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Referência para ultralytics/solutions/speed_estimation.py

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ultralytics.solutions.speed_estimation.SpeedEstimator

Uma classe para estimar a velocidade de objectos em fluxos de vídeo em tempo real com base nas suas trajectórias.

Código fonte em ultralytics/solutions/speed_estimation.py
class SpeedEstimator:
    """A class to estimation speed of objects in real-time video stream based on their tracks."""

    def __init__(self):
        """Initializes the speed-estimator class with default values for Visual, Image, track and speed parameters."""

        # Visual & im0 information
        self.im0 = None
        self.annotator = None
        self.view_img = False

        # Region information
        self.reg_pts = [(20, 400), (1260, 400)]
        self.region_thickness = 3

        # Predict/track information
        self.clss = None
        self.names = None
        self.boxes = None
        self.trk_ids = None
        self.trk_pts = None
        self.line_thickness = 2
        self.trk_history = defaultdict(list)

        # Speed estimator information
        self.current_time = 0
        self.dist_data = {}
        self.trk_idslist = []
        self.spdl_dist_thresh = 10
        self.trk_previous_times = {}
        self.trk_previous_points = {}

        # Check if environment support imshow
        self.env_check = check_imshow(warn=True)

    def set_args(
        self,
        reg_pts,
        names,
        view_img=False,
        line_thickness=2,
        region_thickness=5,
        spdl_dist_thresh=10,
    ):
        """
        Configures the speed estimation and display parameters.

        Args:
            reg_pts (list): Initial list of points defining the speed calculation region.
            names (dict): object detection classes names
            view_img (bool): Flag indicating frame display
            line_thickness (int): Line thickness for bounding boxes.
            region_thickness (int): Speed estimation region thickness
            spdl_dist_thresh (int): Euclidean distance threshold for speed line
        """
        if reg_pts is None:
            print("Region points not provided, using default values")
        else:
            self.reg_pts = reg_pts
        self.names = names
        self.view_img = view_img
        self.line_thickness = line_thickness
        self.region_thickness = region_thickness
        self.spdl_dist_thresh = spdl_dist_thresh

    def extract_tracks(self, tracks):
        """
        Extracts results from the provided data.

        Args:
            tracks (list): List of tracks obtained from the object tracking process.
        """
        self.boxes = tracks[0].boxes.xyxy.cpu()
        self.clss = tracks[0].boxes.cls.cpu().tolist()
        self.trk_ids = tracks[0].boxes.id.int().cpu().tolist()

    def store_track_info(self, track_id, box):
        """
        Store track data.

        Args:
            track_id (int): object track id.
            box (list): object bounding box data
        """
        track = self.trk_history[track_id]
        bbox_center = (float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2))
        track.append(bbox_center)

        if len(track) > 30:
            track.pop(0)

        self.trk_pts = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
        return track

    def plot_box_and_track(self, track_id, box, cls, track):
        """
        Plot track and bounding box.

        Args:
            track_id (int): object track id.
            box (list): object bounding box data
            cls (str): object class name
            track (list): tracking history for tracks path drawing
        """
        speed_label = f"{int(self.dist_data[track_id])}km/ph" if track_id in self.dist_data else self.names[int(cls)]
        bbox_color = colors(int(track_id)) if track_id in self.dist_data else (255, 0, 255)

        self.annotator.box_label(box, speed_label, bbox_color)

        cv2.polylines(self.im0, [self.trk_pts], isClosed=False, color=(0, 255, 0), thickness=1)
        cv2.circle(self.im0, (int(track[-1][0]), int(track[-1][1])), 5, bbox_color, -1)

    def calculate_speed(self, trk_id, track):
        """
        Calculation of object speed.

        Args:
            trk_id (int): object track id.
            track (list): tracking history for tracks path drawing
        """

        if not self.reg_pts[0][0] < track[-1][0] < self.reg_pts[1][0]:
            return
        if self.reg_pts[1][1] - self.spdl_dist_thresh < track[-1][1] < self.reg_pts[1][1] + self.spdl_dist_thresh:
            direction = "known"

        elif self.reg_pts[0][1] - self.spdl_dist_thresh < track[-1][1] < self.reg_pts[0][1] + self.spdl_dist_thresh:
            direction = "known"

        else:
            direction = "unknown"

        if self.trk_previous_times[trk_id] != 0 and direction != "unknown" and trk_id not in self.trk_idslist:
            self.trk_idslist.append(trk_id)

            time_difference = time() - self.trk_previous_times[trk_id]
            if time_difference > 0:
                dist_difference = np.abs(track[-1][1] - self.trk_previous_points[trk_id][1])
                speed = dist_difference / time_difference
                self.dist_data[trk_id] = speed

        self.trk_previous_times[trk_id] = time()
        self.trk_previous_points[trk_id] = track[-1]

    def estimate_speed(self, im0, tracks, region_color=(255, 0, 0)):
        """
        Calculate object based on tracking data.

