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ultralytics.solutions.heatmap.Heatmap

Una classe per disegnare mappe di calore nel flusso video in tempo reale in base alle loro tracce.

Codice sorgente in ultralytics/solutions/heatmap.py
class Heatmap:
    """A class to draw heatmaps in real-time video stream based on their tracks."""

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

        # Visual information
        self.annotator = None
        self.view_img = False
        self.shape = "circle"

        # Image information
        self.imw = None
        self.imh = None
        self.im0 = None
        self.view_in_counts = True
        self.view_out_counts = True

        # Heatmap colormap and heatmap np array
        self.colormap = None
        self.heatmap = None
        self.heatmap_alpha = 0.5

        # Predict/track information
        self.boxes = None
        self.track_ids = None
        self.clss = None
        self.track_history = defaultdict(list)

        # Region & Line Information
        self.count_reg_pts = None
        self.counting_region = None
        self.line_dist_thresh = 15
        self.region_thickness = 5
        self.region_color = (255, 0, 255)

        # Object Counting Information
        self.in_counts = 0
        self.out_counts = 0
        self.counting_list = []
        self.count_txt_thickness = 0
        self.count_txt_color = (0, 0, 0)
        self.count_color = (255, 255, 255)

        # Decay factor
        self.decay_factor = 0.99

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

    def set_args(
        self,
        imw,
        imh,
        colormap=cv2.COLORMAP_JET,
        heatmap_alpha=0.5,
        view_img=False,
        view_in_counts=True,
        view_out_counts=True,
        count_reg_pts=None,
        count_txt_thickness=2,
        count_txt_color=(0, 0, 0),
        count_color=(255, 255, 255),
        count_reg_color=(255, 0, 255),
        region_thickness=5,
        line_dist_thresh=15,
        decay_factor=0.99,
        shape="circle",
    ):
        """
        Configures the heatmap colormap, width, height and display parameters.

        Args:
            colormap (cv2.COLORMAP): The colormap to be set.
            imw (int): The width of the frame.
            imh (int): The height of the frame.
            heatmap_alpha (float): alpha value for heatmap display
            view_img (bool): Flag indicating frame display
            view_in_counts (bool): Flag to control whether to display the incounts on video stream.
            view_out_counts (bool): Flag to control whether to display the outcounts on video stream.
            count_reg_pts (list): Object counting region points
            count_txt_thickness (int): Text thickness for object counting display
            count_txt_color (RGB color): count text color value
            count_color (RGB color): count text background color value
            count_reg_color (RGB color): Color of object counting region
            region_thickness (int): Object counting Region thickness
            line_dist_thresh (int): Euclidean Distance threshold for line counter
            decay_factor (float): value for removing heatmap area after object passed
            shape (str): Heatmap shape, rect or circle shape supported
        """
        self.imw = imw
        self.imh = imh
        self.heatmap_alpha = heatmap_alpha
        self.view_img = view_img
        self.view_in_counts = view_in_counts
        self.view_out_counts = view_out_counts
        self.colormap = colormap

        # Region and line selection
        if count_reg_pts is not None:
            if len(count_reg_pts) == 2:
                print("Line Counter Initiated.")
                self.count_reg_pts = count_reg_pts
                self.counting_region = LineString(count_reg_pts)

            elif len(count_reg_pts) == 4:
                print("Region Counter Initiated.")
                self.count_reg_pts = count_reg_pts
                self.counting_region = Polygon(self.count_reg_pts)

            else:
                print("Region or line points Invalid, 2 or 4 points supported")
                print("Using Line Counter Now")
                self.counting_region = Polygon([(20, 400), (1260, 400)])  # dummy points

        # Heatmap new frame
        self.heatmap = np.zeros((int(self.imh), int(self.imw)), dtype=np.float32)

        self.count_txt_thickness = count_txt_thickness
        self.count_txt_color = count_txt_color
        self.count_color = count_color
        self.region_color = count_reg_color
        self.region_thickness = region_thickness
        self.decay_factor = decay_factor
        self.line_dist_thresh = line_dist_thresh
        self.shape = shape

        # shape of heatmap, if not selected
        if self.shape not in ["circle", "rect"]:
            print("Unknown shape value provided, 'circle' & 'rect' supported")
            print("Using Circular shape now")
            self.shape = "circle"

    def extract_results(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.track_ids = tracks[0].boxes.id.int().cpu().tolist()

    def generate_heatmap(self, im0, tracks):
        """
        Generate heatmap based on tracking data.

