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Classification de la pose humaine avec MoveNet et TensorFlow Lite

Ce cahier vous apprend à entraîner un modèle de classification de pose à l'aide de MoveNet et de TensorFlow Lite. Le résultat est un nouveau modèle TensorFlow Lite qui accepte la sortie du modèle MoveNet comme entrée et génère une classification de pose, telle que le nom d'une pose de yoga.

La procédure dans ce cahier se compose de 3 parties :

  • Partie 1 : Prétraitez les données d'apprentissage de la classification des poses dans un fichier CSV qui spécifie les points de repère (points clés du corps) détectés par le modèle MoveNet, ainsi que les étiquettes de pose de vérité terrain.
  • Partie 2 : créez et entraînez un modèle de classification de pose qui prend les coordonnées du point de repère du fichier CSV en entrée et génère les étiquettes prédites.
  • Partie 3 : Convertissez le modèle de classification de pose en TFLite.

Par défaut, ce bloc-notes utilise un ensemble de données d'images avec des poses de yoga étiquetées, mais nous avons également inclus une section dans la partie 1 où vous pouvez télécharger votre propre ensemble de données d'images de poses.

Voir sur TensorFlow.org Exécuter dans Google Colab Voir la source sur GitHub Télécharger le cahier Voir le modèle TF Hub

Préparation

Dans cette section, vous importerez les bibliothèques nécessaires et définirez plusieurs fonctions pour prétraiter les images d'entraînement dans un fichier CSV contenant les coordonnées des points de repère et les étiquettes de vérité terrain.

Rien d'observable ne se produit ici, mais vous pouvez développer les cellules de code cachées pour voir l'implémentation de certaines des fonctions que nous appellerons plus tard.

Si vous souhaitez uniquement créer le fichier CSV sans connaître tous les détails, exécutez simplement cette section et passez à la partie 1.

pip install -q opencv-python
import csv
import cv2
import itertools
import numpy as np
import pandas as pd
import os
import sys
import tempfile
import tqdm

from matplotlib import pyplot as plt
from matplotlib.collections import LineCollection

import tensorflow as tf
import tensorflow_hub as hub
from tensorflow import keras

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

Code pour exécuter l'estimation de pose à l'aide de MoveNet

Fonctions pour exécuter l'estimation de pose avec MoveNet

Cloning into 'examples'...
remote: Enumerating objects: 20141, done.[K
remote: Counting objects: 100% (1961/1961), done.[K
remote: Compressing objects: 100% (1055/1055), done.[K
remote: Total 20141 (delta 909), reused 1584 (delta 595), pack-reused 18180[K
Receiving objects: 100% (20141/20141), 33.15 MiB | 25.83 MiB/s, done.
Resolving deltas: 100% (11003/11003), done.

Fonctions pour visualiser les résultats d'estimation de pose.

Code pour charger les images, détecter les points de repère de pose et les enregistrer dans un fichier CSV

(Facultatif) Extrait de code pour essayer la logique d'estimation de pose Movenet

--2021-12-21 12:07:36--  https://cdn.pixabay.com/photo/2017/03/03/17/30/yoga-2114512_960_720.jpg
Resolving cdn.pixabay.com (cdn.pixabay.com)... 104.18.20.183, 104.18.21.183, 2606:4700::6812:14b7, ...
Connecting to cdn.pixabay.com (cdn.pixabay.com)|104.18.20.183|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 28665 (28K) [image/jpeg]
Saving to: ‘/tmp/image.jpeg’

/tmp/image.jpeg     100%[===================>]  27.99K  --.-KB/s    in 0s      

2021-12-21 12:07:36 (111 MB/s) - ‘/tmp/image.jpeg’ saved [28665/28665]

png

Partie 1 : Prétraiter les images d'entrée

Parce que l'entrée pour notre classificateur pose est les points de repère de sortie du modèle MoveNet, nous devons générer nos données de formation en exécutant les images marquées par MoveNet puis capturer toutes les données historiques et les étiquettes de vérité au sol dans un fichier CSV.

L'ensemble de données que nous avons fourni pour ce didacticiel est un ensemble de données de pose de yoga généré par CG. Il contient des images de plusieurs modèles générés par CG faisant 5 poses de yoga différentes. Le répertoire est déjà divisé en un train de jeu de données et un test de jeu de données.

Donc , dans cette section, nous téléchargeons le jeu de données de yoga et courir par MoveNet afin que nous puissions capturer tous les points de repère dans un fichier CSV ... Cependant, il faut environ 15 minutes pour alimenter notre ensemble de données de yoga à MoveNet et générons ce fichier CSV . Donc , comme alternative, vous pouvez télécharger un fichier CSV préexistant pour l'ensemble de données de yoga en définissant is_skip_step_1 paramètre ci - dessous pour True. De cette façon, vous ignorerez cette étape et téléchargerez à la place le même fichier CSV qui sera créé lors de cette étape de prétraitement.

D'autre part, si vous voulez former le classificateur de pose avec votre propre jeu de données d'image, vous devez télécharger vos images et exécuter cette étape de pré - traitement (congé is_skip_step_1 de False) -Suivre les instructions ci - dessous pour télécharger votre propre jeu de données pose.

(Facultatif) Téléchargez votre propre jeu de données de pose

Si vous souhaitez entraîner le classificateur de poses avec vos propres poses étiquetées (il peut s'agir de n'importe quelle pose, pas seulement de poses de yoga), suivez ces étapes :

  1. Réglez ci - dessus use_custom_dataset option True.

  2. Préparez un fichier d'archive (ZIP, TAR ou autre) qui inclut un dossier avec votre jeu de données d'images. Le dossier doit inclure des images triées de vos poses comme suit.

    Si vous avez déjà diviser votre ensemble de données en ensembles de train et de test, puis définissez dataset_is_split sur True. C'est-à-dire que votre dossier d'images doit inclure des répertoires « train » et « test » comme celui-ci :

    yoga_poses/
    |__ train/
        |__ downdog/
            |______ 00000128.jpg
            |______ ...
    |__ test/
        |__ downdog/
            |______ 00000181.jpg
            |______ ...
    

