"""
Summary:
このエグザンプルコードでは、顔の特徴量（エンコーディングされたデータ）を取得する方法を学びます。
Example:
.. code-block:: bash
python3 example/get_encoded_data.py
.. note::
このコードでは、かならず一人が映っている映像を使用してください。
Results:
.. code-block:: bash
face encoded data: [-0.01199415 0.01052003 0.07660526 0.06551921 -0.07496994 -0.12972911
-0.05913385 -0.14237705 0.05547058 -0.13777749 0.13601969 -0.12244888
-0.15050077 0.00519788 -0.03569282 0.17992854 -0.08323735 -0.08283503
-0.10274227 -0.07727417 0.08088023 0.01752477 -0.05877539 0.02624322
-0.0077478 -0.25635812 -0.13101193 -0.09281664 0.05218935 -0.04231806
-0.06299889 0.04474396 -0.19532356 -0.12053486 0.02230695 0.06118448
-0.06408831 -0.05561456 0.19666338 -0.00320839 -0.16399916 0.10793754
0.09513831 0.19015315 0.1805291 0.06770462 0.04203921 -0.13319662
0.14975622 -0.17903461 -0.0205469 0.12766521 0.23538746 0.1532644
0.03183981 -0.04921352 0.12409633 0.0604726 -0.21282698 0.0895256
0.1691601 -0.07778979 0.01881283 0.05160358 0.2315844 0.05034623
-0.04736616 -0.10536997 0.15513012 -0.20276567 -0.11107794 0.02522913
-0.07532144 -0.1432429 -0.28969032 0.03757014 0.41931418 0.1212881
-0.1964694 0.06237716 -0.05123742 -0.04189032 0.14579299 0.06779657
-0.04064779 -0.05232659 -0.07283606 0.02151246 0.25605643 -0.08359357
0.03513885 0.15748617 0.05288177 0.03470919 0.0150839 0.04732724
.. image:: ../assets/images/one_point_R.png
:width: 70%
:alt: one point
dlibでは128次元、JAPANESE FACE V1では512次元のNDArrayデータが取得されます⭐️''
Source code:
`get_encoded_data.py <https://github.com/yKesamaru/FACE01_DEV/blob/master/example/get_encoded_data.py>`_
"""
# Operate directory: Common to all examples
import os.path
import sys
dir: str = os.path.dirname(__file__)
parent_dir, _ = os.path.split(dir)
sys.path.append(parent_dir)
from typing import Dict
from face01lib.Core import Core
from face01lib.Initialize import Initialize
from face01lib.logger import Logger
from face01lib.video_capture import VidCap
[ドキュメント]
def main(exec_times: int = 50) -> None:
"""Simple example.
This simple example script prints out results of face encoded datas.
Args:
exec_times (int, optional): Number of frames for process. Defaults to 50 times.
Returns:
None
"""
# Make generator
frame_generator_obj = VidCap().frame_generator(CONFIG)
# Make generator
core = Core()
# Repeat 'exec_times' times
for i in range(0, exec_times):
# Call __next__() from the generator object
resized_frame = frame_generator_obj.__next__()
# VidCap().frame_imshow_for_debug(resized_frame)
frame_datas_array = core.frame_pre_processing(logger, CONFIG, resized_frame)
face_encodings, frame_datas_array = \
core.face_encoding_process(logger, CONFIG, frame_datas_array)
for encoded_data in face_encodings:
print(f"face encoded data: {encoded_data}\n")
if __name__ == '__main__':
# Initialize
CONFIG: Dict = Initialize('FACE-COORDINATE', 'info').initialize()
# Set up logger
logger = Logger(CONFIG['log_level']).logger(__file__, CONFIG['RootDir'])
# Call main function.
main(exec_times=10)