def test_youtube_playback(url):
"""
Testing Youtube Video Playback capabilities of VidGear
"""
try:
height = 0
width = 0
fps = 0
# get params
stream = CamGear(source=url, y_tube=True,
logging=True).start() # YouTube Video URL as input
while True:
frame = stream.read()
if frame is None:
break
if height == 0 or width == 0:
fps = stream.framerate
height, width = frame.shape[:2]
break
stream.stop()
# get true params
true_video_param = return_youtubevideo_params(url)
# log everything
logger.debug("WIDTH: {} HEIGHT: {} FPS: {}".format(
true_video_param[0], true_video_param[1], true_video_param[2]))
logger.debug("WIDTH: {} HEIGHT: {} FPS: {}".format(width, height, fps))
# assert true verses ground results
assert (true_video_param[0] == width and true_video_param[1] == height
and round(true_video_param[2], 1) == round(fps, 1))
except Exception as e:
if isinstance(e, (RuntimeError, ValueError)) and url == "im_not_a_url":
pass
else:
pytest.fail(str(e))
def test_write(conversion):
"""
Testing WriteGear Compression-Mode(FFmpeg) Writer capabilties in different colorspace
"""
#Open stream
stream = CamGear(source=return_testvideo_path(), colorspace = conversion, logging=True).start()
writer = WriteGear(output_filename = 'Output_tw.mp4', custom_ffmpeg = return_static_ffmpeg()) #Define writer
while True:
frame = stream.read()
# check if frame is None
if frame is None:
#if True break the infinite loop
break
if conversion in ['COLOR_BGR2RGB', 'COLOR_BGR2RGBA']:
writer.write(frame, rgb_mode = True)
else:
writer.write(frame)
stream.stop()
writer.close()
basepath, _ = os.path.split(return_static_ffmpeg())
ffprobe_path = os.path.join(basepath,'ffprobe.exe' if os.name == 'nt' else 'ffprobe')
result = check_output([ffprobe_path, "-v", "error", "-count_frames", "-i", os.path.abspath('Output_tw.mp4')])
if result:
if not isinstance(result, string_types):
result = result.decode()
logger.debug('Result: {}'.format(result))
for i in ["Error", "Invalid", "error", "invalid"]:
assert not(i in result)
os.remove(os.path.abspath('Output_tw.mp4'))
def test_rtf_stream(conversion):
"""
Testing Real-Time Frames Mode
"""
mpd_file_path = return_mpd_path()
try:
# Open stream
stream = CamGear(source=return_testvideo_path(),
colorspace=conversion).start()
stream_params = {
"-clear_prev_assets": True,
"-input_framerate": "invalid",
}
streamer = StreamGear(output=mpd_file_path, **stream_params)
while True:
frame = stream.read()
# check if frame is None
if frame is None:
break
if conversion == "COLOR_BGR2RGBA":
streamer.stream(frame, rgb_mode=True)
else:
streamer.stream(frame)
stream.stop()
streamer.terminate()
mpd_file = [
os.path.join(mpd_file_path, f) for f in os.listdir(mpd_file_path)
if f.endswith(".mpd")
]
assert len(mpd_file) == 1, "Failed to create MPD file!"
assert check_valid_mpd(mpd_file[0])
except Exception as e:
pytest.fail(str(e))
def test_stream_mode(url, quality, parameters):
"""
Testing Stream Mode Playback capabilities of CamGear
"""
try:
height = 0
width = 0
fps = 0
options = {"STREAM_RESOLUTION": quality, "STREAM_PARAMS": parameters}
# get params
stream = CamGear(
source=url, stream_mode=True, logging=True, **options
).start() # YouTube Video URL as input
while True:
frame = stream.read()
if frame is None:
break
if height == 0 or width == 0:
fps = stream.framerate
height, width = frame.shape[:2]
break
stream.stop()
logger.debug("WIDTH: {} HEIGHT: {} FPS: {}".format(width, height, fps))
except Exception as e:
if isinstance(e, (RuntimeError, ValueError)) and (
url == "im_not_a_url" or platform.system() in ["Windows", "Darwin"]
):
pass
else:
pytest.fail(str(e))
def test_threaded_queue_mode(source, options):
"""
Test for the Thread Queue Mode in CamGear API
"""
try:
if platform.system() == "Linux":
stream_camgear = CamGear(
source=source, backend=cv2.CAP_FFMPEG, logging=True, **options
).start()
else:
stream_camgear = CamGear(source=source, logging=True, **options).start()
camgear_frames_num = 0
while True:
frame = stream_camgear.read()
if frame is None:
logger.debug("VidGear Total frames: {}".format(camgear_frames_num))
break
time.sleep(0.2) # dummy computational task
camgear_frames_num += 1
stream_camgear.stop()
actual_frame_num = return_total_frame_count()
if "THREADED_QUEUE_MODE" in options and not options["THREADED_QUEUE_MODE"]:
# emulate frame skipping
assert camgear_frames_num < actual_frame_num
else:
assert camgear_frames_num == actual_frame_num
except Exception as e:
if isinstance(e, RuntimeError) and source == "im_not_a_source.mp4":
pass
else:
pytest.fail(str(e))
def test_rtf_livestream(format):
"""
Testing Real-Time Frames Mode with livestream.
"""
assets_file_path = return_assets_path(False if format == "dash" else True)
try:
# Open stream
options = {"THREAD_TIMEOUT": 300}
stream = CamGear(source=return_testvideo_path(), **options).start()
stream_params = {
"-livestream": True,
}
streamer = StreamGear(output=assets_file_path, format=format, **stream_params)
while True:
frame = stream.read()
# check if frame is None
if frame is None:
break
streamer.stream(frame)
stream.stop()
streamer.terminate()
except Exception as e:
if not isinstance(e, queue.Empty):
pytest.fail(str(e))
def test_write(conversion):
"""
Testing WriteGear Compression-Mode(FFmpeg) Writer capabilties in different colorspace with CamGearAPI.
