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Referenz fĂŒr ultralytics/trackers/utils/gmc.py

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ultralytics.trackers.utils.gmc.GMC

Generalized Motion Compensation (GMC) Klasse fĂŒr Tracking und Objekterkennung in Videobildern.

Diese Klasse bietet Methoden zur Verfolgung und Erkennung von Objekten, die auf verschiedenen Verfolgungsalgorithmen basieren, darunter ORB, SIFT, ECC und Sparse Optical Flow. Außerdem unterstĂŒtzt sie die Verkleinerung von Einzelbildern, um die Berechnungen effizienter zu gestalten.

Attribute:

Name Typ Beschreibung
method str

Die fĂŒr das Tracking verwendete Methode. Zu den Optionen gehören "orb", "sift", "ecc", "sparseOptFlow" und "none".

downscale int

Faktor, um den die Bilder fĂŒr die Verarbeitung herunterskaliert werden.

prevFrame ndarray

Speichert das vorherige Bild fĂŒr die Verfolgung.

prevKeyPoints list

Speichert die Keypoints aus dem vorherigen Frame.

prevDescriptors ndarray

Speichert die Deskriptoren des vorherigen Rahmens.

initializedFirstFrame bool

Flagge, die anzeigt, ob der erste Frame verarbeitet wurde.

Methoden:

Name Beschreibung
__init__

Initialisiert ein GMC-Objekt mit der angegebenen Methode und Verkleinerungsfaktor.

apply

Wendet die gewÀhlte Methode auf einen Rohrahmen an und verwendet optional bereitgestellte Erkennungen.

applyEcc

Wendet den ECC-Algorithmus auf einen Rohrahmen an.

applyFeatures

Wendet merkmalsbasierte Methoden wie ORB oder SIFT auf ein Rohbild an.

applySparseOptFlow

Wendet die Sparse Optical Flow Methode auf ein Rohbild an.

Quellcode in 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)

Initialisiere einen Videotracker mit den angegebenen Parametern.

Parameter:

Name Typ Beschreibung Standard
method str

Die fĂŒr das Tracking verwendete Methode. Zu den Optionen gehören "orb", "sift", "ecc", "sparseOptFlow" und "none".

'sparseOptFlow'
downscale int

Verkleinerungsfaktor fĂŒr die Verarbeitung von Frames.

2
Quellcode in 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)

Wendet die Objekterkennung mit der angegebenen Methode auf ein Rohbild an.

Parameter:

Name Typ Beschreibung Standard
raw_frame ndarray

Der zu verarbeitende Rohrahmen.

erforderlich
detections list

Liste der Erkennungen, die bei der Verarbeitung verwendet werden sollen.

None

Retouren:

Typ Beschreibung
ndarray

Bearbeiteter Rahmen.

Beispiele:

>>> gmc = GMC()
>>> gmc.apply(np.array([[1, 2, 3], [4, 5, 6]]))
array([[1, 2, 3],
       [4, 5, 6]])
Quellcode in 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)

Wende den ECC-Algorithmus auf einen Rohrahmen an.

Parameter:

Name Typ Beschreibung Standard
raw_frame ndarray

Der zu verarbeitende Rohrahmen.

erforderlich

Retouren:

Typ Beschreibung
ndarray

Bearbeiteter Rahmen.

Beispiele:

>>> gmc = GMC()
>>> gmc.applyEcc(np.array([[1, 2, 3], [4, 5, 6]]))
array([[1, 2, 3],
       [4, 5, 6]])
Quellcode in 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)

Wende merkmalsbasierte Methoden wie ORB oder SIFT auf ein Rohbild an.

Parameter:

Name Typ Beschreibung Standard
raw_frame ndarray

Der zu verarbeitende Rohrahmen.

erforderlich
detections list

Liste der Erkennungen, die bei der Verarbeitung verwendet werden sollen.

None

Retouren:

Typ Beschreibung
ndarray

Bearbeiteter Rahmen.

Beispiele:

>>> gmc = GMC()
>>> gmc.applyFeatures(np.array([[1, 2, 3], [4, 5, 6]]))
array([[1, 2, 3],
       [4, 5, 6]])
Quellcode in 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)

Wende die Sparse Optical Flow Methode auf ein Rohbild an.

Parameter:

Name Typ Beschreibung Standard
raw_frame ndarray

Der zu verarbeitende Rohrahmen.

erforderlich

Retouren:

Typ Beschreibung
ndarray

Bearbeiteter Rahmen.

Beispiele:

>>> gmc = GMC()
>>> gmc.applySparseOptFlow(np.array([[1, 2, 3], [4, 5, 6]]))
array([[1, 2, 3],
       [4, 5, 6]])
Quellcode in 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()

Parameter zurĂŒcksetzen.

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





Erstellt am 2023-11-12, Aktualisiert am 2023-11-25
Autoren: glenn-jocher (3)