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ultralytics.trackers.utils.gmc.GMC

Classe de compensation de mouvement généralisée (GMC) pour le suivi et la détection d'objets dans les images vidéo.

Cette classe fournit des méthodes de suivi et de détection d'objets basées sur plusieurs algorithmes de suivi, notamment ORB, SIFT, ECC et Sparse Optical Flow. Elle prend également en charge la réduction d'échelle des images pour une meilleure efficacité de calcul.

Attributs :

Nom Type Description
method str

La méthode utilisée pour le suivi. Les options incluent 'orb', 'sift', 'ecc', 'sparseOptFlow', 'none'.

downscale int

Facteur de réduction de l'échelle des images pour le traitement.

prevFrame ndarray

Enregistre la trame précédente pour le suivi.

prevKeyPoints list

Stocke les points clés de l'image précédente.

prevDescriptors ndarray

Stocke les descripteurs de la trame précédente.

initializedFirstFrame bool

Drapeau indiquant si la première trame a été traitée.

MĂ©thodes :

Nom Description
__init__

Initialise un objet GMC avec la méthode et le facteur de réduction spécifiés. et le facteur de réduction d'échelle spécifiés.

apply

Applique la méthode choisie à une image brute et utilise éventuellement les détections fournies. les détections fournies.

applyEcc

Applique l'algorithme ECC Ă  une trame brute.

applyFeatures

Applique les méthodes basées sur les caractéristiques comme ORB ou SIFT à une image brute.

applySparseOptFlow

Applique la méthode du flux optique clairsemé à une image brute.

Code source dans ultralytics/trackers/utils/gmc.py
class GMC:
    """
    Generalized Motion Compensation (GMC) class for tracking and object detection in video frames.

    This class provides methods for tracking and detecting objects based on several tracking algorithms including ORB,
    SIFT, ECC, and Sparse Optical Flow. It also supports downscaling of frames for computational efficiency.

    Attributes:
        method (str): The method used for tracking. Options include 'orb', 'sift', 'ecc', 'sparseOptFlow', 'none'.
        downscale (int): Factor by which to downscale the frames for processing.
        prevFrame (np.ndarray): Stores the previous frame for tracking.
        prevKeyPoints (list): Stores the keypoints from the previous frame.
        prevDescriptors (np.ndarray): Stores the descriptors from the previous frame.
        initializedFirstFrame (bool): Flag to indicate if the first frame has been processed.

    Methods:
        __init__(self, method='sparseOptFlow', downscale=2): Initializes a GMC object with the specified method
                                                              and downscale factor.
        apply(self, raw_frame, detections=None): Applies the chosen method to a raw frame and optionally uses
                                                 provided detections.
        applyEcc(self, raw_frame, detections=None): Applies the ECC algorithm to a raw frame.
        applyFeatures(self, raw_frame, detections=None): Applies feature-based methods like ORB or SIFT to a raw frame.
        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:
        """
        Initialize a video tracker with specified parameters.

        Args:
            method (str): The method used for tracking. Options include 'orb', 'sift', 'ecc', 'sparseOptFlow', 'none'.
            downscale (int): Downscale factor for processing frames.
        """
        super().__init__()

        self.method = method
        self.downscale = max(1, int(downscale))

        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":
            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":
            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 in {"none", "None", None}:
            self.method = None
        else:
            raise ValueError(f"Error: Unknown GMC method:{method}")

        self.prevFrame = None
        self.prevKeyPoints = None
        self.prevDescriptors = None
        self.initializedFirstFrame = False

    def apply(self, raw_frame: np.array, detections: list = None) -> np.array:
        """
        Apply object detection on a raw frame using specified method.

        Args:
            raw_frame (np.ndarray): The raw frame to be processed.
            detections (list): List of detections to be used in the processing.

        Returns:
            (np.ndarray): Processed frame.

        Examples:
            >>> gmc = GMC()
            >>> gmc.apply(np.array([[1, 2, 3], [4, 5, 6]]))
            array([[1, 2, 3],
                   [4, 5, 6]])
        """
        if self.method in ["orb", "sift"]:
            return self.applyFeatures(raw_frame, detections)
        elif self.method == "ecc":
            return self.applyEcc(raw_frame)
        elif self.method == "sparseOptFlow":
            return self.applySparseOptFlow(raw_frame)
        else:
            return np.eye(2, 3)

    def applyEcc(self, raw_frame: np.array) -> np.array:
        """
        Apply ECC algorithm to a raw frame.

