# -*- coding: UTF-8 -*- import cv2 as cv import argparse import numpy as np import time from utils import choose_run_mode, load_pretrain_model, set_video_writer from Pose.pose_visualizer import TfPoseVisualizer from Action.recognizer import load_action_premodel, framewise_recognize parser = argparse.ArgumentParser(description='Action Recognition') parser.add_argument('--video', help='Path to video file.') args = parser.parse_args() estimator = load_pretrain_model('VGG_origin') action_classifier = load_action_premodel('Action/framewise_recognition_under_scene.h5') realtime_fps = '0.0000' start_time = time.time() fps_interval = 1 fps_count = 0 run_timer = 0 frame_count = 0 cap = choose_run_mode(args) video_writer = set_video_writer(cap, write_fps=int(7.0)) # f = open('origin_data.txt', 'a+')
# -*- coding: UTF-8 -*- import cv2 as cv import argparse import numpy as np import time from utils import choose_run_mode, load_pretrain_model, set_video_writer from Pose.pose_visualizer import TfPoseVisualizer from Action.recognizer import load_action_premodel, framewise_recognize parser = argparse.ArgumentParser(description='Action Recognition by OpenPose') parser.add_argument('--video', help='Path to video file.') args = parser.parse_args() # 导入相关模型 estimator = load_pretrain_model('VGG_origin') action_classifier = load_action_premodel('Action/framewise_recognition.h5') # 参数初始化 realtime_fps = '0.0000' start_time = time.time() fps_interval = 1 fps_count = 0 run_timer = 0 frame_count = 0 # 读写视频文件(仅测试过webcam输入) cap = choose_run_mode(args) video_writer = set_video_writer(cap, write_fps=int(30.0)) # # 保存关节数据的txt文件,用于训练过程(for training) # f = open('origin_data.txt', 'a+')
import cv2 as cv import argparse import numpy as np import time from utils import choose_run_mode, load_pretrain_model, set_video_writer from Pose.pose_visualizer import TfPoseVisualizer from Action.recognizer import load_action_premodel, framewise_recognize parser = argparse.ArgumentParser(description='Action Recognition by OpenPose') parser.add_argument('--video', default='Escalator/light_1.5_left_30.mp4',help='Path to video file.') args = parser.parse_args() # 导入相关模型 建立图 # estimator = load_pretrain_model('VGG_origin') #返回一个估计的模型 estimator = load_pretrain_model('mobilenet_thin') #返回一个类的句柄TfPoseVisualizer 并且建立了计算图 # action_classifier = load_action_premodel('Action/Es_all_demo.h5') #返回动作分类模型 且里面定义了tracker action_classifier = load_action_premodel('Action/framewise_recognition_bobei.h5') #返回动作分类模型 且里面定义了tracker # 参数初始化 realtime_fps = '0.0000' start_time = time.time() fps_interval = 1 fps_count = 0 run_timer = 0 frame_count = 0 #读写视频文件(仅测试过webcam输入) cap = choose_run_mode(args) #选择摄像头或者是本地文件 video_writer = set_video_writer(cap, write_fps=int(12)) #保存到本地的视频用到的参数初始化 video_1 = cv.VideoWriter('test_out/ex1.mp4', cv.VideoWriter_fourcc(*'mp4v'), int(12),
import sys from utils import choose_run_mode, load_pretrain_model, set_video_writer from Pose.pose_visualizer import TfPoseVisualizer from Action.recognizer import load_action_premodel, framewise_recognize parser = argparse.ArgumentParser( description='Gesture control camera based on OpenPose') parser.add_argument('--video', help='Path to video file.') args = parser.parse_args() # 导入相关模型 #尝试mobile_thin #estimator = load_pretrain_model('mobilenet_thin') #estimator = load_pretrain_model('mobilenet_small') estimator = load_pretrain_model('VGG_origin') action_classifier = load_action_premodel('Action/own_stand_wave_08.h5') # 参数初始化 realtime_fps = '0.0000' start_time = time.time() fps_interval = 1 fps_count = 0 run_timer = 0 frame_count = 0 #获取被控相机 cap_Receptor = EasyPySpin.VideoCapture(0) # 获取主控相机 cap_main = choose_run_mode(args)
import argparse import numpy as np import time from utils import choose_run_mode, load_pretrain_model, set_video_writer from Pose.pose_visualizer import TfPoseVisualizer from Action.recognizer import load_action_premodel, framewise_recognize parser = argparse.ArgumentParser(description='Action Recognition by OpenPose') parser.add_argument('--video', help='Path to video file.') args = parser.parse_args() # imported related models # estimator = load_pretrain_model('VGG_origin') estimator = load_pretrain_model('mobilenet_thin') action_classifier = load_action_premodel('Action/training/amazon_recognition.h5') # parameter initialization realtime_fps = 0.0000 start_time = time.time() fps_interval = 1 fps_count = 0 run_timer = 0 frame_count = 0 # Read and write video files (tested only for webcam input) cap = choose_run_mode(args) video_writer = set_video_writer(cap, write_fps=int(15.0)) # # A txt file that stores joint data for the training process (for training) #f = open('origin_data.txt', 'a+')