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import os
import sys
# NOTE: Deploy ONLY: Add the src directory to the Python path
sys.path.append(os.path.join(os.path.dirname(__file__), "src"))
from pathlib import Path
from PIL import Image
import gradio as gr
from mwm.utils.common import load_json
from mwm.components.inference import inference
from mwm.components.visualization import (
colorize_label_map,
colorize_mask_2d
)
def segment(image: Image.Image):
# Dump image
# TODO: add normalization before allow user to upload their own image
image_savepath = "input_image.png"
image.save(image_savepath)
labels_pred, mask_pred = inference(
config_path="config.json",
image_path=image_savepath
)
mask_pred_colored = colorize_mask_2d(mask_pred) # TODO: show later
labels_pred_colored = colorize_label_map(labels_pred)
# Clean up
os.remove(image_savepath)
return Image.fromarray(labels_pred_colored)
# Load config
config = load_json(Path("config.json"))
# Gradio Interface
demo = gr.Interface(
fn=segment,
inputs=gr.Image(type="pil"),
outputs=gr.Image(type="pil"),
title="🔬 Microscopy Image Segmentation with Machine Learning",
description="""
## Work-in-progress project exploring state-of-the-art (SOTA) ML models for nuclei (cell body) segmentation in microscopy images.
Check out full details in this repo: [microscopy-with-ml](https://github.com/Huangt19150/microscopy-with-ml)
## 📋 Quick Start Guide:
1. Try one of the **examples** below to run nuclei segmentation.
2. (Upload your own image is not yet supported. Coming soon!)
## 📖 Reference:
1. Dataset: [BBBC039v1](https://bbbc.broadinstitute.org/BBBC039) Caicedo et al. 2018, available from the Broad Bioimage Benchmark Collection [Ljosa et al., Nature Methods, 2012](https://www.nature.com/articles/nmeth.2083).
2. Method improved from [topcoders](https://www.kaggle.com/competitions/data-science-bowl-2018/discussion/54741)
""",
examples=config.example_images,
allow_flagging="never"
)
if __name__ == "__main__":
demo.launch()
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