        Args:
            raw_frame (np.ndarray): The raw frame to be processed.

        Returns:
            (np.ndarray): Processed frame.

        Examples:
            >>> gmc = GMC()
            >>> gmc.applyEcc(np.array([[1, 2, 3], [4, 5, 6]]))
            array([[1, 2, 3],
                   [4, 5, 6]])
        """
        height, width, _ = raw_frame.shape
        frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
        H = np.eye(2, 3, dtype=np.float32)

        # Downscale image
        if self.downscale > 1.0:
            frame = cv2.GaussianBlur(frame, (3, 3), 1.5)
            frame = cv2.resize(frame, (width // self.downscale, height // self.downscale))
            width = width // self.downscale
            height = height // self.downscale

        # Handle first frame
        if not self.initializedFirstFrame:
            # Initialize data
            self.prevFrame = frame.copy()

            # Initialization done
            self.initializedFirstFrame = True

            return H

        # Run the ECC algorithm. The results are stored in warp_matrix.
        # (cc, H) = cv2.findTransformECC(self.prevFrame, frame, H, self.warp_mode, self.criteria)
        try:
            (_, 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}")

        return H

    def applyFeatures(self, raw_frame: np.array, detections: list = None) -> np.array:
        """
        Apply feature-based methods like ORB or SIFT to a raw frame.

        Args:
            raw_frame (np.ndarray): The raw frame to be processed.
            detections (list): List of detections to be used in the processing.

        Returns:
            (np.ndarray): Processed frame.

        Examples:
            >>> gmc = GMC()
            >>> gmc.applyFeatures(np.array([[1, 2, 3], [4, 5, 6]]))
            array([[1, 2, 3],
                   [4, 5, 6]])
        """
        height, width, _ = raw_frame.shape
        frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
        H = np.eye(2, 3)

        # Downscale image
        if self.downscale > 1.0:
            frame = cv2.resize(frame, (width // self.downscale, height // self.downscale))
            width = width // self.downscale
            height = height // self.downscale

        # 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
        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

        keypoints = self.detector.detect(frame, mask)

        # Compute the descriptors
        keypoints, descriptors = self.extractor.compute(frame, keypoints)

        # Handle first frame
        if not self.initializedFirstFrame:
            # Initialize data
            self.prevFrame = frame.copy()
            self.prevKeyPoints = copy.copy(keypoints)
            self.prevDescriptors = copy.copy(descriptors)

            # Initialization done
            self.initializedFirstFrame = True

            return H

        # Match descriptors
        knnMatches = self.matcher.knnMatch(self.prevDescriptors, descriptors, 2)

        # Filter matches based on smallest spatial distance
        matches = []
        spatialDistances = []

        maxSpatialDistance = 0.25 * np.array([width, height])

        # Handle empty matches case
        if len(knnMatches) == 0:
            # Store to next iteration
            self.prevFrame = frame.copy()
            self.prevKeyPoints = copy.copy(keypoints)
            self.prevDescriptors = copy.copy(descriptors)

            return H

        for m, n in knnMatches:
            if m.distance < 0.9 * n.distance:
                prevKeyPointLocation = self.prevKeyPoints[m.queryIdx].pt
                currKeyPointLocation = keypoints[m.trainIdx].pt

                spatialDistance = (
                    prevKeyPointLocation[0] - currKeyPointLocation[0],
                    prevKeyPointLocation[1] - currKeyPointLocation[1],
                )

                if (np.abs(spatialDistance[0]) < maxSpatialDistance[0]) and (
                    np.abs(spatialDistance[1]) < maxSpatialDistance[1]
                ):
                    spatialDistances.append(spatialDistance)
                    matches.append(m)

        meanSpatialDistances = np.mean(spatialDistances, 0)
        stdSpatialDistances = np.std(spatialDistances, 0)

        inliers = (spatialDistances - meanSpatialDistances) < 2.5 * stdSpatialDistances

        goodMatches = []
        prevPoints = []
        currPoints = []
        for i in range(len(matches)):
            if inliers[i, 0] and inliers[i, 1]:
                goodMatches.append(matches[i])
                prevPoints.append(self.prevKeyPoints[matches[i].queryIdx].pt)
                currPoints.append(keypoints[matches[i].trainIdx].pt)

        prevPoints = np.array(prevPoints)
        currPoints = np.array(currPoints)