"""
# Open stream
stream = CamGear(
source=return_testvideo_path(), colorspace=conversion, logging=True
).start()
writer = WriteGear(
output_filename="Output_tw.mp4", custom_ffmpeg=return_static_ffmpeg()
) # Define writer
while True:
frame = stream.read()
# check if frame is None
if frame is None:
# if True break the infinite loop
break
if conversion == "COLOR_BGR2RGBA":
writer.write(frame, rgb_mode=True)
elif conversion == "COLOR_BGR2INVALID":
# test invalid color_space value
stream.color_space = "wrong_colorspace"
conversion = "COLOR_BGR2INVALID2"
writer.write(frame)
elif conversion == "COLOR_BGR2INVALID2":
# test wrong color_space value
stream.color_space = 1546755
conversion = ""
writer.write(frame)
else:
writer.write(frame)
stream.stop()
writer.close()
basepath, _ = os.path.split(return_static_ffmpeg())
ffprobe_path = os.path.join(
basepath, "ffprobe.exe" if os.name == "nt" else "ffprobe"
)
result = check_output(
[
ffprobe_path,
"-v",
"error",
"-count_frames",
"-i",
os.path.abspath("Output_tw.mp4"),
]
)
if result:
if not isinstance(result, string_types):
result = result.decode()
logger.debug("Result: {}".format(result))
for i in ["Error", "Invalid", "error", "invalid"]:
assert not (i in result)
os.remove(os.path.abspath("Output_tw.mp4"))
def test_rtf_stream(conversion, format):
"""
Testing Real-Time Frames Mode
"""
assets_file_path = return_assets_path(False if format == "dash" else True)
try:
# Open stream
options = {"THREAD_TIMEOUT": 300}
stream = CamGear(
source=return_testvideo_path(), colorspace=conversion, **options
).start()
stream_params = {
"-clear_prev_assets": True,
"-input_framerate": "invalid",
}
if format == "hls":
stream_params.update(
{
"-hls_base_url": return_assets_path(
False if format == "dash" else True
)
+ os.sep
}
)
streamer = StreamGear(output=assets_file_path, format=format, **stream_params)
while True:
frame = stream.read()
# check if frame is None
if frame is None:
break
if conversion == "COLOR_BGR2RGBA":
streamer.stream(frame, rgb_mode=True)
else:
streamer.stream(frame)
stream.stop()
streamer.terminate()
asset_file = [
os.path.join(assets_file_path, f)
for f in os.listdir(assets_file_path)
if f.endswith(".mpd" if format == "dash" else ".m3u8")
]
assert len(asset_file) == 1, "Failed to create asset file!"
if format == "dash":
assert check_valid_mpd(asset_file[0]), "Test Failed!"
else:
assert extract_meta_video(asset_file[0]), "Test Failed!"
except Exception as e:
if not isinstance(e, queue.Empty):
pytest.fail(str(e))
def playback(level):
"""
tests CamGear API's playback capabilities
"""
options = {"THREADED_QUEUE_MODE": False}
stream = CamGear(source=level, **options).start()
fps = FPS().start()
while True:
frame = stream.read()
if frame is None:
break
fps.update()
stream.stop()
logger.info("approx. FPS: {:.2f}".format(fps.average_fps()))
def playback(level):
"""
Function to test VidGear playback capabilities
"""
stream = CamGear(source=level).start()
fps = FPS().start()
while True:
frame = stream.read()
if frame is None:
break
fps.update()
stream.stop()
fps.stop()
print("[LOG] total elasped time: {:.2f}".format(fps.total_time_elapsed()))
print("[LOG] approx. FPS: {:.2f}".format(fps.fps()))
def playback(level):
"""
tests CamGear API's playback capabilities
"""
options = {'THREADED_QUEUE_MODE': False}
stream = CamGear(source=level, **options).start()
fps = FPS().start()
while True:
frame = stream.read()
if frame is None:
break
fps.update()
stream.stop()
fps.stop()
logger.debug("total elasped time: {:.2f}".format(fps.total_time_elapsed()))
logger.debug("approx. FPS: {:.2f}".format(fps.fps()))
def Videocapture_withVidGear(path):
"""
Function to benchmark VidGear multi-threaded video playback
"""
stream = CamGear(source=path).start()
fps_Vid = FPS().start()
while True:
frame = stream.read()
if frame is None:
break
fps_Vid.update()
fps_Vid.stop()
stream.stop()
print("VidGear")
print("[LOG] total elasped time: {:.2f}".format(
fps_Vid.total_time_elapsed()))
print("[LOG] approx. FPS: {:.2f}".format(fps_Vid.fps()))
def Videocapture_withVidGear(path):
"""
Function to benchmark VidGear multi-threaded video playback
"""
options = {'THREADED_QUEUE_MODE': False}
stream = CamGear(source=path, **options).start()
fps_Vid = FPS().start()
while True:
frame = stream.read()
if frame is None:
break
fps_Vid.update()
fps_Vid.stop()
stream.stop()
logger.debug("VidGear")
logger.debug("total elasped time: {:.2f}".format(
fps_Vid.total_time_elapsed()))
logger.debug("approx. FPS: {:.2f}".format(fps_Vid.fps()))
def test_network_playback():
"""
Testing Direct Network Video Playback capabilities of VidGear(with rtsp streaming)
"""
Url = 'rtsp://184.72.239.149/vod/mp4:BigBuckBunny_175k.mov'
try:
output_stream = CamGear(source = Url).start()
i = 0
Output_data = []
while i<10:
frame = output_stream.read()
if frame is None:
break
Output_data.append(frame)
i+=1
output_stream.stop()
print('Output data shape:', np.array(Output_data).shape)
except Exception as e:
pytest.fail(str(e))
def get_frames1(self):
# import required libraries
from vidgear.gears import CamGear
import cv2
