Z tego notatnika dowiesz się, jak trenować model klasyfikacji pozycji za pomocą MoveNet i TensorFlow Lite. Rezultatem jest nowy model TensorFlow Lite, który akceptuje dane wyjściowe z modelu MoveNet jako dane wejściowe i generuje klasyfikację pozycji, na przykład nazwę pozycji jogi.
Procedura w tym zeszycie składa się z 3 części:
- Część 1: Wstępnie przetwórz dane treningowe klasyfikacji pozy do pliku CSV, który określa punkty orientacyjne (punkty kluczowe ciała) wykryte przez model MoveNet, wraz z podstawowymi etykietami pozy.
- Część 2: Zbuduj i wytrenuj model klasyfikacji ułożenia, który jako dane wejściowe pobiera współrzędne punktów orientacyjnych z pliku CSV i wyświetla przewidywane etykiety.
- Część 3: Konwersja modelu klasyfikacji poza do TFLite.
Domyślnie ten notatnik korzysta z zestawu danych obrazu z oznaczonymi pozycjami jogi, ale w części 1 uwzględniliśmy również sekcję, w której można przesłać własny zestaw danych obrazu przedstawiający pozy.
 Zobacz na TensorFlow.org |  Uruchom w Google Colab |  Wyświetl źródło na GitHub |  Pobierz notatnik |  Zobacz model piasty TF |
Przygotowanie
W tej sekcji zaimportujesz niezbędne biblioteki i zdefiniujesz kilka funkcji do wstępnego przetworzenia obrazów szkoleniowych do pliku CSV, który zawiera współrzędne punktów orientacyjnych i etykiety prawdy gruntu.
Nic się tutaj nie dzieje, ale możesz rozwinąć ukryte komórki kodu, aby zobaczyć implementację niektórych funkcji, które będziemy wywoływać później.
Jeśli chcesz tylko utworzyć plik CSV bez znajomości wszystkich szczegółów, po prostu uruchom tę sekcję i przejdź do części 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
Kod do szacowania pozycji za pomocą MoveNet
Funkcje do szacowania pozycji za pomocą MoveNet
# Download model from TF Hub and check out inference code from GitHub
!wget -q -O movenet_thunder.tflite https://tfhub.dev/google/lite-model/movenet/singlepose/thunder/tflite/float16/4?lite-format=tflite
!git clone https://github.com/tensorflow/examples.git
pose_sample_rpi_path = os.path.join(os.getcwd(), 'examples/lite/examples/pose_estimation/raspberry_pi')
sys.path.append(pose_sample_rpi_path)
# Load MoveNet Thunder model
import utils
from data import BodyPart
from ml import Movenet
movenet = Movenet('movenet_thunder')
# Define function to run pose estimation using MoveNet Thunder.
# You'll apply MoveNet's cropping algorithm and run inference multiple times on
# the input image to improve pose estimation accuracy.
def detect(input_tensor, inference_count=3):
"""Runs detection on an input image.
Args:
input_tensor: A [height, width, 3] Tensor of type tf.float32.
Note that height and width can be anything since the image will be
immediately resized according to the needs of the model within this
function.
inference_count: Number of times the model should run repeatly on the
same input image to improve detection accuracy.
Returns:
A Person entity detected by the MoveNet.SinglePose.
"""
image_height, image_width, channel = input_tensor.shape
# Detect pose using the full input image
movenet.detect(input_tensor.numpy(), reset_crop_region=True)
# Repeatedly using previous detection result to identify the region of
# interest and only croping that region to improve detection accuracy
for _ in range(inference_count - 1):
person = movenet.detect(input_tensor.numpy(),
reset_crop_region=False)
return person
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.
Funkcje wizualizacji wyników szacowania pozy.
def draw_prediction_on_image(
image, person, crop_region=None, close_figure=True,
keep_input_size=False):
"""Draws the keypoint predictions on image.
Args:
image: An numpy array with shape [height, width, channel] representing the
pixel values of the input image.
person: A person entity returned from the MoveNet.SinglePose model.
close_figure: Whether to close the plt figure after the function returns.
keep_input_size: Whether to keep the size of the input image.
Returns:
An numpy array with shape [out_height, out_width, channel] representing the
image overlaid with keypoint predictions.
"""
# Draw the detection result on top of the image.
image_np = utils.visualize(image, [person])
# Plot the image with detection results.
height, width, channel = image.shape
aspect_ratio = float(width) / height
fig, ax = plt.subplots(figsize=(12 * aspect_ratio, 12))
im = ax.imshow(image_np)
if close_figure:
plt.close(fig)
if not keep_input_size:
image_np = utils.keep_aspect_ratio_resizer(image_np, (512, 512))
return image_np
Kod do ładowania obrazów, wykrywania punktów orientacyjnych ułożenia i zapisywania ich w pliku CSV
class MoveNetPreprocessor(object):
"""Helper class to preprocess pose sample images for classification."""
def __init__(self,
images_in_folder,
images_out_folder,
csvs_out_path):
"""Creates a preprocessor to detection pose from images and save as CSV.
Args:
images_in_folder: Path to the folder with the input images. It should
follow this structure:
yoga_poses
|__ downdog
|______ 00000128.jpg
|______ 00000181.bmp
|______ ...
|__ goddess
|______ 00000243.jpg
|______ 00000306.jpg
|______ ...
...
images_out_folder: Path to write the images overlay with detected
landmarks. These images are useful when you need to debug accuracy
issues.
csvs_out_path: Path to write the CSV containing the detected landmark
coordinates and label of each image that can be used to train a pose
classification model.
"""
self._images_in_folder = images_in_folder
self._images_out_folder = images_out_folder
self._csvs_out_path = csvs_out_path
self._messages = []
# Create a temp dir to store the pose CSVs per class
self._csvs_out_folder_per_class = tempfile.mkdtemp()
# Get list of pose classes and print image statistics
self._pose_class_names = sorted(
[n for n in os.listdir(self._images_in_folder) if not n.startswith('.')]
)
def process(self, per_pose_class_limit=None, detection_threshold=0.1):
"""Preprocesses images in the given folder.
Args:
per_pose_class_limit: Number of images to load. As preprocessing usually
takes time, this parameter can be specified to make the reduce of the
dataset for testing.
detection_threshold: Only keep images with all landmark confidence score
above this threshold.
"""
# Loop through the classes and preprocess its images
for pose_class_name in self._pose_class_names:
print('Preprocessing', pose_class_name, file=sys.stderr)
# Paths for the pose class.
images_in_folder = os.path.join(self._images_in_folder, pose_class_name)
images_out_folder = os.path.join(self._images_out_folder, pose_class_name)
csv_out_path = os.path.join(self._csvs_out_folder_per_class,
pose_class_name + '.csv')
if not os.path.exists(images_out_folder):
os.makedirs(images_out_folder)
# Detect landmarks in each image and write it to a CSV file
with open(csv_out_path, 'w') as csv_out_file:
csv_out_writer = csv.writer(csv_out_file,
delimiter=',',
quoting=csv.QUOTE_MINIMAL)
# Get list of images
image_names = sorted(
[n for n in os.listdir(images_in_folder) if not n.startswith('.')])
if per_pose_class_limit is not None:
image_names = image_names[:per_pose_class_limit]
valid_image_count = 0
# Detect pose landmarks from each image
for image_name in tqdm.tqdm(image_names):
image_path = os.path.join(images_in_folder, image_name)
try:
image = tf.io.read_file(image_path)
image = tf.io.decode_jpeg(image)
except:
self._messages.append('Skipped ' + image_path + '. Invalid image.')
continue
else:
image = tf.io.read_file(image_path)
image = tf.io.decode_jpeg(image)
image_height, image_width, channel = image.shape
# Skip images that isn't RGB because Movenet requires RGB images
if channel != 3:
self._messages.append('Skipped ' + image_path +
'. Image isn\'t in RGB format.')
continue
person = detect(image)
# Save landmarks if all landmarks were detected
min_landmark_score = min(
[keypoint.score for keypoint in person.keypoints])
should_keep_image = min_landmark_score >= detection_threshold
if not should_keep_image:
self._messages.append('Skipped ' + image_path +
'. No pose was confidentlly detected.')
