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  1. 18
      README.md
  2. 18
      README.zh-CN.md
  3. 10
      docs/en/guides/parking-management.md
  4. 1
      ultralytics/cfg/solutions/default.yaml
  5. 259
      ultralytics/solutions/parking_management.py

18
README.md
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@ -116,7 +116,7 @@ See YOLO [Python Docs](https://docs.ultralytics.com/usage/python/) for more exam
YOLO11 [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/) and [Pose](https://docs.ultralytics.com/tasks/pose/) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset are available here, as well as YOLO11 [Classify](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset. [Track](https://docs.ultralytics.com/modes/track/) mode is available for all Detect, Segment and Pose models.
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="Ultralytics YOLO supported tasks">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="Ultralytics YOLO supported tasks">
All [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
@ -207,7 +207,7 @@ See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with
## <div align="center">Integrations</div>
Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [Roboflow](https://roboflow.com/?ref=ultralytics), ClearML, [Comet](https://bit.ly/yolov8-readme-comet), Neural Magic and [OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow.
Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [W&B](https://docs.wandb.ai/guides/integrations/ultralytics/), [Comet](https://bit.ly/yolov8-readme-comet), [Roboflow](https://roboflow.com/?ref=ultralytics) and [OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow.
<br>
<a href="https://www.ultralytics.com/hub" target="_blank">
@ -216,11 +216,11 @@ Our key integrations with leading AI platforms extend the functionality of Ultra
<br>
<div align="center">
<a href="https://roboflow.com/?ref=ultralytics">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" alt="Roboflow logo"></a>
<a href="https://www.ultralytics.com/hub">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png" width="10%" alt="Ultralytics HUB logo"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
<a href="https://clear.ml/">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" alt="ClearML logo"></a>
<a href="https://docs.wandb.ai/guides/integrations/ultralytics/">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png" width="10%" alt="ClearML logo"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
<a href="https://bit.ly/yolov8-readme-comet">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" alt="Comet ML logo"></a>
@ -229,9 +229,9 @@ Our key integrations with leading AI platforms extend the functionality of Ultra
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="NeuralMagic logo"></a>
</div>
| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
| :--------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
| Label and export your custom datasets directly to YOLO11 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLO11 using [ClearML](https://clear.ml/) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLO11 models, resume training, and interactively visualize and debug predictions | Run YOLO11 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
| Ultralytics HUB 🚀 | W&B | Comet ⭐ NEW | Neural Magic |
| :----------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics HUB](https://ultralytics.com/hub). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLO11 models, resume training, and interactively visualize and debug predictions | Run YOLO11 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
## <div align="center">Ultralytics HUB</div>

18
README.zh-CN.md
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@ -116,7 +116,7 @@ path = model.export(format="onnx") # 返回导出模型的路径
YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://docs.ultralytics.com/tasks/segment/) 和 [姿态](https://docs.ultralytics.com/tasks/pose/) 模型在 [COCO](https://docs.ultralytics.com/datasets/detect/coco/) 数据集上进行预训练，这些模型可在此处获得，此外还有在 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 数据集上预训练的 YOLO11 [分类](https://docs.ultralytics.com/tasks/classify/) 模型。所有检测、分割和姿态模型均支持 [跟踪](https://docs.ultralytics.com/modes/track/) 模式。
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="Ultralytics YOLO supported tasks">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="Ultralytics YOLO supported tasks">
所有[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models)在首次使用时自动从最新的 Ultralytics [发布](https://github.com/ultralytics/assets/releases)下载。
