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Update app.py
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app.py
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import gradio as gr
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import numpy as np
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import os
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@@ -264,26 +265,28 @@ def process_satellite_images(red_file, green_file, blue_file, nir_file, batch_si
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return rgb_display, visualization, stats
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Upload separate JP2 files for Red, Green, Blue, and NIR channels to detect clouds in satellite imagery.
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This application uses the OmniCloudMask model to classify each pixel as:
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- Cloud Shadow (3)
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The model works best with imagery at 10-50m resolution. For higher resolution imagery, downsampling is recommended.
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"""
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# Launch the app
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demo.launch(
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import psutil
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import gradio as gr
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import numpy as np
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import os
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return rgb_display, visualization, stats
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def update_cpu():
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return f"CPU Usage: {psutil.cpu_percent()}%"
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with gr.Blocks() as demo:
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cpu_text = gr.Textbox(label="CPU Usage")
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check_cpu_btn = gr.Button("Check CPU")
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# Attach the event handler using the click method
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check_cpu_btn.click(fn=update_cpu, inputs=None, outputs=cpu_text)
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# Define the CPU check function
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def check_cpu_usage():
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"""Check and return the current CPU usage."""
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return f"CPU Usage: {psutil.cpu_percent()}%"
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# Create the Gradio application with Blocks
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with gr.Blocks(title="Satellite Cloud Detection") as demo:
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# Add the description
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gr.Markdown("""
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# Satellite Cloud Detection
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Upload separate JP2 files for Red, Green, Blue, and NIR channels to detect clouds in satellite imagery.
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This application uses the OmniCloudMask model to classify each pixel as:
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- Cloud Shadow (3)
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The model works best with imagery at 10-50m resolution. For higher resolution imagery, downsampling is recommended.
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""")
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# Main cloud detection interface
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with gr.Row():
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with gr.Column():
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# Input components
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red_input = gr.Image(type="filepath", label="Red Channel (JP2)")
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green_input = gr.Image(type="filepath", label="Green Channel (JP2)")
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blue_input = gr.Image(type="filepath", label="Blue Channel (JP2)")
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nir_input = gr.Image(type="filepath", label="NIR Channel (JP2)")
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batch_size = gr.Slider(minimum=1, maximum=32, value=1, step=1,
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label="Batch Size",
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info="Higher values use more memory but process faster")
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patch_size = gr.Slider(minimum=500, maximum=2000, value=1000, step=100,
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label="Patch Size",
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info="Size of image patches for processing")
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patch_overlap = gr.Slider(minimum=100, maximum=500, value=300, step=50,
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label="Patch Overlap",
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info="Overlap between patches to avoid edge artifacts")
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process_btn = gr.Button("Process Cloud Detection")
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with gr.Column():
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# Output components
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rgb_output = gr.Image(label="Original RGB Image")
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cloud_output = gr.Image(label="Cloud Detection Visualization")
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stats_output = gr.Textbox(label="Statistics")
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# CPU usage monitoring section
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with gr.Row():
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with gr.Column():
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gr.Markdown("## System Monitoring")
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cpu_button = gr.Button("Check CPU Usage")
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cpu_output = gr.Textbox(label="CPU Usage")
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# Set up event handlers
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process_btn.click(
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fn=process_satellite_images,
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inputs=[red_input, green_input, blue_input, nir_input, batch_size, patch_size, patch_overlap],
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outputs=[rgb_output, cloud_output, stats_output]
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)
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cpu_button.click(
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fn=check_cpu_usage,
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inputs=None,
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outputs=cpu_output
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)
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# Add examples
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gr.Examples(
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examples=[["jp2s/B04.jp2", "jp2s/B03.jp2", "jp2s/B02.jp2", "jp2s/B8A.jp2", 1, 1000, 300]],
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inputs=[red_input, green_input, blue_input, nir_input, batch_size, patch_size, patch_overlap]
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)
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# Launch the app
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demo.launch()
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