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clases/clase-02/estudiantes/Toomanypanchos/Perkin_Identifier.md
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| 22/09 | ||
| **Proyecto grupal - Entrenamiento de clasificador de imagenes para catalogar personas pertenecientes a instituciones estatales.** | ||
| Javiera Parga - Francsico Cabrera | ||
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| La idea del proyeto es crear y entrenar una IA que sea capaz de, mediante su entrenamiento via imagenes, identificar a personas que pertenezcan | ||
| a carabineros de Chile. Permitiendo reconocer si estos se encuentran en dichas imagenes y no para luego catalogar estas imagenes, | ||
| bajo el prefijo Perkin o NotPerkin. | ||
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| Se usara como referencia la pagina ml5 que presenta una guia de como crear y entrenar un modelo de image identifier, | ||
| utilizando [Teachable Machine](https://teachablemachine.withgoogle.com/train/image) de Google | ||
|  | ||
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| **Recopilacion de Base de Datos** | ||
| Clasificando fotos de google en una carpeta en Drive en dos grupos, perkin y notperkin. | ||
| dentro de cada una de estas clasificaciones se introdujeron mas de 100 fotos: | ||
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| Perkin: Carabineros - Piñera - Kast - Luis Silva- Gloria Hutt | ||
| NotPerkin: Futbolistas - Ciudadanos Chilenos - Lemebel - Camiroaga - Zafrada - Otros. | ||
| [Drive](https://drive.google.com/drive/u/1/folders/1pzcaR1NtTS66UrxsfZezTPG74U1i0XSZ?role=writer) | ||
|  | ||
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| **Adaptando base de Datos** | ||
| Para poder subir estas fotos al teachable machine y que funcione es necesario adaptarlas para que tengan una dimension de pixeles especificas (224 x 224px) y consistente, lo que facilita a la IA para estandarizar el entrenamiento. | ||
|  | ||
| Para esto utilizamos el software [FFmpeg](https://ffmpeg.lav.io/) desarrrollado por Sam.Lavigne. | ||
| Este, permite filtrar archivos de diferentes maneras, en este caso especifico se modificaran sus dimensiones para que cumplan con los requerimientos especificos. | ||
| de esta manera se podra agilizar el proceso y modificar toda la base de datos. | ||
| _Intentando usar el software nos dimos cuenta que solo funcionaba para formatos de video y audio por lo que no podiamos utilizarlo para las imagenes de nuestro troyecto_ | ||
| pipipipipipipipipipipi | ||
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| Encontramos una pagina que lo hacia jeje | ||
| [Paginita](https://bulkresizephotos.com/en?padding=true&quality=1&type=exact&value=224&secondaryValue=224) | ||
| luego de modificadas las fotos de suben al procesod e entrenamiento de Google | ||
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| # titulo | ||
| # Clase 6 | ||
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| texto | ||
| cosas a evaluar: codigo, materiales, img, referentes, github, conclusiones |
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| # titulo | ||
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| ======= | ||
| mis apuntes | ||
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| Trabajo grupo 8 | ||
| me incorporé a este grupo en el cual estamos trabajando con reconocimiento de voz | ||
| Carpeta de Violeta: | ||
| https://github.com/msvioletasr/audiv027-2023-2/tree/main/clases/clase-06/estudiantes/msvioletasr | ||
| >>>>>>> 19850263fa2e654f52a8d62a6217b4fe3d4ca751 |
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| TRABAJO MARTINA FLORES / GABRIELA ECHENIQUE / RICARDO RAMIREZ | ||
| https://editor.p5js.org/Gaabyu/sketches/KlSObWwyo |
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| # titulo | ||
| #### Contenidos de este repositorio | ||
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| * carpeta [assets/](assets/): | ||
| * archivo [WorkSans-Light.ttf](WorkSans-Light.ttf) | ||
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| * carpeta [libraries/](libraries/): | ||
| * archivo [ml5.min.js](ml5.min.js) | ||
| * archivo [p5.js](p5.js) | ||
| * archivo [p5.min.js](p5.min.js) | ||
| * carpeta [models/dalas/](models/dalas/): | ||
| * archivo | ||
| * archivo [index.html](index.html) | ||
| * archivo [sketch.js](sketch.js) | ||
| * archivo [style.css](style.css) | ||
| * archivo [README.md](README.md) | ||
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| ____ | ||
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| _22-09-2023_ | ||
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| # Sobre Dalasrnn | ||
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| Dalasrnn es un pip pip | ||
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| <sub> **Gloria Herrera**, **Amelia López** y **Camila Méndez** para audiv027-2023-2. Electivo Ambas Menciones: Inteligencia Artificial - DOCENTE: **Aaron Montoya Moraga** - FAU.UCHILE - </sub> | ||
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| ____ | ||
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| Video de dalasrnn funcionando | ||