@ -207,7 +207,7 @@ YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://d
## <div align="center">集成</div>
我们与领先的 AI 平台的关键集成扩展了 Ultralytics 产品的功能，增强了数据集标记、训练、可视化和模型管理等任务的能力。了解 Ultralytics 如何与 [Roboflow](https://roboflow.com/?ref=ultralytics)、ClearML、[Comet](https://bit.ly/yolov8-readme-comet)、Neural Magic 和 [OpenVINO](https://docs.ultralytics.com/integrations/openvino/) 合作，优化您的 AI 工作流程。
我们与领先的 AI 平台的关键集成扩展了 Ultralytics 产品的功能，提升了数据集标注、训练、可视化和模型管理等任务。探索 Ultralytics 如何通过与 [W&B](https://docs.wandb.ai/guides/integrations/ultralytics/)、[Comet](https://bit.ly/yolov8-readme-comet)、[Roboflow](https://roboflow.com/?ref=ultralytics) 和 [OpenVINO](https://docs.ultralytics.com/integrations/openvino/) 的合作，优化您的 AI 工作流程。
<br>
<a href="https://www.ultralytics.com/hub" target="_blank">
@ -216,11 +216,11 @@ YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://d
<br>
<div align="center">
<a href="https://roboflow.com/?ref=ultralytics">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" alt="Roboflow logo"></a>
<a href="https://www.ultralytics.com/hub">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png" width="10%" alt="Ultralytics HUB logo"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
<a href="https://clear.ml/">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" alt="ClearML logo"></a>
<a href="https://docs.wandb.ai/guides/integrations/ultralytics/">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png" width="10%" alt="W&B logo"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
<a href="https://bit.ly/yolov8-readme-comet">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" alt="Comet ML logo"></a>
@ -229,9 +229,9 @@ YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://d
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="NeuralMagic logo"></a>
</div>
| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
| :--------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
| Label and export your custom datasets directly to YOLO11 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLO11 using [ClearML](https://clear.ml/) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLO11 models, resume training, and interactively visualize and debug predictions | Run YOLO11 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
| Ultralytics HUB 🚀 | W&B | Comet ⭐ 全新 | Neural Magic |
| :------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------: |
| 简化 YOLO 工作流程：通过 [Ultralytics HUB](https://ultralytics.com/hub) 轻松标注、训练和部署。立即试用！ | 使用 [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) 跟踪实验、超参数和结果 | 永久免费，[Comet](https://bit.ly/yolov5-readme-comet) 允许您保存 YOLO11 模型、恢复训练，并交互式地可视化和调试预测结果 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 运行 YOLO11 推理，速度提升至 6 倍 |
## <div align="center">Ultralytics HUB</div>

10
docs/en/guides/parking-management.md
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@ -102,12 +102,10 @@ Parking management with [Ultralytics YOLO11](https://github.com/ultralytics/ultr
### Optional Arguments `ParkingManagement`
| Name | Type | Default | Description |
| ------------------------ | ------- | ------------- | -------------------------------------------------------------- |
| `model` | `str` | `None` | Path to the YOLO11 model. |
| `json_file` | `str` | `None` | Path to the JSON file, that have all parking coordinates data. |
| `occupied_region_color` | `tuple` | `(0, 0, 255)` | RGB color for occupied regions. |
| `available_region_color` | `tuple` | `(0, 255, 0)` | RGB color for available regions. |
| Name | Type | Default | Description |
| ----------- | ----- | ------- | -------------------------------------------------------------- |
| `model` | `str` | `None` | Path to the YOLO11 model. |
| `json_file` | `str` | `None` | Path to the JSON file, that have all parking coordinates data. |
### Arguments `model.track`

1
ultralytics/cfg/solutions/default.yaml
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@ -15,3 +15,4 @@ down_angle: 90 # Workouts down_angle for counts, 90 is default value. You can ch
kpts: [6, 8, 10] # Keypoints for workouts monitoring, i.e. If you want to consider keypoints for pushups that have mostly values of [6, 8, 10].
colormap: # Colormap for heatmap, Only OPENCV supported colormaps can be used. By default COLORMAP_PARULA will be used for visualization.
analytics_type: "line" # Analytics type i.e "line", "pie", "bar" or "area" charts. By default, "line" analytics will be used for processing.
json_file: # parking system regions file path.

