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Я нашел этот код и хотел бы использовать его для отслеживания обнаруженных объектов в моем проекте Python.

Это работает нормально, но я хочу, чтобы выведенный словарь отвечал моим потребностям.

Иллюстрация

 ct = CentroidTracker() # The tracker class

detected = my_detection_function(image)
# [[0, 2, 254, 161], [551, 4, 713, 393]]
""" my_detection_function returns the [ymin, xmin, ymax, xmax] coordinates of detected objects """

centroid_coord = ct.update(detected)
# OrderedDict([(1, array([128,  88])), (2, array([635, 204]))])
""" Returns a dictionary with couple of the ID and the center coordinates of tracked object """
 

Выходной ток:

 OrderedDict([(1, array([128,  88])), (2, array([635, 204]))])
 

Результат, который я хочу, должен быть таким:

 OrderedDict([(1, array([0, 2, 254, 161])), (2, array([551, 4, 713, 393]))])
 

Как вы можете видеть, я хочу, чтобы ct.update(rects) возвращалась пара идентификаторов с [ymin, xmin, ymax, xmax] координатами обнаруженных объектов

Вот код трекера:

 # python3

class CentroidTracker():
    def __init__(self, maxDisappeared=50):
        # initialize the next unique object ID along with two ordered
        # dictionaries used to keep track of mapping a given object
        # ID to its centroid and number of consecutive frames it has
        # been marked as "disappeared", respectively
        self.nextObjectID = 0
        self.objects = OrderedDict()
        self.disappeared = OrderedDict()

        # store the number of maximum consecutive frames a given
        # object is allowed to be marked as "disappeared" until we
        # need to deregister the object from tracking
        self.maxDisappeared = maxDisappeared

    def register(self, centroid):
        # when registering an object we use the next available object
        # ID to store the centroid
        self.objects[self.nextObjectID] = centroid
        self.disappeared[self.nextObjectID] = 0
        self.nextObjectID  = 1

    def deregister(self, objectID):
        # to deregister an object ID we delete the object ID from
        # both of our respective dictionaries
        del self.objects[objectID]
        del self.disappeared[objectID]

    def update(self, rects):
        # check to see if the list of input bounding box rectangles
        # is empty
        if len(rects) == 0:
            # loop over any existing tracked objects and mark them
            # as disappeared
            for objectID in self.disappeared.keys():
                self.disappeared[objectID]  = 1

                # if we have reached a maximum number of consecutive
                # frames where a given object has been marked as
                # missing, deregister it
                if self.disappeared[objectID] > self.maxDisappeared:
                    self.deregister(objectID)

            # return early as there are no centroids or tracking info
            # to update
            return self.objects

        # initialize an array of input centroids for the current frame
        inputCentroids = np.zeros((len(rects), 2), dtype="int")

        # loop over the bounding box rectangles
        for (i, (startX, startY, endX, endY)) in enumerate(rects):
            # use the bounding box coordinates to derive the centroid
            cX = int((startX   endX) / 2.0)
            cY = int((startY   endY) / 2.0)
            inputCentroids[i] = (cX, cY)

        # if we are currently not tracking any objects take the input
        # centroids and register each of them
        if len(self.objects) == 0:
            for i in range(0, len(inputCentroids)):
                self.register(inputCentroids[i])

        # otherwise, are are currently tracking objects so we need to
        # try to match the input centroids to existing object
        # centroids
        else:
            # grab the set of object IDs and corresponding centroids
            objectIDs = list(self.objects.keys())
            objectCentroids = list(self.objects.values())

            # compute the distance between each pair of object
            # centroids and input centroids, respectively -- our
            # goal will be to match an input centroid to an existing
            # object centroid
            D = dist.cdist(np.array(objectCentroids), inputCentroids)

            # in order to perform this matching we must (1) find the
            # smallest value in each row and then (2) sort the row
            # indexes based on their minimum values so that the row
            # with the smallest value as at the *front* of the index
            # list
            rows = D.min(axis=1).argsort()

            # next, we perform a similar process on the columns by
            # finding the smallest value in each column and then
            # sorting using the previously computed row index list
            cols = D.argmin(axis=1)[rows]

            # in order to determine if we need to update, register,
            # or deregister an object we need to keep track of which
            # of the rows and column indexes we have already examined
            usedRows = set()
            usedCols = set()

            # loop over the combination of the (row, column) index
            # tuples
            for (row, col) in zip(rows, cols):
                # if we have already examined either the row or
                # column value before, ignore it
                # val
                if row in usedRows or col in usedCols:
                    continue

                # otherwise, grab the object ID for the current row,
                # set its new centroid, and reset the disappeared
                # counter
                objectID = objectIDs[row]
                self.objects[objectID] = inputCentroids[col]
                self.disappeared[objectID] = 0

                # indicate that we have examined each of the row and
                # column indexes, respectively
                usedRows.add(row)
                usedCols.add(col)

            # compute both the row and column index we have NOT yet
            # examined
            unusedRows = set(range(0, D.shape[0])).difference(usedRows)
            unusedCols = set(range(0, D.shape[1])).difference(usedCols)

            # in the event that the number of object centroids is
            # equal or greater than the number of input centroids
            # we need to check and see if some of these objects have
            # potentially disappeared
            if D.shape[0] >= D.shape[1]:
                # loop over the unused row indexes
                for row in unusedRows:
                    # grab the object ID for the corresponding row
                    # index and increment the disappeared counter
                    objectID = objectIDs[row]
                    self.disappeared[objectID]  = 1

                    # check to see if the number of consecutive
                    # frames the object has been marked "disappeared"
                    # for warrants deregistering the object
                    if self.disappeared[objectID] > self.maxDisappeared:
                        self.deregister(objectID)