        # Draw the keypoint matches on the output image
        # if False:
        #     import matplotlib.pyplot as plt
        #     matches_img = np.hstack((self.prevFrame, frame))
        #     matches_img = cv2.cvtColor(matches_img, cv2.COLOR_GRAY2BGR)
        #     W = self.prevFrame.shape[1]
        #     for m in goodMatches:
        #         prev_pt = np.array(self.prevKeyPoints[m.queryIdx].pt, dtype=np.int_)
        #         curr_pt = np.array(keypoints[m.trainIdx].pt, dtype=np.int_)
        #         curr_pt[0] += W
        #         color = np.random.randint(0, 255, 3)
        #         color = (int(color[0]), int(color[1]), int(color[2]))
        #
        #         matches_img = cv2.line(matches_img, prev_pt, curr_pt, tuple(color), 1, cv2.LINE_AA)
        #         matches_img = cv2.circle(matches_img, prev_pt, 2, tuple(color), -1)
        #         matches_img = cv2.circle(matches_img, curr_pt, 2, tuple(color), -1)
        #
        #     plt.figure()
        #     plt.imshow(matches_img)
        #     plt.show()

        # Find rigid matrix
        if prevPoints.shape[0] > 4:
            H, inliers = cv2.estimateAffinePartial2D(prevPoints, currPoints, cv2.RANSAC)

            # Handle downscale
            if self.downscale > 1.0:
                H[0, 2] *= self.downscale
                H[1, 2] *= self.downscale
        else:
            LOGGER.warning("WARNING: not enough matching points")

        # Store to next iteration
        self.prevFrame = frame.copy()
        self.prevKeyPoints = copy.copy(keypoints)
        self.prevDescriptors = copy.copy(descriptors)

        return H

    def applySparseOptFlow(self, raw_frame: np.array) -> np.array:
        """
        Apply Sparse Optical Flow method to a raw frame.

        Args:
            raw_frame (np.ndarray): The raw frame to be processed.

        Returns:
            (np.ndarray): Processed frame.

        Examples:
            >>> gmc = GMC()
            >>> gmc.applySparseOptFlow(np.array([[1, 2, 3], [4, 5, 6]]))
            array([[1, 2, 3],
                   [4, 5, 6]])
        """
        height, width, _ = raw_frame.shape
        frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
        H = np.eye(2, 3)

        # Downscale image
        if self.downscale > 1.0:
            frame = cv2.resize(frame, (width // self.downscale, height // self.downscale))

        # Find the keypoints
        keypoints = cv2.goodFeaturesToTrack(frame, mask=None, **self.feature_params)

        # Handle first frame
        if not self.initializedFirstFrame:
            self.prevFrame = frame.copy()
            self.prevKeyPoints = copy.copy(keypoints)
            self.initializedFirstFrame = True
            return H

        # Find correspondences
        matchedKeypoints, status, _ = cv2.calcOpticalFlowPyrLK(self.prevFrame, frame, self.prevKeyPoints, None)

        # Leave good correspondences only
        prevPoints = []
        currPoints = []

        for i in range(len(status)):
            if status[i]:
                prevPoints.append(self.prevKeyPoints[i])
                currPoints.append(matchedKeypoints[i])

        prevPoints = np.array(prevPoints)
        currPoints = np.array(currPoints)

        # Find rigid matrix
        if (prevPoints.shape[0] > 4) and (prevPoints.shape[0] == prevPoints.shape[0]):
            H, _ = cv2.estimateAffinePartial2D(prevPoints, currPoints, cv2.RANSAC)

            if self.downscale > 1.0:
                H[0, 2] *= self.downscale
                H[1, 2] *= self.downscale
        else:
            LOGGER.warning("WARNING: not enough matching points")

        self.prevFrame = frame.copy()
        self.prevKeyPoints = copy.copy(keypoints)

        return H

    def reset_params(self) -> None:
        """Reset parameters."""
        self.prevFrame = None
        self.prevKeyPoints = None
        self.prevDescriptors = None
        self.initializedFirstFrame = False

__init__(method='sparseOptFlow', downscale=2)

Initialise un traceur vidéo avec les paramètres spécifiés.