        Args:
            im0 (nd array): Image
            tracks (list): List of tracks obtained from the object tracking process.
        """
        self.im0 = im0
        if tracks[0].boxes.id is None:
            self.heatmap = np.zeros((int(self.imh), int(self.imw)), dtype=np.float32)
            if self.view_img and self.env_check:
                self.display_frames()
            return im0
        self.heatmap *= self.decay_factor  # decay factor
        self.extract_results(tracks)
        self.annotator = Annotator(self.im0, self.count_txt_thickness, None)

        if self.count_reg_pts is not None:
            # Draw counting region
            if self.view_in_counts or self.view_out_counts:
                self.annotator.draw_region(
                    reg_pts=self.count_reg_pts, color=self.region_color, thickness=self.region_thickness
                )

            for box, cls, track_id in zip(self.boxes, self.clss, self.track_ids):
                if self.shape == "circle":
                    center = (int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2))
                    radius = min(int(box[2]) - int(box[0]), int(box[3]) - int(box[1])) // 2

                    y, x = np.ogrid[0 : self.heatmap.shape[0], 0 : self.heatmap.shape[1]]
                    mask = (x - center[0]) ** 2 + (y - center[1]) ** 2 <= radius**2

                    self.heatmap[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] += (
                        2 * mask[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])]
                    )

                else:
                    self.heatmap[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] += 2

                # Store tracking hist
                track_line = self.track_history[track_id]
                track_line.append((float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2)))
                if len(track_line) > 30:
                    track_line.pop(0)

                # Count objects
                if len(self.count_reg_pts) == 4:
                    if self.counting_region.contains(Point(track_line[-1])) and track_id not in self.counting_list:
                        self.counting_list.append(track_id)
                        if box[0] < self.counting_region.centroid.x:
                            self.out_counts += 1
                        else:
                            self.in_counts += 1

                elif len(self.count_reg_pts) == 2:
                    distance = Point(track_line[-1]).distance(self.counting_region)
                    if distance < self.line_dist_thresh and track_id not in self.counting_list:
                        self.counting_list.append(track_id)
                        if box[0] < self.counting_region.centroid.x:
                            self.out_counts += 1
                        else:
                            self.in_counts += 1
        else:
            for box, cls in zip(self.boxes, self.clss):
                if self.shape == "circle":
                    center = (int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2))
                    radius = min(int(box[2]) - int(box[0]), int(box[3]) - int(box[1])) // 2

                    y, x = np.ogrid[0 : self.heatmap.shape[0], 0 : self.heatmap.shape[1]]
                    mask = (x - center[0]) ** 2 + (y - center[1]) ** 2 <= radius**2

                    self.heatmap[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] += (
                        2 * mask[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])]
                    )

                else:
                    self.heatmap[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] += 2

        # Normalize, apply colormap to heatmap and combine with original image
        heatmap_normalized = cv2.normalize(self.heatmap, None, 0, 255, cv2.NORM_MINMAX)
        heatmap_colored = cv2.applyColorMap(heatmap_normalized.astype(np.uint8), self.colormap)

        incount_label = f"In Count : {self.in_counts}"
        outcount_label = f"OutCount : {self.out_counts}"

