File size: 4,935 Bytes
3834351 4768cde dd1d7f5 a32df56 4c1c4a7 3834351 2a055cf 916b83d 4768cde 916b83d 2716edf 49abd9f 916b83d 2716edf 916b83d 2716edf 7c31b44 916b83d 4768cde e768711 3c8af25 c170961 3c8af25 93d0941 c170961 93d0941 916b83d 93d0941 c170961 93d0941 c170961 93d0941 916b83d c170961 3c8af25 916b83d e768711 916b83d 2a055cf 3834351 916b83d 9226311 2716edf 916b83d e768711 d807150 916b83d d807150 e768711 2a055cf e768711 5f6c42c 3c8af25 c170961 2a055cf c170961 93d0941 2a055cf e768711 2a055cf e768711 916b83d 2a055cf 916b83d 3834351 69b6a19 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 |
import gradio as gr
import tempfile
from pathlib import Path
from wrapper import run_pipeline_on_image
from PIL import Image
##add the process function
def process(file_path):
if not file_path:
return None, None, None, None, None, [], ""
with tempfile.TemporaryDirectory() as tmpdir:
src = Path(file_path)
ext = src.suffix.lstrip('.') or 'tif'
img_path = Path(tmpdir) / f"input.{ext}"
try:
# Copy raw uploaded bytes
img_bytes = src.read_bytes()
img_path.write_bytes(img_bytes)
except Exception:
# Fallback: save via PIL if direct copy fails
Image.open(src).save(img_path)
# Run the full sorghum pipeline
outputs = run_pipeline_on_image(str(img_path), tmpdir, save_artifacts=True)
def load_pil(path_str):
try:
if not path_str:
return None
im = Image.open(path_str)
copied = im.copy()
im.close()
return copied
except Exception:
return None
composite = load_pil(outputs.get('Composite'))
overlay = load_pil(outputs.get('Overlay'))
mask = load_pil(outputs.get('Mask'))
input_img = load_pil(outputs.get('InputImage'))
size_img = load_pil(str(Path(tmpdir) / 'results/size.size_analysis.png'))
yolo_img = load_pil(str(Path(tmpdir) / 'results/yolo_tips.png'))
# Texture images (green band)
lbp_path = Path(tmpdir) / 'texture_output/lbp_green.png'
hog_path = Path(tmpdir) / 'texture_output/hog_green.png'
lac1_path = Path(tmpdir) / 'texture_output/lac1_green.png'
texture_img = load_pil(str(lbp_path)) if lbp_path.exists() else None
hog_img = load_pil(str(hog_path)) if hog_path.exists() else None
lac1_img = load_pil(str(lac1_path)) if lac1_path.exists() else None
# Vegetation indices
order = ['NDVI', 'GNDVI', 'SAVI']
gallery_items = [load_pil(outputs[k]) for k in order if k in outputs]
stats_text = outputs.get('StatsText', '')
# Output order matches UI components defined below
# Row 1: Input image (slightly larger)
# Row 2: Composite, Mask, Overlay
# Row 3: Texture images (LBP, HOG, Lac1)
# Row 4: Vegetation indices (gallery)
# Row 5: Morphology Size and YOLO Tips
# Final: Stats table
return (
input_img,
composite,
mask,
overlay,
texture_img,
hog_img,
lac1_img,
gallery_items,
size_img,
yolo_img,
stats_text,
)
with gr.Blocks() as demo:
gr.Markdown("# 🌿 Automated Plant Analysis Demo")
gr.Markdown("Upload a sorghum plant image (TIFF preferred) to compute and visualize composite, mask, overlay, texture (LBP), vegetation indices, and statistics.")
with gr.Row():
with gr.Column():
# Use File input to preserve raw TIFFs
inp = gr.File(
type="filepath",
file_types=[".tif", ".tiff", ".png", ".jpg"],
label="Upload Image"
)
run = gr.Button("Run Pipeline", variant="primary")
# Row 1: input image, slightly larger
with gr.Row():
input_img = gr.Image(type="pil", label="Input Image", interactive=False, height=380)
# Row 2: composite, mask, overlay
with gr.Row():
composite_img = gr.Image(type="pil", label="Composite (Segmentation Input)", interactive=False)
mask_img = gr.Image(type="pil", label="Mask", interactive=False)
overlay_img = gr.Image(type="pil", label="Segmentation Overlay", interactive=False)
# Row 3: textures
with gr.Row():
texture_img = gr.Image(type="pil", label="Texture LBP (Green Band)", interactive=False)
hog_img = gr.Image(type="pil", label="Texture HOG (Green Band)", interactive=False)
lac1_img = gr.Image(type="pil", label="Texture Lac1 (Green Band)", interactive=False)
# Row 4: vegetation indices
gallery = gr.Gallery(label="Vegetation Indices", columns=3, height="auto")
# Row 5: morphology and YOLO tips
with gr.Row():
size_img = gr.Image(type="pil", label="Morphology Size", interactive=False)
yolo_img = gr.Image(type="pil", label="YOLO Tips", interactive=False)
# Final: statistics table
stats = gr.Textbox(label="Statistics", lines=4)
run.click(
process,
inputs=inp,
outputs=[
input_img,
composite_img,
mask_img,
overlay_img,
texture_img,
hog_img,
lac1_img,
gallery,
size_img,
yolo_img,
stats,
]
)
if __name__ == "__main__":
demo.launch()
|