continue
valid_image_count += 1
# Draw the prediction result on top of the image for debugging later
output_overlay = draw_prediction_on_image(
image.numpy().astype(np.uint8), person,
close_figure=True, keep_input_size=True)
# Write detection result into an image file
output_frame = cv2.cvtColor(output_overlay, cv2.COLOR_RGB2BGR)
cv2.imwrite(os.path.join(images_out_folder, image_name), output_frame)
# Get landmarks and scale it to the same size as the input image
pose_landmarks = np.array(
[[keypoint.coordinate.x, keypoint.coordinate.y, keypoint.score]
for keypoint in person.keypoints],
dtype=np.float32)
# Write the landmark coordinates to its per-class CSV file
coordinates = pose_landmarks.flatten().astype(np.str).tolist()
csv_out_writer.writerow([image_name] + coordinates)
if not valid_image_count:
raise RuntimeError(
'No valid images found for the "{}" class.'
.format(pose_class_name))
# Print the error message collected during preprocessing.
print('\n'.join(self._messages))
# Combine all per-class CSVs into a single output file
all_landmarks_df = self._all_landmarks_as_dataframe()
all_landmarks_df.to_csv(self._csvs_out_path, index=False)
def class_names(self):
"""List of classes found in the training dataset."""
return self._pose_class_names
def _all_landmarks_as_dataframe(self):
"""Merge all per-class CSVs into a single dataframe."""
total_df = None
for class_index, class_name in enumerate(self._pose_class_names):
csv_out_path = os.path.join(self._csvs_out_folder_per_class,
class_name + '.csv')
per_class_df = pd.read_csv(csv_out_path, header=None)
# Add the labels
per_class_df['class_no'] = [class_index]*len(per_class_df)
per_class_df['class_name'] = [class_name]*len(per_class_df)
# Append the folder name to the filename column (first column)
per_class_df[per_class_df.columns[0]] = (os.path.join(class_name, '')
+ per_class_df[per_class_df.columns[0]].astype(str))
if total_df is None:
# For the first class, assign its data to the total dataframe
total_df = per_class_df
else:
# Concatenate each class's data into the total dataframe
total_df = pd.concat([total_df, per_class_df], axis=0)
list_name = [[bodypart.name + '_x', bodypart.name + '_y',
bodypart.name + '_score'] for bodypart in BodyPart]
header_name = []
for columns_name in list_name:
header_name += columns_name
header_name = ['file_name'] + header_name
header_map = {total_df.columns[i]: header_name[i]
for i in range(len(header_name))}
total_df.rename(header_map, axis=1, inplace=True)
return total_df
(Opcjonalnie) Fragment kodu umożliwiający wypróbowanie logiki szacowania pozycji Movenet
test_image_url = "https://cdn.pixabay.com/photo/2017/03/03/17/30/yoga-2114512_960_720.jpg"
!wget -O /tmp/image.jpeg {test_image_url}
if len(test_image_url):
image = tf.io.read_file('/tmp/image.jpeg')
image = tf.io.decode_jpeg(image)
person = detect(image)
_ = draw_prediction_on_image(image.numpy(), person, crop_region=None,
close_figure=False, keep_input_size=True)
--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]
Część 1: Wstępne przetwarzanie obrazów wejściowych
Ponieważ wejście do naszego ułożenia klasyfikatora jest orientacyjnych wyjściowe z modelu MoveNet, musimy wygenerować z naszego szkolenia zestaw danych, uruchamiając oznaczonych obrazów przez MoveNet a następnie przechwytywanie wszystkich danych przełomowe i etykiet ziemia prawdy do pliku CSV.
Zestaw danych, który udostępniliśmy w tym samouczku, to wygenerowany komputerowo zestaw danych jogi. Zawiera obrazy wielu wygenerowanych komputerowo modeli wykonujących 5 różnych pozycji jogi. Katalog jest już podzielony na train zbiorze oraz test zestawu danych.
Tak więc w tej części będziemy pobrać zestaw danych jogi i uruchomić go przez MoveNet więc możemy uchwycić wszystkie punkty orientacyjne do pliku CSV ... Jednak to trwa około 15 minut, aby karmić naszą jogi zestawu danych do MoveNet i wygenerowania tego pliku CSV . Więc jako alternatywę, można pobrać plik CSV wcześniej istniejących na zbiorze jogi poprzez ustawienie is_skip_step_1 parametru poniżej true. W ten sposób pominiesz ten krok i zamiast tego pobierzesz ten sam plik CSV, który zostanie utworzony na tym etapie wstępnego przetwarzania.
