Python cv2.CAP_PROP_BUFFERSIZE Examples
The following are 8
code examples of cv2.CAP_PROP_BUFFERSIZE().
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Example #1
| Source File: datasets.py From pruning_yolov3 with GNU General Public License v3.0 | 10 votes |
|
def __init__(self, pipe=0, img_size=416, half=False):
self.img_size = img_size
self.half = half # half precision fp16 images
if pipe == '0':
pipe = 0 # local camera
# pipe = 'rtsp://192.168.1.64/1' # IP camera
# pipe = 'rtsp://username:password@192.168.1.64/1' # IP camera with login
# pipe = 'rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa' # IP traffic camera
# pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg' # IP golf camera
# https://answers.opencv.org/question/215996/changing-gstreamer-pipeline-to-opencv-in-pythonsolved/
# pipe = '"rtspsrc location="rtsp://username:password@192.168.1.64/1" latency=10 ! appsink' # GStreamer
# https://answers.opencv.org/question/200787/video-acceleration-gstremer-pipeline-in-videocapture/
# https://stackoverflow.com/questions/54095699/install-gstreamer-support-for-opencv-python-package # install help
# pipe = "rtspsrc location=rtsp://root:root@192.168.0.91:554/axis-media/media.amp?videocodec=h264&resolution=3840x2160 protocols=GST_RTSP_LOWER_TRANS_TCP ! rtph264depay ! queue ! vaapih264dec ! videoconvert ! appsink" # GStreamer
self.pipe = pipe
self.cap = cv2.VideoCapture(pipe) # video capture object
self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size
Example #2
| Source File: datasets.py From yolov3-channel-and-layer-pruning with Apache License 2.0 | 6 votes |
|
def __init__(self, pipe=0, img_size=416, half=False):
self.img_size = img_size
self.half = half # half precision fp16 images
if pipe == '0':
pipe = 0 # local camera
# pipe = 'rtsp://192.168.1.64/1' # IP camera
# pipe = 'rtsp://username:password@192.168.1.64/1' # IP camera with login
# pipe = 'rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa' # IP traffic camera
# pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg' # IP golf camera
# https://answers.opencv.org/question/215996/changing-gstreamer-pipeline-to-opencv-in-pythonsolved/
# pipe = '"rtspsrc location="rtsp://username:password@192.168.1.64/1" latency=10 ! appsink' # GStreamer
# https://answers.opencv.org/question/200787/video-acceleration-gstremer-pipeline-in-videocapture/
# https://stackoverflow.com/questions/54095699/install-gstreamer-support-for-opencv-python-package # install help
# pipe = "rtspsrc location=rtsp://root:root@192.168.0.91:554/axis-media/media.amp?videocodec=h264&resolution=3840x2160 protocols=GST_RTSP_LOWER_TRANS_TCP ! rtph264depay ! queue ! vaapih264dec ! videoconvert ! appsink" # GStreamer
self.pipe = pipe
self.cap = cv2.VideoCapture(pipe) # video capture object
self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size
Example #3
| Source File: wifi_camera.py From 1ZLAB_PyEspCar with GNU General Public License v3.0 | 5 votes |
|
def __init__(self, phone_ip):
ip_camera_url = 'http://admin:admin@{}:8081/'.format(phone_ip)
self.cap = cv2.VideoCapture(ip_camera_url)
# 设置缓存区的大小
self.cap.set(cv2.CAP_PROP_BUFFERSIZE, self.CAP_BUFFER_SIZE)
for i in range(self.INIT_JUMP_FRAME_NUM):
ret, img = self.cap.read()
Example #4
| Source File: camera_publisher_and_services.py From niryo_one_ros with GNU General Public License v3.0 | 4 votes |
|
def __setup_stream_settings(self):
# Set compression format
self.__video_stream.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))
# Set Buffer size
# -- Not available in opencv 3.4 -- #
self.__video_stream.set(cv2.CAP_PROP_BUFFERSIZE, rospy.get_param("~buffer_size"))
# Set image size
w, h = rospy.get_param("~frame_size")
self.__video_stream.set(cv2.CAP_PROP_FRAME_WIDTH, w)
self.__video_stream.set(cv2.CAP_PROP_FRAME_HEIGHT, h)
# Set frame rate
self.__video_stream.set(cv2.CAP_PROP_FPS, self.__frame_rate)
Example #5
| Source File: main.py From speed-detector with MIT License | 4 votes |
|
def main ():