# Add YouTube Video URL as input source (for e.g https://youtu.be/bvetuLwJIkA)
# and enable Stream Mode (`stream_mode = True`)
stream = CamGear(source=self.__url, stream_mode=True,
logging=True).start()
skip = 0
# loop over
while True:
# read frames from stream
frame = stream.read()
# check for frame if Nonetype
if frame is None:
break
if frame is None:
break
if self._max_dim is not None:
frame = resize_if_larger(frame, self._max_dim)
if skip > 0:
skip = skip - 1
else:
yield frame
k = cv2.waitKey(1) & 0xFF
if k == ord("q"):
break
elif k == ord("s"):
skip = 10
# close output window
cv2.destroyAllWindows()
# safely close video stream
stream.stop()
def test_network_playback():
"""
Testing Direct Network Video Playback capabilities of VidGear(with rtsp streaming)
"""
Publictest_rstp_urls = [
"rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mov",
"rtsp://freja.hiof.no:1935/rtplive/definst/hessdalen03.stream",
"rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa",
"rtmp://semerkandglb.mediatriple.net:1935/semerkandliveedge/semerkand2",
]
index = 0
while index < len(Publictest_rstp_urls):
try:
output_stream = CamGear(
source=Publictest_rstp_urls[index], logging=True
).start()
i = 0
Output_data = []
while i < 10:
frame = output_stream.read()
if frame is None:
break
Output_data.append(frame)
i += 1
output_stream.stop()
logger.debug("Output data shape:", np.array(Output_data).shape)
if Output_data[-1].shape[:2] > (50, 50):
break
except Exception as e:
if isinstance(e, RuntimeError):
logger.debug(
"`{}` URL is not working".format(Publictest_rstp_urls[index])
)
index += 1
continue
else:
pytest.fail(str(e))
if index == len(Publictest_rstp_urls):
pytest.fail("Test failed to play any URL!")
def test_threaded_queue_mode(): """ Test for New Thread Queue Mode in CamGear Class """ actual_frame_num = return_total_frame_count() stream_camgear = CamGear(source=return_testvideo_path(), logging=True).start() #start stream on CamGear camgear_frames_num = 0 while True: frame = stream_camgear.read() if frame is None: print(camgear_frames_num) break time.sleep(0.2) #dummy computational task camgear_frames_num += 1 stream_camgear.stop() assert camgear_frames_num == actual_frame_num
def scrape_live(url, duration=60, show=False):
print('Scraping')
# create pafy object. Just used to extract name of YouTube video
pafy_vid = pafy.new(url)
title = folder
title += pafy_vid.title
# cleanup title so nicer for video_naming
title = title.replace(' ', '-')
title = title.replace('.', '')
# get time
now = datetime.now()
# add time stamp
title += now.strftime("-%m_%d_%Y-%H_%M_%S")
file_name = title + '.' + file_type
stream = CamGear(source=url, y_tube=True, time_delay=1,
logging=True).start()
fourcc = cv2.VideoWriter_fourcc(*codec)
out = cv2.VideoWriter(file_name, fourcc, fps, (1920, 1080))
start = time.time()
frames = 0
while time.time() - start < duration:
frame = stream.read()
frames += 1
out.write(frame)
if frame is None:
break
if show:
cv2.imshow('Output Frame', frame)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
if show:
cv2.destroyAllWindows()
stream.stop()
out.release()
print('Done!')
def test_rtf_livestream():
"""
Testing Real-Time Frames Mode with livestream.
"""
mpd_file_path = return_mpd_path()
try:
# Open stream
stream = CamGear(source=return_testvideo_path()).start()
stream_params = {
"-livestream": True,
}
streamer = StreamGear(output=mpd_file_path, **stream_params)
while True:
frame = stream.read()
# check if frame is None
if frame is None:
break
streamer.stream(frame)
stream.stop()
streamer.terminate()
except Exception as e:
pytest.fail(str(e))
def camera_gear(id):
cam = cctv_cam(id)
print(cam)
options = {
"CAP_PROP_FRAME_WIDTH": 320,
"CAP_PROP_FRAME_HEIGHT": 240,
"CAP_PROP_FPS": 70
}
stream = CamGear(source=cam, **options).start()
while True:
frame = stream.read()
frame = imutils.resize(frame, width=450)
if frame is None:
break
frame = cv2.imencode('.png', frame)[1].tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/png\r\n\r\n' + frame + b'\r\n')
stream.stop()
def main(camera_id, filename, hrnet_c, hrnet_j, hrnet_weights,
hrnet_joints_set, image_resolution, disable_tracking, max_nof_people,
max_batch_size, disable_vidgear, save_video, video_format,
video_framerate, device):
if device is not None:
device = torch.device(device)
else:
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
device = torch.device('cuda')
else:
device = torch.device('cpu')
# print(device)
has_display = 'DISPLAY' in os.environ.keys() or sys.platform == 'win32'
video_writer = None
if filename is not None:
rotation_code = check_video_rotation(filename)
video = cv2.VideoCapture(filename)
assert video.isOpened()
else:
rotation_code = None
if disable_vidgear:
video = cv2.VideoCapture(camera_id)
assert video.isOpened()
else:
video = CamGear(camera_id).start()
model = SimpleHigherHRNet(hrnet_c,
hrnet_j,
hrnet_weights,
resolution=image_resolution,
return_bounding_boxes=not disable_tracking,
max_nof_people=max_nof_people,
max_batch_size=max_batch_size,
device=device)
if not disable_tracking:
prev_boxes = None
prev_pts = None
prev_person_ids = None
next_person_id = 0
while True:
t = time.time()
if filename is not None or disable_vidgear:
ret, frame = video.read()
if not ret:
break