Z drugiej strony, jeśli chcesz trenować klasyfikatora ułożenia z własnego zbioru danych obrazu, należy przesłać swoje zdjęcia i uruchomić ten krok przerób (leave is_skip_step_1 false) -follow poniższymi instrukcjami, aby przesłać własne pozy zestawu danych.
is_skip_step_1 = False
(Opcjonalnie) Prześlij własny zestaw danych pozy
use_custom_dataset = False
dataset_is_split = False
Jeśli chcesz wytrenować klasyfikatora pozycji z własnymi oznaczonymi pozycjami (mogą to być dowolne pozy, nie tylko joga), wykonaj następujące kroki:
Ustaw powyżej
use_custom_datasetopcję true.Przygotuj plik archiwum (ZIP, TAR lub inny), który zawiera folder ze zbiorem danych obrazów. Folder musi zawierać posortowane obrazy twoich póz w następujący sposób.
Jeśli już podzielić zbiór danych do składów i testowych, a następnie ustawić
dataset_is_splittrue. Oznacza to, że folder obrazów musi zawierać katalogi „train” i „test” w następujący sposób:yoga_poses/ |__ train/ |__ downdog/ |______ 00000128.jpg |______ ... |__ test/ |__ downdog/ |______ 00000181.jpg |______ ...Lub, jeśli zbiór danych nie jest jeszcze podzielona, a następnie ustawić
dataset_is_splitna False, a my podzielić go na podstawie ułamek określonej podziału. Oznacza to, że folder z przesłanymi obrazami powinien wyglądać tak:yoga_poses/ |__ downdog/ |______ 00000128.jpg |______ 00000181.jpg |______ ... |__ goddess/ |______ 00000243.jpg |______ 00000306.jpg |______ ...Kliknij kartę Pliki z lewej strony (ikona folderu), a następnie kliknij przycisk Prześlij do przechowywania sesji (file icon).
Wybierz plik archiwum i poczekaj, aż zakończy się przesyłanie, zanim przejdziesz dalej.
Edytuj następujący blok kodu, aby określić nazwę pliku archiwum i katalogu obrazów. (Domyślnie oczekujemy pliku ZIP, więc musisz zmodyfikować tę część, jeśli Twoje archiwum jest w innym formacie).
Teraz uruchom resztę notatnika.
import os
import random
import shutil
def split_into_train_test(images_origin, images_dest, test_split):
"""Splits a directory of sorted images into training and test sets.
Args:
images_origin: Path to the directory with your images. This directory
must include subdirectories for each of your labeled classes. For example:
yoga_poses/
|__ downdog/
|______ 00000128.jpg
|______ 00000181.jpg
|______ ...
|__ goddess/
|______ 00000243.jpg
|______ 00000306.jpg
|______ ...
...
images_dest: Path to a directory where you want the split dataset to be
saved. The results looks like this:
split_yoga_poses/
|__ train/
|__ downdog/
|______ 00000128.jpg
|______ ...
|__ test/
|__ downdog/
|______ 00000181.jpg
|______ ...
test_split: Fraction of data to reserve for test (float between 0 and 1).
"""
_, dirs, _ = next(os.walk(images_origin))
TRAIN_DIR = os.path.join(images_dest, 'train')
TEST_DIR = os.path.join(images_dest, 'test')
os.makedirs(TRAIN_DIR, exist_ok=True)
os.makedirs(TEST_DIR, exist_ok=True)
for dir in dirs:
# Get all filenames for this dir, filtered by filetype
filenames = os.listdir(os.path.join(images_origin, dir))
filenames = [os.path.join(images_origin, dir, f) for f in filenames if (
f.endswith('.png') or f.endswith('.jpg') or f.endswith('.jpeg') or f.endswith('.bmp'))]
# Shuffle the files, deterministically
filenames.sort()
random.seed(42)
random.shuffle(filenames)
# Divide them into train/test dirs
os.makedirs(os.path.join(TEST_DIR, dir), exist_ok=True)
os.makedirs(os.path.join(TRAIN_DIR, dir), exist_ok=True)
test_count = int(len(filenames) * test_split)
for i, file in enumerate(filenames):
if i < test_count:
destination = os.path.join(TEST_DIR, dir, os.path.split(file)[1])
else:
destination = os.path.join(TRAIN_DIR, dir, os.path.split(file)[1])
shutil.copyfile(file, destination)
print(f'Moved {test_count} of {len(filenames)} from class "{dir}" into test.')
print(f'Your split dataset is in "{images_dest}"')
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
Pobierz zestaw danych jogi
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]
Preprocesuj się TRAIN zestawu danych
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.
