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