    Ou, si votre ensemble de données est divisé PAS encore, puis réglez dataset_is_split sur False et nous allons le diviser la base d'une fraction de fraction spécifiée. C'est-à-dire que votre dossier d'images téléchargées devrait ressembler à ceci :

    yoga_poses/
    |__ downdog/
        |______ 00000128.jpg
        |______ 00000181.jpg
        |______ ...
    |__ goddess/
        |______ 00000243.jpg
        |______ 00000306.jpg
        |______ ...
    
  3. Cliquez sur l'onglet Fichiers sur le (icône de dossier) à gauche, puis cliquez sur Télécharger pour le stockage de session (icône du fichier).

  4. Sélectionnez votre fichier d'archive et attendez la fin du téléchargement avant de continuer.

  5. Modifiez le bloc de code suivant pour spécifier le nom de votre fichier d'archive et du répertoire d'images. (Par défaut, nous attendons un fichier ZIP, vous devrez donc également modifier cette partie si votre archive est dans un autre format.)

  6. Exécutez maintenant le reste du bloc-notes.

if use_custom_dataset:
  # ATTENTION:
  # You must edit these two lines to match your archive and images folder name:
  # !tar -xf YOUR_DATASET_ARCHIVE_NAME.tar
  !unzip -q YOUR_DATASET_ARCHIVE_NAME.zip
  dataset_in = 'YOUR_DATASET_DIR_NAME'

  # You can leave the rest alone:
  if not os.path.isdir(dataset_in):
    raise Exception("dataset_in is not a valid directory")
  if dataset_is_split:
    IMAGES_ROOT = dataset_in
  else:
    dataset_out = 'split_' + dataset_in
    split_into_train_test(dataset_in, dataset_out, test_split=0.2)
    IMAGES_ROOT = dataset_out

Télécharger le jeu de données de yoga

if not is_skip_step_1 and not use_custom_dataset:
  !wget -O yoga_poses.zip http://download.tensorflow.org/data/pose_classification/yoga_poses.zip
  !unzip -q yoga_poses.zip -d yoga_cg
  IMAGES_ROOT = "yoga_cg"
--2021-12-21 12:07:46--  http://download.tensorflow.org/data/pose_classification/yoga_poses.zip
Resolving download.tensorflow.org (download.tensorflow.org)... 172.217.218.128, 2a00:1450:4013:c08::80
Connecting to download.tensorflow.org (download.tensorflow.org)|172.217.218.128|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 102517581 (98M) [application/zip]
Saving to: ‘yoga_poses.zip’

yoga_poses.zip      100%[===================>]  97.77M  76.7MB/s    in 1.3s    

2021-12-21 12:07:48 (76.7 MB/s) - ‘yoga_poses.zip’ saved [102517581/102517581]

Préprocesser TRAIN ensemble de données

if not is_skip_step_1:
  images_in_train_folder = os.path.join(IMAGES_ROOT, 'train')
  images_out_train_folder = 'poses_images_out_train'
  csvs_out_train_path = 'train_data.csv'

  preprocessor = MoveNetPreprocessor(
      images_in_folder=images_in_train_folder,
      images_out_folder=images_out_train_folder,
      csvs_out_path=csvs_out_train_path,
  )

  preprocessor.process(per_pose_class_limit=None)
Preprocessing chair
  0%|          | 0/200 [00:00<?, ?it/s]/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/ipykernel_launcher.py:128: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
100%|██████████| 200/200 [00:32<00:00,  6.10it/s]
Preprocessing cobra
100%|██████████| 200/200 [00:31<00:00,  6.27it/s]
Preprocessing dog
100%|██████████| 200/200 [00:32<00:00,  6.15it/s]
Preprocessing tree
100%|██████████| 200/200 [00:33<00:00,  6.00it/s]
Preprocessing warrior
100%|██████████| 200/200 [00:30<00:00,  6.54it/s]
Skipped yoga_cg/train/chair/girl3_chair091.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair092.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair093.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair094.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair096.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair097.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair099.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair100.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair104.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair106.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair110.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair114.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair115.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair118.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair122.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair123.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair124.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair125.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair131.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair132.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair133.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair134.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair136.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair138.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair139.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/girl3_chair142.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/guy2_chair089.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/guy2_chair136.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/guy2_chair140.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/guy2_chair143.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/guy2_chair144.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/guy2_chair145.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/chair/guy2_chair146.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra026.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra029.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra030.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra038.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra040.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra041.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra048.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra050.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra051.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra055.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra059.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra061.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra068.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra070.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra081.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra087.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra088.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra089.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra090.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra091.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra092.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra093.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra094.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra096.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra099.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra102.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra110.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra112.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra115.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra119.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra122.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra128.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra129.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra136.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl1_cobra140.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl2_cobra029.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl2_cobra046.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl2_cobra050.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl2_cobra053.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl2_cobra108.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl2_cobra117.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl2_cobra129.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl2_cobra133.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl2_cobra136.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl2_cobra140.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra028.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra030.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra032.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra039.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra040.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra051.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra052.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra058.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra062.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra068.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra072.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra076.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra078.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra079.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra082.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra088.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra092.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra097.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra099.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra107.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra129.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra130.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra132.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra134.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/girl3_cobra138.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra034.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra042.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra043.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra047.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra053.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra065.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra077.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra078.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra080.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra081.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra084.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra088.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra089.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra102.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra105.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra108.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/cobra/guy2_cobra139.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl1_dog027.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl1_dog028.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl1_dog030.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl1_dog032.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog075.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog080.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog083.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog085.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog087.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog088.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog090.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog091.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog093.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog094.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog095.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog099.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog100.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog101.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog103.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog104.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog105.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog107.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl2_dog111.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog025.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog026.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog027.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog028.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog031.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog033.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog035.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog037.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog040.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog041.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog047.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog052.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog062.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog072.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog074.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog075.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog077.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog081.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog082.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog086.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog088.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog090.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog092.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog094.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog095.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog096.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog100.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog102.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog103.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog104.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog106.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog107.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/girl3_dog111.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/guy1_dog070.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/guy1_dog076.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/guy2_dog070.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/guy2_dog071.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/dog/guy2_dog082.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/girl2_tree119.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/girl2_tree122.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/girl2_tree161.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/girl2_tree163.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/guy1_tree139.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/guy1_tree140.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/guy1_tree141.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/guy1_tree143.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/guy2_tree085.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/guy2_tree086.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/guy2_tree087.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/guy2_tree088.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/guy2_tree090.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/guy2_tree092.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/guy2_tree145.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/tree/guy2_tree147.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior049.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior053.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior064.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior066.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior067.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior072.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior075.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior077.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior080.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior083.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior084.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior087.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior089.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior093.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior094.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior095.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior098.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior099.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior100.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior103.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior108.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior109.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior111.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior112.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior113.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior114.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior116.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl1_warrior117.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior047.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior049.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior050.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior052.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior057.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior058.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior063.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior068.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior079.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior083.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior085.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior088.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior092.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior096.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior097.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior102.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior106.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl2_warrior108.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior042.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior043.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior047.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior049.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior051.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior054.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior056.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior057.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior061.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior066.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior067.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior073.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior074.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior075.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior079.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior087.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior089.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior090.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior091.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior092.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior094.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior095.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior096.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior100.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior103.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior107.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior115.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior117.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior134.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior140.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/girl3_warrior143.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior043.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior048.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior051.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior052.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior055.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior057.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior062.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior068.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior069.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior073.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior076.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior077.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior080.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior081.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior082.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior088.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior091.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior092.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior093.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior094.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior097.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior118.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior120.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior121.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior124.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior125.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior126.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior131.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior134.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior135.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior138.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior143.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior145.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy1_warrior148.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy2_warrior051.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy2_warrior086.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy2_warrior111.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy2_warrior118.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy2_warrior122.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy2_warrior129.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy2_warrior131.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy2_warrior135.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy2_warrior137.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy2_warrior139.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy2_warrior145.jpg. No pose was confidentlly detected.
Skipped yoga_cg/train/warrior/guy2_warrior148.jpg. No pose was confidentlly detected.