if rotation_code is not None:
frame = cv2.rotate(frame, rotation_code)
else:
frame = video.read()
if frame is None:
break
pts = model.predict(frame)
if not disable_tracking:
boxes, pts = pts
if not disable_tracking:
if len(pts) > 0:
if prev_pts is None and prev_person_ids is None:
person_ids = np.arange(next_person_id,
len(pts) + next_person_id,
dtype=np.int32)
next_person_id = len(pts) + 1
else:
boxes, pts, person_ids = find_person_id_associations(
boxes=boxes,
pts=pts,
prev_boxes=prev_boxes,
prev_pts=prev_pts,
prev_person_ids=prev_person_ids,
next_person_id=next_person_id,
pose_alpha=0.2,
similarity_threshold=0.4,
smoothing_alpha=0.1,
)
next_person_id = max(next_person_id,
np.max(person_ids) + 1)
else:
person_ids = np.array((), dtype=np.int32)
prev_boxes = boxes.copy()
prev_pts = pts.copy()
prev_person_ids = person_ids
else:
person_ids = np.arange(len(pts), dtype=np.int32)
for i, (pt, pid) in enumerate(zip(pts, person_ids)):
frame = draw_points_and_skeleton(
frame,
pt,
joints_dict()[hrnet_joints_set]['skeleton'],
person_index=pid,
points_color_palette='gist_rainbow',
skeleton_color_palette='jet',
points_palette_samples=10)
fps = 1. / (time.time() - t)
print('\rframerate: %f fps / detected people: %d' % (fps, len(pts)),
end='')
if has_display:
cv2.imshow('frame.png', frame)
k = cv2.waitKey(1)
if k == 27: # Esc button
if disable_vidgear:
video.release()
else:
video.stop()
break
else:
cv2.imwrite('frame.png', frame)
if save_video:
if video_writer is None:
fourcc = cv2.VideoWriter_fourcc(*video_format) # video format
video_writer = cv2.VideoWriter(
'output.avi', fourcc, video_framerate,
(frame.shape[1], frame.shape[0]))
video_writer.write(frame)
if save_video:
video_writer.release()
def main(camera_id, filename, hrnet_c, hrnet_j, hrnet_weights,
hrnet_joints_set, single_person, max_batch_size, disable_vidgear,
device):
if device is not None:
device = torch.device(device)
else:
if torch.cuda.is_available() and True:
torch.backends.cudnn.deterministic = True
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
print(device)
has_display = 'DISPLAY' in os.environ.keys() or sys.platform == 'win32'
if filename is not None:
video = cv2.VideoCapture(filename)
assert video.isOpened()
else:
if disable_vidgear:
video = cv2.VideoCapture(camera_id)
assert video.isOpened()
else:
video = CamGear(camera_id).start()
model = SimpleHRNet(hrnet_c,
hrnet_j,
hrnet_weights,
multiperson=not single_person,
max_batch_size=max_batch_size,
device=device)
while True:
if disable_vidgear:
ret, frame = video.read()
if not ret:
break
else:
frame = video.read()
if frame is None:
break
pts = model.predict(frame)
for i, pt in enumerate(pts):
frame = draw_points_and_skeleton(
frame,
pt,
joints_dict()[hrnet_joints_set]['skeleton'],
person_index=i,
joints_color_palette='gist_rainbow',
skeleton_color_palette='jet',
joints_palette_samples=10)
if has_display:
cv2.imshow('frame.png', frame)
k = cv2.waitKey(1)
if k == 27: # Esc button
if disable_vidgear:
video.release()
else:
video.stop()
break
else:
cv2.imwrite('frame.png', frame)
def open_app(camera_id=0,
filename=None,
hrnet_c=48,
hrnet_j=17,
hrnet_weights="./weights/pose_hrnet_w48_384x288.pth",
hrnet_joints_set="coco",
image_resolution='(384, 288)',
single_person=True,
max_batch_size=16,
disable_vidgear=False,
device=None):
if device is not None:
device = torch.device(device)
else:
if torch.cuda.is_available() and True:
torch.backends.cudnn.deterministic = True
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
image_resolution = ast.literal_eval(image_resolution)
has_display = 'DISPLAY' in os.environ.keys() or sys.platform == 'win32'
if filename is not None:
video = cv2.VideoCapture(filename)
assert video.isOpened()
else:
if disable_vidgear:
video = cv2.VideoCapture(camera_id)
assert video.isOpened()
else:
video = CamGear(camera_id).start()
model = SimpleHRNet(hrnet_c,
hrnet_j,
hrnet_weights,
resolution=image_resolution,
multiperson=not single_person,
max_batch_size=max_batch_size,
device=device)
loaded_model = pickle.load(open("mlp_model_best.sav", 'rb'))
no_to_label = {
0: "tree",
1: "warrior1",
2: "warrior2",
3: "childs",
4: "downwarddog",
5: "plank",
6: "mountain",
7: "trianglepose"
}
image_to_blob = {}
for id, path in no_to_label.items():
images = [
cv2.imread(file)
for file in glob.glob('sampleposes\\' + path + '.jpg')
]
image_to_blob[id] = images
while True:
if filename is not None or disable_vidgear:
ret, frame = video.read()
if not ret:
break
else:
frame = video.read()
if frame is None:
break
pts = model.predict(frame)
resolution = frame.shape
x_len = resolution[0]
y_len = resolution[1]
vector = []
if len(pts) == 0:
continue
keypoints = pts[0]
for pt in keypoints:
pt = list(pt)
temp = []
temp.append((pt[0] / x_len))
temp.append((pt[1] / y_len))
vector.extend(temp)
vector = list(vector)
predicted_pose = loaded_model.predict([vector])
text = no_to_label[predicted_pose[0]] + " pose"
cv2.putText(image_to_blob[predicted_pose[0]][0], text,
bottomLeftCornerOfText, font, fontScale, fontColor,
lineType)
cv2.imshow("Suggestion", image_to_blob[predicted_pose[0]][0])
k = cv2.waitKey(1)
for i, pt in enumerate(pts):
frame = draw_points_and_skeleton(
