From 71b9ef38f73c332dc634bfa06c9ab86262e598e3 Mon Sep 17 00:00:00 2001 From: canveo Date: Fri, 26 Jan 2024 18:12:43 -0500 Subject: [PATCH] balanceo --- docs/_posts/2024-01-25-week-2.md | 10 +++- docs/_posts/2024-01-25-week-3.md | 53 ++++++++++++++++++ docs/assets/images/week3/histograma_steer.png | Bin 0 -> 13366 bytes .../week3/histograma_steer_balanceado.png | Bin 0 -> 13032 bytes .../week3/tabla_conteo_steer_values.png | Bin 0 -> 428060 bytes 5 files changed, 62 insertions(+), 1 deletion(-) create mode 100644 docs/assets/images/week3/histograma_steer.png create mode 100644 docs/assets/images/week3/histograma_steer_balanceado.png create mode 100644 docs/assets/images/week3/tabla_conteo_steer_values.png diff --git a/docs/_posts/2024-01-25-week-2.md b/docs/_posts/2024-01-25-week-2.md index 92f811e..dfb3758 100644 --- a/docs/_posts/2024-01-25-week-2.md +++ b/docs/_posts/2024-01-25-week-2.md @@ -34,7 +34,7 @@ ros2 bag record /carla/ego_vehicle/rgb_front/image /carla/ego_vehicle/vehicle_st El comando anterior devuelve la información recopilada en una base de datos SQLite con extensión .db3, para lo cual se debe procesar la información para acceder a los datos producidos. El primer inconveniente encontrado es el gran tamaño de esta base de datos en cuanto al contenido de imágenes, pues al capturar una hora de conducción autónoma, se puede llegar a generar demasiada información, ocasionando que al procesarla se requiera demasiada capacidad de cómputo y un script eficiente. En mi caso, cuento con 16 GB de memoria RAM junto con una memoria Swap de 16 GB, lo que limita el procesamiento de estas imágenes, restringiendo el procesamiento a bases de datos más pequeñas. Para mejorar esto, se ha decidido ampliar la capacidad de RAM y mejorar el script de procesamiento, el cual permita tomar de la base de datos lotes de imágenes más pequeños que faciliten la recuperación de esta información. -Mientras se mejora el rendimiento del hardware, se optó por trabajar con una base de datos más pequeña con el fin de tener el dataset para probar el entrenamiento de la red PilotNet y poder realizar algunos ajustes, mientras se tiene el dataset completo. +Mientras se mejora el rendimiento del hardware, se optó por trabajar con una base de datos más pequeña con el fin de tener el dataset para probar el entrenamiento de la red PilotNet y poder realizar algunos ajustes, mientras se tiene el dataset completo. El dataset generado se obtuvo de la simulación del town03, ejecutando el piloto automático durante 20 minutos. El script desarrollado toma las imágenes y la marca de tiempo en la que estas son producidas. Luego, estas imágenes son acumuladas en un directorio específico, y adicionalmente se genera un archivo de texto .csv en donde se guarda el nombre de la imagen asociado a su marca de tiempo. Lo mismo se realiza con la información de los valores de control de throttle, steer y brake, los cuales están asociados a una marca de tiempo, los cuales se almacenan dentro de un archivo de texto adicional csv. @@ -44,6 +44,14 @@ El script desarrollado toma las imágenes y la marca de tiempo en la que estas s +Las imágenes procesadas tienen dimensiones (160, 320, 3) en formato RGB y extensión .png. + +Los valores de control se encuentran en los siguientes rangos: +| Control | Rango | Tipo | +|-----------|--------------------------|---------------| +| Throttle | 0.0 ≤ throttle ≤ 1.0 | float32 | +| Steer | -1.0 ≤ steer ≤ 1.0 | float32 | +| Brake | 0.0 ≤ brake ≤ 1.0 | float32 | Debido a que la captura de los tópicos de control y las imágenes producidas ocurren en instantes de tiempo distintos y, en consecuencia, tienen marcas de tiempo distintas, el script busca para cada imagen la acción de control más cercana, producida en el instante posterior al de dicha imagen. diff --git a/docs/_posts/2024-01-25-week-3.md b/docs/_posts/2024-01-25-week-3.md index e69de29..35990a3 100644 --- a/docs/_posts/2024-01-25-week-3.md +++ b/docs/_posts/2024-01-25-week-3.md @@ -0,0 +1,53 @@ +--- +title: Week 2 - Captura y balanceo del dataset +categories: + - weekly Log +tags: + - CARLA 0.9.14 + - balanceo + - ROS 2 + - ROS_BRIDGE + - Dataset +--- + +### Exploracion de los datos + +El dataset generado consta de un conjunto de 7,080 imágenes con sus respectivas etiquetas. Al representar los valores correspondientes al ángulo de giro (steer) en un histograma con 50 intervalos, se observa una distribución altamente desequilibrada. + +```python +histogram = plt.hist(df['steer_values'], bins=50) +plt.xlabel("Ángulo de giro") +plt.ylabel("# de Conteos") +plt.show(histogram) +``` + +
+ +
histograma-sin-balanceo
+
+ +Como se aprecia en la imagen, la cantidad de valores cercanos al cero en el ángulo de giro supera los 5000, mientras que en los demás intervalos no se alcanza a superar los 100 valores. Esto indica que en la simulación predominó la conducción en línea recta. Si el modelo se entrena con estos datos, es probable que generalice este comportamiento y solo pueda predecir una dirección cero para todas las salidas, por lo tanto, no aprendería el mapeo correcto de la dirección de las imágenes. + +
+ +
tabla_intervalos
+
+ +Para equilibrar el conjunto de datos, se empleó la técnica de submuestreo. Para ello, se dividió el conjunto de datos en 50 intervalos y se tomó el valor medio. + +```python +samples = 400 +bin_width = 0.04 + +resampled = pd.concat([ + part_df.sample(min(samples, part_df.shape[0]), random_state=1) + for small_r in np.arange(-1, 1.01, bin_width) + for part_df in [df[(df['steer_values'] >= small_r) & (df['steer_values'] < small_r + bin_width)]] +]) +``` +Este proceso genera un conjunto de datos más equilibrado. + +
+ +
datos-balanceados
+
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