Préprocesser TEST ensemble de données

if not is_skip_step_1:
  images_in_test_folder = os.path.join(IMAGES_ROOT, 'test')
  images_out_test_folder = 'poses_images_out_test'
  csvs_out_test_path = 'test_data.csv'

  preprocessor = MoveNetPreprocessor(
      images_in_folder=images_in_test_folder,
      images_out_folder=images_out_test_folder,
      csvs_out_path=csvs_out_test_path,
  )

  preprocessor.process(per_pose_class_limit=None)
Preprocessing chair
  0%|          | 0/84 [00:00<?, ?it/s]/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/ipykernel_launcher.py:128: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
100%|██████████| 84/84 [00:15<00:00,  5.51it/s]
Preprocessing cobra
100%|██████████| 116/116 [00:19<00:00,  6.10it/s]
Preprocessing dog
100%|██████████| 90/90 [00:14<00:00,  6.03it/s]
Preprocessing tree
100%|██████████| 96/96 [00:16<00:00,  5.98it/s]
Preprocessing warrior
100%|██████████| 109/109 [00:17<00:00,  6.38it/s]
Skipped yoga_cg/test/cobra/guy3_cobra048.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra050.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra051.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra052.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra053.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra054.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra055.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra056.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra057.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra058.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra059.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra060.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra062.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra069.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra075.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra077.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra081.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra124.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra131.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra132.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra134.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra135.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/cobra/guy3_cobra136.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/dog/guy3_dog025.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/dog/guy3_dog026.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/dog/guy3_dog036.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/dog/guy3_dog042.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/dog/guy3_dog106.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/dog/guy3_dog108.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior042.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior043.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior044.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior045.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior046.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior047.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior048.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior050.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior051.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior052.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior053.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior054.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior055.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior056.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior059.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior060.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior062.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior063.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior065.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior066.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior068.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior070.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior071.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior072.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior073.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior074.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior075.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior076.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior077.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior079.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior080.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior081.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior082.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior083.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior084.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior085.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior086.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior087.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior088.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior089.jpg. No pose was confidentlly detected.
Skipped yoga_cg/test/warrior/guy3_warrior137.jpg. No pose was confidentlly detected.

Partie 2 : Entraînez un modèle de classification de pose qui prend les coordonnées du point de repère en entrée et génère les étiquettes prédites.

Vous allez créer un modèle TensorFlow qui prend les coordonnées du point de repère et prédit la classe de pose que la personne dans l'image d'entrée effectue. Le modèle se compose de deux sous-modèles :

  • Le sous-modèle 1 calcule un plongement de pose (alias vecteur de caractéristiques) à partir des coordonnées de point de repère détectées.
  • 2 flux sous - modèle posent l' intégration par plusieurs Dense couche pour prédire la classe de pose.

Vous entraînerez ensuite le modèle en fonction de l'ensemble de données prétraité dans la partie 1.

(Facultatif) Téléchargez l'ensemble de données prétraité si vous n'avez pas exécuté la partie 1

# Download the preprocessed CSV files which are the same as the output of step 1
if is_skip_step_1:
  !wget -O train_data.csv http://download.tensorflow.org/data/pose_classification/yoga_train_data.csv
  !wget -O test_data.csv http://download.tensorflow.org/data/pose_classification/yoga_test_data.csv

  csvs_out_train_path = 'train_data.csv'
  csvs_out_test_path = 'test_data.csv'
  is_skipped_step_1 = True

Charger les CSVs en prétraitées TRAIN et TEST jeux de données.

def load_pose_landmarks(csv_path):
  """Loads a CSV created by MoveNetPreprocessor.