259
ultralytics/solutions/parking_management.py
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@ -5,7 +5,7 @@ import json
import cv2
import numpy as np
from ultralytics.utils.checks import check_imshow, check_requirements
from ultralytics.solutions.solutions import LOGGER, BaseSolution, check_requirements
from ultralytics.utils.plotting import Annotator
@ -13,229 +13,158 @@ class ParkingPtsSelection:
"""Class for selecting and managing parking zone points on images using a Tkinter-based UI."""
def __init__(self):
"""Initializes the UI for selecting parking zone points in a tkinter window."""
"""Class initialization method."""
check_requirements("tkinter")
import tkinter as tk
from tkinter import filedialog, messagebox
import tkinter as tk # scope for multi-environment compatibility
self.tk, self.filedialog, self.messagebox = tk, filedialog, messagebox
self.setup_ui()
self.initialize_properties()
self.master.mainloop()
self.tk = tk
self.master = tk.Tk()
def setup_ui(self):
"""Sets up the Tkinter UI components."""
self.master = self.tk.Tk()
self.master.title("Ultralytics Parking Zones Points Selector")
# Disable window resizing
self.master.resizable(False, False)
# Setup canvas for image display
# Canvas for image display
self.canvas = self.tk.Canvas(self.master, bg="white")
self.canvas.pack(side=self.tk.BOTTOM)
# Setup buttons
# Button frame with buttons
button_frame = self.tk.Frame(self.master)
button_frame.pack(side=self.tk.TOP)
self.tk.Button(button_frame, text="Upload Image", command=self.upload_image).grid(row=0, column=0)
self.tk.Button(button_frame, text="Remove Last BBox", command=self.remove_last_bounding_box).grid(
row=0, column=1
)
self.tk.Button(button_frame, text="Save", command=self.save_to_json).grid(row=0, column=2)
# Initialize properties
self.image_path = None
self.image = None
self.canvas_image = None
self.rg_data = [] # region coordinates
self.current_box = []
self.imgw = 0 # image width
self.imgh = 0 # image height
for text, cmd in [
("Upload Image", self.upload_image),
("Remove Last BBox", self.remove_last_bounding_box),
("Save", self.save_to_json),
]:
self.tk.Button(button_frame, text=text, command=cmd).pack(side=self.tk.LEFT)
# Constants
self.canvas_max_width = 1280
self.canvas_max_height = 720
self.master.mainloop()
def initialize_properties(self):
"""Initialize the necessary properties."""
self.image = self.canvas_image = None
self.rg_data, self.current_box = [], []
self.imgw = self.imgh = 0
self.canvas_max_width, self.canvas_max_height = 1280, 720
def upload_image(self):
"""Upload an image and resize it to fit canvas."""
from tkinter import filedialog
"""Uploads an image, resizes it to fit the canvas, and displays it."""
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:
self.image = Image.open(self.filedialog.askopenfilename(filetypes=[("Image Files", "*.png;*.jpg;*.jpeg")]))
if not self.image:
return
self.image = Image.open(self.image_path)
self.imgw, self.imgh = self.image.size
# Calculate the aspect ratio and resize image
aspect_ratio = self.imgw / self.imgh
if aspect_ratio > 1:
# Landscape orientation
canvas_width = min(self.canvas_max_width, self.imgw)
canvas_height = int(canvas_width / aspect_ratio)
else:
# Portrait orientation
canvas_height = min(self.canvas_max_height, self.imgh)
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)
canvas_width = (
min(self.canvas_max_width, self.imgw) if aspect_ratio > 1 else int(self.canvas_max_height * aspect_ratio)
)
canvas_height = (
min(self.canvas_max_height, self.imgh) if aspect_ratio <= 1 else int(canvas_width / aspect_ratio)
)
self.canvas.pack(side=self.tk.BOTTOM)
self.canvas.config(width=canvas_width, height=canvas_height)
self.canvas_image = ImageTk.PhotoImage(self.image.resize((canvas_width, canvas_height), Image.LANCZOS))
self.canvas.create_image(0, 0, anchor=self.tk.NW, image=self.canvas_image)
self.canvas.bind("<Button-1>", self.on_canvas_click)
# Reset bounding boxes and current box
self.rg_data = []
self.current_box = []
self.rg_data.clear(), self.current_box.clear()
def on_canvas_click(self, event):
"""Handle mouse clicks on canvas to create points for bounding boxes."""