frame,
pt,
joints_dict()[hrnet_joints_set]['skeleton'],
person_index=i,
points_color_palette='gist_rainbow',
skeleton_color_palette='jet',
points_palette_samples=10)
if has_display:
cv2.imshow('frame.png', frame)
k = cv2.waitKey(1)
if k == 27: # Esc button
if disable_vidgear:
video.release()
else:
video.stop()
break
else:
cv2.imwrite('frame.png', frame)
def main(camera_id, filename, hrnet_m, hrnet_c, hrnet_j, hrnet_weights,
hrnet_joints_set, image_resolution, single_person, use_tiny_yolo,
disable_tracking, max_batch_size, disable_vidgear, save_video,
video_format, video_framerate, device):
if device is not None:
device = torch.device(device)
else:
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
image_resolution = ast.literal_eval(image_resolution)
has_display = 'DISPLAY' in os.environ.keys() or sys.platform == 'win32'
video_writer = None
falldown = FallDown()
if foldername is not None:
images_path = foldername
images_name = os.listdir(images_path)
images_path = [os.path.join(images_path, name) for name in images_name]
images_path.sort()
else:
if filename is not None:
rotation_code = check_video_rotation(filename)
video = cv2.VideoCapture(filename)
assert video.isOpened()
else:
rotation_code = None
if disable_vidgear:
video = cv2.VideoCapture(camera_id)
assert video.isOpened()
else:
video = CamGear(camera_id).start()
if use_tiny_yolo:
yolo_model_def = "./models/detectors/yolo/config/yolov3-tiny.cfg"
yolo_class_path = "./models/detectors/yolo/data/coco.names"
yolo_weights_path = "./models/detectors/yolo/weights/yolov3-tiny.weights"
else:
yolo_model_def = "./models/detectors/yolo/config/yolov3.cfg"
yolo_class_path = "./models/detectors/yolo/data/coco.names"
yolo_weights_path = "./models/detectors/yolo/weights/yolov3.weights"
model = SimpleHRNet(hrnet_c,
hrnet_j,
hrnet_weights,
model_name=hrnet_m,
resolution=image_resolution,
multiperson=not single_person,
return_bounding_boxes=not disable_tracking,
max_batch_size=max_batch_size,
yolo_model_def=yolo_model_def,
yolo_class_path=yolo_class_path,
yolo_weights_path=yolo_weights_path,
device=device)
if not disable_tracking:
prev_boxes = None
prev_pts = None
prev_person_ids = None
next_person_id = 0
step = 0
while True:
t = time.time()
if foldername is None:
if filename is not None or disable_vidgear:
ret, frame = video.read()
if not ret:
break
if rotation_code is not None:
frame = cv2.rotate(frame, rotation_code)
else:
frame = video.read()
if frame is None:
break
else:
if step >= len(images_path):
break
# Pre-process
images = []
images_origin = []
path = images_path[step]
frame = cv2.imread(path, cv2.IMREAD_COLOR)
if frame is None:
logging.error("read image error: {}. skip it.".format(path))
continue
pts = model.predict(frame)
if not disable_tracking:
boxes, pts = pts
if not disable_tracking:
if len(pts) > 0:
if prev_pts is None and prev_person_ids is None:
person_ids = np.arange(next_person_id,
len(pts) + next_person_id,
dtype=np.int32)
next_person_id = len(pts) + 1
else:
boxes, pts, person_ids = find_person_id_associations(
boxes=boxes,
pts=pts,
prev_boxes=prev_boxes,
prev_pts=prev_pts,
prev_person_ids=prev_person_ids,
next_person_id=next_person_id,
pose_alpha=0.2,
similarity_threshold=0.4,
smoothing_alpha=0.1,
)
next_person_id = max(next_person_id,
np.max(person_ids) + 1)
else:
person_ids = np.array((), dtype=np.int32)
prev_boxes = boxes.copy()
prev_pts = pts.copy()
prev_person_ids = person_ids
else:
person_ids = np.arange(len(pts), dtype=np.int32)
for i, (pt, pid) in enumerate(zip(pts, person_ids)):
frame = draw_points_and_skeleton(
frame,
pt,
joints_dict()[hrnet_joints_set]['skeleton'],
person_index=pid,
points_color_palette='gist_rainbow',
skeleton_color_palette='jet',
points_palette_samples=10)
frame = falldown.check_fall_down(
frame, pt,
joints_dict()[hrnet_joints_set]['skeleton'], video_framerate)
fps = 1. / (time.time() - t)
print('\rframerate: %f fps' % fps, end='')
if has_display:
cv2.imshow('frame.png', frame)
k = cv2.waitKey(1)
if k == 27: # Esc button
if disable_vidgear:
video.release()
else:
video.stop()
break
else:
cv2.imwrite('frame.png', frame)
if save_video:
if video_writer is None:
fourcc = cv2.VideoWriter_fourcc(*video_format) # video format
video_writer = cv2.VideoWriter(
'output.avi', fourcc, video_framerate,
(frame.shape[1], frame.shape[0]))
video_writer.write(frame)
if save_video:
video_writer.release()
backend=cv2.CAP_V4L,
logging=True,
**options).start()
writer = WriteGear(output_filename='rtsp://192.168.0.100:5541/test',
compression_mode=True,
logging=True,
**output_params)
i = 0
faces = []
while True:
try:
i += 1
frame = stream.read()
if frame is not None:
# 半秒读取下人脸
if i % 12 == 0:
faces = detect_face(frame)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 1)
writer.write(frame)
else:
print("no frame")
except KeyboardInterrupt:
break
# safely close video stream
stream.stop()
writer.close()
print("time:", datetime.datetime.now().timestamp() - t.timestamp())
if area_set == 0 and area != 0:
area_set = area
intial_dim = (left, top, rect_width, rect_height)
if area > area_set:
tracker = cv2.TrackerMedianFlow_create()
tracker.init(frame, intial_dim)
print("setting to original ...")
if area < area_set - ((10 * area_set) / 100):
q.put(True)
else:
q.put(False)
# if (area == 0) and area_set > 0:
# area_set = 0
# tracker = cv2.TrackerMedianFlow_create()
# q.put(False)
cv2.namedWindow("Main Window", cv2.WINDOW_NORMAL)
cv2.putText(frame, str(area), (int(left), int(top)), 1,
cv2.FONT_HERSHEY_DUPLEX, (124, 50, 145), 2)
cv2.rectangle(frame, (int(left), int(top)), (int(right), int(bottom)),
(124, 50, 145), 3)
cv2.imshow("Main Window", frame)
out.write(frame)
key = cv2.waitKey(1)
if key == ord("q"):
break
# cleanup the camera and close any open windows
vs.stop()
out.release()
cv2.destroyAllWindows()
def main(camera_id, filename, hrnet_c, hrnet_j, hrnet_weights, hrnet_joints_set, image_resolution, single_person,
max_batch_size, disable_vidgear, save_video, video_format, video_framerate, device):
if device is not None:
device = torch.device(device)
else:
if torch.cuda.is_available() and True:
torch.backends.cudnn.deterministic = True
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
print(device)
image_resolution = ast.literal_eval(image_resolution)
has_display = 'DISPLAY' in os.environ.keys() or sys.platform == 'win32'
video_writer = None
if filename is not None:
video = cv2.VideoCapture(filename)
assert video.isOpened()
else:
if disable_vidgear:
video = cv2.VideoCapture(camera_id)
assert video.isOpened()
else:
video = CamGear(camera_id).start()
model = SimpleHRNet(
hrnet_c,
hrnet_j,
hrnet_weights,
resolution=image_resolution,
multiperson=not single_person,
max_batch_size=max_batch_size,
device=device
)
while True:
t = time.time()
if filename is not None or disable_vidgear:
ret, frame = video.read()
if not ret:
break
else:
frame = video.read()
if frame is None:
break
pts = model.predict(frame)
for i, pt in enumerate(pts):
frame = draw_points_and_skeleton(frame, pt, joints_dict()[hrnet_joints_set]['skeleton'], person_index=i,
points_color_palette='gist_rainbow', skeleton_color_palette='jet',
points_palette_samples=10)
fps = 1. / (time.time() - t)
print('\rframerate: %f fps' % fps, end='')
if has_display:
cv2.imshow('frame.png', frame)
k = cv2.waitKey(1)
if k == 27: # Esc button
if disable_vidgear:
video.release()
else:
video.stop()
break
else:
cv2.imwrite('frame.png', frame)
if save_video:
if video_writer is None:
fourcc = cv2.VideoWriter_fourcc(*video_format) # video format
video_writer = cv2.VideoWriter('output.avi', fourcc, video_framerate, (frame.shape[1], frame.shape[0]))
video_writer.write(frame)
if save_video:
video_writer.release()
def main(self):
if self.args.device is not None:
device = torch.device(self.args.device)
else:
if torch.cuda.is_available() and True:
torch.backends.cudnn.deterministic = True
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
print(device)
image_resolution = ast.literal_eval(self.args.image_resolution)
has_display = 'DISPLAY' in os.environ.keys() or sys.platform == 'win32'
has_display = False
if self.args.filename is not None:
video = cv2.VideoCapture(self.args.filename)
assert video.isOpened()
else:
if self.args.disable_vidgear:
video = cv2.VideoCapture(self.args.camera_id)
assert video.isOpened()
else:
video = CamGear(self.args.camera_id).start()
model = SimpleHRNet(self.args.hrnet_c,
self.args.hrnet_j,
self.args.hrnet_weights,
resolution=image_resolution,
multiperson=not self.args.single_person,
max_batch_size=self.args.max_batch_size,
device=device)
num_of_frame = 0
num_of_std = 0
start = False
flag = False
root = os.path.join(self.args.save_root, 'test')
if not os.path.exists(root):
os.mkdir(root)
while True:
if self.args.filename is not None or self.args.disable_vidgear:
ret, self.frame = video.read()
if not ret:
break
else:
self.frame = video.read()
if self.frame is None:
break
pts = model.predict(self.frame)
for i, pt in enumerate(pts):
self.frame = draw_points_and_skeleton(
self.frame,
pt,
joints_dict()[self.args.hrnet_joints_set]['skeleton'],
person_index=i,
points_color_palette='gist_rainbow',
skeleton_color_palette='jet',
points_palette_samples=10)
# if not start:
# #print('pts', pts)
# angel = cal_angle(pts, 'start')
# start = True if angel <= 20 else False
if not start:
self.text_ready = 'please ready'
cv2.putText(self.frame, self.text_ready, (50, 50),
cv2.FONT_HERSHEY_PLAIN, 2.0, (0, 0, 255), 2)
angel = self.cal_angle(pts, 'start')
start = True if angel <= 5 else False
if start:
self.text_elbow_touch_knee = 'please elbow touch knee'
cv2.putText(self.frame, self.text_elbow_touch_knee, (50, 50),
cv2.FONT_HERSHEY_PLAIN, 2.0, (0, 0, 255), 2)
if has_display:
cv2.imshow('frame.png', self.frame)
k = cv2.waitKey(1)
if k == 27: # Esc button
if self.args.disable_vidgear:
video.release()
else:
video.stop()
break
else:
angle = self.cal_angle(pts, 'stardard')
if angle <= 50 and start:
text = "count_{}".format(num_of_std)
self.count(self.frame, text, num_of_frame, root, video)
start = False
num_of_std += 1
flag = True
elif angle <= 50 and not start and not flag:
self.text_error = 'fault wrong hands action'
cv2.putText(self.frame, self.text_error, (330, 50),
