def video(args):
config = load_config(args)
model = create_model(args, config)
cap = cv2.VideoCapture(0)
# cap = cv2.VideoCapture("/home/fabian/Documents/dataset/videos/test4.mp4")
if cap.isOpened() is False:
print('Error opening video stream or file')
exit(1)
logger.info('camera will capture {} FPS'.format(cap.get(cv2.CAP_PROP_FPS)))
if os.path.exists('mask.png'):
mask = Image.open('mask.png')
mask = mask.resize((200, 200))
else:
mask = None
fps_time = 0
degree = 0
detection_thresh = config.getfloat('predict', 'detection_thresh')
min_num_keypoints = config.getint('predict', 'min_num_keypoints')
while cap.isOpened():
degree += 5
degree = degree % 360
ret_val, image = cap.read()
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, model.insize)
with chainer.using_config('autotune', True):
humans = estimate(model,
image.transpose(2, 0, 1).astype(np.float32),
detection_thresh, min_num_keypoints)
pilImg = Image.fromarray(image)
pilImg = draw_humans(
model.keypoint_names,
model.edges,
pilImg,
humans,
mask=mask.rotate(degree) if mask else None,
visbbox=config.getboolean('predict', 'visbbox'),
)
img_with_humans = cv2.cvtColor(np.asarray(pilImg), cv2.COLOR_RGB2BGR)
msg = 'GPU ON' if chainer.backends.cuda.available else 'GPU OFF'
msg += ' ' + config.get('model_param', 'model_name')
cv2.putText(img_with_humans,
'FPS: % f' % (1.0 / (time.time() - fps_time)), (10, 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# img_with_humans = cv2.resize(img_with_humans, (3 * model.insize[0], 3 * model.insize[1]))
img_with_humans = cv2.resize(
img_with_humans, (1 * model.insize[0], 1 * model.insize[1]))
cv2.imshow('Pose Proposal Network' + msg, img_with_humans)
fps_time = time.time()
# press Esc to exit
if cv2.waitKey(1) == 27:
break
def main():
config = configparser.ConfigParser()
config.read('config.ini', 'UTF-8')
# load model
model = create_model(config)
cap = cv2.VideoCapture(0)
if cap.isOpened() is False:
print('Error opening video stream or file')
exit(1)
logger.info('camera will capture {} FPS'.format(cap.get(cv2.CAP_PROP_FPS)))
if os.path.exists('mask.png'):
mask = Image.open('mask.png')
mask = mask.resize((200, 200))
else:
mask = None
fps_time = 0
degree = 0
while cap.isOpened():
degree += 5
degree = degree % 360
ret_val, image = cap.read()
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, model.insize)
with chainer.using_config('autotune', True):
humans = estimate(model,
image.transpose(2, 0, 1).astype(np.float32))
pilImg = Image.fromarray(image)
pilImg = draw_humans(
model.keypoint_names,
model.edges,
pilImg,
humans,
mask=mask.rotate(degree) if mask else None
)
img_with_humans = cv2.cvtColor(np.asarray(pilImg), cv2.COLOR_RGB2BGR)
msg = 'GPU ON' if chainer.backends.cuda.available else 'GPU OFF'
msg += ' ' + config.get('model_param', 'model_name')
cv2.putText(img_with_humans, 'FPS: % f' % (1.0 / (time.time() - fps_time)),
(10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
img_with_humans = cv2.resize(img_with_humans, (3 * model.insize[0], 3 * model.insize[1]))
cv2.imshow('Pose Proposal Network' + msg, img_with_humans)
fps_time = time.time()
# press Esc to exit
if cv2.waitKey(1) == 27:
break
def high_speed(args):
config = load_config(args)
dataset_type = config.get('dataset', 'type')
detection_thresh = config.getfloat('predict', 'detection_thresh')
min_num_keypoints = config.getint('predict', 'min_num_keypoints')
model = create_model(args, config)
if os.path.exists('mask.png'):
mask = Image.open('mask.png')
mask = mask.resize((200, 200))
else:
mask = None
capture = Capture(model.insize)
predictor = Predictor(model=model, cap=capture)
capture.start()
predictor.start()
fps_time = 0
degree = 0
main_event = threading.Event()
try:
while not main_event.is_set():
degree += 5
degree = degree % 360
try:
image, feature_map = predictor.get()
humans = get_humans_by_feature(
model,
feature_map,
detection_thresh,
min_num_keypoints
)
except queue.Empty:
continue
except Exception:
break
pilImg = Image.fromarray(image)
pilImg = draw_humans(
model.keypoint_names,
model.edges,
pilImg,
humans,
mask=mask.rotate(degree) if mask else None,
visbbox=config.getboolean('predict', 'visbbox'),
)
img_with_humans = cv2.cvtColor(np.asarray(pilImg), cv2.COLOR_RGB2BGR)
