initial commit

app.py

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+import torch
+import gradio as gr
+import matplotlib.pyplot as plt
+from PIL import Image
+from transformers import SamModel, SamProcessor
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+processor = SamProcessor.from_pretrained('facebook/sam-vit-base')
+model = SamModel.from_pretrained('hmdliu/sidewalks-seg-base')
+model.to(device)
+
+def segment_sidewalk(image, threshold):
+    # init data
+    width, height = image.size
+    prompt = [0, 0, width, height]
+    inputs = processor(image, input_boxes=[[prompt]], return_tensors='pt')
+    # make prediction
+    outputs = model(pixel_values=inputs['pixel_values'].to(device),
+                    input_boxes=inputs['input_boxes'].to(device),
+                    multimask_output=False)
+    prob_map = torch.sigmoid(outputs.pred_masks.squeeze()).cpu().detach()
+    prediction = (prob_map > threshold).float()
+    prob_map, prediction = prob_map.numpy(), prediction.numpy()
+    # visualize results
+    save_image(image, 'image.png')
+    save_image(prob_map, 'prob.png', cmap='jet')
+    save_image(prediction, 'mask.png', cmap='gray')
+    return Image.open('image.png'), Image.open('mask.png'), Image.open('prob.png')
+
+def save_image(image, path, **kwargs):
+    plt.figure(figsize=(8, 8))
+    plt.imshow(image, interpolation='nearest', **kwargs)
+    plt.axis('off')
+    plt.tight_layout()
+    plt.savefig(path, bbox_inches='tight', pad_inches=0)
+    plt.close()
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type='pil', label='TIFF Image')
+            threshold_slider = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label='Prediction Threshold')
+            segment_button = gr.Button('Segment')
+        with gr.Column():
+            prediction = gr.Image(type='pil', label='Segmentation Result')
+            prob_map = gr.Image(type='pil', label='Probability Map')
+    segment_button.click(
+        segment_image, 
+        inputs=[image_input, threshold_slider], 
+        outputs=[image_input, prediction, prob_map]
+    )
+demo.launch(debug=True, show_error=True)