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# AUTOGENERATED! DO NOT EDIT! File to edit: ../main.ipynb.
# %% auto 0
__all__ = ['ORGAN', 'IMAGE_SIZE', 'MODEL_NAME', 'THRESHOLD', 'CODES', 'learn', 'title', 'description', 'examples',
'interpretation', 'demo', 'x_getter', 'y_getter', 'splitter', 'make3D', 'predict', 'infer',
'remove_small_segs', 'to_oberlay_image']
# %% ../main.ipynb 1
import numpy as np
import pandas as pd
import skimage
from fastai.vision.all import *
import segmentation_models_pytorch as smp
import gradio as gr
# %% ../main.ipynb 2
ORGAN = "kidney"
IMAGE_SIZE = 512
MODEL_NAME = "unetpp_b4_th60_d9414.pkl"
THRESHOLD = float(MODEL_NAME.split("_")[2][2:]) / 100.
CODES = ["Background", "FTU"] # FTU = functional tissue unit
# %% ../main.ipynb 3
def x_getter(r): return r["fnames"]
def y_getter(r):
rle = r["rle"]
shape = (int(r["img_height"]), int(r["img_width"]))
return rle_decode(rle, shape).T
def splitter(a):
enc_params = L(model.encoder.parameters())
dec_params = L(model.decoder.parameters())
sg_params = L(model.segmentation_head.parameters())
untrained_params = L([*dec_params, *sg_params])
return L([enc_params, untrained_params])
# %% ../main.ipynb 4
learn = load_learner(MODEL_NAME)
# %% ../main.ipynb 5
def make3D(t: np.array) -> np.array:
t = np.expand_dims(t, axis=2)
t = np.concatenate((t,t,t), axis=2)
return t
def predict(fn, cutoff_area=200):
data = infer(fn)
data = remove_small_segs(data, cutoff_area=cutoff_area)
return to_oberlay_image(data), data["df"]
def infer(fn):
img = PILImage.create(fn)
tf_img,_,_,preds = learn.predict(img, with_input=True)
mask = (F.softmax(preds.float(), dim=0)>THRESHOLD).int()[1]
mask = np.array(mask, dtype=np.uint8)
resized_image = Image.fromarray(tf_img.numpy().transpose(1, 2, 0).astype(np.uint8)).resize(img.shape)
resized_image = np.array(resized_image)
return {
"tf_image": tf_img.numpy().transpose(1, 2, 0).astype(np.uint8),
"tf_mask": mask
}
def remove_small_segs(data, cutoff_area=250):
labeled_mask = skimage.measure.label(data["tf_mask"])
props = skimage.measure.regionprops(labeled_mask)
df = {"Glomerulus":[], "Area (in px)":[]}
for i, prop in enumerate(props):
if prop.area < cutoff_area:
labeled_mask[labeled_mask==i+1] = 0
continue
df["Glomerulus"].append(len(df["Glomerulus"]) + 1)
df["Area (in px)"].append(prop.area)
labeled_mask[labeled_mask>0] = 1
data["tf_mask"] = labeled_mask.astype(np.uint8)
data["df"] = pd.DataFrame(df)
return data
def to_oberlay_image(data):
img, msk = data["tf_image"], data["tf_mask"]
msk_im = np.zeros_like(img)
# rgb code: 255, 80, 80
msk_im[:,:,0] = 255
msk_im[:,:,1] = 80
msk_im[:,:,2] = 80
img = Image.fromarray(img).convert("RGBA")
msk_im = Image.fromarray(msk_im).convert("RGBA")
msk = Image.fromarray((msk*255*0.5).astype(np.uint8))
img.paste(msk_im, (0, 0), msk, )
return img
# %% ../main.ipynb 6
title = "Glomerulus Segmentation"
description = """
A web app, that segments glomeruli in histologic kidney slices!
The model deployed here is a [UnetPlusPlus](https://arxiv.org/abs/1807.10165) with an [efficientnet-b4](https://arxiv.org/abs/1905.11946) encoder from the [segmentation_models_pytorch](https://github.com/qubvel/segmentation_models.pytorch) library.
The provided example images are random subset of kidney slices from the [Human Protein Atlas](https://www.proteinatlas.org/). These have been collected separately from model training and have neither been part of the training nor test set.
Find the corresponding blog post [here](https://www.fast.ai/).
"""
#article="<p style='text-align: center'><a href='Blog post URL' target='_blank'>Blog post</a></p>"
examples = [str(p) for p in get_image_files("example_images")]
interpretation='default'
# %% ../main.ipynb 7
demo = gr.Interface(
fn=predict,
inputs=gr.components.Image(shape=(IMAGE_SIZE, IMAGE_SIZE)),
outputs=[gr.components.Image(), gr.components.DataFrame()],
title=title,
description=description,
examples=examples,
interpretation=interpretation,
)
# %% ../main.ipynb 9
demo.launch()
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