args = parser.parse_args()
# 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(
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/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
fps_count = 0
run_timer = 0
frame_count = 0
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 = {}
args = parser.parse_args() # args 이름으로 파싱 성공시 args.parameter 형태로 주어진 인자 값을 받아서 사용가능
# 관련 모델 가져오기
# tensorflow 추상화 라이브러리
estimator = load_pretrain_model('VGG_origin') # 훈련 모델 로드(VGG_origin) 분류??
# 인자 초기화
realtime_fps = '0.0000'
start_time = time.time()
fps_interval = 1
fps_count = 0
run_timer = 0
frame_count = 0
# 동영상 파일 읽고 쓰기(웹캠 입력만 테스트)
cap = choose_run_mode(args) # cap 객체에 choose_run_mode 파싱
while cv.waitKey(1) < 0: #키가 입력될때까지 반복
data = []
has_frame, show = cap.read() # has_frame 과 show에 비디오를 한프레임씩 읽음 성공시 True, 실패시 False
if has_frame:
fps_count += 1 # fps 카운트
frame_count += 1 # frame 카운트
# pose estimation 추정치 배열
humans = estimator.inference(show) # 비디오에서 사람 객체 추정
# get pose info 사람의 동작을 추정 frame,joints, bboxes, xcenter 반환
pose = TfPoseVisualizer.draw_pose_rgb(show, humans) # return frame, joints, bboxes, xcenter
image = img_to_array(show) # image (450, 300, 3)
image = cv.resize(image, dsize=(224, 224),
action_classifier = load_action_premodel(
'Action/framewise_recognition.h5') #action classifier model
# 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
cam_video = 1 #cam_video=0->camera cam_video=1->video
file_p = "test/*.avi"
is_save = True
# 读写视频文件（仅测试过webcam输入）
#cap = choose_run_mode(args)
cap = choose_run_mode(cam_video, file_p)
video_writer = set_video_writer(cap, write_fps=int(7.0))
# # 保存关节数据的txt文件，用于训练过程(for training)
if is_save:
f = open('origin_data.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
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)
# 读写视频文件
video_writer = set_video_writer(cap_main, write_fps=int(7.0))
# print("DEBUG:stage 1")
# # 保存多组关节数据的txt文件，用于训练过程(for training),重命名为wave_*.txt/stand_*.txt
# f = open('test_out/origin_data.txt', 'a+')
while cv.waitKey(1) < 0:
#print("DEBUG:stage 2")
has_frame, show = cap_main.read()
#print("DEBUG:stage 3")
if has_frame:
fps_count += 1