msg = 'GPU ON' if chainer.backends.cuda.available else 'GPU OFF'
msg += ' ' + config.get('model_param', 'model_name')
cv2.putText(img_with_humans, 'FPS: %f' % (1.0 / (time.time() - fps_time)),
(10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
img_with_humans = cv2.resize(img_with_humans, (3 * model.insize[0], 3 * model.insize[1]))
cv2.imshow('Pose Proposal Network' + msg, img_with_humans)
fps_time = time.time()
# press Esc to exit
if cv2.waitKey(1) == 27:
main_event.set()
except Exception as e:
print(e)
except KeyboardInterrupt:
main_event.set()
capture.stop()
predictor.stop()
capture.join()
predictor.join()
def video_handle(video_file, video_output):
global FPS_list
# Video reader
cam = cv2.VideoCapture(video_file)
input_fps = cam.get(cv2.CAP_PROP_FPS)
ret_val, input_image = cam.read()
video_length = int(cam.get(cv2.CAP_PROP_FRAME_COUNT))
ending_frame = video_length
# Video writer
frame_rate_ratio = 1
output_fps = input_fps / frame_rate_ratio
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
# 后面两者是写入的图片大小
out = cv2.VideoWriter(video_output, fourcc, output_fps, (672, 672))
i = 0 # default is 0
while (cam.isOpened()) and ret_val == True and i < ending_frame:
if i % frame_rate_ratio == 0:
tic = time.time()
mask = None
input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
input_image = cv2.resize(input_image, model.insize)
humans = estimate(
model,
input_image.transpose(2, 0, 1).astype(np.float32))
pilImg = Image.fromarray(input_image)
pilImg = draw_humans(
model.keypoint_names,
model.edges,
pilImg,
humans,
mask=mask.rotate(degree) if mask else None)
img_with_humans = cv2.cvtColor(np.asarray(pilImg),
cv2.COLOR_RGB2BGR)
msg = 'GPU ON' if chainer.backends.cuda.available else 'GPU OFF'
msg += ' ' + config.get('model_param', 'model_name')
FPS = round(1.0 / (time.time() - tic), 2)
FPS_list.append(FPS)
cv2.putText(img_with_humans,
'FPS: % f' % (1.0 / (time.time() - tic)), (10, 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
img_with_humans = cv2.resize(
img_with_humans,
(3 * model.insize[0], 3 * model.insize[1]))
# cv2.imshow('Pose Proposal Network' + msg, img_with_humans)
print('Processing frame: {}/{}'.format(
i, video_length / frame_rate_ratio))
toc = time.time()
print('processing time is %.5f' % (toc - tic))
out.write(img_with_humans)
# 每次在这里变化的
ret_val, input_image = cam.read()
i += 1
# compute average FPS
average_fps = sum(FPS_list) / len(FPS_list)
print('total {} frame, {} frame per second\n\n\n'.format(
len(FPS_list), round(average_fps, 1)))
def high_speed(args):
config = load_config(args)
dataset_type = config.get('dataset', 'type')
detection_thresh = config.getfloat('predict', 'detection_thresh')
min_num_keypoints = config.getint('predict', 'min_num_keypoints')
model = create_model(args, config)
if os.path.exists('mask.png'):
mask = Image.open('mask.png')
mask = mask.resize((200, 200))
else:
mask = None
cap = cv2.VideoCapture(0) # get input from usb camera
# cap = cv2.VideoCapture("/home/fabian/Documents/dataset/videos/test4.mp4")
if cap.isOpened() is False:
print('Error opening video stream or file')
exit(1)
cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))
cap.set(cv2.CAP_PROP_FPS, 60)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
logger.info('camera will capture {} FPS'.format(cap.get(cv2.CAP_PROP_FPS)))
capture = Capture(cap, model.insize)
predictor = Predictor(model=model, cap=capture)
capture.start()
predictor.start()
fps_time = 0
degree = 0
main_event = threading.Event()
try:
while not main_event.is_set() and cap.isOpened():
degree += 5
degree = degree % 360
try:
image, feature_map = predictor.get()
humans = get_humans_by_feature(model, feature_map,
detection_thresh,
min_num_keypoints)
except queue.Empty:
continue
except Exception:
break
pilImg = Image.fromarray(image)
pilImg = draw_humans(
model.keypoint_names,
model.edges,
pilImg,
humans,
mask=mask.rotate(degree) if mask else None,
visbbox=config.getboolean('predict', 'visbbox'),
)
img_with_humans = cv2.cvtColor(np.asarray(pilImg),
cv2.COLOR_RGB2BGR)
msg = 'GPU ON' if chainer.backends.cuda.available else 'GPU OFF'
msg += ' ' + config.get('model_param', 'model_name')
cv2.putText(img_with_humans,
'FPS: %f' % (1.0 / (time.time() - fps_time)), (10, 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
img_with_humans = cv2.resize(
img_with_humans, (3 * model.insize[0], 3 * model.insize[1]))
cv2.imshow('Pose Proposal Network' + msg, img_with_humans)
fps_time = time.time()
# press Esc to exit
if cv2.waitKey(1) == 27:
main_event.set()
except Exception as e:
print(e)
except KeyboardInterrupt:
main_event.set()
capture.stop()
predictor.stop()
capture.join()
predictor.join()
while (cam.isOpened()) and ret_val == True and i < ending_frame:
# 每隔几帧处理一次帧图片
if i % frame_rate_ratio == 0:
tic = time.time()
mask = None
input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
input_image = cv2.resize(input_image, model.insize)
humans = estimate(
model,
input_image.transpose(2, 0, 1).astype(np.float32))
pilImg = Image.fromarray(input_image)
pilImg = draw_humans(model.keypoint_names,
model.edges,
pilImg,
humans,
mask=mask.rotate(degree) if mask else None)
img_with_humans = cv2.cvtColor(np.asarray(pilImg),
cv2.COLOR_RGB2BGR)
msg = 'GPU ON' if chainer.backends.cuda.available else 'GPU OFF'
msg += ' ' + config.get('model_param', 'model_name')
cv2.putText(img_with_humans,
'FPS: % f' % (1.0 / (time.time() - tic)), (10, 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
img_with_humans = cv2.resize(
img_with_humans, (3 * model.insize[0], 3 * model.insize[1]))
# cv2.imshow('Pose Proposal Network' + msg, img_with_humans)
print('Processing frame: {}/{}'.format(
i, video_length / frame_rate_ratio))
def high_speed(args, viewer):
config = load_config(args)
dataset_type = config.get('dataset', 'type')
detection_thresh = config.getfloat('predict', 'detection_thresh')
min_num_keypoints = config.getint('predict', 'min_num_keypoints')
model = create_model(args, config)
svo_file_path = None #"/home/adujardin/Downloads/5m.svo" #config.get('zed', 'svo_file_path')
init_cap_params = sl.InitParameters()
if svo_file_path:
print("Loading SVO file " + svo_file_path)
init_cap_params.svo_input_filename = svo_file_path
init_cap_params.svo_real_time_mode = True
init_cap_params.camera_resolution = sl.RESOLUTION.RESOLUTION_HD720
init_cap_params.depth_mode = sl.DEPTH_MODE.DEPTH_MODE_ULTRA
init_cap_params.coordinate_units = sl.UNIT.UNIT_METER
init_cap_params.depth_stabilization = True
init_cap_params.coordinate_system = sl.COORDINATE_SYSTEM.COORDINATE_SYSTEM_RIGHT_HANDED_Y_UP
cap = sl.Camera()
if not cap.is_opened():
print("Opening ZED Camera...")
status = cap.open(init_cap_params)
if status != sl.ERROR_CODE.SUCCESS:
print(repr(status))
exit()
py_transform = sl.Transform()
tracking_parameters = sl.TrackingParameters(init_pos=py_transform)
cap.enable_tracking(tracking_parameters)
capture = Capture(cap, model.insize)
predictor = Predictor(model=model, cap=capture)
capture.start()
predictor.start()
fps_time = 0
main_event = threading.Event()
viewer.edges = model.edges
try:
while not main_event.is_set() and cap.is_opened():
try:
image, feature_map, depth = predictor.get()
humans = get_humans_by_feature(model, feature_map,
detection_thresh,
min_num_keypoints)
humans_3d = get_humans3d(humans, depth, model)
except queue.Empty:
continue
except Exception as e:
print(e)
break
pilImg = Image.fromarray(image)
pilImg = draw_humans(
model.keypoint_names,
model.edges,
pilImg,
humans,
None,
visbbox=config.getboolean('predict', 'visbbox'),
)
img_with_humans = cv2.cvtColor(np.asarray(pilImg),
cv2.COLOR_RGB2BGR)
img_with_humans = cv2.resize(
img_with_humans,
(700, 400)) #(3 * model.insize[0], 3 * model.insize[1]))
msg = 'GPU ON' if chainer.backends.cuda.available else 'GPU OFF'
msg += ' ' + config.get('model_param', 'model_name')
fps_display = 'FPS: %f' % (1.0 / (time.time() - fps_time))
str_to_dsplay = msg + " " + fps_display
cv2.putText(img_with_humans, fps_display, (10, 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.imshow('Pose Proposal Network' + msg, img_with_humans)
viewer.update_text(str_to_dsplay)
viewer.update_humans(humans_3d)
fps_time = time.time()
key = cv2.waitKey(1)
# press Esc to exit
if key == 27:
exit
main_event.set()
except Exception as e:
print(e)
except KeyboardInterrupt:
main_event.set()
capture.stop()
predictor.stop()
capture.join()
predictor.join()
cap.close()
