Yin Guobing yinguobing

## split_files.sh
n=0
for i in *
do
  if [ $((n+=1)) -gt 50 ]; then
    n=1
  fi
  todir=../my_file$n
  [ -d "$todir" ] || mkdir "$todir"
  mv "$i" "$todir"
done

## process_video.py
"""Template code for video frame processing.

Features:
    - video source from files or webcams.
    - automatically flip the frame if reading from webcam.
    - built in video writer to output the processed result.
    - built in FPS metter.

For more: https://github.com/yinguobing
"""

## save_mesh_animation.py
import tkinter

import cv2
import matplotlib
from matplotlib import animation, pyplot
from mpl_toolkits.mplot3d import Axes3D

# The module mesh_detector could be found here:
# https://github.com/yinguobing/head-pose-estimation/tree/google-face-mesh
from mesh_detector import MeshDetector

## import_savedmodel_to_tensorboard.py
"""Imports a SavedModel as a graph in Tensorboard."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys

from tensorflow.core.framework import graph_pb2

## load_saved_model.py
import tensorflow as tf

MODEL_PATH = '/home/robin/Desktop/arc_inf/1554262166'
IMAGE_PATH = '/data/dataset/public/ms_celeb_1m/arc_face_data/img/0/0.jpg'

with tf.Session(graph=tf.Graph()) as sess:
    # Restore model from the saved_modle file, that is exported by TensorFlow estimator.
    tf.saved_model.loader.load(sess, ["serve"], MODEL_PATH)

    # Get the output node from the graph.

## tfrecord.py
"""Minimal example showing how to generate TensorFlow TFRecord file."""

import tensorflow as tf
tf.enable_eager_execution()


# All raw values should be converted to a type compatible with tf.Example. Use
# the following functions to do these convertions.
def _bytes_feature(value):
    """Returns a bytes_list from a string / byte."""

## sample_map.py
import matplotlib.pyplot as plt
import numpy as np

DURATION = 12
FPS = 25
START = 0
STEPS = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]


if __name__ == "__main__":

## perf_grab_decode.py
"""
read() and grab() in OpenCV VideoCapture takes different time as there is no
decoding for grab() operation. Let's try to find out how much these two methods
differs.
"""
import cv2


def main():
    tm = cv2.TickMeter()

## video_threaded.py
# Original code here:
# https://github.com/opencv/opencv/blob/master/samples/python/video_threaded.py

#!/usr/bin/env python3

'''
Multithreaded video processing minimal sample.
Usage:
   python3 video_threaded.py
   Shows how python threading capabilities can be used

## cluster.py
import os
import numpy
from random import randint
from PIL import Image

# Get all the image files' path.
homePath = "./images"

# Define locations for different type of data.
for dir_parent in ["train", "test"]:
	n=0
	for i in *
	do
	if [ $((n+=1)) -gt 50 ]; then
	n=1
	fi
	todir=../my_file$n
	[ -d "$todir" ] \|\| mkdir "$todir"
	mv "$i" "$todir"
	done
	"""Template code for video frame processing.

	Features:
	- video source from files or webcams.
	- automatically flip the frame if reading from webcam.
	- built in video writer to output the processed result.
	- built in FPS metter.

	For more: https://github.com/yinguobing
	"""
	import tkinter

	import cv2
	import matplotlib
	from matplotlib import animation, pyplot
	from mpl_toolkits.mplot3d import Axes3D

	# The module mesh_detector could be found here:
	# https://github.com/yinguobing/head-pose-estimation/tree/google-face-mesh
	from mesh_detector import MeshDetector
	"""Imports a SavedModel as a graph in Tensorboard."""

	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	import argparse
	import sys

	from tensorflow.core.framework import graph_pb2
	import tensorflow as tf

	MODEL_PATH = '/home/robin/Desktop/arc_inf/1554262166'
	IMAGE_PATH = '/data/dataset/public/ms_celeb_1m/arc_face_data/img/0/0.jpg'

	with tf.Session(graph=tf.Graph()) as sess:
	# Restore model from the saved_modle file, that is exported by TensorFlow estimator.
	tf.saved_model.loader.load(sess, ["serve"], MODEL_PATH)

	# Get the output node from the graph.
	"""Minimal example showing how to generate TensorFlow TFRecord file."""

	import tensorflow as tf
	tf.enable_eager_execution()


	# All raw values should be converted to a type compatible with tf.Example. Use
	# the following functions to do these convertions.
	def _bytes_feature(value):
	"""Returns a bytes_list from a string / byte."""
	import matplotlib.pyplot as plt
	import numpy as np

	DURATION = 12
	FPS = 25
	START = 0
	STEPS = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]


	if __name__ == "__main__":
	"""
	read() and grab() in OpenCV VideoCapture takes different time as there is no
	decoding for grab() operation. Let's try to find out how much these two methods
	differs.
	"""
	import cv2


	def main():
	tm = cv2.TickMeter()
	# Original code here:
	# https://github.com/opencv/opencv/blob/master/samples/python/video_threaded.py

	#!/usr/bin/env python3

	'''
	Multithreaded video processing minimal sample.
	Usage:
	python3 video_threaded.py
	Shows how python threading capabilities can be used
	import os
	import numpy
	from random import randint
	from PIL import Image

	# Get all the image files' path.
	homePath = "./images"

	# Define locations for different type of data.
	for dir_parent in ["train", "test"]: