이 노트북은 MoveNet 및 TensorFlow Lite를 사용하여 포즈 분류 모델을 훈련하는 방법을 알려줍니다. 결과는 MoveNet 모델의 출력을 입력으로 받아들이고 요가 포즈의 이름과 같은 포즈 분류를 출력하는 새로운 TensorFlow Lite 모델입니다.
이 노트북의 절차는 세 부분으로 구성됩니다.
- 파트 1: 포즈 분류 훈련 데이터를 실제 포즈 레이블과 함께 MoveNet 모델이 감지한 랜드마크(신체 키포인트)를 지정하는 CSV 파일로 사전 처리합니다.
- 2부: CSV 파일의 랜드마크 좌표를 입력으로 사용하고 예측된 레이블을 출력하는 포즈 분류 모델을 빌드하고 훈련합니다.
- 3부: 포즈 분류 모델을 TFLite로 변환합니다.
기본적으로 이 노트북은 요가 포즈라는 레이블이 지정된 이미지 데이터 세트를 사용하지만, 포즈의 이미지 데이터 세트를 업로드할 수 있는 섹션도 파트 1에 포함했습니다.
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준비
이 섹션에서는 필요한 라이브러리를 가져오고 랜드마크 좌표와 정답 레이블이 포함된 CSV 파일로 훈련 이미지를 사전 처리하는 여러 함수를 정의합니다.
여기서 관찰할 수 있는 일은 없지만 숨겨진 코드 셀을 확장하여 나중에 호출할 일부 기능에 대한 구현을 볼 수 있습니다.
모든 세부 사항을 모르는 상태에서 CSV 파일만 생성하려면 이 섹션을 실행하고 1부로 진행하십시오.
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
MoveNet을 사용하여 포즈 추정을 실행하는 코드
MoveNet으로 포즈 추정을 실행하는 함수
# 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.
포즈 추정 결과를 시각화하는 기능.
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
이미지를 로드하고 포즈 랜드마크를 감지하여 CSV 파일로 저장하는 코드
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
(선택 사항) Movenet 포즈 추정 로직을 시도하기 위한 코드 스니펫
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]
1부: 입력 이미지 전처리
우리의 포즈 분류에 대한 입력이 MoveNet 모델의 출력 랜드 마크이기 때문에, 우리는 MoveNet을 통해 표시된 이미지를 실행 한 후 CSV 파일로 모든 랜드 마크 데이터와 지상 진실 라벨을 캡처하여 우리의 훈련 데이터 집합을 생성해야합니다.
이 튜토리얼을 위해 제공한 데이터세트는 CG로 생성된 요가 포즈 데이터세트입니다. 여기에는 5가지 다른 요가 포즈를 하는 여러 CG 생성 모델의 이미지가 포함되어 있습니다. 디렉토리는 이미으로 분할 train 세트와 test 데이터 세트.
그래서이 절에서, 우리는 요가 데이터 집합을 다운로드 할 수 있습니다 그리고 우리는 CSV 파일로 모든 랜드 마크를 캡처 할 수 있도록 MoveNet를 통해 실행 ... 그러나, 그것은 MoveNet 우리의 요가 데이터 집합을 공급하기 위해 약 15 분 정도 소요되며이 CSV 파일을 생성 . 그래서 대안으로, 당신은 설정하여 요가 데이터 세트에 대한 기존 CSV 파일을 다운로드 할 수 있습니다 is_skip_step_1 True로 아래 매개 변수를. 그렇게 하면 이 단계를 건너뛰고 대신 이 전처리 단계에서 생성될 동일한 CSV 파일을 다운로드합니다.
당신이 당신의 자신의 이미지 데이터 세트와 함께 포즈 분류를 훈련 할 경우 반면에, 당신은 당신의 이미지를 업로드하고이 전처리 단계 (휴가 실행해야 is_skip_step_1 다음 지침 자신의 포즈 데이터 집합을 업로드 할 -follow 거짓을).
is_skip_step_1 = False
(선택 사항) 자신의 포즈 데이터 세트 업로드
use_custom_dataset = False
dataset_is_split = False
자신의 레이블이 지정된 포즈(요가 포즈뿐만 아니라 모든 포즈가 될 수 있음)로 포즈 분류기를 훈련하려면 다음 단계를 따르세요.
위의 설정
use_custom_datasetTrue로 옵션을 선택합니다.이미지 데이터 세트가 있는 폴더를 포함하는 아카이브 파일(ZIP, TAR 또는 기타)을 준비합니다. 폴더에는 다음과 같이 정렬된 포즈 이미지가 포함되어야 합니다.