  Returns:
    X: Detected landmark coordinates and scores of shape (N, 17 * 3)
    y: Ground truth labels of shape (N, label_count)
    classes: The list of all class names found in the dataset
    dataframe: The CSV loaded as a Pandas dataframe features (X) and ground
      truth labels (y) to use later to train a pose classification model.
  """

  # Load the CSV file
  dataframe = pd.read_csv(csv_path)
  df_to_process = dataframe.copy()

  # Drop the file_name columns as you don't need it during training.
  df_to_process.drop(columns=['file_name'], inplace=True)

  # Extract the list of class names
  classes = df_to_process.pop('class_name').unique()

  # Extract the labels
  y = df_to_process.pop('class_no')

  # Convert the input features and labels into the correct format for training.
  X = df_to_process.astype('float64')
  y = keras.utils.to_categorical(y)

  return X, y, classes, dataframe

Charger et diviser l'original TRAIN ensemble de données en TRAIN (85% des données) et VALIDATE (les 15% restants).

# Load the train data
X, y, class_names, _ = load_pose_landmarks(csvs_out_train_path)

# Split training data (X, y) into (X_train, y_train) and (X_val, y_val)
X_train, X_val, y_train, y_val = train_test_split(X, y,
                                                  test_size=0.15)
# Load the test data
X_test, y_test, _, df_test = load_pose_landmarks(csvs_out_test_path)

Définir des fonctions pour convertir les points de repère de la pose en une intégration de pose (alias vecteur de caractéristiques) pour la classification des poses

Ensuite, convertissez les coordonnées du point de repère en un vecteur d'entité en :

  1. Déplacer le centre de la pose vers l'origine.
  2. Redimensionner la pose pour que la taille de la pose devienne 1
  3. Aplatir ces coordonnées dans un vecteur de caractéristiques

Utilisez ensuite ce vecteur de fonctionnalités pour entraîner un classificateur de pose basé sur un réseau de neurones.

def get_center_point(landmarks, left_bodypart, right_bodypart):
  """Calculates the center point of the two given landmarks."""

  left = tf.gather(landmarks, left_bodypart.value, axis=1)
  right = tf.gather(landmarks, right_bodypart.value, axis=1)
  center = left * 0.5 + right * 0.5
  return center


def get_pose_size(landmarks, torso_size_multiplier=2.5):
  """Calculates pose size.

  It is the maximum of two values:
    * Torso size multiplied by `torso_size_multiplier`
    * Maximum distance from pose center to any pose landmark
  """
  # Hips center
  hips_center = get_center_point(landmarks, BodyPart.LEFT_HIP, 
                                 BodyPart.RIGHT_HIP)

  # Shoulders center
  shoulders_center = get_center_point(landmarks, BodyPart.LEFT_SHOULDER,
                                      BodyPart.RIGHT_SHOULDER)

  # Torso size as the minimum body size
  torso_size = tf.linalg.norm(shoulders_center - hips_center)

  # Pose center
  pose_center_new = get_center_point(landmarks, BodyPart.LEFT_HIP, 
                                     BodyPart.RIGHT_HIP)
  pose_center_new = tf.expand_dims(pose_center_new, axis=1)
  # Broadcast the pose center to the same size as the landmark vector to
  # perform substraction
  pose_center_new = tf.broadcast_to(pose_center_new,
                                    [tf.size(landmarks) // (17*2), 17, 2])

  # Dist to pose center
  d = tf.gather(landmarks - pose_center_new, 0, axis=0,
                name="dist_to_pose_center")
  # Max dist to pose center
  max_dist = tf.reduce_max(tf.linalg.norm(d, axis=0))

  # Normalize scale
  pose_size = tf.maximum(torso_size * torso_size_multiplier, max_dist)

  return pose_size


def normalize_pose_landmarks(landmarks):
  """Normalizes the landmarks translation by moving the pose center to (0,0) and
  scaling it to a constant pose size.
  """
  # Move landmarks so that the pose center becomes (0,0)
  pose_center = get_center_point(landmarks, BodyPart.LEFT_HIP, 
                                 BodyPart.RIGHT_HIP)
  pose_center = tf.expand_dims(pose_center, axis=1)
  # Broadcast the pose center to the same size as the landmark vector to perform
  # substraction
  pose_center = tf.broadcast_to(pose_center, 
                                [tf.size(landmarks) // (17*2), 17, 2])
  landmarks = landmarks - pose_center

  # Scale the landmarks to a constant pose size
  pose_size = get_pose_size(landmarks)
  landmarks /= pose_size

  return landmarks


def landmarks_to_embedding(landmarks_and_scores):
  """Converts the input landmarks into a pose embedding."""
  # Reshape the flat input into a matrix with shape=(17, 3)
  reshaped_inputs = keras.layers.Reshape((17, 3))(landmarks_and_scores)

  # Normalize landmarks 2D
  landmarks = normalize_pose_landmarks(reshaped_inputs[:, :, :2])

  # Flatten the normalized landmark coordinates into a vector
  embedding = keras.layers.Flatten()(landmarks)

  return embedding

Définir un modèle Keras pour la classification des poses

Notre modèle Keras prend les repères de pose détectés, puis calcule l'intégration de la pose et prédit la classe de pose.

# Define the model
inputs = tf.keras.Input(shape=(51))
embedding = landmarks_to_embedding(inputs)

layer = keras.layers.Dense(128, activation=tf.nn.relu6)(embedding)
layer = keras.layers.Dropout(0.5)(layer)
layer = keras.layers.Dense(64, activation=tf.nn.relu6)(layer)
layer = keras.layers.Dropout(0.5)(layer)
outputs = keras.layers.Dense(len(class_names), activation="softmax")(layer)

model = keras.Model(inputs, outputs)
model.summary()
Model: "model"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_1 (InputLayer)           [(None, 51)]         0           []                               
                                                                                                  
 reshape (Reshape)              (None, 17, 3)        0           ['input_1[0][0]']                
                                                                                                  
 tf.__operators__.getitem (Slic  (None, 17, 2)       0           ['reshape[0][0]']                
 ingOpLambda)                                                                                     
                                                                                                  
 tf.compat.v1.gather (TFOpLambd  (None, 2)           0           ['tf.__operators__.getitem[0][0]'
 a)                                                              ]                                
                                                                                                  
 tf.compat.v1.gather_1 (TFOpLam  (None, 2)           0           ['tf.__operators__.getitem[0][0]'
 bda)                                                            ]                                
                                                                                                  