Paramètres :

Nom Type Description DĂ©faut
method str

La méthode utilisée pour le suivi. Les options incluent 'orb', 'sift', 'ecc', 'sparseOptFlow', 'none'.

'sparseOptFlow'
downscale int

Facteur de réduction d'échelle pour le traitement des images.

2
Code source dans ultralytics/trackers/utils/gmc.py
def __init__(self, method: str = "sparseOptFlow", downscale: int = 2) -> None:
    """
    Initialize a video tracker with specified parameters.

    Args:
        method (str): The method used for tracking. Options include 'orb', 'sift', 'ecc', 'sparseOptFlow', 'none'.
        downscale (int): Downscale factor for processing frames.
    """
    super().__init__()

    self.method = method
    self.downscale = max(1, int(downscale))

    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":
        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":
        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 in {"none", "None", None}:
        self.method = None
    else:
        raise ValueError(f"Error: Unknown GMC method:{method}")

    self.prevFrame = None
    self.prevKeyPoints = None
    self.prevDescriptors = None
    self.initializedFirstFrame = False

apply(raw_frame, detections=None)

Applique la détection d'objets sur une image brute en utilisant la méthode spécifiée.

Paramètres :

Nom Type Description DĂ©faut
raw_frame ndarray

L'image brute Ă  traiter.

requis
detections list

Liste des détections à utiliser dans le traitement.

None

Retourne :

Type Description
ndarray

Cadre traité.

Exemples :

>>> gmc = GMC()
>>> gmc.apply(np.array([[1, 2, 3], [4, 5, 6]]))
array([[1, 2, 3],
       [4, 5, 6]])
Code source dans ultralytics/trackers/utils/gmc.py
def apply(self, raw_frame: np.array, detections: list = None) -> np.array:
    """
    Apply object detection on a raw frame using specified method.

    Args:
        raw_frame (np.ndarray): The raw frame to be processed.
        detections (list): List of detections to be used in the processing.

    Returns:
        (np.ndarray): Processed frame.

    Examples:
        >>> gmc = GMC()
        >>> gmc.apply(np.array([[1, 2, 3], [4, 5, 6]]))
        array([[1, 2, 3],
               [4, 5, 6]])
    """
    if self.method in ["orb", "sift"]:
        return self.applyFeatures(raw_frame, detections)
    elif self.method == "ecc":
        return self.applyEcc(raw_frame)
    elif self.method == "sparseOptFlow":
        return self.applySparseOptFlow(raw_frame)
    else:
        return np.eye(2, 3)

applyEcc(raw_frame)

Applique l'algorithme ECC Ă  une trame brute.

Paramètres :

Nom Type Description DĂ©faut
raw_frame ndarray

L'image brute Ă  traiter.

requis

Retourne :

Type Description
ndarray

Cadre traité.

Exemples :

>>> gmc = GMC()
>>> gmc.applyEcc(np.array([[1, 2, 3], [4, 5, 6]]))
array([[1, 2, 3],
       [4, 5, 6]])
Code source dans ultralytics/trackers/utils/gmc.py
def applyEcc(self, raw_frame: np.array) -> np.array:
    """
    Apply ECC algorithm to a raw frame.

    Args:
        raw_frame (np.ndarray): The raw frame to be processed.

    Returns:
        (np.ndarray): Processed frame.

    Examples:
        >>> gmc = GMC()
        >>> gmc.applyEcc(np.array([[1, 2, 3], [4, 5, 6]]))
        array([[1, 2, 3],
               [4, 5, 6]])
    """
    height, width, _ = raw_frame.shape
    frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
    H = np.eye(2, 3, dtype=np.float32)

    # Downscale image
    if self.downscale > 1.0:
        frame = cv2.GaussianBlur(frame, (3, 3), 1.5)
        frame = cv2.resize(frame, (width // self.downscale, height // self.downscale))
        width = width // self.downscale
        height = height // self.downscale

    # Handle first frame
    if not self.initializedFirstFrame:
        # Initialize data
        self.prevFrame = frame.copy()

        # Initialization done
        self.initializedFirstFrame = True

        return H

    # Run the ECC algorithm. The results are stored in warp_matrix.
    # (cc, H) = cv2.findTransformECC(self.prevFrame, frame, H, self.warp_mode, self.criteria)
    try:
        (_, 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}")

    return H

applyFeatures(raw_frame, detections=None)

Applique des méthodes basées sur les caractéristiques comme ORB ou SIFT à une image brute.