        Args:
            im0 (nd array): Image
            tracks (list): List of tracks obtained from the object tracking process.
            region_color (tuple): Color to use when drawing regions.
        """
        self.im0 = im0
        if tracks[0].boxes.id is None:
            if self.view_img and self.env_check:
                self.display_frames()
            return im0
        self.extract_tracks(tracks)

        self.annotator = Annotator(self.im0, line_width=2)
        self.annotator.draw_region(reg_pts=self.reg_pts, color=region_color, thickness=self.region_thickness)

        for box, trk_id, cls in zip(self.boxes, self.trk_ids, self.clss):
            track = self.store_track_info(trk_id, box)

            if trk_id not in self.trk_previous_times:
                self.trk_previous_times[trk_id] = 0

            self.plot_box_and_track(trk_id, box, cls, track)
            self.calculate_speed(trk_id, track)

        if self.view_img and self.env_check:
            self.display_frames()

        return im0

    def display_frames(self):
        """Display frame."""
        cv2.imshow("Ultralytics Speed Estimation", self.im0)
        if cv2.waitKey(1) & 0xFF == ord("q"):
            return

__init__()

Inicializa a classe do estimador de velocidade com valores por defeito para os parâmetros Visual, Imagem, Via e Velocidade.

Código fonte em ultralytics/solutions/speed_estimation.py
def __init__(self):
    """Initializes the speed-estimator class with default values for Visual, Image, track and speed parameters."""

    # Visual & im0 information
    self.im0 = None
    self.annotator = None
    self.view_img = False

    # Region information
    self.reg_pts = [(20, 400), (1260, 400)]
    self.region_thickness = 3

    # Predict/track information
    self.clss = None
    self.names = None
    self.boxes = None
    self.trk_ids = None
    self.trk_pts = None
    self.line_thickness = 2
    self.trk_history = defaultdict(list)

    # Speed estimator information
    self.current_time = 0
    self.dist_data = {}
    self.trk_idslist = []
    self.spdl_dist_thresh = 10
    self.trk_previous_times = {}
    self.trk_previous_points = {}

    # Check if environment support imshow
    self.env_check = check_imshow(warn=True)

calculate_speed(trk_id, track)

Cálculo da velocidade do objeto.

Parâmetros:

Nome Tipo Descrição Predefinição
trk_id int

id da pista do objeto.

necessário
track list

histórico de rastreio para desenho de trajectos

necessário
Código fonte em ultralytics/solutions/speed_estimation.py
def calculate_speed(self, trk_id, track):
    """
    Calculation of object speed.

    Args:
        trk_id (int): object track id.
        track (list): tracking history for tracks path drawing
    """

    if not self.reg_pts[0][0] < track[-1][0] < self.reg_pts[1][0]:
        return
    if self.reg_pts[1][1] - self.spdl_dist_thresh < track[-1][1] < self.reg_pts[1][1] + self.spdl_dist_thresh:
        direction = "known"

    elif self.reg_pts[0][1] - self.spdl_dist_thresh < track[-1][1] < self.reg_pts[0][1] + self.spdl_dist_thresh:
        direction = "known"

    else:
        direction = "unknown"

    if self.trk_previous_times[trk_id] != 0 and direction != "unknown" and trk_id not in self.trk_idslist:
        self.trk_idslist.append(trk_id)

        time_difference = time() - self.trk_previous_times[trk_id]
        if time_difference > 0:
            dist_difference = np.abs(track[-1][1] - self.trk_previous_points[trk_id][1])
            speed = dist_difference / time_difference
            self.dist_data[trk_id] = speed

    self.trk_previous_times[trk_id] = time()
    self.trk_previous_points[trk_id] = track[-1]

display_frames()

Apresenta o quadro.

Código fonte em ultralytics/solutions/speed_estimation.py
def display_frames(self):
    """Display frame."""
    cv2.imshow("Ultralytics Speed Estimation", self.im0)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        return

estimate_speed(im0, tracks, region_color=(255, 0, 0))

Calcula o objeto com base nos dados de seguimento.

Parâmetros:

Nome Tipo Descrição Predefinição
im0 nd array

Imagem

necessário
tracks list

Lista de trajectos obtidos a partir do processo de seguimento de objectos.

necessário
region_color tuple

Cor a utilizar quando desenha regiões.

(255, 0, 0)
Código fonte em ultralytics/solutions/speed_estimation.py
def estimate_speed(self, im0, tracks, region_color=(255, 0, 0)):
    """
    Calculate object based on tracking data.