# I think KNN works better than MOG2, specifically with trucks/large vehicles
# TODO: Block out snowbank where shadows are strongly reflected!
bg_subtractor = cv2.createBackgroundSubtractorKNN(detectShadows=True)
car_counter = None
load_cropped()
cap = cv2.VideoCapture(road['stream_url'])
cap.set(cv2.CAP_PROP_BUFFERSIZE, 2)
cv2.namedWindow('Source Image')
cv2.setMouseCallback('Source Image', click_and_crop)
frame_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
frame_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
frame_number = -1
while True:
frame_number += 1
ret, frame = cap.read()
if not ret:
print('Frame capture failed, stopping...')
break
if car_counter is None:
car_counter = VehicleCounter(frame.shape[:2], road, cap.get(cv2.CAP_PROP_FPS), samples=10)
processed = process_frame(frame_number, frame, bg_subtractor, car_counter)
cv2.imshow('Source Image', frame)
cv2.imshow('Processed Image', processed)
key = cv2.waitKey(WAIT_TIME)
if key == ord('s'):
# save rects!
save_cropped()
elif key == ord('q') or key == 27:
break
# Keep video's speed stable
# I think that this causes the abrupt jumps in the video
time.sleep( 1.0 / cap.get(cv2.CAP_PROP_FPS) )
print('Closing video capture...')
cap.release()
cv2.destroyAllWindows()
print('Done.')
Example #6
| Source File: threadedcam.py From speed-detector with MIT License | 4 votes |
|
def __init__(self, src=0):
self.capture = cv2.VideoCapture(src)
self.capture.set(cv2.CAP_PROP_BUFFERSIZE, 2)
# FPS = 1/X
# X = desired FPS
self.FPS = 1/30
self.FPS_MS = int(self.FPS * 1000)
# Start frame retrieval thread
self.thread = Thread(target=self.update, args=())
self.thread.daemon = True
self.thread.start()
Example #7
| Source File: video_stream.py From open_model_zoo with Apache License 2.0 | 4 votes |
|
def _frame_generator(input_source, out_frame, frame_shape, finish_flag):
"""Produces live frames from the input stream"""
cap = cv2.VideoCapture(input_source)
cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
source_fps = cap.get(cv2.CAP_PROP_FPS)
trg_time_step = 1.0 / float(source_fps)
while True:
start_time = time.perf_counter()
_, frame = cap.read()
if frame is None:
break
with out_frame.get_lock():
buffer = np.frombuffer(out_frame.get_obj(), dtype=np.uint8)
np.copyto(buffer.reshape(frame_shape), frame)
end_time = time.perf_counter()
elapsed_time = end_time - start_time
rest_time = trg_time_step - elapsed_time
if rest_time > 0.0:
time.sleep(rest_time)
finish_flag.value = True
cap.release()
Example #8
| Source File: video_library.py From open_model_zoo with Apache License 2.0 | 4 votes |
|
def _play(visualizer_queue, cur_source_id, source_paths, max_image_size, trg_time_step):
"""Produces live frame from the active video source"""
cap = None
last_source_id = cur_source_id.value
while True:
start_time = time.perf_counter()
if cur_source_id.value != last_source_id:
last_source_id = cur_source_id.value
cap.release()
cap = None
source_name, source_path = source_paths[cur_source_id.value]
if cap is None:
cap = cv2.VideoCapture(source_path)
cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
_, frame = cap.read()
if frame is None:
cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
_, frame = cap.read()
assert frame is not None
trg_frame_size = list(frame.shape[:2])
if np.max(trg_frame_size) > max_image_size:
if trg_frame_size[0] == np.max(trg_frame_size):
trg_frame_size[1] = int(float(max_image_size) / float(trg_frame_size[0]) * float(trg_frame_size[1]))
trg_frame_size[0] = max_image_size
else:
trg_frame_size[0] = int(float(max_image_size) * float(trg_frame_size[0]) / float(trg_frame_size[1]))
trg_frame_size[1] = max_image_size
frame = cv2.resize(frame, (trg_frame_size[1], trg_frame_size[0]))
cv2.putText(frame, 'GT Gesture: {}'.format(source_name), (10, frame.shape[0] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
visualizer_queue.put(np.copy(frame), True)
end_time = time.perf_counter()
elapsed_time = end_time - start_time
rest_time = trg_time_step - elapsed_time
if rest_time > 0.0:
time.sleep(rest_time)
