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README.md

@@ -1,12 +1,35 @@
 ---
-title: Traffic Sign Classifier
-emoji: 🐠
-colorFrom: indigo
-colorTo: red
-sdk: streamlit
-sdk_version: 1.35.0
-app_file: app.py
+title: "Traffic Sign Classifier"
+emoji: "🚦"
+colorFrom: "blue"
+colorTo: "green"
+sdk: "streamlit"
+app_file: "app.py"
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Traffic Sign Classifier
+
+Esta aplicación clasifica señales de tráfico usando un modelo de CNN.
+
+## Cómo ejecutar localmente
+
+1. Clona el repositorio:
+    ```bash
+    git clone https://github.com/tu-usuario/tu-repo.git
+    ```
+2. Crea un entorno virtual e instala las dependencias:
+    ```bash
+    cd tu-repo
+    python -m venv venv
+    source venv/bin/activate  
+    pip install -r requirements.txt
+    ```
+3. Ejecuta la aplicación:
+    ```bash
+    streamlit run app.py
+    ```
+
+## Desplegado en Hugging Face Spaces
+
+Puedes ver la aplicación desplegada en [Hugging Face Spaces](https://huggingface.co/spaces/tu-usuario/tu-repo).

app.py

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+# -*- coding: utf-8 -*-
+"""ProyectoAvanzado1.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1R5pOFRcOn9faAiaFmIL6GcuWweWYGswX
+"""
+
+import os
+import pickle
+import numpy as np
+from sklearn.model_selection import train_test_split
+from tensorflow.keras.utils import to_categorical
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+from sklearn import preprocessing
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+import cv2
+from PIL import Image
+import matplotlib.pyplot as plt
+import seaborn as sns
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
+
+# Función para cargar datos desde archivos .p
+def load_pickle(file_path):
+    with open(file_path, 'rb') as file:
+        data = pickle.load(file)
+    return data
+
+# Cargar datos de entrenamiento, validación y prueba
+train_data = load_pickle(os.path.join("/Users/victorg/Documents/DataScience/ProjectoAvanzado/CSV/train.p"))
+val_data = load_pickle(os.path.join("/Users/victorg/Documents/DataScience/ProjectoAvanzado/CSV/valid.p"))
+test_data = load_pickle(os.path.join("/Users/victorg/Documents/DataScience/ProjectoAvanzado/CSV/test.p"))
+
+# Separar imágenes y etiquetas
+train_images, train_labels = train_data['features'], train_data['labels']
+val_images, val_labels = val_data['features'], val_data['labels']
+test_images, test_labels = test_data['features'], test_data['labels']
+
+# Preprocesar datos
+def preprocess_data(images, labels):
+    images = images.astype('float32') / 255.0
+    labels = to_categorical(labels, num_classes=43)
+    return images, labels
+
+train_images, train_labels = preprocess_data(train_images, train_labels)
+val_images, val_labels = preprocess_data(val_images, val_labels)
+test_images, test_labels = preprocess_data(test_images, test_labels)
+
+
+# Convertir etiquetas a DataFrame para visualización
+train_df = pd.DataFrame({"label": train_labels.argmax(axis=1)})
+
+# Conteo de imágenes por clase
+plt.figure(figsize=(10, 6))
+sns.countplot(x=train_df['label'])
+plt.title("Conteo de Imágenes por Clase")
+plt.xlabel("Clase")
+plt.ylabel("Conteo")
+plt.show()
+
+# Visualización 2: Ejemplos de imágenes por clase
+fig, axes = plt.subplots(5, 5, figsize=(15, 15))
+axes = axes.ravel()
+for i in range(25):
+    axes[i].imshow(train_images[i])
+    axes[i].set_title(f"Clase: {train_labels[i].argmax()}")
+    axes[i].axis('off')
+plt.subplots_adjust(hspace=0.5)
+plt.show()
+
+# Visualización 3: Muestra aleatoria de imágenes
+fig, axes = plt.subplots(3, 3, figsize=(12, 12))
+axes = axes.ravel()
+for i in range(9):
+    idx = np.random.randint(0, len(train_images))
+    axes[i].imshow(train_images[idx])
+    axes[i].set_title(f"Clase: {train_labels[idx].argmax()}")
+    axes[i].axis('off')
+plt.subplots_adjust(hspace=0.5)
+plt.show()
+
+
+
+# Definir la arquitectura de la CNN
+model = Sequential([
+    Conv2D(32, (3, 3), activation="relu", input_shape=(32, 32, 3)),
+    MaxPooling2D((2, 2)),
+    Conv2D(64, (3, 3), activation="relu"),
+    MaxPooling2D((2, 2)),
+    Conv2D(128, (3, 3), activation="relu"),
+    MaxPooling2D((2, 2)),
+    Flatten(),
+    Dense(215, activation="relu"),
+    Dropout(0.5),
+    Dense(43, activation="softmax")
+])
+
+# Compilar el modelo
+model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
+
+# Verificar si el modelo ya está guardado
+model_path = "traffic_sign_classifier.h5"
+if os.path.exists(model_path):
+    model = tf.keras.models.load_model(model_path)
+else:
+    model.fit(train_images, train_labels, epochs=10, validation_data=(val_images, val_labels))
+    model.save(model_path)
+
+# Evaluar el modelo en datos de prueba
+test_loss, test_acc = model.evaluate(test_images, test_labels)
+print(f"Test Accuracy: {test_acc:.2f}")
+
+
+
+
+# Cargar el modelo
+model = tf.keras.models.load_model("traffic_sign_classifier.h5")
+
+# Diccionario de clases de señales de tráfico
+classes = {
+    0: 'Speed limit (20km/h)',
+    1: 'Speed limit (30km/h)',
+    2: 'Speed limit (50km/h)',
+    3: 'Speed limit (60km/h)',
+    4: 'Speed limit (70km/h)',
+    5: 'Speed limit (80km/h)',
+    6: 'End of speed limit (80km/h)',
+    7: 'Speed limit (100km/h)',
+    8: 'Speed limit (120km/h)',
+    9: 'No passing',
+    10: 'No passing for vehicles over 3.5 metric tons',
+    11: 'Right-of-way at the next intersection',
+    12: 'Priority road',
+    13: 'Yield',
+    14: 'Stop',
+    15: 'No vehicles',
+    16: 'Vehicles over 3.5 metric tons prohibited',
+    17: 'No entry',
+    18: 'General caution',
+    19: 'Dangerous curve to the left',
+    20: 'Dangerous curve to the right',
+    21: 'Double curve',
+    22: 'Bumpy road',
+    23: 'Slippery road',
+    24: 'Road narrows on the right',
+    25: 'Road work',
+    26: 'Traffic signals',
+    27: 'Pedestrians',
+    28: 'Children crossing',
+    29: 'Bicycles crossing',
+    30: 'Beware of ice/snow',
+    31: 'Wild animals crossing',
+    32: 'End of all speed and passing limits',
+    33: 'Turn right ahead',
+    34: 'Turn left ahead',
+    35: 'Ahead only',
+    36: 'Go straight or right',
+    37: 'Go straight or left',
+    38: 'Keep right',
+    39: 'Keep left',
+    40: 'Roundabout mandatory',
+    41: 'End of no passing',
+    42: 'End of no passing by vehicles over 3.5 metric tons'
+}
+
+# Función para predecir la clase de una imagen
+def predict(image):
+    image = np.array(image)
+    image = cv2.resize(image, (32, 32))
+    image = image / 255.0
+    image = np.expand_dims(image, axis=0)
+    predictions = model.predict(image)
+    class_idx = np.argmax(predictions)
+    return classes[class_idx]
+
+# Título y descripción de la aplicación
+st.title("Traffic Sign Classifier")
+st.write("Esta aplicación clasifica señales de tráfico usando un modelo de CNN.")
+
+# Mostrar ejemplos de imágenes del conjunto de datos
+st.header("Ejemplos de Imágenes del Conjunto de Datos")
+fig, axes = plt.subplots(2, 5, figsize=(15, 6))
+axes = axes.ravel()
+for i in range(10):
+    idx = np.random.randint(0, len(train_images))
+    axes[i].imshow(train_images[idx])
+    axes[i].set_title(f"Clase: {train_labels[idx].argmax()}")
+    axes[i].axis('off')
+st.pyplot(fig)
+# Mostrar ejemplos de imágenes del conjunto de datos
+
+fig, axes = plt.subplots(2, 5, figsize=(15, 6))
+axes = axes.ravel()
+for i in range(10):
+    idx = np.random.randint(0, len(train_images))
+    axes[i].imshow(train_images[idx])
+    axes[i].set_title(f"Clase: {train_labels[idx].argmax()}")
+    axes[i].axis('off')
+st.pyplot(fig)
+
+# Mostrar algunas visualizaciones de EDA
+st.header("Visualizaciones de EDA")
+fig, ax = plt.subplots(figsize=(10, 6))
+sns.countplot(x='label', data=train_df, ax=ax)
+ax.set_title("Conteo de Imágenes por Clase")
+ax.set_xlabel("Clase")
+ax.set_ylabel("Conteo")
+st.pyplot(fig)
+
+# Permitir al usuario cargar una imagen
+st.header("Carga tu Propia Imagen de Señal de Tráfico")
+uploaded_file = st.file_uploader("Elige una imagen...", type=["jpg", "jpeg", "png"])
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Imagen Cargada', use_column_width=True)
+    st.write("")
+    st.write("Clasificando...")
+    label = predict(image)
+    st.write(f"Esta señal es: {label}")
+
+# Mostrar métricas del modelo
+st.header("Métricas del Modelo")
+st.write(f"Exactitud del conjunto de prueba: {test_acc:.2f}")

requirements.txt

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+streamlit
+tensorflow
+pandas
+numpy
+matplotlib
+seaborn
+pillow
+opencv-python