─░├žeri─če ge├ž

Referans i├žin ultralytics/trackers/utils/gmc.py

Not

Bu dosya https://github.com/ultralytics/ultralytics/blob/main/ ultralytics/trackers/utils/gmc .py adresinde mevcuttur. Bir sorun tespit ederseniz l├╝tfen bir ├çekme ─░ste─či ­čŤá´ŞĆ ile katk─▒da bulunarak d├╝zeltilmesine yard─▒mc─▒ olun. Te┼čekk├╝rler ­čÖĆ!



ultralytics.trackers.utils.gmc.GMC

Video karelerinde izleme ve nesne alg─▒lama i├žin Genelle┼čtirilmi┼č Hareket Telafisi (GMC) s─▒n─▒f─▒.

Bu s─▒n─▒f, ORB dahil olmak ├╝zere ├že┼čitli izleme algoritmalar─▒na dayal─▒ olarak nesneleri izlemek ve tespit etmek i├žin y├Ântemler sa─člar, SIFT, ECC ve Seyrek Optik Ak─▒┼č. Ayr─▒ca hesaplama verimlili─či i├žin karelerin k├╝├ž├╝lt├╝lmesini de destekler.

Nitelikler:

─░sim Tip A├ž─▒klama
method str

─░zleme i├žin kullan─▒lan y├Ântem. Se├ženekler aras─▒nda 'orb', 'sift', 'ecc', 'sparseOptFlow', 'none' bulunur.

downscale int

─░┼čleme i├žin ├žer├ževelerin k├╝├ž├╝lt├╝lmesini sa─člayan fakt├Âr.

prevFrame ndarray

─░zleme i├žin ├Ânceki kareyi saklar.

prevKeyPoints list

├ľnceki karedeki anahtar noktalar─▒ saklar.

prevDescriptors ndarray

├ľnceki karenin tan─▒mlay─▒c─▒lar─▒n─▒ saklar.

initializedFirstFrame bool

─░lk ├žer├ževenin i┼členip i┼členmedi─čini g├Âsteren bayrak.

Y├Ântemler:

─░sim A├ž─▒klama
__init__

Bir GMC nesnesini belirtilen y├Ântemle ba┼člat─▒r ve ├Âl├žek k├╝├ž├╝ltme fakt├Âr├╝.

apply

Se├žilen y├Ântemi ham ├žer├ževeye uygular ve iste─če ba─čl─▒ olarak tespitler sa─člad─▒.

applyEcc

ECC algoritmas─▒n─▒ ham bir ├žer├ževeye uygular.

applyFeatures

ORB veya SIFT gibi ├Âzellik tabanl─▒ y├Ântemleri ham bir kareye uygular.

applySparseOptFlow

Seyrek Optik Ak─▒┼č y├Ântemini ham bir kareye uygular.

Kaynak kodu 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, 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 or self.prevKeyPoints is None:
            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)

Belirtilen parametrelerle bir video izleyiciyi ba┼člat─▒n.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
method str

─░zleme i├žin kullan─▒lan y├Ântem. Se├ženekler aras─▒nda 'orb', 'sift', 'ecc', 'sparseOptFlow', 'none' bulunur.

'sparseOptFlow'
downscale int

├çer├ževeleri i┼člemek i├žin k├╝├ž├╝ltme fakt├Âr├╝.

2
Kaynak kodu 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, 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)

Belirtilen y├Ântemi kullanarak ham bir kareye nesne alg─▒lama uygulay─▒n.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
raw_frame ndarray

─░┼členecek ham ├žer├ževe.

gerekli
detections list

─░┼člemede kullan─▒lacak tespitlerin listesi.

None

─░ade:

Tip A├ž─▒klama
ndarray

─░┼členmi┼č ├žer├ževe.

├ľrnekler:

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

Ham bir ├žer├ževeye ECC algoritmas─▒ uygulay─▒n.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
raw_frame ndarray

─░┼členecek ham ├žer├ževe.

gerekli

─░ade:

Tip A├ž─▒klama
ndarray

─░┼členmi┼č ├žer├ževe.

├ľrnekler:

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

Ham bir kareye ORB veya SIFT gibi ├Âzellik tabanl─▒ y├Ântemler uygulay─▒n.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
raw_frame ndarray

─░┼členecek ham ├žer├ževe.

gerekli
detections list

─░┼člemede kullan─▒lacak tespitlerin listesi.

None

─░ade:

Tip A├ž─▒klama
ndarray

─░┼členmi┼č ├žer├ževe.

├ľrnekler:

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

Seyrek Optik Ak─▒┼č y├Ântemini ham bir kareye uygulay─▒n.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
raw_frame ndarray

─░┼členecek ham ├žer├ževe.

gerekli

─░ade:

Tip A├ž─▒klama
ndarray

─░┼členmi┼č ├žer├ževe.

├ľrnekler:

>>> gmc = GMC()
>>> gmc.applySparseOptFlow(np.array([[1, 2, 3], [4, 5, 6]]))
array([[1, 2, 3],
       [4, 5, 6]])
Kaynak kodu 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 or self.prevKeyPoints is None:
        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()

Parametreleri s─▒f─▒rlay─▒n.

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





Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (5), Burhan-Q (1)