 tf.math.multiply (TFOpLambda)  (None, 2)            0           ['tf.compat.v1.gather[0][0]']    
                                                                                                  
 tf.math.multiply_1 (TFOpLambda  (None, 2)           0           ['tf.compat.v1.gather_1[0][0]']  
 )                                                                                                
                                                                                                  
 tf.__operators__.add (TFOpLamb  (None, 2)           0           ['tf.math.multiply[0][0]',       
 da)                                                              'tf.math.multiply_1[0][0]']     
                                                                                                  
 tf.compat.v1.size (TFOpLambda)  ()                  0           ['tf.__operators__.getitem[0][0]'
                                                                 ]                                
                                                                                                  
 tf.expand_dims (TFOpLambda)    (None, 1, 2)         0           ['tf.__operators__.add[0][0]']   
                                                                                                  
 tf.compat.v1.floor_div (TFOpLa  ()                  0           ['tf.compat.v1.size[0][0]']      
 mbda)                                                                                            
                                                                                                  
 tf.broadcast_to (TFOpLambda)   (None, 17, 2)        0           ['tf.expand_dims[0][0]',         
                                                                  'tf.compat.v1.floor_div[0][0]'] 
                                                                                                  
 tf.math.subtract (TFOpLambda)  (None, 17, 2)        0           ['tf.__operators__.getitem[0][0]'
                                                                 , 'tf.broadcast_to[0][0]']       
                                                                                                  
 tf.compat.v1.gather_6 (TFOpLam  (None, 2)           0           ['tf.math.subtract[0][0]']       
 bda)                                                                                             
                                                                                                  
 tf.compat.v1.gather_7 (TFOpLam  (None, 2)           0           ['tf.math.subtract[0][0]']       
 bda)                                                                                             
                                                                                                  
 tf.math.multiply_6 (TFOpLambda  (None, 2)           0           ['tf.compat.v1.gather_6[0][0]']  
 )                                                                                                
                                                                                                  
 tf.math.multiply_7 (TFOpLambda  (None, 2)           0           ['tf.compat.v1.gather_7[0][0]']  
 )                                                                                                
                                                                                                  
 tf.__operators__.add_3 (TFOpLa  (None, 2)           0           ['tf.math.multiply_6[0][0]',     
 mbda)                                                            'tf.math.multiply_7[0][0]']     
                                                                                                  
 tf.compat.v1.size_1 (TFOpLambd  ()                  0           ['tf.math.subtract[0][0]']       
 a)                                                                                               
                                                                                                  
 tf.compat.v1.gather_4 (TFOpLam  (None, 2)           0           ['tf.math.subtract[0][0]']       
 bda)                                                                                             
                                                                                                  
 tf.compat.v1.gather_5 (TFOpLam  (None, 2)           0           ['tf.math.subtract[0][0]']       
 bda)                                                                                             
                                                                                                  
 tf.compat.v1.gather_2 (TFOpLam  (None, 2)           0           ['tf.math.subtract[0][0]']       
 bda)                                                                                             
                                                                                                  
 tf.compat.v1.gather_3 (TFOpLam  (None, 2)           0           ['tf.math.subtract[0][0]']       
 bda)                                                                                             
                                                                                                  
 tf.expand_dims_1 (TFOpLambda)  (None, 1, 2)         0           ['tf.__operators__.add_3[0][0]'] 
                                                                                                  
 tf.compat.v1.floor_div_1 (TFOp  ()                  0           ['tf.compat.v1.size_1[0][0]']    
 Lambda)                                                                                          
                                                                                                  
 tf.math.multiply_4 (TFOpLambda  (None, 2)           0           ['tf.compat.v1.gather_4[0][0]']  
 )                                                                                                
                                                                                                  
 tf.math.multiply_5 (TFOpLambda  (None, 2)           0           ['tf.compat.v1.gather_5[0][0]']  
 )                                                                                                
                                                                                                  
 tf.math.multiply_2 (TFOpLambda  (None, 2)           0           ['tf.compat.v1.gather_2[0][0]']  
 )                                                                                                
                                                                                                  
 tf.math.multiply_3 (TFOpLambda  (None, 2)           0           ['tf.compat.v1.gather_3[0][0]']  
 )                                                                                                
                                                                                                  
 tf.broadcast_to_1 (TFOpLambda)  (None, 17, 2)       0           ['tf.expand_dims_1[0][0]',       
                                                                  'tf.compat.v1.floor_div_1[0][0]'
                                                                 ]                                
                                                                                                  
 tf.__operators__.add_2 (TFOpLa  (None, 2)           0           ['tf.math.multiply_4[0][0]',     
 mbda)                                                            'tf.math.multiply_5[0][0]']     
                                                                                                  
 tf.__operators__.add_1 (TFOpLa  (None, 2)           0           ['tf.math.multiply_2[0][0]',     
 mbda)                                                            'tf.math.multiply_3[0][0]']     
                                                                                                  
 tf.math.subtract_2 (TFOpLambda  (None, 17, 2)       0           ['tf.math.subtract[0][0]',       
 )                                                                'tf.broadcast_to_1[0][0]']      
                                                                                                  
 tf.math.subtract_1 (TFOpLambda  (None, 2)           0           ['tf.__operators__.add_2[0][0]', 
 )                                                                'tf.__operators__.add_1[0][0]'] 
                                                                                                  
 tf.compat.v1.gather_8 (TFOpLam  (17, 2)             0           ['tf.math.subtract_2[0][0]']     
 bda)                                                                                             
                                                                                                  
 tf.compat.v1.norm (TFOpLambda)  ()                  0           ['tf.math.subtract_1[0][0]']     
                                                                                                  
 tf.compat.v1.norm_1 (TFOpLambd  (2,)                0           ['tf.compat.v1.gather_8[0][0]']  
 a)                                                                                               
                                                                                                  
 tf.math.multiply_8 (TFOpLambda  ()                  0           ['tf.compat.v1.norm[0][0]']      
 )                                                                                                
                                                                                                  
 tf.math.reduce_max (TFOpLambda  ()                  0           ['tf.compat.v1.norm_1[0][0]']    
 )                                                                                                
                                                                                                  