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| ____ | ||
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| #### Materiales | ||
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| texto |
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| # titulo | ||
| # Trabajo Grupal | ||
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| texto | ||
| INTEGRANTES: Gloria Herrera - Amelia López - Camila Méndez. | ||
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| * Ubicación entrega: https://github.com/disenoUChile/audiv027-2023-2/tree/main/clases/clase-06/estudiantes/camilamendezm |
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| # titulo | ||
| # Primer Trabajo | ||
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| texto | ||
| Integrantes: Violeta Silva, Fae Ávila y Kamila Mansilla | ||
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| Link a p5.js en Fullscreen: https://editor.p5js.org/msvioletasr/full/TyKuLwPm0 | ||
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| Link a p5.js embed: https://editor.p5js.org/msvioletasr/sketches/TyKuLwPm0 | ||
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| # Materiales: | ||
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| Para crear este modelo usamos el tutorial sobre SoundClassification de learn.ml5js.org. Duplicamos y editamos el ejemplo incluido en esa página. Además, usamos el modo de audio de Teachable Machine para entrenar nuestro modelo. | ||
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| # Referentes: | ||
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| Respecto a los referentes se tomaron en cuenta en primer lugar Shazam, ya que queríamos simular la identificación de música que logra hacer esta aplicación, y para esto queríamos usar Sound Classification, pero teniendo en cuenta la complejidad para programar algo así, decidimos tomar como referente diversos reconocimientos de voz, grabando nuestras propias voces. | ||
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| # Registro: | ||
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| Para empezar leímos la página del tutorial de SoundClassification, luego de lo cual decidimos usar Teachable Machine para crear un modelo entrenado propio que reemplazara el que ya estaba. Para esto grabamos el sonido de ambiente dentro de la sala, pero para lograr más claridad salimos a un lugar más callado para grabar nuestras voces. | ||
| A continuación se pueden ver videos de este proceso: | ||
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| https://github.com/msvioletasr/audiv027-2023-2/assets/142625864/efd87560-2db0-47c3-b332-2e2a7bdc9489 | ||
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| https://github.com/msvioletasr/audiv027-2023-2/assets/142625864/045816b0-e979-47bd-bef4-f68a7d60dc35 | ||
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| https://github.com/msvioletasr/audiv027-2023-2/assets/142625864/3753137c-ac2e-4484-a85a-9270849a16da | ||
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| Aquí se puede ver el modelo ya entrenado: | ||
|  | ||
| Luego de esto exportamos el modelo entrenado a un sitio web: | ||
|  | ||
| Después editamos el código para que usara nuestro modelo en vez del que estaba incluido por default: | ||
|  | ||
| En la siguiente foto se puede ver el código funcionando e identificando nuestras voces: | ||
|  | ||
| Finalmente, cambiamos el texto que originalmente estaba en inglés por uno en español: | ||
|  | ||
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| # Conclusión | ||
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| En conclusión al usar Teachable Machine se logró reconocer y diferenciar 5 tipos de sonido, siendo estos: Sonido de fondo, voces de Violeta, Fae, Damian y Kamila. No tuvimos dificultad al usar Teachable Machine, que fue un programa muy intuitivo. Pero por otro lado, fue complicado poder incorporar nuestro modelo entrenado a p5.js ya que al principio intentamos descargar el modelo y ponerlo como archivo, pero necesitábamos ponerlo como sitio web (html), para lo cual no encontramos instrucciones. Pero con la orientación del profesor pudimos solucionar este problema y crear un programa funcional. |
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| # titulo | ||
| # Clase 6 | ||
| # Cambiamos harto. Vamos a alterar este texto | ||
| Esta es la primera clase de grupo. | ||
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| ## 5 grupos faciales: | ||
| [Basado en el siguiente artículo de Koreaboo](https://www.koreaboo.com/lists/korean-face-shape-animal-puppy-cat-bunny-deer-fox-celebrities-idols/). | ||
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| #### 1. Puppy | ||
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| People with puppy-type faces tend to have soft facial features and big eyes that may be turned downwards. Known for their cuteness, they may have a fresh and youthful look. | ||
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| #### 2. Cat | ||