cv2.FONT_HERSHEY_PLAIN, 2.0, (0, 0, 255), 2)
self.text = "count_{}".format(num_of_std)
self.count(self.frame, self.text, num_of_frame, root,
video)
#print(type(frame))
else:
self.text = "count_{}".format(num_of_std)
self.count(self.frame, self.text, num_of_frame, root,
video)
#print('num_of_frame', num_of_frame)
#print('pts', pts)
num_of_frame += 1
def main(camera_id, filename, hrnet_m, hrnet_c, hrnet_j, hrnet_weights,
hrnet_joints_set, image_resolution, single_person, use_tiny_yolo,
disable_tracking, max_batch_size, disable_vidgear, save_video,
video_format, video_framerate, device, exercise_type):
if device is not None:
device = torch.device(device)
else:
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
device = torch.device('cuda')
else:
device = torch.device('cpu')
# print(device)
image_resolution = ast.literal_eval(image_resolution)
has_display = 'DISPLAY' in os.environ.keys() or sys.platform == 'win32'
video_writer = None
if filename is not None:
video = cv2.VideoCapture(filename)
assert video.isOpened()
else:
if disable_vidgear:
video = cv2.VideoCapture(camera_id)
assert video.isOpened()
else:
video = CamGear(camera_id).start()
if use_tiny_yolo:
yolo_model_def = "./models/detectors/yolo/config/yolov3-tiny.cfg"
yolo_class_path = "./models/detectors/yolo/data/coco.names"
yolo_weights_path = "./models/detectors/yolo/weights/yolov3-tiny.weights"
else:
yolo_model_def = "./models/detectors/yolo/config/yolov3.cfg"
yolo_class_path = "./models/detectors/yolo/data/coco.names"
yolo_weights_path = "./models/detectors/yolo/weights/yolov3.weights"
model = SimpleHRNet(hrnet_c,
hrnet_j,
hrnet_weights,
model_name=hrnet_m,
resolution=image_resolution,
multiperson=not single_person,
return_bounding_boxes=not disable_tracking,
max_batch_size=max_batch_size,
yolo_model_def=yolo_model_def,
yolo_class_path=yolo_class_path,
yolo_weights_path=yolo_weights_path,
device=device)
if not disable_tracking:
prev_boxes = None
prev_pts = None
prev_person_ids = None
next_person_id = 0
flag = 0
prev_flag = flag
counter = 0
angle = 0
dist = 0
prev_dist = dist
while True:
t = time.time()
if filename is not None or disable_vidgear:
ret, frame = video.read()
if not ret:
break
else:
frame = video.read()
if frame is None:
break
pts = model.predict(frame)
if not disable_tracking:
boxes, pts = pts
if not disable_tracking:
if len(pts) > 0:
if prev_pts is None and prev_person_ids is None:
person_ids = np.arange(next_person_id,
len(pts) + next_person_id,
dtype=np.int32)
next_person_id = len(pts) + 1
else:
boxes, pts, person_ids = find_person_id_associations(
boxes=boxes,
pts=pts,
prev_boxes=prev_boxes,
prev_pts=prev_pts,
prev_person_ids=prev_person_ids,
next_person_id=next_person_id,
pose_alpha=0.2,
similarity_threshold=0.4,
smoothing_alpha=0.1,
)
next_person_id = max(next_person_id,
np.max(person_ids) + 1)
else:
person_ids = np.array((), dtype=np.int32)
prev_boxes = boxes.copy()
prev_pts = pts.copy()
prev_person_ids = person_ids
else:
person_ids = np.arange(len(pts), dtype=np.int32)
start_point = (45, 5)
end_point = (1300, 250)
colorr = (0, 0, 0)
thicknessr = -1
frame = cv2.rectangle(frame, start_point, end_point, colorr,
thicknessr)
if exercise_type == 1: #for pushUps
for i, (pt, pid) in enumerate(zip(pts, person_ids)):
frame, angle = draw_points_and_skeleton(
frame,
pt,
joints_dict()[hrnet_joints_set]['skeleton'],
person_index=pid,
points_color_palette='gist_rainbow',
skeleton_color_palette='jet',
points_palette_samples=10,
exercise_type=1)
fps = 1. / (time.time() - t)
print('\rframerate: %f fps' % fps, end='')
#angle=findangle(frame, pts, joints_dict()[hrnet_joints_set]['skeleton'])
font = cv2.FONT_HERSHEY_SIMPLEX
x, y, l = frame.shape
org = (50, 80)
fontScale = 1
color = (255, 255, 255)
thickness = 2
frame = cv2.putText(frame, str(angle), org, font, fontScale, color,
thickness, cv2.LINE_AA)
if (len(pts) > 0):
if (angle > 150):
flag = 0
if (angle < 90):
flag = 1
if (prev_flag == 1 and flag == 0):
counter = counter + 1
prev_flag = flag
font = cv2.FONT_HERSHEY_SIMPLEX
x, y, l = frame.shape
org = (50, 180)
fontScale = 4
color = (255, 255, 255)
thickness = 8
text = "PushUps Count=" + str(counter)
frame = cv2.putText(frame, text, org, font, fontScale, color,
thickness, cv2.LINE_AA)
elif exercise_type == 2: #for sitUps
for i, (pt, pid) in enumerate(zip(pts, person_ids)):
frame, angle = draw_points_and_skeleton(
frame,
pt,
joints_dict()[hrnet_joints_set]['skeleton'],
person_index=pid,
points_color_palette='gist_rainbow',
skeleton_color_palette='jet',
points_palette_samples=10,
exercise_type=2)
fps = 1. / (time.time() - t)
print('\rframerate: %f fps' % fps, end='')
#angle=findangle(frame, pts, joints_dict()[hrnet_joints_set]['skeleton'])
font = cv2.FONT_HERSHEY_SIMPLEX
x, y, l = frame.shape
org = (50, 80)
fontScale = 1
color = (255, 255, 255)
thickness = 2
frame = cv2.putText(frame, str(angle), org, font, fontScale, color,
thickness, cv2.LINE_AA)
if (len(pts) > 0):
if (angle > 168):
flag = 0
if (angle < 48):
flag = 1
if (prev_flag == 1 and flag == 0):