Paramètres :

Nom Type Description DĂ©faut
raw_frame ndarray

L'image brute Ă  traiter.

requis
detections list

Liste des détections à utiliser dans le traitement.

None

Retourne :

Type Description
ndarray

Cadre traité.

Exemples :

>>> gmc = GMC()
>>> gmc.applyFeatures(np.array([[1, 2, 3], [4, 5, 6]]))
array([[1, 2, 3],
       [4, 5, 6]])
Code source dans ultralytics/trackers/utils/gmc.py
def applyFeatures(self, raw_frame: np.array, detections: list = None) -> np.array:
    """
    Apply feature-based methods like ORB or SIFT to a raw frame.

    Args:
        raw_frame (np.ndarray): The raw frame to be processed.
        detections (list): List of detections to be used in the processing.

    Returns:
        (np.ndarray): Processed frame.

    Examples:
        >>> gmc = GMC()
        >>> gmc.applyFeatures(np.array([[1, 2, 3], [4, 5, 6]]))
        array([[1, 2, 3],
               [4, 5, 6]])
    """
    height, width, _ = raw_frame.shape
    frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
    H = np.eye(2, 3)

    # Downscale image
    if self.downscale > 1.0:
        frame = cv2.resize(frame, (width // self.downscale, height // self.downscale))
        width = width // self.downscale
        height = height // self.downscale

    # 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
    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

    keypoints = self.detector.detect(frame, mask)

    # Compute the descriptors
    keypoints, descriptors = self.extractor.compute(frame, keypoints)

    # Handle first frame
    if not self.initializedFirstFrame:
        # Initialize data
        self.prevFrame = frame.copy()
        self.prevKeyPoints = copy.copy(keypoints)
        self.prevDescriptors = copy.copy(descriptors)

        # Initialization done
        self.initializedFirstFrame = True

        return H

    # Match descriptors
    knnMatches = self.matcher.knnMatch(self.prevDescriptors, descriptors, 2)

    # Filter matches based on smallest spatial distance
    matches = []
    spatialDistances = []

    maxSpatialDistance = 0.25 * np.array([width, height])

    # Handle empty matches case
    if len(knnMatches) == 0:
        # Store to next iteration
        self.prevFrame = frame.copy()
        self.prevKeyPoints = copy.copy(keypoints)
        self.prevDescriptors = copy.copy(descriptors)

        return H

    for m, n in knnMatches:
        if m.distance < 0.9 * n.distance:
            prevKeyPointLocation = self.prevKeyPoints[m.queryIdx].pt
            currKeyPointLocation = keypoints[m.trainIdx].pt

            spatialDistance = (
                prevKeyPointLocation[0] - currKeyPointLocation[0],
                prevKeyPointLocation[1] - currKeyPointLocation[1],
            )

            if (np.abs(spatialDistance[0]) < maxSpatialDistance[0]) and (
                np.abs(spatialDistance[1]) < maxSpatialDistance[1]
            ):
                spatialDistances.append(spatialDistance)
                matches.append(m)

    meanSpatialDistances = np.mean(spatialDistances, 0)
    stdSpatialDistances = np.std(spatialDistances, 0)

    inliers = (spatialDistances - meanSpatialDistances) < 2.5 * stdSpatialDistances

    goodMatches = []
    prevPoints = []
    currPoints = []
    for i in range(len(matches)):
        if inliers[i, 0] and inliers[i, 1]:
            goodMatches.append(matches[i])
            prevPoints.append(self.prevKeyPoints[matches[i].queryIdx].pt)
            currPoints.append(keypoints[matches[i].trainIdx].pt)

    prevPoints = np.array(prevPoints)
    currPoints = np.array(currPoints)

