Changed scan func

.idea/workspace.xml

@@ -65,7 +65,7 @@
       <workItem from="1683665300392" duration="7649000" />
       <workItem from="1683708398011" duration="1235000" />
       <workItem from="1684437905081" duration="110000" />
-      <workItem from="1686602174110" duration="2679000" />
+      <workItem from="1686602174110" duration="3352000" />
     </task>
     <servers />
   </component>

app.py

@@ -122,8 +122,8 @@ class Net(nn.Module):
 model = None
 model_path = "models1.pth"
 
-model2 = None
-model2_path = "model4.pth"
+# model2 = None
+# model2_path = "model4.pth"
 
 if os.path.exists(model_path):
     state_dict = torch.load(model_path, map_location=torch.device('cpu'))
@@ -192,9 +192,7 @@ else:
 # 
 # iface = gr.Interface(fn=gradio_process_image, inputs=inputs, outputs=outputs)
 # iface.launch()
-def scanmap(satellite_image, model, device, threshold=0.5):
-    # No need to read the image, you already have it as a NumPy array.
-    # Just normalize it.
+def scanmap(satellite_image, model, threshold=0.5):
     satellite_image = satellite_image.astype(np.float32) / 255.0
 
     window_size = (80, 80)
@@ -202,8 +200,6 @@ def scanmap(satellite_image, model, device, threshold=0.5):
 
     height, width, channels = satellite_image.shape
 
-    model.to(device)  # ensure model is on correct device
-
     fig, ax = plt.subplots(1)
     ax.imshow(satellite_image)
 
@@ -213,15 +209,12 @@ def scanmap(satellite_image, model, device, threshold=0.5):
         for x in range(0, width - window_size[0] + 1, stride):
             cropped_window = satellite_image[y:y + window_size[1], x:x + window_size[0]]
             cropped_window_torch = torch.tensor(cropped_window.transpose(2, 0, 1), dtype=torch.float32).unsqueeze(0)
-            cropped_window_torch = cropped_window_torch.to(device)  # move data to the same device as model
 
             with torch.no_grad():
                 probabilities = model(cropped_window_torch)
 
-            # if probability is greater than threshold, draw a bounding box and add to ship_images
             if probabilities[0, 1].item() > threshold:
-                rect = patches.Rectangle((x, y), window_size[0], window_size[1], linewidth=1, edgecolor='r',
-                                         facecolor='none')
+                rect = patches.Rectangle((x, y), window_size[0], window_size[1], linewidth=1, edgecolor='r', facecolor='none')
                 ax.add_patch(rect)
                 ship_images.append(cropped_window)
 
@@ -230,16 +223,16 @@ def scanmap(satellite_image, model, device, threshold=0.5):
     return ship_images
 
 
-def process_image(input_image, model, device, threshold=0.5):
+def process_image(input_image, model, threshold=0.5):
     start_time = time.time()
-    ship_images = scanmap(input_image, model, device, threshold)
+    ship_images = scanmap(input_image, model, threshold)
     elapsed_time = time.time() - start_time
 
     return ship_images, int(elapsed_time)
 
 
-def gradio_process_image(input_image, model, device, threshold=0.5):
-    ship_images, elapsed_time = process_image(input_image, model, device, threshold)
+def gradio_process_image(input_image, model, threshold=0.5):
+    ship_images, elapsed_time = process_image(input_image, model, threshold)
 
     return ship_images, f"Elapsed Time (seconds): {elapsed_time}"
 
@@ -249,13 +242,10 @@ outputs = [
     gr.Image(label="Detected Ships"),
     gr.Textbox(label="Elapsed Time")
 ]
-model = None  # TODO: initialize your model
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-# Here I'm using 0.5 as the threshold, but adjust according to your needs
-gradio_process_image_partial = partial(gradio_process_image, model=model, device=device, threshold=0.5)
+
+# Use 0.5 as the threshold, but adjust according to your needs
+gradio_process_image_partial = partial(gradio_process_image, model=model, threshold=0.5)
 
 iface = gr.Interface(fn=gradio_process_image_partial, inputs=inputs, outputs=outputs)
 iface.launch()
 
-
-