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| People with cat-type faces tend to eyes that are turned upwards at the edges or may have a sharper eye shape. Their eyes can either be big or small as long as they have an overall sharp appearance. | ||
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| #### 3. Bunny | ||
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| Those with bunny-type faces are often known for their cute front teeth that can resemble a rabbit’s teeth. They also tend to have big, round eyes. | ||
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| #### 4. Deer | ||
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| Those with deer-type faces often have an oval-shaped face that is longer than it is wide, a narrow jawline, and twinkling eyes. | ||
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| #### 5. Fox | ||
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| People with fox type faces tend to have a combination of round and sharp features, including a sharp nose and eyes with pouty lips that curl upwards at the edges. | ||
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|  | ||
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| texto |
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1
clases/clase-07/estudiantes/1gnocchi~19850263fa2e654f52a8d62a6217b4fe3d4ca751
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| # titulo | ||
| # APUNTES CLASE N°7 | ||
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| texto | ||
| Trabajé con Sergio Venegas (angcroft) |
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| # titulo | ||
| # Primer Trabajo | ||
|
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| texto | ||
| Integrantes: Violeta Silva, Fae Ávila y Kamila Mansilla | ||
|
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| Link a p5.js en Fullscreen: https://editor.p5js.org/msvioletasr/full/TyKuLwPm0 | ||
|
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| Link a p5.js embed: https://editor.p5js.org/msvioletasr/sketches/TyKuLwPm0 | ||
|
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| # Materiales: | ||
|
|
||
| Para crear este modelo usamos el tutorial sobre SoundClassification de learn.ml5js.org. Duplicamos y editamos el ejemplo incluido en esa página. Además, usamos el modo de audio de Teachable Machine para entrenar nuestro modelo. | ||
|
|
||
| # Referentes: | ||
|
|
||
| Respecto a los referentes se tomaron en cuenta en primer lugar Shazam, ya que queríamos simular la identificación de música que logra hacer esta aplicación, y para esto queríamos usar Sound Classification, pero teniendo en cuenta la complejidad para programar algo así, decidimos tomar como referente diversos reconocimientos de voz, grabando nuestras propias voces. | ||
|
|
||
| # Registro: | ||
|
|
||
| Para empezar leímos la página del tutorial de SoundClassification, luego de lo cual decidimos usar Teachable Machine para crear un modelo entrenado propio que reemplazara el que ya estaba. | ||
|
|
||
|  | ||
|
|
||
| Para esto grabamos el sonido de ambiente dentro de la sala, pero para lograr más claridad salimos a un lugar más callado para grabar nuestras voces. | ||
| A continuación se pueden ver videos de este proceso: | ||
|
|
||
| https://github.com/msvioletasr/audiv027-2023-2/assets/142625864/efd87560-2db0-47c3-b332-2e2a7bdc9489 | ||
|
|
||
| https://github.com/msvioletasr/audiv027-2023-2/assets/142625864/045816b0-e979-47bd-bef4-f68a7d60dc35 | ||
|
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||
| https://github.com/msvioletasr/audiv027-2023-2/assets/142625864/3753137c-ac2e-4484-a85a-9270849a16da | ||
|
|
||
| Aquí se puede ver el modelo ya entrenado: | ||
|
|
||
|  | ||
|
|
||
| Luego de esto exportamos el modelo entrenado a un sitio web: | ||
|
|
||
|  | ||
|
|
||
| Después editamos el código para que usara nuestro modelo en vez del que estaba incluido por default: | ||
|
|
||
|  | ||
|
|
||
| En la siguiente foto se puede ver el código funcionando e identificando nuestras voces: | ||
|
|
||
|  | ||
|
|
||
| Finalmente, cambiamos el texto que originalmente estaba en inglés por uno en español: | ||
|
|
||
|  | ||
|
|
||
| # Conclusión | ||
|
|
||
| En conclusión al usar Teachable Machine se logró reconocer y diferenciar 5 tipos de sonido, siendo estos: Sonido de fondo, voces de Violeta, Fae, Damian y Kamila. No tuvimos dificultad al usar Teachable Machine, que fue un programa muy intuitivo. Pero por otro lado, fue complicado poder incorporar nuestro modelo entrenado a p5.js ya que al principio intentamos descargar el modelo y ponerlo como archivo, pero necesitábamos ponerlo como sitio web (html), para lo cual no encontramos instrucciones. Pero con la orientación del profesor pudimos solucionar este problema y crear un programa funcional. |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,3 @@ | ||
| # titulo | ||
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| texto |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,3 @@ | ||
| # titulo | ||
|
|
||
| texto |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,3 @@ | ||
| # titulo | ||
|
|
||
| texto |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,3 @@ | ||
| # titulo | ||
|
|
||
| texto |