Create app.py

app.py

@@ -0,0 +1,69 @@
+import gradio as gr
+from datasets import load_dataset
+from PIL import Image, ImageDraw
+import numpy as np
+
+# Load the dataset
+dataset = load_dataset("dwb2023/brain-tumor-image-dataset-semantic-segmentation", split="test")
+# print(f"Dataset loaded successfully. Number of images: {len(dataset)}")
+
+def draw_annotations(index):
+    try:
+        # Fetch the image and annotations from the dataset
+        record = dataset[index]
+        
+        # Convert image to PIL Image if it's a numpy array
+        if isinstance(record['image'], np.ndarray):
+            img = Image.fromarray(record['image'])
+        else:
+            img = record['image']
+        
+        img = img.convert("RGB")  # Ensure the image is in RGB mode
+        
+        draw = ImageDraw.Draw(img)
+
+        # Draw bounding box
+        bbox = record["bbox"]
+        draw.rectangle([bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]], outline="red", width=2)
+
+        # Draw segmentation mask
+        segmentation = record["segmentation"]
+        for seg in segmentation:
+            draw.polygon(seg, outline="blue", width=2)
+
+        # Prepare additional information
+        category_id = record["category_id"]
+        area = record["area"]
+        file_name = record["file_name"]
+
+        info = f"File Name: {file_name}\n"
+        info += f"Image ID: {record['id']}\n"
+        info += f"Category ID: {category_id}\n"
+        info += f"Bounding Box: [{bbox[0]:.2f}, {bbox[1]:.2f}, {bbox[2]:.2f}, {bbox[3]:.2f}]\n"
+        info += f"Segmentation: {segmentation}\n"
+        info += f"Area: {area:.2f}"
+
+        return img, info
+    except Exception as e:
+        print(f"Error processing image at index {index}: {e}")
+        return Image.new('RGB', (300, 300), color='gray'), f"Error loading image information: {str(e)}"
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Brain Tumor Image Dataset Viewer")
+    gr.Markdown("## Refer to the [dwb2023/brain-tumor-image-dataset-semantic-segmentation](https://huggingface.co/datasets/dwb2023/brain-tumor-image-dataset-semantic-segmentation/viewer/default/test) dataset for more information")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_output = gr.Image(label="Annotated Image")
+        with gr.Column(scale=1):
+            image_index = gr.Slider(minimum=0, maximum=len(dataset)-1, step=1, value=0, label="Image ID Slider")
+            info_output = gr.Textbox(label="Image Information", lines=10)
+    
+    # Update image and info when slider changes
+    image_index.change(draw_annotations, inputs=image_index, outputs=[image_output, info_output])
+
+    # Display initial image and info
+    demo.load(draw_annotations, inputs=image_index, outputs=[image_output, info_output])
+
+demo.launch(debug=True)