Create app.py

app.py

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+# Import the dependencies
+import gradio as gr
+from PIL import Image
+import torch
+from transformers import SamModel, SamProcessor
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+# Load the SAM model and processor
+model = SamModel.from_pretrained("Zigeng/SlimSAM-uniform-77")
+processor = SamProcessor.from_pretrained("Zigeng/SlimSAM-uniform-77")
+
+
+# Global variable to store input points
+input_points = []
+
+# Helper functions
+def show_mask(mask, ax, random_color=False):
+    if random_color:
+        color = np.concatenate([np.random.random(3),
+                                np.array([0.6])],
+                               axis=0)
+    else:
+        color = np.array([30/255, 144/255, 255/255, 0.6])
+    h, w = mask.shape[-2:]
+    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
+    ax.imshow(mask_image)
+# Function to get pixel coordinates
+def get_pixel_coordinates(image, evt: gr.SelectData):
+    global input_points
+    x, y = evt.index[0], evt.index[1]
+    input_points = [[[x, y]]]
+    return perform_prediction(image)
+
+# Function to perform SAM model prediction
+def perform_prediction(image):
+    global input_points
+    # Preprocess the image
+    inputs = processor(images=image, input_points=input_points, return_tensors="pt")
+    # Perform prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+    iou = outputs.iou_scores
+    max_iou_index = torch.argmax(iou)
+
+    # Post-process the masks
+    predicted_masks = processor.image_processor.post_process_masks(
+        outputs.pred_masks,
+        inputs['original_sizes'],
+        inputs['reshaped_input_sizes']
+    )
+    predicted_mask = predicted_masks[0]
+
+    # Display the mask on the image
+    mask_image = show_mask_on_image(image, predicted_mask[:,max_iou_index], return_image=True)
+    return mask_image
+
+# Function to overlay mask on the image
+def show_mask_on_image(raw_image, mask, return_image=False):
+    if not isinstance(mask, torch.Tensor):
+        mask = torch.Tensor(mask)
+
+    if len(mask.shape) == 4:
+        mask = mask.squeeze()
+
+    fig, axes = plt.subplots(1, 1, figsize=(15, 15))
+
+    mask = mask.cpu().detach()
+    axes.imshow(np.array(raw_image))
+    show_mask(mask, axes)
+    axes.axis("off")
+    plt.show()
+
+    if return_image:
+        fig = plt.gcf()
+        fig.canvas.draw()
+        # Convert plot to image
+        img = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
+        img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+        img = Image.fromarray(img)
+        plt.close(fig)
+        return img
+
+
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        <div style='text-align: center; font-family: "Times New Roman";'>
+            <h1 style='color: #FF6347;'>One Click Image Segmentation App</h1>
+            <h3 style='color: #4682B4;'>Model: SlimSAM-uniform-77</h3>
+            <h3 style='color: #32CD32;'>Made By: Md. Mahmudun Nabi</h3>
+        </div>
+        """
+    )
+    with gr.Row():
+          
+        img = gr.Image(type="pil", label="Input Image",height=400, width=600)
+        output_image = gr.Image(label="Masked Image")
+
+    img.select(get_pixel_coordinates, inputs=[img], outputs=[output_image])
+
+
+    if __name__ == "__main__":
+        demo.launch(share=False)