이미 기차 및 테스트 집합으로 데이터 집합을 분할 한 경우, 설정
dataset_is_splitTrue로. 즉, 이미지 폴더에는 다음과 같은 "train" 및 "test" 디렉터리가 포함되어야 합니다.yoga_poses/ |__ train/ |__ downdog/ |______ 00000128.jpg |______ ... |__ test/ |__ downdog/ |______ 00000181.jpg |______ ...데이터 세트가 아직 분리되지 않은 경우 또는, 다음 설정
dataset_is_splitFalse로 우리는 지정된 분할 비율에 따라 그것을 나눌 수 있습니다. 즉, 업로드한 이미지 폴더는 다음과 같아야 합니다.yoga_poses/ |__ downdog/ |______ 00000128.jpg |______ 00000181.jpg |______ ... |__ goddess/ |______ 00000243.jpg |______ 00000306.jpg |______ ...왼쪽 (폴더 아이콘)에서 파일 탭을 클릭 한 다음 세션 저장 (파일 아이콘)에 업로드를 클릭합니다.
보관 파일을 선택하고 계속 진행하기 전에 업로드가 완료될 때까지 기다리세요.
아카이브 파일 및 이미지 디렉토리의 이름을 지정하려면 다음 코드 블록을 편집하십시오. (기본적으로 ZIP 파일이 필요하므로 아카이브가 다른 형식인 경우 해당 부분도 수정해야 합니다.)
이제 노트북의 나머지 부분을 실행하십시오.
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
요가 데이터 세트 다운로드
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]
전처리 TRAIN 세트를
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.
전처리 TEST 데이터 집합을
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.
2부: 랜드마크 좌표를 입력으로 사용하고 예측된 레이블을 출력하는 포즈 분류 모델을 훈련합니다.
랜드마크 좌표를 사용하고 입력 이미지의 사람이 수행하는 포즈 클래스를 예측하는 TensorFlow 모델을 빌드합니다. 모델은 두 개의 하위 모델로 구성됩니다.
- 하위 모델 1은 감지된 랜드마크 좌표에서 포즈 임베딩(특징 벡터라고도 함)을 계산합니다.
- 서브 모델이 피드는 몇 가지를 통해 내장 포즈
Dense포즈 클래스를 예측하는 계층.
그런 다음 1부에서 사전 처리된 데이터 세트를 기반으로 모델을 학습합니다.
(선택 사항) 파트 1을 실행하지 않은 경우 전처리된 데이터 세트를 다운로드합니다.
# 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
로 전처리 된 CSV를로드 TRAIN 및 TEST 데이터 세트.
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
로드 원래 분할 TRAIN 로 집합을 TRAIN (데이터의 85 %) 및 VALIDATE (나머지 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)
포즈 분류를 위해 포즈 랜드마크를 포즈 임베딩(특징 벡터라고도 함)으로 변환하는 함수 정의
다음으로 다음과 같이 랜드마크 좌표를 특징 벡터로 변환합니다.
- 포즈 중심을 원점으로 이동합니다.
- 포즈 크기가 1이 되도록 포즈 크기 조정
- 이 좌표를 특징 벡터로 병합
그런 다음 이 특징 벡터를 사용하여 신경망 기반 포즈 분류기를 훈련합니다.
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
포즈 분류를 위한 Keras 모델 정의
Keras 모델은 감지된 포즈 랜드마크를 가져온 다음 포즈 임베딩을 계산하고 포즈 클래스를 예측합니다.
# 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
모델 성능을 더 잘 이해하기 위해 정오분류표 그리기
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
(선택 사항) 잘못된 예측 조사
당신은에서 포즈를 볼 수 있습니다 TEST 잘못 모델의 정확성을 향상시킬 수 있는지 여부를 예측 된 데이터 세트.
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()
3부: 포즈 분류 모델을 TensorFlow Lite로 변환
Keras 포즈 분류 모델을 TensorFlow Lite 형식으로 변환하여 모바일 앱, 웹 브라우저 및 IoT 장치에 배포할 수 있습니다. 모델을 변환 할 때 적용됩니다 동적 범위 양자화 미미한 정도 손실이 약 4 배 포즈 분류 TensorFlow 라이트 모델의 크기를 줄일 수 있습니다.
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
그런 다음 클래스 인덱스에서 사람이 읽을 수 있는 클래스 이름으로의 매핑이 포함된 레이블 파일을 작성합니다.
with open('pose_labels.txt', 'w') as f:
f.write('\n'.join(class_names))
모델 크기를 줄이기 위해 양자화를 적용했으므로 양자화된 TFLite 모델을 평가하여 정확도 저하가 허용 가능한지 확인하겠습니다.
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
이제 TFLite 모델 (다운로드 할 수 있습니다 pose_classifier.tflite )과 레이블 파일 ( pose_labels.txt 분류 사용자 정의 포즈로를). 참고 항목 안드로이드 및 파이썬 / 라즈베리 파이 TFLite의 포즈 분류 모델을 사용하는 방법에 대한 엔드 - 투 - 엔드 예를 들어 샘플 응용 프로그램을.
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