─░├žeri─če ge├ž

Referans i├žin ultralytics/solutions/speed_estimation.py

Not

Bu dosya https://github.com/ultralytics/ultralytics/blob/main/ ultralytics/solutions/speed_estimation .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.solutions.speed_estimation.SpeedEstimator

Ger├žek zamanl─▒ bir video ak─▒┼č─▒ndaki nesnelerin izlerine g├Âre h─▒zlar─▒n─▒ tahmin eden bir s─▒n─▒f.

Kaynak kodu ultralytics/solutions/speed_estimation.py
class SpeedEstimator:
    """A class to estimate the speed of objects in a real-time video stream based on their tracks."""

    def __init__(self, names, reg_pts=None, view_img=False, line_thickness=2, region_thickness=5, spdl_dist_thresh=10):
        """
        Initializes the SpeedEstimator with the given parameters.

        Args:
            names (dict): Dictionary of class names.
            reg_pts (list, optional): List of region points for speed estimation. Defaults to [(20, 400), (1260, 400)].
            view_img (bool, optional): Whether to display the image with annotations. Defaults to False.
            line_thickness (int, optional): Thickness of the lines for drawing boxes and tracks. Defaults to 2.
            region_thickness (int, optional): Thickness of the region lines. Defaults to 5.
            spdl_dist_thresh (int, optional): Distance threshold for speed calculation. Defaults to 10.
        """
        # Visual & image information
        self.im0 = None
        self.annotator = None
        self.view_img = view_img

        # Region information
        self.reg_pts = reg_pts if reg_pts is not None else [(20, 400), (1260, 400)]
        self.region_thickness = region_thickness

        # Tracking information
        self.clss = None
        self.names = names
        self.boxes = None
        self.trk_ids = None
        self.trk_pts = None
        self.line_thickness = line_thickness
        self.trk_history = defaultdict(list)

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

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

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

        Args:
            track_id (int): Object track id.
            box (list): Object bounding box data.

        Returns:
            (list): Updated tracking history for the given track_id.
        """
        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):
        """
        Plots 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 drawing tracks path.
        """
        speed_label = f"{int(self.dist_data[track_id])} km/h" 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):
        """
        Calculates the speed of an object.

        Args:
            trk_id (int): Object track id.
            track (list): Tracking history for drawing tracks path.
        """
        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.get(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)):
        """
        Estimates the speed of objects based on tracking data.

        Args:
            im0 (ndarray): Image.
            tracks (list): List of tracks obtained from the object tracking process.
            region_color (tuple, optional): Color to use when drawing regions. Defaults to (255, 0, 0).

        Returns:
            (ndarray): The image with annotated boxes and tracks.
        """
        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=self.line_thickness)
        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):
        """Displays the current frame."""
        cv2.imshow("Ultralytics Speed Estimation", self.im0)
        if cv2.waitKey(1) & 0xFF == ord("q"):
            return

__init__(names, reg_pts=None, view_img=False, line_thickness=2, region_thickness=5, spdl_dist_thresh=10)

SpeedEstimator'─▒ verilen parametrelerle ba┼člat─▒r.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
names dict

S─▒n─▒f adlar─▒ s├Âzl├╝─č├╝.

gerekli
reg_pts list

H─▒z tahmini i├žin b├Âlge noktalar─▒n─▒n listesi. Varsay─▒lan de─čer [(20, 400), (1260, 400)].

None
view_img bool

G├Âr├╝nt├╝n├╝n ek a├ž─▒klamalarla g├Âr├╝nt├╝lenip g├Âr├╝nt├╝lenmeyece─či. Varsay─▒lan de─čer False'dir.

False
line_thickness int

Kutular─▒ ve izleri ├žizmek i├žin ├žizgilerin kal─▒nl─▒─č─▒. Varsay─▒lan de─čer 2'dir.

2
region_thickness int

B├Âlge ├žizgilerinin kal─▒nl─▒─č─▒. Varsay─▒lan de─čer 5'tir.

5
spdl_dist_thresh int

H─▒z hesaplamas─▒ i├žin mesafe e┼či─či. Varsay─▒lan de─čer 10'dur.

10
Kaynak kodu ultralytics/solutions/speed_estimation.py
def __init__(self, names, reg_pts=None, view_img=False, line_thickness=2, region_thickness=5, spdl_dist_thresh=10):
    """
    Initializes the SpeedEstimator with the given parameters.

