|
import streamlit as st |
|
from PIL import Image |
|
import numpy as np |
|
import cv2 |
|
from huggingface_hub import from_pretrained_keras |
|
|
|
st.header("Segmentaci贸n de dientes con rayos X") |
|
|
|
st.markdown(''' |
|
|
|
Hola estudiantes de Platzi 馃殌. Este modelo usan UNet para segmentar im谩genes |
|
de dientos en rayos X. Se utila un modelo de Keras importado con la funci贸n |
|
`huggingface_hub.from_pretrained_keras`. Recuerda que el Hub de Hugging Face est谩 integrado |
|
con muchas librer铆as como Keras, scikit-learn, fastai y otras. |
|
|
|
El modelo fue creado por [SerdarHelli](https://huggingface.co/SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net). |
|
|
|
''') |
|
|
|
model_id = "SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net" |
|
model=from_pretrained_keras(model_id) |
|
|
|
|
|
def convertir_one_channel(img): |
|
if len(img.shape)>2: |
|
img= cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
|
return img |
|
else: |
|
return img |
|
|
|
def convertir_rgb(img): |
|
if len(img.shape)==2: |
|
img= cv2.cvtColor(img,cv2.COLOR_GRAY2RGB) |
|
return img |
|
else: |
|
return img |
|
|
|
|
|
image_file = st.file_uploader("Sube aqu铆 tu imagen.", type=["png","jpg","jpeg"]) |
|
|
|
|
|
if image_file is not None: |
|
|
|
img= Image.open(image_file) |
|
|
|
st.image(img,width=850) |
|
|
|
img=np.asarray(img) |
|
|
|
img_cv=convertir_one_channel(img) |
|
img_cv=cv2.resize(img_cv,(512,512), interpolation=cv2.INTER_LANCZOS4) |
|
img_cv=np.float32(img_cv/255) |
|
|
|
img_cv=np.reshape(img_cv,(1,512,512,1)) |
|
prediction=model.predict(img_cv) |
|
predicted=prediction[0] |
|
predicted = cv2.resize(predicted, (img.shape[1],img.shape[0]), interpolation=cv2.INTER_LANCZOS4) |
|
mask=np.uint8(predicted*255) |
|
_, mask = cv2.threshold(mask, thresh=0, maxval=255, type=cv2.THRESH_BINARY+cv2.THRESH_OTSU) |
|
kernel =( np.ones((5,5), dtype=np.float32)) |
|
mask=cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel,iterations=1 ) |
|
mask=cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel,iterations=1 ) |
|
cnts,hieararch=cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) |
|
output = cv2.drawContours(convertir_one_channel(img), cnts, -1, (255, 0, 0) , 3) |
|
|
|
|
|
if output is not None : |
|
st.subheader("Segmentaci贸n:") |
|
st.write(output.shape) |
|
st.image(output,width=850) |
