Upload demo about teeth segmentation

.gitattributes

@@ -0,0 +1,38 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+teeth_1.png filter=lfs diff=lfs merge=lfs -text
+teeth_2.png filter=lfs diff=lfs merge=lfs -text
+teeth_3.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1 @@
+env/*

README.md

@@ -0,0 +1,37 @@
+---
+title: Streamlit Teeth Segmentation
+emoji: 🌖
+colorFrom: purple
+colorTo: red
+sdk: streamlit
+sdk_version: 1.30.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+Steps to run locally:
+```
+python3 -m venv env # To create an env
+source env/bin/activate # To activate the env
+pip3 install -r requirements.txt # To download packages needed
+streamlit run app.py # To run the app
+deactivate # To leave the env
+```
+
+Some useful commands:
+```
+pip3 install streamlit # Only the first time
+pip3 install torch # Only the first time
+pip3 freeze > requirements.txt # To export the libraries to a file
+```
+
+If you have issues with git push:
+```
+git remote get-url origin # Copy this value and then change according the next line
+git remote set-url origin https://<user_name>:<token>@huggingface.co/<repo_path> # ex. git remote set-url origin https://marcela9409:valid_token@huggingface.co/spaces/marcela9409/streamlit-teeth-segmentation
+git push
+```

app.py

@@ -0,0 +1,103 @@
+import streamlit as st
+from PIL import Image
+import numpy as np
+import cv2
+from huggingface_hub import from_pretrained_keras
+
+st.header("X-ray segmentation of teeth / Segmentación de dientes con rayos X")
+st.subheader("Iteration to improve demo / Iteración para mejorar la demo")
+
+st.markdown(
+    """
+    Demo for Platzi class / Demo para la clase de Platzi
+"""
+)
+
+## Seleccionamos y cargamos el modelo
+model_id = "SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net"
+model = from_pretrained_keras(model_id)
+
+## Permitimos a la usuaria cargar una imagen
+image_file = st.file_uploader("Upload your image here / Sube aquí tu imagen", type=["png", "jpg", "jpeg"])
+
+## Si una imagen tiene más de un canal entonces se convierte a escala de grises (1 canal)
+def convert_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
+
+
+## Manipularemos la interfaz para que podamos usar imágenes ejemplo
+## Si el usuario da click en un ejemplo entonces el modelo correrá con él
+examples = ["teeth_1.png", "teeth_2.png", "teeth_3.png"]
+
+## Creamos tres columnas; en cada una estará una imagen ejemplo
+col1, col2, col3 = st.columns(3)
+with col1:
+    ## Se carga la imagen y se muestra en la interfaz
+    ex = Image.open(examples[0])
+    st.image(ex, width=200)
+    ## Si oprime el botón entonces usaremos ese ejemplo en el modelo
+    if st.button("Run example 1 / Corre ejemplo 1"):
+        image_file = examples[0]
+
+with col2:
+    ex1 = Image.open(examples[1])
+    st.image(ex1, width=200)
+    if st.button("Run example 2 / Corre ejemplo 2"):
+        image_file = examples[1]
+
+with col3:
+    ex2 = Image.open(examples[2])
+    st.image(ex2, width=200)
+    if st.button("Run example 3 / Corre ejemplo 3"):
+        image_file = examples[2]
+
+## Si tenemos una imagen para ingresar en el modelo entonces
+## la procesamos e ingresamos al modelo
+if image_file is not None:
+    ## Cargamos la imagen con PIL, la mostramos y la convertimos a un array de NumPy
+    img = Image.open(image_file)
+    st.image(img, width=850)
+    img = np.asarray(img)
+
+    ## Procesamos la imagen para ingresarla al modelo
+    img_cv = convert_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))
+
+    ## Ingresamos el array de NumPy al modelo
+    predicted = model.predict(img_cv)
+    predicted = predicted[0]
+
+    ## Regresamos la imagen a su forma original y agregamos las máscaras de la segmentación
+    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(convert_one_channel(img.astype(np.uint8)), cnts, -1, (255, 0, 0), 3)
+
+    ## Si obtuvimos exitosamente un resultadod entonces lo mostramos en la interfaz
+    if output is not None:
+        st.subheader("Segmentation / Segmentación:")
+        st.write(output.shape)
+        st.image(output, width=850)
+
+    st.markdown("Thanks for use our demo! / Gracias por usar esta demo")

requirements.txt

@@ -0,0 +1,78 @@
+absl-py==2.1.0
+altair==5.2.0
+astunparse==1.6.3
+attrs==23.2.0
+blinker==1.7.0
+cachetools==5.3.2
+certifi==2023.11.17
+charset-normalizer==3.3.2
+click==8.1.7
+filelock==3.13.1
+flatbuffers==23.5.26
+fsspec==2023.12.2
+gast==0.5.4
+gitdb==4.0.11
+GitPython==3.1.41
+google-auth==2.27.0
+google-auth-oauthlib==1.2.0
+google-pasta==0.2.0
+grpcio==1.60.0
+h5py==3.10.0
+huggingface-hub==0.20.3
+idna==3.6
+importlib-metadata==7.0.1
+Jinja2==3.1.3
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+keras==2.15.0
+libclang==16.0.6
+Markdown==3.5.2
+markdown-it-py==3.0.0
+MarkupSafe==2.1.4
+mdurl==0.1.2
+ml-dtypes==0.2.0
+numpy==1.26.3
+oauthlib==3.2.2
+opencv-python==4.9.0.80
+opt-einsum==3.3.0
+packaging==23.2
+pandas==2.2.0
+pillow==10.2.0
+protobuf==4.23.4
+pyarrow==15.0.0
+pyasn1==0.5.1
+pyasn1-modules==0.3.0
+pydeck==0.8.1b0
+Pygments==2.17.2
+python-dateutil==2.8.2
+pytz==2023.3.post1
+PyYAML==6.0.1
+referencing==0.32.1
+requests==2.31.0
+requests-oauthlib==1.3.1
+rich==13.7.0
+rpds-py==0.17.1
+rsa==4.9
+scipy==1.12.0
+six==1.16.0
+smmap==5.0.1
+streamlit==1.30.0
+tenacity==8.2.3
+tensorboard==2.15.1
+tensorboard-data-server==0.7.2
+tensorflow==2.15.0
+tensorflow-estimator==2.15.0
+tensorflow-io-gcs-filesystem==0.35.0
+termcolor==2.4.0
+toml==0.10.2
+toolz==0.12.1
+tornado==6.4
+tqdm==4.66.1
+typing_extensions==4.9.0
+tzdata==2023.4
+tzlocal==5.2
+urllib3==2.1.0
+validators==0.22.0
+Werkzeug==3.0.1
+wrapt==1.14.1
+zipp==3.17.0

requirements_basic.txt

@@ -0,0 +1,7 @@
+numpy
+Pillow
+scipy
+opencv-python
+tensorflow
+streamlit
+huggingface_hub