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import numpy as np
import pandas as pd
#import cv2
import os
import pydicom
import streamlit as st
from streamlit_cropper import st_cropper
#import tensorflow as tf
#import tensorflow_io as tfio
#from tensorflow import keras
import matplotlib.pyplot as plt
from PIL import Image
from fastai.vision.all import *
import pathlib
import platform
plt = platform.system()
if plt == 'Linux':
pathlib.WindowsPath = pathlib.PosixPath
# Get the directory of the current script
script_dir = os.path.dirname(os.path.realpath(__file__))
st.set_page_config(page_title="Aplicaci贸n cancer de mama - IXCHEN")
os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python'
class Modelo:
def __init__(self, model_name):
self.model_name = model_name
self.modelo = self.cargar_modelo(self.model_name)
def cargar_modelo(self, modelo):
if modelo == "alexnet":
return load_learner(os.path.join(script_dir, 'clasificacion_alexnet.pkl'))
elif modelo == 'restnet50':
return load_learner(os.path.join(script_dir, 'clasificacion.pkl'))
def predecir(self, imagen):
_, _, probs = self.modelo.predict(imagen)
return probs
modelo_alexnet = Modelo('alexnet')
modelo_resnet50 = Modelo('restnet50')
realtime_update = st.sidebar.checkbox(label="Actualizar en tiempo real", value=True)
box_color = st.sidebar.color_picker(label="Color caja", value='#0000FF')
aspect_choice = st.sidebar.radio(label="Relaci贸n aspecto", options=["1:1", "16:9", "4:3", "2:3", "Free"])
aspect_dict = {
"1:1": (1, 1),
"16:9": (16, 9),
"4:3": (4, 3),
"2:3": (2, 3),
"Free": None
}
aspect_ratio = aspect_dict[aspect_choice]
class_names = ['Benigno', 'Maligno', 'Normal']
def cargar_dicom_file(image_bytes, tipo):
if tipo != 'storage':
dcm = pydicom.dcmread(image_bytes)
else:
dcm = pydicom.dcmread(image_bytes)
numpy_lossy_image = dcm.pixel_array
return np.squeeze(numpy_lossy_image)
def mostrar_resultados_prediccion(crop_image):
resultado_alexnet = modelo_alexnet.predecir(crop_image)
resultado_resnet50 = modelo_resnet50.predecir(crop_image)
return resultado_alexnet, resultado_resnet50
def obtener_valor_tag(image_byte, tag: int) -> str:
raw_tag_value = tfio.image.decode_dicom_data(image_byte, tag).numpy()
return raw_tag_value.decode('utf-8')
st.write("# Modelo de Machine Learning para el diagnostico del cancer de mama")
uploaded_file = st.file_uploader("Subir archivo DICOM", type=[".dcm"])
if uploaded_file:
dicom_file = cargar_dicom_file(uploaded_file, tipo='')
img = Image.fromarray(dicom_file)
rect = st_cropper(img,realtime_update=realtime_update, box_color=box_color,
aspect_ratio=aspect_ratio, return_type='box')
realizar_prediccion = st.button("Realizar predicci贸n")
st.session_state['uploaded_file'] = uploaded_file.getvalue()
if realizar_prediccion:
st.empty()
crop = dicom_file[
rect['top']:rect['height'] + rect['top'],
rect['left']:rect['width'] + rect['left']]
resultado_alexnet, resultado_restnet50 = mostrar_resultados_prediccion( crop)
resultado_alexnet = np.squeeze(resultado_alexnet) * 100
resultado_resnet50 = np.squeeze(resultado_restnet50) * 100
datos_alexnet = {
"Probabilidad": resultado_alexnet,
"Clase": class_names
}
df_alexnet = pd.DataFrame(data=datos_alexnet).set_index('Clase')
datos_resnet50 = {
"Probabilidad": resultado_resnet50,
"Clase": class_names
}
df_resnet50 = pd.DataFrame(data=datos_resnet50).set_index('Clase')
st.write("### Probabilidades seg煤n tipo de tumor - AlexNet")
st.bar_chart(df_alexnet)
st.write("### Probabilidades seg煤n tipo de tumor - ResNet50")
st.bar_chart(df_resnet50) |