"""Handles mouse clicks to add points for bounding boxes."""
self.current_box.append((event.x, event.y))
self.canvas.create_oval(event.x - 3, event.y - 3, event.x + 3, event.y + 3, fill="red")
if len(self.current_box) == 4:
self.rg_data.append(self.current_box)
[
self.canvas.create_line(self.current_box[i], self.current_box[(i + 1) % 4], fill="blue", width=2)
for i in range(4)
]
self.current_box = []
self.rg_data.append(self.current_box.copy())
self.draw_box(self.current_box)
self.current_box.clear()
def remove_last_bounding_box(self):
"""Remove the last drawn bounding box from canvas."""
from tkinter import messagebox # scope for multi-environment compatibility
def draw_box(self, box):
"""Draws a bounding box on the canvas."""
for i in range(4):
self.canvas.create_line(box[i], box[(i + 1) % 4], fill="blue", width=2)
if self.rg_data:
self.rg_data.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
def remove_last_bounding_box(self):
"""Removes the last bounding box and redraws the canvas."""
if not self.rg_data:
self.messagebox.showwarning("Warning", "No bounding boxes to remove.")
return
self.rg_data.pop()
self.redraw_canvas()
# Redraw all bounding boxes
for box in self.rg_data:
[self.canvas.create_line(box[i], box[(i + 1) % 4], fill="blue", width=2) for i in range(4)]
messagebox.showinfo("Success", "Last bounding box removed.")
else:
messagebox.showwarning("Warning", "No bounding boxes to remove.")
def redraw_canvas(self):
"""Redraws the canvas with the image and all bounding boxes."""
self.canvas.delete("all")
self.canvas.create_image(0, 0, anchor=self.tk.NW, image=self.canvas_image)
for box in self.rg_data:
self.draw_box(box)
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
rg_data = [] # regions data
for box in self.rg_data:
rs_box = [
(
int(x * self.imgw / self.canvas.winfo_width()), # width scaling
int(y * self.imgh / self.canvas.winfo_height()), # height scaling
)
for x, y in box
]
rg_data.append({"points": rs_box})
"""Saves the bounding boxes to a JSON file."""
scale_w, scale_h = self.imgw / self.canvas.winfo_width(), self.imgh / self.canvas.winfo_height()
data = [{"points": [(int(x * scale_w), int(y * scale_h)) for x, y in box]} for box in self.rg_data]
with open("bounding_boxes.json", "w") as f:
json.dump(rg_data, f, indent=4)
json.dump(data, f, indent=4)
self.messagebox.showinfo("Success", "Bounding boxes saved to bounding_boxes.json")
messagebox.showinfo("Success", "Bounding boxes saved to bounding_boxes.json")
class ParkingManagement:
class ParkingManagement(BaseSolution):
"""Manages parking occupancy and availability using YOLO model for real-time monitoring and visualization."""
def __init__(
self,
model, # Ultralytics YOLO model file path
json_file, # Parking management annotation file created from Parking Annotator
occupied_region_color=(0, 0, 255), # occupied region color
available_region_color=(0, 255, 0), # available region color
):
"""
Initializes the parking management system with a YOLO model and visualization settings.