 tf.math.maximum (TFOpLambda)   ()                   0           ['tf.math.multiply_8[0][0]',     
                                                                  'tf.math.reduce_max[0][0]']     
                                                                                                  
 tf.math.truediv (TFOpLambda)   (None, 17, 2)        0           ['tf.math.subtract[0][0]',       
                                                                  'tf.math.maximum[0][0]']        
                                                                                                  
 flatten (Flatten)              (None, 34)           0           ['tf.math.truediv[0][0]']        
                                                                                                  
 dense (Dense)                  (None, 128)          4480        ['flatten[0][0]']                
                                                                                                  
 dropout (Dropout)              (None, 128)          0           ['dense[0][0]']                  
                                                                                                  
 dense_1 (Dense)                (None, 64)           8256        ['dropout[0][0]']                
                                                                                                  
 dropout_1 (Dropout)            (None, 64)           0           ['dense_1[0][0]']                
                                                                                                  
 dense_2 (Dense)                (None, 5)            325         ['dropout_1[0][0]']              
                                                                                                  
==================================================================================================
Total params: 13,061
Trainable params: 13,061
Non-trainable params: 0
__________________________________________________________________________________________________
model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

# Add a checkpoint callback to store the checkpoint that has the highest
# validation accuracy.
checkpoint_path = "weights.best.hdf5"
checkpoint = keras.callbacks.ModelCheckpoint(checkpoint_path,
                             monitor='val_accuracy',
                             verbose=1,
                             save_best_only=True,
                             mode='max')
earlystopping = keras.callbacks.EarlyStopping(monitor='val_accuracy', 
                                              patience=20)

