0]  # Resize and pad to 640x640
+def preprocess_image(image_path):
+#def preprocess_image(image):
+    img0 = cv2.imread(image_path)
+    print("in preprocess-0 img0.shape:",img0.shape)
+    #img0 = letterbox(image, 640, stride=32, auto=True)[0]  # Resize and pad to 640x640
     img = letterbox(img0, 640, stride=32, auto=True)[0]  # Resize and pad to 640x640
     print("in preprocess-1 img.shape:",img.shape)
     img = img.transpose(2, 0, 1)[::-1]  # Convert BGR to RGB, to 3x416x416
@@ -63,6 +63,8 @@ def preprocess_image(image):
     if img.ndimension() == 3:
         #img = img.transpose(2, 0, 1)[::-1]  # Convert BGR to RGB, 
         img = img.unsqueeze(0)
+    print("in preprocess-2 img.shape:",img.shape)
+
     return img, img0
 
 def infer(model, img):