Update Tracker docstrings (#15469)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher
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10 changed files with 501 additions and 196 deletions
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@ -19,27 +19,39 @@ class GMC:
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method (str): The method used for tracking. Options include 'orb', 'sift', 'ecc', 'sparseOptFlow', 'none'.
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downscale (int): Factor by which to downscale the frames for processing.
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prevFrame (np.ndarray): Stores the previous frame for tracking.
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prevKeyPoints (list): Stores the keypoints from the previous frame.
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prevKeyPoints (List): Stores the keypoints from the previous frame.
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prevDescriptors (np.ndarray): Stores the descriptors from the previous frame.
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initializedFirstFrame (bool): Flag to indicate if the first frame has been processed.
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Methods:
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__init__(self, method='sparseOptFlow', downscale=2): Initializes a GMC object with the specified method
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and downscale factor.
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apply(self, raw_frame, detections=None): Applies the chosen method to a raw frame and optionally uses
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provided detections.
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applyEcc(self, raw_frame, detections=None): Applies the ECC algorithm to a raw frame.
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applyFeatures(self, raw_frame, detections=None): Applies feature-based methods like ORB or SIFT to a raw frame.
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applySparseOptFlow(self, raw_frame, detections=None): Applies the Sparse Optical Flow method to a raw frame.
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__init__: Initializes a GMC object with the specified method and downscale factor.
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apply: Applies the chosen method to a raw frame and optionally uses provided detections.
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applyEcc: Applies the ECC algorithm to a raw frame.
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applyFeatures: Applies feature-based methods like ORB or SIFT to a raw frame.
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applySparseOptFlow: Applies the Sparse Optical Flow method to a raw frame.
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reset_params: Resets the internal parameters of the GMC object.
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Examples:
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Create a GMC object and apply it to a frame
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>>> gmc = GMC(method='sparseOptFlow', downscale=2)
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>>> frame = np.array([[1, 2, 3], [4, 5, 6]])
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>>> processed_frame = gmc.apply(frame)
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>>> print(processed_frame)
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array([[1, 2, 3],
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[4, 5, 6]])
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"""
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def __init__(self, method: str = "sparseOptFlow", downscale: int = 2) -> None:
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"""
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Initialize a video tracker with specified parameters.
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Initialize a Generalized Motion Compensation (GMC) object with tracking method and downscale factor.
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Args:
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method (str): The method used for tracking. Options include 'orb', 'sift', 'ecc', 'sparseOptFlow', 'none'.
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downscale (int): Downscale factor for processing frames.
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Examples:
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Initialize a GMC object with the 'sparseOptFlow' method and a downscale factor of 2
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>>> gmc = GMC(method='sparseOptFlow', downscale=2)
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"""
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super().__init__()
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@ -79,20 +91,21 @@ class GMC:
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def apply(self, raw_frame: np.array, detections: list = None) -> np.array:
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"""
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Apply object detection on a raw frame using specified method.
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Apply object detection on a raw frame using the specified method.
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Args:
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raw_frame (np.ndarray): The raw frame to be processed.
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detections (list): List of detections to be used in the processing.
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raw_frame (np.ndarray): The raw frame to be processed, with shape (H, W, C).
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detections (List | None): List of detections to be used in the processing.
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Returns:
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(np.ndarray): Processed frame.
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(np.ndarray): Processed frame with applied object detection.
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Examples:
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>>> gmc = GMC()
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>>> gmc.apply(np.array([[1, 2, 3], [4, 5, 6]]))
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array([[1, 2, 3],
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[4, 5, 6]])
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>>> gmc = GMC(method='sparseOptFlow')
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>>> raw_frame = np.random.rand(480, 640, 3)
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>>> processed_frame = gmc.apply(raw_frame)
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>>> print(processed_frame.shape)
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(480, 640, 3)
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"""
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if self.method in {"orb", "sift"}:
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return self.applyFeatures(raw_frame, detections)
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@ -105,19 +118,20 @@ class GMC:
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def applyEcc(self, raw_frame: np.array) -> np.array:
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"""
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Apply ECC algorithm to a raw frame.
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Apply the ECC (Enhanced Correlation Coefficient) algorithm to a raw frame for motion compensation.
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Args:
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raw_frame (np.ndarray): The raw frame to be processed.
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raw_frame (np.ndarray): The raw frame to be processed, with shape (H, W, C).
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Returns:
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(np.ndarray): Processed frame.
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(np.ndarray): The processed frame with the applied ECC transformation.
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Examples:
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>>> gmc = GMC()
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>>> gmc.applyEcc(np.array([[1, 2, 3], [4, 5, 6]]))
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array([[1, 2, 3],
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[4, 5, 6]])
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>>> gmc = GMC(method='ecc')
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>>> processed_frame = gmc.applyEcc(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]))
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>>> print(processed_frame)
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[[1. 0. 0.]
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[0. 1. 0.]]
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"""
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height, width, _ = raw_frame.shape
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frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
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@ -127,8 +141,6 @@ class GMC:
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if self.downscale > 1.0:
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frame = cv2.GaussianBlur(frame, (3, 3), 1.5)
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frame = cv2.resize(frame, (width // self.downscale, height // self.downscale))
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width = width // self.downscale
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height = height // self.downscale
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# Handle first frame
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if not self.initializedFirstFrame:
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@ -154,17 +166,18 @@ class GMC:
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Apply feature-based methods like ORB or SIFT to a raw frame.
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Args:
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raw_frame (np.ndarray): The raw frame to be processed.
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detections (list): List of detections to be used in the processing.
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raw_frame (np.ndarray): The raw frame to be processed, with shape (H, W, C).
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detections (List | None): List of detections to be used in the processing.
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Returns:
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(np.ndarray): Processed frame.
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Examples:
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>>> gmc = GMC()
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>>> gmc.applyFeatures(np.array([[1, 2, 3], [4, 5, 6]]))
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array([[1, 2, 3],
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[4, 5, 6]])
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>>> gmc = GMC(method='orb')
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>>> raw_frame = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
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>>> processed_frame = gmc.applyFeatures(raw_frame)
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>>> print(processed_frame.shape)
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(2, 3)
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"""
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height, width, _ = raw_frame.shape
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frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
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@ -296,16 +309,17 @@ class GMC:
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Apply Sparse Optical Flow method to a raw frame.
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Args:
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raw_frame (np.ndarray): The raw frame to be processed.
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raw_frame (np.ndarray): The raw frame to be processed, with shape (H, W, C).
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Returns:
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(np.ndarray): Processed frame.
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(np.ndarray): Processed frame with shape (2, 3).
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Examples:
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>>> gmc = GMC()
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>>> gmc.applySparseOptFlow(np.array([[1, 2, 3], [4, 5, 6]]))
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array([[1, 2, 3],
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[4, 5, 6]])
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>>> result = gmc.applySparseOptFlow(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]))
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>>> print(result)
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[[1. 0. 0.]
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[0. 1. 0.]]
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"""
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height, width, _ = raw_frame.shape
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frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
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@ -356,7 +370,7 @@ class GMC:
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return H
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def reset_params(self) -> None:
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"""Reset parameters."""
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"""Reset the internal parameters including previous frame, keypoints, and descriptors."""
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self.prevFrame = None
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self.prevKeyPoints = None
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self.prevDescriptors = None
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