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ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher 2024-01-10 03:16:08 +01:00 • committed by GitHub
parent e795277391
commit fe27db2f6e
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139 changed files with 6870 additions and 5125 deletions
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50
ultralytics/trackers/utils/gmc.py
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@ -33,7 +33,7 @@ class GMC:
applySparseOptFlow(self, raw_frame, detections=None): Applies the Sparse Optical Flow method to a raw frame.
"""
def __init__(self, method: str = 'sparseOptFlow', downscale: int = 2) -> None:
def __init__(self, method: str = "sparseOptFlow", downscale: int = 2) -> None:
"""
Initialize a video tracker with specified parameters.
@ -46,34 +46,31 @@ class GMC:
self.method = method
self.downscale = max(1, int(downscale))
if self.method == 'orb':
if self.method == "orb":
self.detector = cv2.FastFeatureDetector_create(20)
self.extractor = cv2.ORB_create()
self.matcher = cv2.BFMatcher(cv2.NORM_HAMMING)
elif self.method == 'sift':
elif self.method == "sift":
self.detector = cv2.SIFT_create(nOctaveLayers=3, contrastThreshold=0.02, edgeThreshold=20)
self.extractor = cv2.SIFT_create(nOctaveLayers=3, contrastThreshold=0.02, edgeThreshold=20)
self.matcher = cv2.BFMatcher(cv2.NORM_L2)
elif self.method == 'ecc':
elif self.method == "ecc":
number_of_iterations = 5000
termination_eps = 1e-6
self.warp_mode = cv2.MOTION_EUCLIDEAN
self.criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, number_of_iterations, termination_eps)
elif self.method == 'sparseOptFlow':
self.feature_params = dict(maxCorners=1000,
qualityLevel=0.01,
minDistance=1,
blockSize=3,
useHarrisDetector=False,
k=0.04)
elif self.method == "sparseOptFlow":
self.feature_params = dict(
maxCorners=1000, qualityLevel=0.01, minDistance=1, blockSize=3, useHarrisDetector=False, k=0.04
)
elif self.method in ['none', 'None', None]:
elif self.method in ["none", "None", None]:
self.method = None
else:
raise ValueError(f'Error: Unknown GMC method:{method}')
raise ValueError(f"Error: Unknown GMC method:{method}")
self.prevFrame = None
self.prevKeyPoints = None
@ -97,11 +94,11 @@ class GMC:
array([[1, 2, 3],
[4, 5, 6]])
"""
if self.method in ['orb', 'sift']:
if self.method in ["orb", "sift"]:
return self.applyFeatures(raw_frame, detections)
elif self.method == 'ecc':
elif self.method == "ecc":
return self.applyEcc(raw_frame, detections)
elif self.method == 'sparseOptFlow':
elif self.method == "sparseOptFlow":
return self.applySparseOptFlow(raw_frame, detections)
else:
return np.eye(2, 3)
@ -149,7 +146,7 @@ class GMC:
try:
(cc, H) = cv2.findTransformECC(self.prevFrame, frame, H, self.warp_mode, self.criteria, None, 1)
except Exception as e:
LOGGER.warning(f'WARNING: find transform failed. Set warp as identity {e}')
LOGGER.warning(f"WARNING: find transform failed. Set warp as identity {e}")
return H
@ -182,11 +179,11 @@ class GMC:
# Find the keypoints
mask = np.zeros_like(frame)
mask[int(0.02 * height):int(0.98 * height), int(0.02 * width):int(0.98 * width)] = 255
mask[int(0.02 * height) : int(0.98 * height), int(0.02 * width) : int(0.98 * width)] = 255
if detections is not None:
for det in detections:
tlbr = (det[:4] / self.downscale).astype(np.int_)
mask[tlbr[1]:tlbr[3], tlbr[0]:tlbr[2]] = 0
mask[tlbr[1] : tlbr[3], tlbr[0] : tlbr[2]] = 0
keypoints = self.detector.detect(frame, mask)
@ -228,11 +225,14 @@ class GMC:
prevKeyPointLocation = self.prevKeyPoints[m.queryIdx].pt
currKeyPointLocation = keypoints[m.trainIdx].pt
spatialDistance = (prevKeyPointLocation[0] - currKeyPointLocation[0],
prevKeyPointLocation[1] - currKeyPointLocation[1])
spatialDistance = (
prevKeyPointLocation[0] - currKeyPointLocation[0],
prevKeyPointLocation[1] - currKeyPointLocation[1],
)
if (np.abs(spatialDistance[0]) < maxSpatialDistance[0]) and \
(np.abs(spatialDistance[1]) < maxSpatialDistance[1]):
if (np.abs(spatialDistance[0]) < maxSpatialDistance[0]) and (
np.abs(spatialDistance[1]) < maxSpatialDistance[1]
):
spatialDistances.append(spatialDistance)
matches.append(m)
@ -283,7 +283,7 @@ class GMC:
H[0, 2] *= self.downscale
H[1, 2] *= self.downscale
else:
LOGGER.warning('WARNING: not enough matching points')
LOGGER.warning("WARNING: not enough matching points")
# Store to next iteration
self.prevFrame = frame.copy()
@ -350,7 +350,7 @@ class GMC:
H[0, 2] *= self.downscale
H[1, 2] *= self.downscale
else:
LOGGER.warning('WARNING: not enough matching points')
LOGGER.warning("WARNING: not enough matching points")
self.prevFrame = frame.copy()
self.prevKeyPoints = copy.copy(keypoints)