Reference for ultralytics/solutions/parking_management.py

def draw_bounding_box(self, box):
    """
    Draw bounding box on canvas.

    Args:
        box (list): Bounding box data
    """
    for i in range(4):
        x1, y1 = box[i]
        x2, y2 = box[(i + 1) % 4]
        self.canvas.create_line(x1, y1, x2, y2, fill="blue", width=2)

def on_canvas_click(self, event):
    """Handle mouse clicks on canvas to create points for bounding boxes."""
    self.current_box.append((event.x, event.y))
    x0, y0 = event.x - 3, event.y - 3
    x1, y1 = event.x + 3, event.y + 3
    self.canvas.create_oval(x0, y0, x1, y1, fill="red")

    if len(self.current_box) == 4:
        self.bounding_boxes.append(self.current_box)
        self.draw_bounding_box(self.current_box)
        self.current_box = []

def remove_last_bounding_box(self):
    """Remove the last drawn bounding box from canvas."""
    from tkinter import messagebox  # scope for multi-environment compatibility

    if self.bounding_boxes:
        self.bounding_boxes.pop()  # Remove the last bounding box
        self.canvas.delete("all")  # Clear the canvas
        self.canvas.create_image(0, 0, anchor=self.tk.NW, image=self.canvas_image)  # Redraw the image

        # Redraw all bounding boxes
        for box in self.bounding_boxes:
            self.draw_bounding_box(box)

        messagebox.showinfo("Success", "Last bounding box removed.")
    else:
        messagebox.showwarning("Warning", "No bounding boxes to remove.")

def save_to_json(self):
    """Saves rescaled bounding boxes to 'bounding_boxes.json' based on image-to-canvas size ratio."""
    from tkinter import messagebox  # scope for multi-environment compatibility

    canvas_width, canvas_height = self.canvas.winfo_width(), self.canvas.winfo_height()
    width_scaling_factor = self.img_width / canvas_width
    height_scaling_factor = self.img_height / canvas_height
    bounding_boxes_data = []
    for box in self.bounding_boxes:
        rescaled_box = []
        for x, y in box:
            rescaled_x = int(x * width_scaling_factor)
            rescaled_y = int(y * height_scaling_factor)
            rescaled_box.append((rescaled_x, rescaled_y))
        bounding_boxes_data.append({"points": rescaled_box})
    with open("bounding_boxes.json", "w") as f:
        json.dump(bounding_boxes_data, f, indent=4)

    messagebox.showinfo("Success", "Bounding boxes saved to bounding_boxes.json")

def upload_image(self):
    """Upload an image and resize it to fit canvas."""
    from tkinter import filedialog

    from PIL import Image, ImageTk  # scope because ImageTk requires tkinter package

    self.image_path = filedialog.askopenfilename(filetypes=[("Image Files", "*.png;*.jpg;*.jpeg")])
    if not self.image_path:
        return

    self.image = Image.open(self.image_path)
    self.img_width, self.img_height = self.image.size

    # Calculate the aspect ratio and resize image
    aspect_ratio = self.img_width / self.img_height
    if aspect_ratio > 1:
        # Landscape orientation
        canvas_width = min(self.canvas_max_width, self.img_width)
        canvas_height = int(canvas_width / aspect_ratio)
    else:
        # Portrait orientation
        canvas_height = min(self.canvas_max_height, self.img_height)
        canvas_width = int(canvas_height * aspect_ratio)

    # Check if canvas is already initialized
    if self.canvas:
        self.canvas.destroy()  # Destroy previous canvas

    self.canvas = self.tk.Canvas(self.master, bg="white", width=canvas_width, height=canvas_height)
    resized_image = self.image.resize((canvas_width, canvas_height), Image.LANCZOS)
    self.canvas_image = ImageTk.PhotoImage(resized_image)
    self.canvas.create_image(0, 0, anchor=self.tk.NW, image=self.canvas_image)

    self.canvas.pack(side=self.tk.BOTTOM)
    self.canvas.bind("<Button-1>", self.on_canvas_click)

    # Reset bounding boxes and current box
    self.bounding_boxes = []
    self.current_box = []

def display_frames(self, im0):
    """
    Display frame.

    Args:
        im0 (ndarray): inference image
    """
    if self.env_check:
        cv2.namedWindow(self.window_name)
        cv2.imshow(self.window_name, im0)
        # Break Window
        if cv2.waitKey(1) & 0xFF == ord("q"):
            return

def load_model(self):
    """Load the Ultralytics YOLO model for inference and analytics."""
    from ultralytics import YOLO

    return YOLO(self.model_path)

@staticmethod
def parking_regions_extraction(json_file):
    """
    Extract parking regions from json file.

    Args:
        json_file (str): file that have all parking slot points
    """
    with open(json_file, "r") as f:
        return json.load(f)

def process_data(self, json_data, im0, boxes, clss):
    """
    Process the model data for parking lot management.

    Args:
        json_data (str): json data for parking lot management
        im0 (ndarray): inference image
        boxes (list): bounding boxes data
        clss (list): bounding boxes classes list

    Returns:
        filled_slots (int): total slots that are filled in parking lot
        empty_slots (int): total slots that are available in parking lot
    """
    annotator = Annotator(im0)
    empty_slots, filled_slots = len(json_data), 0
    for region in json_data:
        points_array = np.array(region["points"], dtype=np.int32).reshape((-1, 1, 2))
        region_occupied = False

        for box, cls in zip(boxes, clss):
            x_center = int((box[0] + box[2]) / 2)
            y_center = int((box[1] + box[3]) / 2)
            text = f"{self.model.names[int(cls)]}"

            annotator.display_objects_labels(
                im0, text, self.txt_color, self.bg_color, x_center, y_center, self.margin
            )
            dist = cv2.pointPolygonTest(points_array, (x_center, y_center), False)
            if dist >= 0:
                region_occupied = True
                break

        color = self.occupied_region_color if region_occupied else self.available_region_color
        cv2.polylines(im0, [points_array], isClosed=True, color=color, thickness=2)
        if region_occupied:
            filled_slots += 1
            empty_slots -= 1

    self.labels_dict["Occupancy"] = filled_slots
    self.labels_dict["Available"] = empty_slots

    annotator.display_analytics(im0, self.labels_dict, self.txt_color, self.bg_color, self.margin)