Preprocesuj ten TEST zestaw danych
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.
Część 2: Trenuj model klasyfikacji ułożenia, który przyjmuje współrzędne punktów orientacyjnych jako dane wejściowe i wyświetla przewidywane etykiety.
Zbudujesz model TensorFlow, który pobiera współrzędne punktów orientacyjnych i przewiduje klasę pozy, jaką wykonuje osoba na obrazie wejściowym. Model składa się z dwóch podmodeli:
- Podmodel 1 oblicza osadzanie pozy (inaczej wektor cech) na podstawie wykrytych współrzędnych punktów orientacyjnych.
- Submodel 2 kanały stanowią osadzanie przez kilka
Densewarstwy przewidzieć klasę stwarzają.
Następnie nauczysz się modelu na podstawie zestawu danych, który został wstępnie przetworzony w części 1.
(Opcjonalnie) Pobierz wstępnie przetworzony zbiór danych, jeśli nie uruchomiłeś części 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
Załaduj przetworzonych pliki CSV do TRAIN i TEST zbiorów danych.
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
Obciążenia i podzielił oryginalną TRAIN zbiór danych do TRAIN (85% danych) i VALIDATE (pozostałe 15%).
# 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)
Zdefiniuj funkcje do konwersji punktów orientacyjnych pozy na osadzanie pozy (inaczej wektor funkcji) w celu klasyfikacji pozy
Następnie przekształć współrzędne punktu orientacyjnego na wektor cech przez:
- Przenoszenie środka ułożenia do początku.
- Skalowanie pozy tak, aby rozmiar pozy wynosił 1
- Spłaszczanie tych współrzędnych do wektora cech
Następnie użyj tego wektora funkcji, aby wytrenować klasyfikator pozycji oparty na sieci neuronowej.
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
Zdefiniuj model Kerasa do klasyfikacji pozycji
Nasz model Keras bierze wykryte punkty orientacyjne pozy, a następnie oblicza osadzanie pozy i przewiduje klasę pozy.
# 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()
# 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
Narysuj macierz pomyłek, aby lepiej zrozumieć wydajność modelu
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
(Opcjonalnie) Sprawdź nieprawidłowe prognozy
Można spojrzeć na pozycjach od TEST zbiorze danych, które zostały nieprawidłowo przewidywana, aby zobaczyć, czy dokładność modelu można poprawić.
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()
Część 3: Konwersja modelu klasyfikacji pozycji do TensorFlow Lite
Przekonwertujesz model klasyfikacji poza Keras na format TensorFlow Lite, aby móc go wdrożyć w aplikacjach mobilnych, przeglądarkach internetowych i urządzeniach IoT. Przy konwersji modelu, można zastosować dynamiczny zakres kwantyzacji zmniejszenia klasyfikacji pozy TensorFlow Lite rozmiar modelu o około 4 razy z nieznacznej utraty dokładności.
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
Następnie napiszesz plik etykiety, który zawiera mapowanie z indeksów klas na nazwy klas czytelne dla człowieka.
with open('pose_labels.txt', 'w') as f:
f.write('\n'.join(class_names))
Ponieważ zastosowałeś kwantyzację w celu zmniejszenia rozmiaru modelu, oceńmy skwantowany model TFLite, aby sprawdzić, czy spadek dokładności jest akceptowalny.
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
Teraz można pobrać model TFLite ( pose_classifier.tflite ) oraz plik etykiety ( pose_labels.txt ) sklasyfikować niestandardowych pozach. Zobacz Android i Python / Raspberry Pi przykładową aplikację dla przykładu end-to-end, w jaki sposób korzystać z modelu klasyfikacyjnego TFLite stwarzają.
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