        # Display counts based on user choice
        counts_label = None
        if not self.view_in_counts and not self.view_out_counts:
            counts_label = None
        elif not self.view_in_counts:
            counts_label = outcount_label
        elif not self.view_out_counts:
            counts_label = incount_label
        else:
            counts_label = f"{incount_label} {outcount_label}"

        if self.count_reg_pts is not None and counts_label is not None:
            self.annotator.count_labels(
                counts=counts_label,
                count_txt_size=self.count_txt_thickness,
                txt_color=self.count_txt_color,
                color=self.count_color,
            )

        self.im0 = cv2.addWeighted(self.im0, 1 - self.heatmap_alpha, heatmap_colored, self.heatmap_alpha, 0)

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

        return self.im0

    def display_frames(self):
        """Display frame."""
        cv2.imshow("Ultralytics Heatmap", self.im0)

        if cv2.waitKey(1) & 0xFF == ord("q"):
            return

__init__()

Inizializza la classe heatmap con i valori predefiniti dei parametri Visual, Image, track, count e heatmap.

Codice sorgente in ultralytics/solutions/heatmap.py
def __init__(self):
    """Initializes the heatmap class with default values for Visual, Image, track, count and heatmap parameters."""

    # Visual information
    self.annotator = None
    self.view_img = False
    self.shape = "circle"

    # Image information
    self.imw = None
    self.imh = None
    self.im0 = None
    self.view_in_counts = True
    self.view_out_counts = True

    # Heatmap colormap and heatmap np array
    self.colormap = None
    self.heatmap = None
    self.heatmap_alpha = 0.5

    # Predict/track information
    self.boxes = None
    self.track_ids = None
    self.clss = None
    self.track_history = defaultdict(list)

    # Region & Line Information
    self.count_reg_pts = None
    self.counting_region = None
    self.line_dist_thresh = 15
    self.region_thickness = 5
    self.region_color = (255, 0, 255)

    # Object Counting Information
    self.in_counts = 0
    self.out_counts = 0
    self.counting_list = []
    self.count_txt_thickness = 0
    self.count_txt_color = (0, 0, 0)
    self.count_color = (255, 255, 255)

    # Decay factor
    self.decay_factor = 0.99

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

display_frames()

Cornice del display.

Codice sorgente in ultralytics/solutions/heatmap.py
def display_frames(self):
    """Display frame."""
    cv2.imshow("Ultralytics Heatmap", self.im0)

    if cv2.waitKey(1) & 0xFF == ord("q"):
        return

extract_results(tracks)

Estrae i risultati dai dati forniti.

Parametri:

Nome Tipo Descrizione Predefinito
tracks list

Elenco delle tracce ottenute dal processo di tracciamento dell'oggetto.

richiesto
Codice sorgente in ultralytics/solutions/heatmap.py
def extract_results(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.track_ids = tracks[0].boxes.id.int().cpu().tolist()

generate_heatmap(im0, tracks)

Genera una mappa di calore basata sui dati di tracciamento.

Parametri:

Nome Tipo Descrizione Predefinito
im0 nd array

Immagine

richiesto
tracks list

Elenco delle tracce ottenute dal processo di tracciamento dell'oggetto.

richiesto
Codice sorgente in ultralytics/solutions/heatmap.py
def generate_heatmap(self, im0, tracks):
    """
    Generate heatmap based on tracking data.

    Args:
        im0 (nd array): Image
        tracks (list): List of tracks obtained from the object tracking process.
    """
    self.im0 = im0
    if tracks[0].boxes.id is None:
        self.heatmap = np.zeros((int(self.imh), int(self.imw)), dtype=np.float32)
        if self.view_img and self.env_check:
            self.display_frames()
        return im0
    self.heatmap *= self.decay_factor  # decay factor
    self.extract_results(tracks)
    self.annotator = Annotator(self.im0, self.count_txt_thickness, None)

    if self.count_reg_pts is not None:
        # Draw counting region
        if self.view_in_counts or self.view_out_counts:
            self.annotator.draw_region(
                reg_pts=self.count_reg_pts, color=self.region_color, thickness=self.region_thickness
            )

        for box, cls, track_id in zip(self.boxes, self.clss, self.track_ids):
            if self.shape == "circle":
                center = (int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2))
                radius = min(int(box[2]) - int(box[0]), int(box[3]) - int(box[1])) // 2

                y, x = np.ogrid[0 : self.heatmap.shape[0], 0 : self.heatmap.shape[1]]
                mask = (x - center[0]) ** 2 + (y - center[1]) ** 2 <= radius**2

                self.heatmap[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] += (
                    2 * mask[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])]
                )

            else:
                self.heatmap[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] += 2