# Start training
history = model.fit(X_train, y_train,
                    epochs=200,
                    batch_size=16,
                    validation_data=(X_val, y_val),
                    callbacks=[checkpoint, earlystopping])
Epoch 1/200
19/37 [==============>...............] - ETA: 0s - loss: 1.5703 - accuracy: 0.3684 
Epoch 00001: val_accuracy improved from -inf to 0.64706, saving model to weights.best.hdf5
37/37 [==============================] - 1s 11ms/step - loss: 1.5090 - accuracy: 0.4602 - val_loss: 1.3352 - val_accuracy: 0.6471
Epoch 2/200
20/37 [===============>..............] - ETA: 0s - loss: 1.3372 - accuracy: 0.4844
Epoch 00002: val_accuracy improved from 0.64706 to 0.67647, saving model to weights.best.hdf5
37/37 [==============================] - 0s 4ms/step - loss: 1.2375 - accuracy: 0.5190 - val_loss: 1.0193 - val_accuracy: 0.6765
Epoch 3/200
20/37 [===============>..............] - ETA: 0s - loss: 1.0596 - accuracy: 0.5469
Epoch 00003: val_accuracy improved from 0.67647 to 0.75490, saving model to weights.best.hdf5
37/37 [==============================] - 0s 4ms/step - loss: 1.0096 - accuracy: 0.5796 - val_loss: 0.8397 - val_accuracy: 0.7549
Epoch 4/200
21/37 [================>.............] - ETA: 0s - loss: 0.8922 - accuracy: 0.6220
Epoch 00004: val_accuracy improved from 0.75490 to 0.81373, saving model to weights.best.hdf5
37/37 [==============================] - 0s 4ms/step - loss: 0.8798 - accuracy: 0.6349 - val_loss: 0.7103 - val_accuracy: 0.8137
Epoch 5/200
20/37 [===============>..............] - ETA: 0s - loss: 0.7895 - accuracy: 0.6875
Epoch 00005: val_accuracy improved from 0.81373 to 0.82353, saving model to weights.best.hdf5
37/37 [==============================] - 0s 4ms/step - loss: 0.7810 - accuracy: 0.6903 - val_loss: 0.6120 - val_accuracy: 0.8235
Epoch 6/200
20/37 [===============>..............] - ETA: 0s - loss: 0.7324 - accuracy: 0.7250
Epoch 00006: val_accuracy improved from 0.82353 to 0.92157, saving model to weights.best.hdf5
37/37 [==============================] - 0s 4ms/step - loss: 0.7263 - accuracy: 0.7093 - val_loss: 0.5297 - val_accuracy: 0.9216
Epoch 7/200
19/37 [==============>...............] - ETA: 0s - loss: 0.6852 - accuracy: 0.7467
Epoch 00007: val_accuracy did not improve from 0.92157
37/37 [==============================] - 0s 4ms/step - loss: 0.6450 - accuracy: 0.7595 - val_loss: 0.4635 - val_accuracy: 0.8922
Epoch 8/200
20/37 [===============>..............] - ETA: 0s - loss: 0.6007 - accuracy: 0.7719
Epoch 00008: val_accuracy did not improve from 0.92157
37/37 [==============================] - 0s 4ms/step - loss: 0.5751 - accuracy: 0.7837 - val_loss: 0.4012 - val_accuracy: 0.9216
Epoch 9/200
20/37 [===============>..............] - ETA: 0s - loss: 0.5358 - accuracy: 0.8125
Epoch 00009: val_accuracy improved from 0.92157 to 0.93137, saving model to weights.best.hdf5
37/37 [==============================] - 0s 4ms/step - loss: 0.5272 - accuracy: 0.8097 - val_loss: 0.3547 - val_accuracy: 0.9314
Epoch 10/200
20/37 [===============>..............] - ETA: 0s - loss: 0.5200 - accuracy: 0.8094
Epoch 00010: val_accuracy improved from 0.93137 to 0.98039, saving model to weights.best.hdf5
37/37 [==============================] - 0s 5ms/step - loss: 0.5051 - accuracy: 0.8218 - val_loss: 0.3014 - val_accuracy: 0.9804
Epoch 11/200
19/37 [==============>...............] - ETA: 0s - loss: 0.4413 - accuracy: 0.8322
Epoch 00011: val_accuracy did not improve from 0.98039
37/37 [==============================] - 0s 4ms/step - loss: 0.4509 - accuracy: 0.8374 - val_loss: 0.2786 - val_accuracy: 0.9706
Epoch 12/200
20/37 [===============>..............] - ETA: 0s - loss: 0.4323 - accuracy: 0.8156
Epoch 00012: val_accuracy improved from 0.98039 to 0.99020, saving model to weights.best.hdf5
37/37 [==============================] - 0s 5ms/step - loss: 0.4377 - accuracy: 0.8253 - val_loss: 0.2440 - val_accuracy: 0.9902
Epoch 13/200
20/37 [===============>..............] - ETA: 0s - loss: 0.4037 - accuracy: 0.8719
Epoch 00013: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.4187 - accuracy: 0.8668 - val_loss: 0.2109 - val_accuracy: 0.9804
Epoch 14/200
20/37 [===============>..............] - ETA: 0s - loss: 0.3664 - accuracy: 0.8813
Epoch 00014: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.3733 - accuracy: 0.8772 - val_loss: 0.2030 - val_accuracy: 0.9804
Epoch 15/200
20/37 [===============>..............] - ETA: 0s - loss: 0.3708 - accuracy: 0.8781
Epoch 00015: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.3684 - accuracy: 0.8754 - val_loss: 0.1765 - val_accuracy: 0.9902
Epoch 16/200
21/37 [================>.............] - ETA: 0s - loss: 0.3238 - accuracy: 0.9137
Epoch 00016: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.3213 - accuracy: 0.9100 - val_loss: 0.1662 - val_accuracy: 0.9804
Epoch 17/200
20/37 [===============>..............] - ETA: 0s - loss: 0.2739 - accuracy: 0.9281
Epoch 00017: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.3015 - accuracy: 0.9100 - val_loss: 0.1423 - val_accuracy: 0.9804
Epoch 18/200
20/37 [===============>..............] - ETA: 0s - loss: 0.3076 - accuracy: 0.9062
Epoch 00018: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.3022 - accuracy: 0.9048 - val_loss: 0.1407 - val_accuracy: 0.9804
Epoch 19/200
20/37 [===============>..............] - ETA: 0s - loss: 0.2719 - accuracy: 0.9250
Epoch 00019: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.2697 - accuracy: 0.9291 - val_loss: 0.1191 - val_accuracy: 0.9902
Epoch 20/200
20/37 [===============>..............] - ETA: 0s - loss: 0.2960 - accuracy: 0.9031
Epoch 00020: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.2775 - accuracy: 0.9100 - val_loss: 0.1120 - val_accuracy: 0.9902
Epoch 21/200
20/37 [===============>..............] - ETA: 0s - loss: 0.2590 - accuracy: 0.9250
Epoch 00021: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.2537 - accuracy: 0.9273 - val_loss: 0.1022 - val_accuracy: 0.9902
Epoch 22/200
20/37 [===============>..............] - ETA: 0s - loss: 0.2504 - accuracy: 0.9344
Epoch 00022: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.2661 - accuracy: 0.9204 - val_loss: 0.0976 - val_accuracy: 0.9902
Epoch 23/200
20/37 [===============>..............] - ETA: 0s - loss: 0.2384 - accuracy: 0.9156
Epoch 00023: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.2182 - accuracy: 0.9308 - val_loss: 0.0944 - val_accuracy: 0.9902
Epoch 24/200
20/37 [===============>..............] - ETA: 0s - loss: 0.2157 - accuracy: 0.9375
Epoch 00024: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.2031 - accuracy: 0.9412 - val_loss: 0.0844 - val_accuracy: 0.9902
Epoch 25/200
20/37 [===============>..............] - ETA: 0s - loss: 0.1944 - accuracy: 0.9469
Epoch 00025: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.2080 - accuracy: 0.9343 - val_loss: 0.0811 - val_accuracy: 0.9902
Epoch 26/200
20/37 [===============>..............] - ETA: 0s - loss: 0.2232 - accuracy: 0.9312
Epoch 00026: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.2033 - accuracy: 0.9394 - val_loss: 0.0703 - val_accuracy: 0.9902
Epoch 27/200
20/37 [===============>..............] - ETA: 0s - loss: 0.2120 - accuracy: 0.9281
Epoch 00027: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.1845 - accuracy: 0.9481 - val_loss: 0.0708 - val_accuracy: 0.9902
Epoch 28/200
20/37 [===============>..............] - ETA: 0s - loss: 0.2696 - accuracy: 0.9156
Epoch 00028: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.2355 - accuracy: 0.9273 - val_loss: 0.0679 - val_accuracy: 0.9902
Epoch 29/200
20/37 [===============>..............] - ETA: 0s - loss: 0.1794 - accuracy: 0.9531
Epoch 00029: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.1938 - accuracy: 0.9498 - val_loss: 0.0623 - val_accuracy: 0.9902
Epoch 30/200
20/37 [===============>..............] - ETA: 0s - loss: 0.1831 - accuracy: 0.9406
Epoch 00030: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.1758 - accuracy: 0.9498 - val_loss: 0.0599 - val_accuracy: 0.9902
Epoch 31/200
20/37 [===============>..............] - ETA: 0s - loss: 0.1967 - accuracy: 0.9375
Epoch 00031: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.1724 - accuracy: 0.9516 - val_loss: 0.0565 - val_accuracy: 0.9902
Epoch 32/200
20/37 [===============>..............] - ETA: 0s - loss: 0.1868 - accuracy: 0.9219
Epoch 00032: val_accuracy did not improve from 0.99020
37/37 [==============================] - 0s 4ms/step - loss: 0.1676 - accuracy: 0.9360 - val_loss: 0.0503 - val_accuracy: 0.9902
# Visualize the training history to see whether you're overfitting.
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['TRAIN', 'VAL'], loc='lower right')
plt.show()

png

# Evaluate the model using the TEST dataset
loss, accuracy = model.evaluate(X_test, y_test)
14/14 [==============================] - 0s 2ms/step - loss: 0.0612 - accuracy: 0.9976

Dessinez la matrice de confusion pour mieux comprendre les performances du modèle

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
  """Plots the confusion matrix."""
  if normalize:
    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
    print("Normalized confusion matrix")
  else:
    print('Confusion matrix, without normalization')

  plt.imshow(cm, interpolation='nearest', cmap=cmap)
  plt.title(title)
  plt.colorbar()
  tick_marks = np.arange(len(classes))
  plt.xticks(tick_marks, classes, rotation=55)
  plt.yticks(tick_marks, classes)
  fmt = '.2f' if normalize else 'd'
  thresh = cm.max() / 2.
  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
    plt.text(j, i, format(cm[i, j], fmt),
              horizontalalignment="center",
              color="white" if cm[i, j] > thresh else "black")

  plt.ylabel('True label')
  plt.xlabel('Predicted label')
  plt.tight_layout()

