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import gradio as gr
from tifffile import imread
from PIL import Image
from path_analysis.analyse import analyse_paths
import numpy as np
# Function to preview the imported image
def preview_image(file1):
if file1:
print('Uploading image', file1.name)
im = imread(file1.name)
print(im.ndim, im.shape)
if im.ndim>2:
return Image.fromarray(np.max(im, axis=0))
else:
return Image.fromarray(im)
else:
return None
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
# Inputs for cell ID, image, and path
cellid_input = gr.Textbox(label="Cell ID", placeholder="Image_1")
image_input = gr.File(label="Input foci image")
image_preview = gr.Image(label="Max projection of foci image")
image_input.change(fn=preview_image, inputs=image_input, outputs=image_preview)
path_input = gr.File(label="SNT traces file")
# Additional options wrapped in an accordion for better UI experience
with gr.Accordion("Additional options ..."):
sphere_radius = gr.Number(label="Trace sphere radius (um)", value=0.1984125, interactive=True)
peak_threshold = gr.Number(label="Peak relative threshold", value=0.4, interactive=True)
# Resolutions for xy and z axis
with gr.Row():
xy_res = gr.Number(label='xy-yesolution (um)', value=0.0396825, interactive=True)
z_res = gr.Number(label='z resolution (um)', value=0.0909184, interactive=True)
# Resolutions for xy and z axis
threshold_type = gr.Radio(["per-trace", "per-cell"], label="Threshold-type", value="per-trace", interactive=True)
use_corrected_positions = gr.Checkbox(label="Correct foci position measurements", value=True, interactive=True)
screening_distance = gr.Number(label='Screening distance (voxels)', value=10, interactive=True)
# The output column showing the result of processing
with gr.Column():
trace_output = gr.Image(label="Overlayed paths")
image_output=gr.Gallery(label="Traced paths")
plot_output=gr.Plot(label="Foci intensity traces")
data_output=gr.DataFrame(label="Detected peak data")#, "Peak 1 pos", "Peak 1 int"])
data_file_output=gr.File(label="Output data file (.csv)")
def process(cellid_input, image_input, path_input, sphere_radius, peak_threshold, xy_res, z_res, threshold_type, use_corrected_positions, screening_distance):
config = { 'sphere_radius': sphere_radius,
'peak_threshold': peak_threshold,
'xy_res': xy_res,
'z_res': z_res,
'threshold_type': threshold_type,
'use_corrected_positions': use_corrected_positions,
'screening_distance': screening_distance,
}
paths, traces, fig, extracted_peaks = analyse_paths(cellid_input, image_input.name, path_input.name, config)
extracted_peaks.to_csv('output.csv')
print('extracted', extracted_peaks)
return paths, [Image.fromarray(im) for im in traces], fig, extracted_peaks, 'output.csv'
with gr.Row():
greet_btn = gr.Button("Process")
greet_btn.click(fn=process, inputs=[cellid_input, image_input, path_input, sphere_radius, peak_threshold, xy_res, z_res, threshold_type, use_corrected_positions, screening_distance], outputs=[trace_output, image_output, plot_output, data_output, data_file_output], api_name="process")
if __name__ == "__main__":
demo.launch()
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