    Args:
        names (dict): Dictionary of class names.
        reg_pts (list, optional): List of region points for speed estimation. Defaults to [(20, 400), (1260, 400)].
        view_img (bool, optional): Whether to display the image with annotations. Defaults to False.
        line_thickness (int, optional): Thickness of the lines for drawing boxes and tracks. Defaults to 2.
        region_thickness (int, optional): Thickness of the region lines. Defaults to 5.
        spdl_dist_thresh (int, optional): Distance threshold for speed calculation. Defaults to 10.
    """
    # Visual & image information
    self.im0 = None
    self.annotator = None
    self.view_img = view_img

    # Region information
    self.reg_pts = reg_pts if reg_pts is not None else [(20, 400), (1260, 400)]
    self.region_thickness = region_thickness

    # Tracking information
    self.clss = None
    self.names = names
    self.boxes = None
    self.trk_ids = None
    self.trk_pts = None
    self.line_thickness = line_thickness
    self.trk_history = defaultdict(list)

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

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

calculate_speed(trk_id, track)

Bir nesnenin h─▒z─▒n─▒ hesaplar.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
trk_id int

Nesne par├ža kimli─či.

gerekli
track list

├çizim izleri yolu i├žin izleme ge├žmi┼či.

gerekli
Kaynak kodu ultralytics/solutions/speed_estimation.py
def calculate_speed(self, trk_id, track):
    """
    Calculates the speed of an object.

    Args:
        trk_id (int): Object track id.
        track (list): Tracking history for drawing tracks path.
    """
    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.get(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()

Ge├žerli ├žer├ževeyi g├Âr├╝nt├╝ler.

Kaynak kodu ultralytics/solutions/speed_estimation.py
def display_frames(self):
    """Displays the current 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))

─░zleme verilerine dayanarak nesnelerin h─▒z─▒n─▒ tahmin eder.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
im0 ndarray

Resim.

gerekli
tracks list

Nesne izleme i┼čleminden elde edilen izlerin listesi.

gerekli
region_color tuple

B├Âlgeleri ├žizerken kullan─▒lacak renk. Varsay─▒lan de─čer (255, 0, 0).

(255, 0, 0)

─░ade:

Tip A├ž─▒klama
ndarray

A├ž─▒klamal─▒ kutular ve izler i├žeren g├Âr├╝nt├╝.

Kaynak kodu ultralytics/solutions/speed_estimation.py
def estimate_speed(self, im0, tracks, region_color=(255, 0, 0)):
    """
    Estimates the speed of objects based on tracking data.

    Args:
        im0 (ndarray): Image.
        tracks (list): List of tracks obtained from the object tracking process.
        region_color (tuple, optional): Color to use when drawing regions. Defaults to (255, 0, 0).

    Returns:
        (ndarray): The image with annotated boxes and tracks.
    """
    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=self.line_thickness)
    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)

Sa─članan izleme verilerinden sonu├žlar─▒ ├ž─▒kar─▒r.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
tracks list

Nesne izleme i┼čleminden elde edilen izlerin listesi.

gerekli
Kaynak kodu ultralytics/solutions/speed_estimation.py
def extract_tracks(self, tracks):
    """
    Extracts results from the provided tracking 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)

─░zi ve s─▒n─▒rlay─▒c─▒ kutuyu ├žizer.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
track_id int

Nesne par├ža kimli─či.

gerekli
box list

Nesne s─▒n─▒rlay─▒c─▒ kutu verileri.

gerekli
cls str

Nesne s─▒n─▒f─▒ ad─▒.

gerekli
track list

├çizim izleri yolu i├žin izleme ge├žmi┼či.

gerekli
Kaynak kodu ultralytics/solutions/speed_estimation.py
def plot_box_and_track(self, track_id, box, cls, track):
    """
    Plots 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 drawing tracks path.
    """
    speed_label = f"{int(self.dist_data[track_id])} km/h" 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)

store_track_info(track_id, box)

─░z verilerini depolar.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
track_id int

Nesne par├ža kimli─či.

gerekli
box list

Nesne s─▒n─▒rlay─▒c─▒ kutu verileri.

gerekli

─░ade:

Tip A├ž─▒klama
list

Verilen track_id i├žin izleme ge├žmi┼čini g├╝nceller.

Kaynak kodu ultralytics/solutions/speed_estimation.py
def store_track_info(self, track_id, box):
    """
    Stores track data.

    Args:
        track_id (int): Object track id.
        box (list): Object bounding box data.

    Returns:
        (list): Updated tracking history for the given track_id.
    """
    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





Created 2024-01-05, Updated 2024-06-02
Authors: glenn-jocher (2), Burhan-Q (1), AyushExel (1), RizwanMunawar (1)