# 모델 불러오기
estimator = load_pretrain_model('VGG_origin')
action_classifier = load_action_premodel('Action/taekwondo_recognition.h5')
# 매개변수 초기화
realtime_fps = '0.0000'
start_time = time.time()
fps_interval = 1
fps_count = 0
run_timer = 0
frame_count = 0
# video file 읽기 및 쓰기
cap = choose_run_mode(args)
video_writer = set_video_writer(cap, write_fps=int(7.0))
# skeleton data for Training을 txt 파일로 저장
# f = open('data\\txt_data\\taekwondoskill2-3-1.txt', 'a+')
while cv.waitKey(1) < 0:
has_frame, show = cap.read()
if has_frame:
fps_count += 1
frame_count += 1
# pose estimation
humans = estimator.inference(show)
# get pose info
pose = TfPoseVisualizer.draw_pose_rgb(
show, humans) # return frame, joints, bboxes, xcenter
cam_video = 1 #cam_video=0->camera cam_video=1->video
file_p = "camera_record/*.mp4" #要读取的视频所在文件夹的路径
is_save = False
vid_index = 0
#读取视频列表
if (cam_video == 1):
video_list = glob.glob(file_p)
video_len = len(video_list)
print(video_list)
if (video_len <= 0):
print('No video in that file')
sys.exit(1)
cap = choose_run_mode(cam_video, video_list, vid_index) #读取视频
#video_writer = set_video_writer(cap, write_fps=int(7.0))
video_writer = set_video_writer(cap,
write_fps=int(30.0),
output_path='after_cam.mp4')
video_writer_origin = set_video_writer(cap,
write_fps=int(30.0),
output_path='origin_cam.mp4')
vid_index += 1
if is_save:
f = open('origin_data.txt', 'a+')
cur_pose = {}
befo_pose = {}
cur_box = {}
cur_tag = {}
delay_rec = {}
detect_thresh = 5
is_suspicious = False
'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/alpha.mp4', cv.VideoWriter_fourcc(*'mp4v'),
int(12), (round(cap.get(cv.CAP_PROP_FRAME_WIDTH)),
round(cap.get(cv.CAP_PROP_FRAME_HEIGHT))))
# # 保存关节数据的txt文件，用于训练过程(for training)
# f = open('origin_data.txt', 'a+') #通过openPose提取keyPoint 然后标记keyPoint进行分类训练 最后通过openpose送到分类
while True and cap.isOpened(): #loop
has_frame, show = cap.read() #framewise
if has_frame:
fps_count += 1
frame_count += 1
#crop image ,then ,the image into network
#todo 缩小检测范围 显然影响到了检测精度 想要改变显示显然需要从节点生成后去做了
# pose estimation body_parts PartPair uidx_list
humans = estimator.inference(show) #返回heatMat pafMat空间地址
file_p = "camera_record/*.mp4" #要读取的视频所在文件夹的路径
is_save = True
vid_index = 0
#读取视频列表
if (cam_video == 1):
video_list = glob.glob(file_p)
video_len = len(video_list)
print(video_list)
if (video_len <= 0):
print('No video in that file')
sys.exit(1)
cap = choose_run_mode(cam_video, video_list, vid_index) #读取视频
#存储处理后的视频
tmp_string = 'after_process_' + str(vid_index) + '.mp4'
video_writer = set_video_writer(cap,
write_fps=int(30.0),
output_path=tmp_string)
#存储获取的关节点信息 目前只考虑单人的情况
if is_save:
data_file = 'gained_data/origin_data_' + str(vid_index) + '.txt'
f = open(data_file, 'a+')
vid_index += 1
while cv.waitKey(1) < 0:
#while True :
has_frame, show = cap.read()
if has_frame:
fps_count += 1
frame_count += 1
# pose estimation
humans = estimator.inference(show) #openpose输出检测到的humans(人体关键点)
# get pose info