counter = counter + 1
prev_flag = flag
font = cv2.FONT_HERSHEY_SIMPLEX
x, y, l = frame.shape
org = (50, 180)
fontScale = 4
color = (255, 255, 255)
thickness = 8
text = "Squats Count=" + str(counter)
frame = cv2.putText(frame, text, org, font, fontScale, color,
thickness, cv2.LINE_AA)
elif exercise_type == 3: #for ChinUps
for i, (pt, pid) in enumerate(zip(pts, person_ids)):
frame, dist = draw_points_and_skeleton(
frame,
pt,
joints_dict()[hrnet_joints_set]['skeleton'],
person_index=pid,
points_color_palette='gist_rainbow',
skeleton_color_palette='jet',
points_palette_samples=10,
exercise_type=3)
fps = 1. / (time.time() - t)
print('\rframerate: %f fps' % fps, end='')
#angle=findangle(frame, pts, joints_dict()[hrnet_joints_set]['skeleton'])
font = cv2.FONT_HERSHEY_SIMPLEX
x, y, l = frame.shape
org = (50, 80)
fontScale = 1
color = (255, 255, 255)
thickness = 2
frame = cv2.putText(frame, str(dist), org, font, fontScale, color,
thickness, cv2.LINE_AA)
if (len(pts) > 0):
if (dist == -1 and prev_dist == 1):
counter = counter + 1
prev_dist = dist
font = cv2.FONT_HERSHEY_SIMPLEX
x, y, l = frame.shape
org = (50, 180)
fontScale = 4
color = (255, 255, 255)
thickness = 8
text = "ChinUps Count=" + str(counter)
frame = cv2.putText(frame, text, org, font, fontScale, color,
thickness, cv2.LINE_AA)
if has_display:
cv2.imshow('frame.png', frame)
k = cv2.waitKey(1)
if k == 27: # Esc button
if disable_vidgear:
video.release()
else:
video.stop()
break
else:
cv2.imwrite('frame.png', frame)
if save_video:
if video_writer is None:
fourcc = cv2.VideoWriter_fourcc(*video_format) # video format
video_writer = cv2.VideoWriter(
'arnleft.avi', fourcc, video_framerate,
(frame.shape[1], frame.shape[0]))
video_writer.write(frame)
if save_video:
video_writer.release()
def main(self, args):
if args.device is not None:
device = torch.device(args.device)
else:
if torch.cuda.is_available() and True:
torch.backends.cudnn.deterministic = True
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
print(device)
image_resolution = ast.literal_eval(args.image_resolution)
has_display = 'DISPLAY' in os.environ.keys() or sys.platform == 'win32'
has_display = False
if args.filename is not None:
video = cv2.VideoCapture(args.filename)
assert video.isOpened()
else:
if args.disable_vidgear:
video = cv2.VideoCapture(args.camera_id)
assert video.isOpened()
else:
video = CamGear(args.camera_id).start()
model = SimpleHRNet(
args.hrnet_c,
args.hrnet_j,
args.hrnet_weights,
resolution=image_resolution,
multiperson=not args.single_person,
#multiperson= False,
max_batch_size=args.max_batch_size,
device=device)
num_of_frame = 0
self.num_of_std = 0
self.error_box_text = ' '
start = False
flag = False
root = os.path.join(args.save_root, 'sit_ups_v4_test')
if not os.path.exists(root):
os.mkdir(root)
while True:
if args.filename is not None or args.disable_vidgear:
ret, self.frame = video.read()
if not ret:
break
else:
self.frame = video.read()
if self.frame is None:
break
pts = model.predict(self.frame)
for i, pt in enumerate(pts):
self.frame = draw_points_and_skeleton(
self.frame,
pt,
joints_dict()[args.hrnet_joints_set]['skeleton'],
person_index=i,
points_color_palette='gist_rainbow',
skeleton_color_palette='jet',
points_palette_samples=10)
print('pts', pts)
if not start:
self.text_ready = '请双肩着地，双手抱头'
angle_stg, angle_sew, angle_hma_start = self.cal_angle(
pts, 'start')
if angle_stg <= 5 and angle_sew <= 90 and angle_hma_start <= 10:
start = True
else:
start = False
self.state_box_text = self.text_ready
elif start:
self.text_elbow_touch_knee = '请双手抱头坐起肘部触膝'
self.state_box_text = self.text_elbow_touch_knee
if has_display:
cv2.imshow('frame.png', self.frame)
k = cv2.waitKey(1)
if k == 27: # Esc button
if args.disable_vidgear:
video.release()
else:
video.stop()
break
else:
ratio_between_distance, angle_hks, angle_hma_standard, x_diff_elbow_knee, avg_conf = self.cal_angle(
pts, 'stardard')
print('avg_conf', avg_conf)
if avg_conf < 0.2:
start = False
self.text = "count_{}".format(self.num_of_std)
self.count(self.frame, self.text, num_of_frame, root,
video)
num_of_frame += 1
continue
raise_feet = False if np.absolute(
angle_hma_start - angle_hma_standard) <= 5 else True
if angle_hks <= 70 and start and (
ratio_between_distance
or x_diff_elbow_knee < 0) and not raise_feet:
self.text = "count_{}".format(self.num_of_std)
self.count(self.frame, self.text, num_of_frame, root,
video)
self.num_of_std += 1
start = False
flag = True
elif angle_hks <= 70 and (
ratio_between_distance or x_diff_elbow_knee < 0
) and not raise_feet and not start and not flag:
self.text_error = '犯规，手部动作不规范'
self.error_box_text = self.text_error
self.text = "count_{}".format(self.num_of_std)
self.count(self.frame, self.text, num_of_frame, root,
video)
else:
self.text = "count_{}".format(self.num_of_std)
self.count(self.frame, self.text, num_of_frame, root,
video)
#yield (self.state_box_text, self.error_box_text, self.frame, self.num_of_std)
#print('time', time.time() - start_time)
self.error_box_text = ' '
num_of_frame += 1
