def __init__(self, sources='streams.txt', img_size=640, stride=32):
self.mode = 'stream'
self.img_size = img_size
self.stride = stride
if os.path.isfile(sources):
with open(sources, 'r') as f:
sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())]
else:
sources = [sources]
n = len(sources)
self.imgs = [None] * n
self.sources = [clean_str(x) for x in sources] # clean source names for later
for i, s in enumerate(sources):
# Start the thread to read frames from the video stream
print(f'{i + 1}/{n}: {s}... ', end='')
cap = cv2.VideoCapture(eval(s) if s.isnumeric() else s)
assert cap.isOpened(), f'Failed to open {s}'
w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS) % 100
_, self.imgs[i] = cap.read() # guarantee first frame
thread = Thread(target=self.update, args=([i, cap]), daemon=True)
print(f' success ({w}x{h} at {fps:.2f} FPS).')
thread.start()
print('') # newline
# check for common shapes
s = np.stack([letterbox(x, self.img_size, stride=self.stride)[0].shape for x in self.imgs], 0) # shapes
self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal
if not self.rect:
print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')
def __init__(self, sources="streams.txt", img_size=640):
self.mode = "stream"
self.img_size = img_size
if os.path.isfile(sources):
with open(sources, "r") as f:
sources = [
x.strip() for x in f.read().strip().splitlines()
if len(x.strip())
]
else:
sources = [sources]
n = len(sources)
self.imgs = [None] * n
self.sources = [clean_str(x)
for x in sources] # clean source names for later
for i, s in enumerate(sources):
# Start the thread to read frames from the video stream
print("%g/%g: %s... " % (i + 1, n, s), end="")
cap = cv2.VideoCapture(eval(s) if s.isnumeric() else s)
assert cap.isOpened(), "Failed to open %s" % s
w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS) % 100
_, self.imgs[i] = cap.read() # guarantee first frame
thread = Thread(target=self.update, args=([i, cap]), daemon=True)
print(" success (%gx%g at %.2f FPS)." % (w, h, fps))
thread.start()
print("") # newline
# check for common shapes
s = np.stack([
letterbox(x, new_shape=self.img_size)[0].shape for x in self.imgs
], 0) # inference shapes
self.rect = (np.unique(s, axis=0).shape[0] == 1
) # rect inference if all shapes equal
if not self.rect:
print(
"WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams."
)
def __init__(self,
sources='streams.txt',
img_size=640,
stride=32,
auto=True):
self.mode = 'stream'
self.img_size = img_size
self.stride = stride
if os.path.isfile(sources):
with open(sources) as f:
sources = [
x.strip() for x in f.read().strip().splitlines()
if len(x.strip())
]
else:
sources = [sources]
n = len(sources)
self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [
0
] * n, [None] * n
self.sources = [clean_str(x)
for x in sources] # clean source names for later
self.auto = auto
for i, s in enumerate(sources): # index, source
# Start thread to read frames from video stream
st = f'{i + 1}/{n}: {s}... '
if 'youtube.com/' in s or 'youtu.be/' in s: # if source is YouTube video
check_requirements(('pafy', 'youtube_dl'))
import pafy
s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL
s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam
cap = cv2.VideoCapture(s)
assert cap.isOpened(), f'{st}Failed to open {s}'
w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
self.fps[i] = max(cap.get(cv2.CAP_PROP_FPS) % 100,
0) or 30.0 # 30 FPS fallback
self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)),
0) or float('inf') # infinite stream fallback
_, self.imgs[i] = cap.read() # guarantee first frame
self.threads[i] = Thread(target=self.update,
args=([i, cap, s]),
daemon=True)
LOGGER.info(
f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)"
)
self.threads[i].start()
LOGGER.info('') # newline
# check for common shapes
s = np.stack([
letterbox(x, self.img_size, stride=self.stride,
auto=self.auto)[0].shape for x in self.imgs
])
self.rect = np.unique(
s, axis=0).shape[0] == 1 # rect inference if all shapes equal
if not self.rect:
LOGGER.warning(
'WARNING: Stream shapes differ. For optimal performance supply similarly-shaped streams.'
)
def __next__(self):
self.count += 1
if cv2.waitKey(1) == ord('q'): # q to quit
self.cap.release()
cv2.destroyAllWindows()
raise StopIteration
# Read frame
if self.pipe == 0: # local camera
ret_val, img0 = self.cap.read()
img0 = cv2.flip(img0, 1) # flip left-right
else: # IP camera
n = 0
while True:
n += 1
self.cap.grab()
if n % 30 == 0: # skip frames
ret_val, img0 = self.cap.retrieve()
if ret_val:
break
# Print
assert ret_val, 'Camera Error %s' % self.pipe
img_path = 'webcam.jpg'
print('webcam %g: ' % self.count, end='')
# Padded resize
img = letterbox(img0, new_shape=self.img_size)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
self.mode = 'stream'
self.img_size = img_size
if os.path.isfile(sources):
with open(sources, 'r') as f:
sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())]
else:
sources = [sources]
n = len(sources)
self.imgs = [None] * n
self.sources = [clean_str(x) for x in sources] # clean source names for later
for i, s in enumerate(sources):
# Start the thread to read frames from the video stream
print('%g/%g: %s... ' % (i + 1, n, s), end='')
cap = cv2.VideoCapture(eval(s) if s.isnumeric() else s)
assert cap.isOpened(), 'Failed to open %s' % s
w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS) % 100
_, self.imgs[i] = cap.read() # guarantee first frame
thread = Thread(target=self.update, args=([i, cap]), daemon=True)
print(' success (%gx%g at %.2f FPS).' % (w, h, fps))
thread.start()
print('') # newline
# check for common shapes
s = np.stack([letterbox(x, new_shape=self.img_size)[0].shape for x in self.imgs], 0) # inference shapes
self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal
if not self.rect:
print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')