Args:
model (str): Path to the YOLO model.
json_file (str): file that have all parking slot points data
occupied_region_color (tuple): RGB color tuple for occupied regions.
available_region_color (tuple): RGB color tuple for available regions.
"""
# Model initialization
from ultralytics import YOLO
def __init__(self, **kwargs):
"""Initializes the parking management system with a YOLO model and visualization settings."""
super().__init__(**kwargs)
self.model = YOLO(model)
self.json_file = self.CFG["json_file"] # Load JSON data
if self.json_file is None:
LOGGER.warning("❌ json_file argument missing. Parking region details required.")
raise ValueError("❌ Json file path can not be empty")
# Load JSON data
with open(json_file) as f:
self.json_data = json.load(f)
with open(self.json_file) as f:
self.json = json.load(f)
self.pr_info = {"Occupancy": 0, "Available": 0} # dictionary for parking information
self.occ = occupied_region_color
self.arc = available_region_color
self.env_check = check_imshow(warn=True) # check if environment supports imshow
self.arc = (0, 0, 255) # available region color
self.occ = (0, 255, 0) # occupied region color
self.dc = (255, 0, 189) # centroid color for each box
def process_data(self, im0):
"""
Process the model data for parking lot management.
Args:
im0 (ndarray): inference image
im0 (ndarray): inference image.
"""
results = self.model.track(im0, persist=True, show=False) # object tracking
es, fs = len(self.json_data), 0 # empty slots, filled slots
annotator = Annotator(im0) # init annotator
self.extract_tracks(im0) # extract tracks from im0
es, fs = len(self.json), 0 # empty slots, filled slots
annotator = Annotator(im0, self.line_width) # init annotator
# extract tracks data
if results[0].boxes.id is None:
self.display_frames(im0)
return im0
boxes = results[0].boxes.xyxy.cpu().tolist()
clss = results[0].boxes.cls.cpu().tolist()
for region in self.json_data:
for region in self.json:
# Convert points to a NumPy array with the correct dtype and reshape properly
pts_array = np.array(region["points"], dtype=np.int32).reshape((-1, 1, 2))
rg_occupied = False # occupied region initialization
for box, cls in zip(boxes, clss):
xc = int((box[0] + box[2]) / 2)
yc = int((box[1] + box[3]) / 2)
annotator.display_objects_labels(
im0, self.model.names[int(cls)], (104, 31, 17), (255, 255, 255), xc, yc, 10
)
for box, cls in zip(self.boxes, self.clss):
xc, yc = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
dist = cv2.pointPolygonTest(pts_array, (xc, yc), False)
if dist >= 0:
# cv2.circle(im0, (xc, yc), radius=self.line_width * 4, color=self.dc, thickness=-1)
annotator.display_objects_labels(
im0, self.model.names[int(cls)], (104, 31, 17), (255, 255, 255), xc, yc, 10
)
rg_occupied = True
break
if rg_occupied:
fs += 1
es -= 1
fs, es = (fs + 1, es - 1) if rg_occupied else (fs, es)
# Plotting regions
color = self.occ if rg_occupied else self.arc
cv2.polylines(im0, [pts_array], isClosed=True, color=color, thickness=2)
cv2.polylines(im0, [pts_array], isClosed=True, color=self.occ if rg_occupied else self.arc, thickness=2)
self.pr_info["Occupancy"] = fs
self.pr_info["Available"] = es
self.pr_info["Occupancy"], self.pr_info["Available"] = fs, es
annotator.display_analytics(im0, self.pr_info, (104, 31, 17), (255, 255, 255), 10)
self.display_frames(im0)
return im0
def display_frames(self, im0):
"""
Display frame.
Args:
im0 (ndarray): inference image
"""
if self.env_check:
cv2.imshow("Ultralytics Parking Manager", im0)
# Break Window
if cv2.waitKey(1) & 0xFF == ord("q"):
return
self.display_output(im0) # display output with base class function
return im0 # return output image for more usage

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