# Classify pose in the TEST dataset using the trained model
y_pred = model.predict(X_test)

# Convert the prediction result to class name
y_pred_label = [class_names[i] for i in np.argmax(y_pred, axis=1)]
y_true_label = [class_names[i] for i in np.argmax(y_test, axis=1)]

# Plot the confusion matrix
cm = confusion_matrix(np.argmax(y_test, axis=1), np.argmax(y_pred, axis=1))
plot_confusion_matrix(cm,
                      class_names,
                      title ='Confusion Matrix of Pose Classification Model')

# Print the classification report
print('\nClassification Report:\n', classification_report(y_true_label,
                                                          y_pred_label))
Confusion matrix, without normalization

Classification Report:
               precision    recall  f1-score   support

       chair       1.00      1.00      1.00        84
       cobra       0.99      1.00      0.99        93
         dog       1.00      1.00      1.00        84
        tree       1.00      1.00      1.00        96
     warrior       1.00      0.99      0.99        68

    accuracy                           1.00       425
   macro avg       1.00      1.00      1.00       425
weighted avg       1.00      1.00      1.00       425

png

(Facultatif) Enquêter sur les prédictions incorrectes

Vous pouvez regarder les poses du TEST ensemble de données qui ont été incorrectement prédit pour voir si la précision du modèle peut être amélioré.

if is_skip_step_1:
  raise RuntimeError('You must have run step 1 to run this cell.')

# If step 1 was skipped, skip this step.
IMAGE_PER_ROW = 3
MAX_NO_OF_IMAGE_TO_PLOT = 30

# Extract the list of incorrectly predicted poses
false_predict = [id_in_df for id_in_df in range(len(y_test)) \
                if y_pred_label[id_in_df] != y_true_label[id_in_df]]
if len(false_predict) > MAX_NO_OF_IMAGE_TO_PLOT:
  false_predict = false_predict[:MAX_NO_OF_IMAGE_TO_PLOT]

# Plot the incorrectly predicted images
row_count = len(false_predict) // IMAGE_PER_ROW + 1
fig = plt.figure(figsize=(10 * IMAGE_PER_ROW, 10 * row_count))
for i, id_in_df in enumerate(false_predict):
  ax = fig.add_subplot(row_count, IMAGE_PER_ROW, i + 1)
  image_path = os.path.join(images_out_test_folder,
                            df_test.iloc[id_in_df]['file_name'])

  image = cv2.imread(image_path)
  plt.title("Predict: %s; Actual: %s"
            % (y_pred_label[id_in_df], y_true_label[id_in_df]))
  plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.show()

png

Partie 3 : convertir le modèle de classification des poses en TensorFlow Lite

Vous allez convertir le modèle de classification de pose Keras au format TensorFlow Lite afin de pouvoir le déployer sur des applications mobiles, des navigateurs Web et des appareils IoT. Lors de la conversion du modèle, vous appliquez la quantification de la plage dynamique pour réduire la classification de pose tensorflow Lite taille du modèle d'environ 4 fois avec une perte de précision insignifiante.

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

print('Model size: %dKB' % (len(tflite_model) / 1024))

with open('pose_classifier.tflite', 'wb') as f:
  f.write(tflite_model)
2021-12-21 12:12:00.560331: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: /tmp/tmpr1ewa_xj/assets
2021-12-21 12:12:02.324896: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:363] Ignored output_format.
2021-12-21 12:12:02.324941: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:366] Ignored drop_control_dependency.
WARNING:absl:Buffer deduplication procedure will be skipped when flatbuffer library is not properly loaded
Model size: 26KB

Ensuite, vous écrivez le fichier d'étiquettes qui contient le mappage des index de classe vers les noms de classe lisibles par l'homme.

with open('pose_labels.txt', 'w') as f:
  f.write('\n'.join(class_names))

Comme vous avez appliqué la quantification pour réduire la taille du modèle, évaluons le modèle TFLite quantifié pour vérifier si la baisse de précision est acceptable.

def evaluate_model(interpreter, X, y_true):
  """Evaluates the given TFLite model and return its accuracy."""
  input_index = interpreter.get_input_details()[0]["index"]
  output_index = interpreter.get_output_details()[0]["index"]

  # Run predictions on all given poses.
  y_pred = []
  for i in range(len(y_true)):
    # Pre-processing: add batch dimension and convert to float32 to match with
    # the model's input data format.
    test_image = X[i: i + 1].astype('float32')
    interpreter.set_tensor(input_index, test_image)

    # Run inference.
    interpreter.invoke()

    # Post-processing: remove batch dimension and find the class with highest
    # probability.
    output = interpreter.tensor(output_index)
    predicted_label = np.argmax(output()[0])
    y_pred.append(predicted_label)

  # Compare prediction results with ground truth labels to calculate accuracy.
  y_pred = keras.utils.to_categorical(y_pred)
  return accuracy_score(y_true, y_pred)

# Evaluate the accuracy of the converted TFLite model
classifier_interpreter = tf.lite.Interpreter(model_content=tflite_model)
classifier_interpreter.allocate_tensors()
print('Accuracy of TFLite model: %s' %
      evaluate_model(classifier_interpreter, X_test, y_test))
Accuracy of TFLite model: 1.0

Maintenant , vous pouvez télécharger le modèle TFLite ( pose_classifier.tflite ) et le fichier d'étiquette ( pose_labels.txt ) pour classer les poses personnalisées. Voir l' Android et Python / Raspberry Pi exemple d' application pour un exemple de bout en bout de la façon d'utiliser le modèle de classification de pose de TFLite.

zip pose_classifier.zip pose_labels.txt pose_classifier.tflite
adding: pose_labels.txt (stored 0%)
  adding: pose_classifier.tflite (deflated 35%)
# Download the zip archive if running on Colab.
try:
  from google.colab import files
  files.download('pose_classifier.zip')
except:
  pass