            # Store tracking hist
            track_line = self.track_history[track_id]
            track_line.append((float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2)))
            if len(track_line) > 30:
                track_line.pop(0)

            # Count objects
            if len(self.count_reg_pts) == 4:
                if self.counting_region.contains(Point(track_line[-1])) and track_id not in self.counting_list:
                    self.counting_list.append(track_id)
                    if box[0] < self.counting_region.centroid.x:
                        self.out_counts += 1
                    else:
                        self.in_counts += 1

            elif len(self.count_reg_pts) == 2:
                distance = Point(track_line[-1]).distance(self.counting_region)
                if distance < self.line_dist_thresh and track_id not in self.counting_list:
                    self.counting_list.append(track_id)
                    if box[0] < self.counting_region.centroid.x:
                        self.out_counts += 1
                    else:
                        self.in_counts += 1
    else:
        for box, cls in zip(self.boxes, self.clss):
            if self.shape == "circle":
                center = (int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2))
                radius = min(int(box[2]) - int(box[0]), int(box[3]) - int(box[1])) // 2

                y, x = np.ogrid[0 : self.heatmap.shape[0], 0 : self.heatmap.shape[1]]
                mask = (x - center[0]) ** 2 + (y - center[1]) ** 2 <= radius**2

                self.heatmap[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] += (
                    2 * mask[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])]
                )

            else:
                self.heatmap[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] += 2

    # Normalize, apply colormap to heatmap and combine with original image
    heatmap_normalized = cv2.normalize(self.heatmap, None, 0, 255, cv2.NORM_MINMAX)
    heatmap_colored = cv2.applyColorMap(heatmap_normalized.astype(np.uint8), self.colormap)

    incount_label = f"In Count : {self.in_counts}"
    outcount_label = f"OutCount : {self.out_counts}"

    # Display counts based on user choice
    counts_label = None
    if not self.view_in_counts and not self.view_out_counts:
        counts_label = None
    elif not self.view_in_counts:
        counts_label = outcount_label
    elif not self.view_out_counts:
        counts_label = incount_label
    else:
        counts_label = f"{incount_label} {outcount_label}"

    if self.count_reg_pts is not None and counts_label is not None:
        self.annotator.count_labels(
            counts=counts_label,
            count_txt_size=self.count_txt_thickness,
            txt_color=self.count_txt_color,
            color=self.count_color,
        )

    self.im0 = cv2.addWeighted(self.im0, 1 - self.heatmap_alpha, heatmap_colored, self.heatmap_alpha, 0)

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

    return self.im0

set_args(imw, imh, colormap=cv2.COLORMAP_JET, heatmap_alpha=0.5, view_img=False, view_in_counts=True, view_out_counts=True, count_reg_pts=None, count_txt_thickness=2, count_txt_color=(0, 0, 0), count_color=(255, 255, 255), count_reg_color=(255, 0, 255), region_thickness=5, line_dist_thresh=15, decay_factor=0.99, shape='circle')

Configura la mappa di colore, la larghezza, l'altezza e i parametri di visualizzazione della heatmap.

Parametri:

Nome Tipo Descrizione Predefinito
colormap COLORMAP

La mappa dei colori da impostare.

COLORMAP_JET
imw int

La larghezza della cornice.

richiesto
imh int

L'altezza della cornice.

richiesto
heatmap_alpha float

valore alfa per la visualizzazione della mappa di calore

0.5
view_img bool

Flag che indica la visualizzazione della cornice

False
view_in_counts bool

Flag per controllare se visualizzare gli importi nel flusso video.

True
view_out_counts bool

Flag per controllare se visualizzare gli outcount nel flusso video.

