# 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(model): 
    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 = "Segmentação de Glomérulos"
description = """
Um aplicativo web que segmenta glomérulos em cortes histológicos de rim!

O modelo implantado aqui é um UNet++ com um codificador efficientnet-b4 da biblioteca segmentation_models_pytorch.

As imagens de exemplo fornecidas são um subconjunto aleatório de cortes de rim do Atlas de Proteínas Humanas. Essas imagens foram coletadas separadamente do treinamento do modelo e não fizeram parte do conjunto de treinamento, validação ou teste.

"""
#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,
    # Fixes error when set to True: 
    # https://github.com/gradio-app/gradio/pull/1949  
    # but generated file names are too long
    _api_mode=False
)

# %% ../main.ipynb 9
demo.launch()