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ultralytics 8.0.193 add Raspberry Pi guide to Docs (#5230)

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Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: DaanKwF <108017202+DaanKwF@users.noreply.github.com>
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Glenn Jocher 2023-10-04 22:36:18 +02:00 • committed by GitHub
parent 9b1f35cbdc
commit 3e3980b2bc
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14 changed files with 288 additions and 34 deletions
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2
ultralytics/data/build.py
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@ -115,7 +115,7 @@ def check_source(source):
if isinstance(source, (str, int, Path)): # int for local usb camera
source = str(source)
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
is_url = source.lower().startswith(('https://', 'http://', 'rtsp://', 'rtmp://'))
is_url = source.lower().startswith(('https://', 'http://', 'rtsp://', 'rtmp://', 'tcp://'))
webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
screenshot = source.lower() == 'screen'
if is_url and is_file:

2
ultralytics/data/loaders.py
View file

@ -29,7 +29,7 @@ class SourceTypes:
class LoadStreams:
"""YOLOv8 streamloader, i.e. `yolo predict source='rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams`."""
"""Stream Loader, i.e. `yolo predict source='rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP, TCP streams`."""
def __init__(self, sources='file.streams', imgsz=640, vid_stride=1, buffer=False):
"""Initialize instance variables and check for consistent input stream shapes."""

19
ultralytics/data/utils.py
View file

@ -115,17 +115,17 @@ def verify_image_label(args):
if nl:
if keypoint:
assert lb.shape[1] == (5 + nkpt * ndim), f'labels require {(5 + nkpt * ndim)} columns each'
assert (lb[:, 5::ndim] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
assert (lb[:, 6::ndim] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
points = lb[:, 5:].reshape(-1, ndim)[:, :2]
else:
assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected'
assert (lb[:, 1:] <= 1).all(), \
f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}'
assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}'
points = lb[:, 1:]
assert points.max() <= 1, f'non-normalized or out of bounds coordinates {points[points > 1]}'
assert lb.min() >= 0, f'negative label values {lb[lb < 0]}'
# All labels
max_cls = int(lb[:, 0].max()) # max label count
max_cls = lb[:, 0].max() # max label count
assert max_cls <= num_cls, \
f'Label class {max_cls} exceeds dataset class count {num_cls}. ' \
f'Label class {int(max_cls)} exceeds dataset class count {num_cls}. ' \
f'Possible class labels are 0-{num_cls - 1}'
_, i = np.unique(lb, axis=0, return_index=True)
if len(i) < nl: # duplicate row check
@ -135,11 +135,10 @@ def verify_image_label(args):
msg = f'{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed'
else:
ne = 1 # label empty
lb = np.zeros((0, (5 + nkpt * ndim)), dtype=np.float32) if keypoint else np.zeros(
(0, 5), dtype=np.float32)
lb = np.zeros((0, (5 + nkpt * ndim) if keypoint else 5), dtype=np.float32)
else:
nm = 1 # label missing
lb = np.zeros((0, (5 + nkpt * ndim)), dtype=np.float32) if keypoint else np.zeros((0, 5), dtype=np.float32)
lb = np.zeros((0, (5 + nkpt * ndim) if keypoints else 5), dtype=np.float32)
if keypoint:
keypoints = lb[:, 5:].reshape(-1, nkpt, ndim)
if ndim == 2: