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import gradio as gr |
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import tempfile |
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from pathlib import Path |
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from wrapper import run_pipeline_on_image |
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import numpy as np |
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from PIL import Image |
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from itertools import product |
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def process(image_path): |
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if not image_path: |
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return None, None, None, None, [], "" |
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with tempfile.TemporaryDirectory() as tmpdir: |
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src = Path(image_path) |
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ext = src.suffix.lstrip('.') or 'tif' |
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img_path = Path(tmpdir) / f"input.{ext}" |
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try: |
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img_bytes = src.read_bytes() |
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img_path.write_bytes(img_bytes) |
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except Exception: |
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Image.open(src).save(img_path) |
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outputs = run_pipeline_on_image(str(img_path), tmpdir, save_artifacts=True) |
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def load_pil(path_str): |
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try: |
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if not path_str: |
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return None |
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im = Image.open(path_str) |
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im = im.convert('RGB') |
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copied = im.copy() |
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im.close() |
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return copied |
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except Exception: |
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return None |
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composite = load_pil(outputs.get('Composite')) |
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overlay = load_pil(outputs.get('Overlay')) |
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mask = load_pil(outputs.get('Mask')) |
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size_img = load_pil(str(Path(tmpdir) / 'results/size.size_analysis.png')) |
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lbp_path = Path(tmpdir) / 'texture_output/lbp_green.png' |
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texture_img = load_pil(str(lbp_path)) if lbp_path.exists() else None |
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order = ['NDVI', 'GNDVI', 'SAVI'] |
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gallery_items = [load_pil(outputs[k]) for k in order if k in outputs] |
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stats_text = outputs.get('StatsText', '') |
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return size_img, composite, mask, overlay, texture_img, gallery_items, stats_text |
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with gr.Blocks() as demo: |
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gr.Markdown("# 🌿 Automated Plant Analysis Demo") |
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gr.Markdown("Upload a sorghum plant image to compute and visualize composite, mask, overlay, texture (LBP), vegetation indices, and statistics.") |
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with gr.Row(): |
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with gr.Column(): |
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inp = gr.Image(type="filepath", label="Upload Image") |
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run = gr.Button("Run Pipeline", variant="primary") |
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with gr.Row(): |
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size_img = gr.Image(type="pil", label="Morphology Size", interactive=False) |
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composite_img = gr.Image(type="pil", label="Composite (Segmentation Input)", interactive=False) |
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mask_img = gr.Image(type="pil", label="Mask", interactive=False) |
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overlay_img = gr.Image(type="pil", label="Segmentation Overlay", interactive=False) |
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with gr.Row(): |
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texture_img = gr.Image(type="pil", label="Texture LBP (Green Band)", interactive=False) |
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gallery = gr.Gallery(label="Vegetation Indices", columns=3, height="auto") |
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stats = gr.Textbox(label="Statistics", lines=4) |
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run.click(process, inputs=inp, outputs=[size_img, composite_img, mask_img, overlay_img, texture_img, gallery, stats]) |
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if __name__ == "__main__": |
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demo.launch() |