True
count_reg_pts list

Punti della regione di conteggio degli oggetti

None
count_txt_thickness int

Spessore del testo per la visualizzazione del conteggio degli oggetti

2
count_txt_color RGB color

valore del colore del testo del conteggio

(0, 0, 0)
count_color RGB color

valore del colore di sfondo del testo del conteggio

(255, 255, 255)
count_reg_color RGB color

Colore della regione di conteggio degli oggetti

(255, 0, 255)
region_thickness int

Conteggio degli oggetti Spessore della regione

5
line_dist_thresh int

Soglia della distanza euclidea per il contatore di linee

15
decay_factor float

valore per la rimozione dell'area della mappa di calore dopo il passaggio dell'oggetto

0.99
shape str

Forma della mappa di calore, forma rettangolare o circolare supportata

'circle'
Codice sorgente in ultralytics/solutions/heatmap.py
def set_args(
    self,
    imw,
    imh,
    colormap=cv2.COLORMAP_JET,
    heatmap_alpha=0.5,
    view_img=False,
    view_in_counts=True,
    view_out_counts=True,
    count_reg_pts=None,
    count_txt_thickness=2,
    count_txt_color=(0, 0, 0),
    count_color=(255, 255, 255),
    count_reg_color=(255, 0, 255),
    region_thickness=5,
    line_dist_thresh=15,
    decay_factor=0.99,
    shape="circle",
):
    """
    Configures the heatmap colormap, width, height and display parameters.

    Args:
        colormap (cv2.COLORMAP): The colormap to be set.
        imw (int): The width of the frame.
        imh (int): The height of the frame.
        heatmap_alpha (float): alpha value for heatmap display
        view_img (bool): Flag indicating frame display
        view_in_counts (bool): Flag to control whether to display the incounts on video stream.
        view_out_counts (bool): Flag to control whether to display the outcounts on video stream.
        count_reg_pts (list): Object counting region points
        count_txt_thickness (int): Text thickness for object counting display
        count_txt_color (RGB color): count text color value
        count_color (RGB color): count text background color value
        count_reg_color (RGB color): Color of object counting region
        region_thickness (int): Object counting Region thickness
        line_dist_thresh (int): Euclidean Distance threshold for line counter
        decay_factor (float): value for removing heatmap area after object passed
        shape (str): Heatmap shape, rect or circle shape supported
    """
    self.imw = imw
    self.imh = imh
    self.heatmap_alpha = heatmap_alpha
    self.view_img = view_img
    self.view_in_counts = view_in_counts
    self.view_out_counts = view_out_counts
    self.colormap = colormap

    # Region and line selection
    if count_reg_pts is not None:
        if len(count_reg_pts) == 2:
            print("Line Counter Initiated.")
            self.count_reg_pts = count_reg_pts
            self.counting_region = LineString(count_reg_pts)

        elif len(count_reg_pts) == 4:
            print("Region Counter Initiated.")
            self.count_reg_pts = count_reg_pts
            self.counting_region = Polygon(self.count_reg_pts)

        else:
            print("Region or line points Invalid, 2 or 4 points supported")
            print("Using Line Counter Now")
            self.counting_region = Polygon([(20, 400), (1260, 400)])  # dummy points

    # Heatmap new frame
    self.heatmap = np.zeros((int(self.imh), int(self.imw)), dtype=np.float32)

    self.count_txt_thickness = count_txt_thickness
    self.count_txt_color = count_txt_color
    self.count_color = count_color
    self.region_color = count_reg_color
    self.region_thickness = region_thickness
    self.decay_factor = decay_factor
    self.line_dist_thresh = line_dist_thresh
    self.shape = shape

    # shape of heatmap, if not selected
    if self.shape not in ["circle", "rect"]:
        print("Unknown shape value provided, 'circle' & 'rect' supported")
        print("Using Circular shape now")
        self.shape = "circle"





Creato 2023-12-10, Aggiornato 2023-12-10
Autori: glenn-jocher (1)