    # Draw the keypoint matches on the output image
    # if False:
    #     import matplotlib.pyplot as plt
    #     matches_img = np.hstack((self.prevFrame, frame))
    #     matches_img = cv2.cvtColor(matches_img, cv2.COLOR_GRAY2BGR)
    #     W = self.prevFrame.shape[1]
    #     for m in goodMatches:
    #         prev_pt = np.array(self.prevKeyPoints[m.queryIdx].pt, dtype=np.int_)
    #         curr_pt = np.array(keypoints[m.trainIdx].pt, dtype=np.int_)
    #         curr_pt[0] += W
    #         color = np.random.randint(0, 255, 3)
    #         color = (int(color[0]), int(color[1]), int(color[2]))
    #
    #         matches_img = cv2.line(matches_img, prev_pt, curr_pt, tuple(color), 1, cv2.LINE_AA)
    #         matches_img = cv2.circle(matches_img, prev_pt, 2, tuple(color), -1)
    #         matches_img = cv2.circle(matches_img, curr_pt, 2, tuple(color), -1)
    #
    #     plt.figure()
    #     plt.imshow(matches_img)
    #     plt.show()

    # Find rigid matrix
    if prevPoints.shape[0] > 4:
        H, inliers = cv2.estimateAffinePartial2D(prevPoints, currPoints, cv2.RANSAC)

        # Handle downscale
        if self.downscale > 1.0:
            H[0, 2] *= self.downscale
            H[1, 2] *= self.downscale
    else:
        LOGGER.warning("WARNING: not enough matching points")

    # Store to next iteration
    self.prevFrame = frame.copy()
    self.prevKeyPoints = copy.copy(keypoints)
    self.prevDescriptors = copy.copy(descriptors)

    return H

applySparseOptFlow(raw_frame)

Applique la méthode Sparse Optical Flow à une image brute.

Paramètres :

Nom Type Description DĂ©faut
raw_frame ndarray

L'image brute Ă  traiter.

requis

Retourne :

Type Description
ndarray

Cadre traité.

Exemples :

>>> gmc = GMC()
>>> gmc.applySparseOptFlow(np.array([[1, 2, 3], [4, 5, 6]]))
array([[1, 2, 3],
       [4, 5, 6]])
Code source dans ultralytics/trackers/utils/gmc.py
def applySparseOptFlow(self, raw_frame: np.array) -> np.array:
    """
    Apply Sparse Optical Flow method to a raw frame.

    Args:
        raw_frame (np.ndarray): The raw frame to be processed.

    Returns:
        (np.ndarray): Processed frame.

    Examples:
        >>> gmc = GMC()
        >>> gmc.applySparseOptFlow(np.array([[1, 2, 3], [4, 5, 6]]))
        array([[1, 2, 3],
               [4, 5, 6]])
    """
    height, width, _ = raw_frame.shape
    frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
    H = np.eye(2, 3)

    # Downscale image
    if self.downscale > 1.0:
        frame = cv2.resize(frame, (width // self.downscale, height // self.downscale))

    # Find the keypoints
    keypoints = cv2.goodFeaturesToTrack(frame, mask=None, **self.feature_params)

    # Handle first frame
    if not self.initializedFirstFrame:
        self.prevFrame = frame.copy()
        self.prevKeyPoints = copy.copy(keypoints)
        self.initializedFirstFrame = True
        return H

    # Find correspondences
    matchedKeypoints, status, _ = cv2.calcOpticalFlowPyrLK(self.prevFrame, frame, self.prevKeyPoints, None)

    # Leave good correspondences only
    prevPoints = []
    currPoints = []

    for i in range(len(status)):
        if status[i]:
            prevPoints.append(self.prevKeyPoints[i])
            currPoints.append(matchedKeypoints[i])

    prevPoints = np.array(prevPoints)
    currPoints = np.array(currPoints)

    # Find rigid matrix
    if (prevPoints.shape[0] > 4) and (prevPoints.shape[0] == prevPoints.shape[0]):
        H, _ = cv2.estimateAffinePartial2D(prevPoints, currPoints, cv2.RANSAC)

        if self.downscale > 1.0:
            H[0, 2] *= self.downscale
            H[1, 2] *= self.downscale
    else:
        LOGGER.warning("WARNING: not enough matching points")

    self.prevFrame = frame.copy()
    self.prevKeyPoints = copy.copy(keypoints)

    return H

reset_params()

Réinitialise les paramètres.

Code source dans ultralytics/trackers/utils/gmc.py
def reset_params(self) -> None:
    """Reset parameters."""
    self.prevFrame = None
    self.prevKeyPoints = None
    self.prevDescriptors = None
    self.initializedFirstFrame = False





Créé le 2023-11-12, Mis à jour le 2023-11-25
Auteurs : glenn-jocher (3)