            # otherwise, if the number of input centroids is greater
            # than the number of existing object centroids we need to
            # register each new input centroid as a trackable object
            else:
                for col in unusedCols:
                    self.register(inputCentroids[col])

        # return the set of trackable objects
        return self.objects
 

Если я правильно понял, вы хотите напрямую изменить код детектора, чтобы предоставить вам необходимую структуру данных ? Если это так, я бы предложил внести это изменение в код:

 # python3

class CentroidTracker():
    def __init__(self, maxDisappeared=50):
        # initialize the next unique object ID along with two ordered
        # dictionaries used to keep track of mapping a given object
        # ID to its centroid and number of consecutive frames it has
        # been marked as "disappeared", respectively
        self.nextObjectID = 0
        self.objects = OrderedDict()
        self.disappeared = OrderedDict()

        # store the number of maximum consecutive frames a given
        # object is allowed to be marked as "disappeared" until we
        # need to deregister the object from tracking
        self.maxDisappeared = maxDisappeared

    def register(self, centroid):
        # when registering an object we use the next available object
        # ID to store the centroid
        self.objects[self.nextObjectID] = centroid
        self.disappeared[self.nextObjectID] = 0
        self.nextObjectID  = 1

    def deregister(self, objectID):
        # to deregister an object ID we delete the object ID from
        # both of our respective dictionaries
        del self.objects[objectID]
        del self.disappeared[objectID]

    def update(self, rects):
        # check to see if the list of input bounding box rectangles
        # is empty
        if len(rects) == 0:
            # loop over any existing tracked objects and mark them
            # as disappeared
            for objectID in self.disappeared.keys():
                self.disappeared[objectID]  = 1

                # if we have reached a maximum number of consecutive
                # frames where a given object has been marked as
                # missing, deregister it
                if self.disappeared[objectID] > self.maxDisappeared:
                    self.deregister(objectID)

            # return early as there are no centroids or tracking info
            # to update
            return self.objects

        # initialize an array of input centroids for the current frame
        inputCentroids = np.zeros((len(rects), 2), dtype="int")

        ## EDIT 1/3
        ## Initialize the array of custom results
        customOutput = np.zeros((len(rects), 4), dtype="int")

        # loop over the bounding box rectangles
        for (i, (startX, startY, endX, endY)) in enumerate(rects):
            # use the bounding box coordinates to derive the centroid
            cX = int((startX   endX) / 2.0)
            cY = int((startY   endY) / 2.0)
            inputCentroids[i] = (cX, cY)

            ## EDIT 2/3
            ## Store the information in the array
            customOutput[i] = (startX, startY, endX, endY)
            

        # if we are currently not tracking any objects take the input
        # centroids and register each of them
        if len(self.objects) == 0:
            for i in range(0, len(inputCentroids)):
                self.register(inputCentroids[i])

        # otherwise, are are currently tracking objects so we need to
        # try to match the input centroids to existing object
        # centroids
        else:
            # grab the set of object IDs and corresponding centroids
            objectIDs = list(self.objects.keys())
            objectCentroids = list(self.objects.values())

            # compute the distance between each pair of object
            # centroids and input centroids, respectively -- our
            # goal will be to match an input centroid to an existing
            # object centroid
            D = dist.cdist(np.array(objectCentroids), inputCentroids)

            # in order to perform this matching we must (1) find the
            # smallest value in each row and then (2) sort the row
            # indexes based on their minimum values so that the row
            # with the smallest value as at the *front* of the index
            # list
            rows = D.min(axis=1).argsort()

            # next, we perform a similar process on the columns by
            # finding the smallest value in each column and then
            # sorting using the previously computed row index list
            cols = D.argmin(axis=1)[rows]

            # in order to determine if we need to update, register,
            # or deregister an object we need to keep track of which
            # of the rows and column indexes we have already examined
            usedRows = set()
            usedCols = set()

            # loop over the combination of the (row, column) index
            # tuples
            for (row, col) in zip(rows, cols):
                # if we have already examined either the row or
                # column value before, ignore it
                # val
                if row in usedRows or col in usedCols:
                    continue

                # otherwise, grab the object ID for the current row,
                # set its new centroid, and reset the disappeared
                # counter
                objectID = objectIDs[row]

                ## Edit 3/3
                ## Put the Custom values instead of the centroids
                self.objects[objectID] = customOutput[col]
                self.disappeared[objectID] = 0

                # indicate that we have examined each of the row and
                # column indexes, respectively
                usedRows.add(row)
                usedCols.add(col)

            # compute both the row and column index we have NOT yet
            # examined
            unusedRows = set(range(0, D.shape[0])).difference(usedRows)
            unusedCols = set(range(0, D.shape[1])).difference(usedCols)

            # in the event that the number of object centroids is
            # equal or greater than the number of input centroids
            # we need to check and see if some of these objects have
            # potentially disappeared
            if D.shape[0] >= D.shape[1]:
                # loop over the unused row indexes
                for row in unusedRows:
                    # grab the object ID for the corresponding row
                    # index and increment the disappeared counter
                    objectID = objectIDs[row]
                    self.disappeared[objectID]  = 1

                    # check to see if the number of consecutive
                    # frames the object has been marked "disappeared"
                    # for warrants deregistering the object
                    if self.disappeared[objectID] > self.maxDisappeared:
                        self.deregister(objectID)

            # otherwise, if the number of input centroids is greater
            # than the number of existing object centroids we need to
            # register each new input centroid as a trackable object
            else:
                for col in unusedCols:
                    self.register(inputCentroids[col])

        # return the set of trackable objects
        return self.objects