    Args:
        im0 (nd array): Image
        tracks (list): List of tracks obtained from the object tracking process.
        region_color (tuple): Color to use when drawing regions.
    """
    self.im0 = im0
    if tracks[0].boxes.id is None:
        if self.view_img and self.env_check:
            self.display_frames()
        return im0
    self.extract_tracks(tracks)

    self.annotator = Annotator(self.im0, line_width=2)
    self.annotator.draw_region(reg_pts=self.reg_pts, color=region_color, thickness=self.region_thickness)

    for box, trk_id, cls in zip(self.boxes, self.trk_ids, self.clss):
        track = self.store_track_info(trk_id, box)

        if trk_id not in self.trk_previous_times:
            self.trk_previous_times[trk_id] = 0

        self.plot_box_and_track(trk_id, box, cls, track)
        self.calculate_speed(trk_id, track)

    if self.view_img and self.env_check:
        self.display_frames()

    return im0

extract_tracks(tracks)

Extrai resultados dos dados fornecidos.

Parâmetros:

Nome Tipo Descrição Predefinição
tracks list

Lista de trajectos obtidos a partir do processo de seguimento de objectos.

necessário
Código fonte em ultralytics/solutions/speed_estimation.py
def extract_tracks(self, tracks):
    """
    Extracts results from the provided data.

    Args:
        tracks (list): List of tracks obtained from the object tracking process.
    """
    self.boxes = tracks[0].boxes.xyxy.cpu()
    self.clss = tracks[0].boxes.cls.cpu().tolist()
    self.trk_ids = tracks[0].boxes.id.int().cpu().tolist()

plot_box_and_track(track_id, box, cls, track)

Traça o trajeto e a caixa delimitadora.

Parâmetros:

Nome Tipo Descrição Predefinição
track_id int

id da pista do objeto.

necessário
box list

dados da caixa delimitadora do objeto

necessário
cls str

nome da classe do objeto

necessário
track list

histórico de rastreio para desenho de trajectos

necessário
Código fonte em ultralytics/solutions/speed_estimation.py
def plot_box_and_track(self, track_id, box, cls, track):
    """
    Plot track and bounding box.

    Args:
        track_id (int): object track id.
        box (list): object bounding box data
        cls (str): object class name
        track (list): tracking history for tracks path drawing
    """
    speed_label = f"{int(self.dist_data[track_id])}km/ph" if track_id in self.dist_data else self.names[int(cls)]
    bbox_color = colors(int(track_id)) if track_id in self.dist_data else (255, 0, 255)

    self.annotator.box_label(box, speed_label, bbox_color)

    cv2.polylines(self.im0, [self.trk_pts], isClosed=False, color=(0, 255, 0), thickness=1)
    cv2.circle(self.im0, (int(track[-1][0]), int(track[-1][1])), 5, bbox_color, -1)

set_args(reg_pts, names, view_img=False, line_thickness=2, region_thickness=5, spdl_dist_thresh=10)

Configura a estimativa de velocidade e os parâmetros de visualização.

Parâmetros:

Nome Tipo Descrição Predefinição
reg_pts list

Lista inicial de pontos que definem a região de cálculo da velocidade.

necessário
names dict

nomes das classes de deteção de objectos

necessário
view_img bool

Bandeira que indica a visualização do quadro

False
line_thickness int

Espessura da linha para caixas delimitadoras.

2
region_thickness int

Espessura da região de estimativa da velocidade

5
spdl_dist_thresh int

Limiar da distância euclidiana para a linha de velocidade

10
Código fonte em ultralytics/solutions/speed_estimation.py
def set_args(
    self,
    reg_pts,
    names,
    view_img=False,
    line_thickness=2,
    region_thickness=5,
    spdl_dist_thresh=10,
):
    """
    Configures the speed estimation and display parameters.

    Args:
        reg_pts (list): Initial list of points defining the speed calculation region.
        names (dict): object detection classes names
        view_img (bool): Flag indicating frame display
        line_thickness (int): Line thickness for bounding boxes.
        region_thickness (int): Speed estimation region thickness
        spdl_dist_thresh (int): Euclidean distance threshold for speed line
    """
    if reg_pts is None:
        print("Region points not provided, using default values")
    else:
        self.reg_pts = reg_pts
    self.names = names
    self.view_img = view_img
    self.line_thickness = line_thickness
    self.region_thickness = region_thickness
    self.spdl_dist_thresh = spdl_dist_thresh

store_track_info(track_id, box)

Armazena os dados do trajeto.

Parâmetros:

Nome Tipo Descrição Predefinição
track_id int

id da pista do objeto.

necessário
box list

dados da caixa delimitadora do objeto

necessário
Código fonte em ultralytics/solutions/speed_estimation.py
def store_track_info(self, track_id, box):
    """
    Store track data.

    Args:
        track_id (int): object track id.
        box (list): object bounding box data
    """
    track = self.trk_history[track_id]
    bbox_center = (float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2))
    track.append(bbox_center)

    if len(track) > 30:
        track.pop(0)

    self.trk_pts = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
    return track





Criado em 2024-01-05, Atualizado em 2024-01-10
Autores: AyushExel (1), chr043416@gmail.com (1)