单目3D初始代码

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+# Ultralytics YOLO Interactive Object Tracking UI 🚀
+
+A real-time [object detection](https://docs.ultralytics.com/tasks/detect/) and [tracking](https://docs.ultralytics.com/modes/track/) UI built with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) and [OpenCV](https://opencv.org/), designed for interactive demos and seamless integration of tracking overlays. Whether you're just getting started with object tracking or looking to enhance it with additional features, this project provides a solid foundation.
+
+https://github.com/user-attachments/assets/723e919e-555b-4cca-8e60-18e711d4f3b2
+
+## ✨ Features
+
+- Real-time object detection and visual tracking
+- Click-to-track any detected object
+- Scope lines and bold [bounding boxes](https://docs.ultralytics.com/usage/simple-utilities/#bounding-boxes) for active tracking
+- Dashed boxes for passive (non-tracked) objects
+- [Live terminal output](https://docs.ultralytics.com/guides/view-results-in-terminal/): object ID, label, [confidence](https://www.ultralytics.com/glossary/confidence), and center coordinates
+- Adjustable object tracking algorithms ([ByteTrack](https://docs.ultralytics.com/reference/trackers/byte_tracker/), [BoT-SORT](https://docs.ultralytics.com/reference/trackers/bot_sort/))
+- Supports:
+  - [PyTorch](https://pytorch.org/) `.pt` models (for GPU devices like [NVIDIA Jetson](https://docs.ultralytics.com/guides/nvidia-jetson/) or [CUDA](https://developer.nvidia.com/cuda)-enabled desktops)
+  - [NCNN](https://docs.ultralytics.com/integrations/ncnn/) `.param + .bin` models (for CPU-only devices like [Raspberry Pi](https://www.raspberrypi.org/) or ARM boards)
+
+## 🏗️ Project Structure
+
+```
+YOLO-Interactive-Tracking-UI/
+├── interactive_tracker.py   # Main Python tracking UI script
+└── README.md                # You're here!
+```
+
+## 💻 Hardware & Model Compatibility
+
+| Platform         | Model Format       | Example Model        | GPU Acceleration | Notes                           |
+| ---------------- | ------------------ | -------------------- | ---------------- | ------------------------------- |
+| Raspberry Pi 4/5 | NCNN (.param/.bin) | `yolov8n_ncnn_model` | ❌ CPU only      | Recommended format for Pi/ARM   |
+| Jetson Nano      | PyTorch (.pt)      | `yolov8n.pt`         | ✅ CUDA          | Real-time performance possible  |
+| Desktop w/ GPU   | PyTorch (.pt)      | `yolov8s.pt`         | ✅ CUDA          | Best performance                |
+| CPU-only laptops | NCNN (.param/.bin) | `yolov8n_ncnn_model` | ❌               | Decent performance (~10–15 FPS) |
+
+_Note: Performance may vary based on the specific hardware, model complexity, and input resolution._
+
+## 🛠️ Installation
+
+### Basic Dependencies
+
+Install the core `ultralytics` package:
+
+```bash
+pip install ultralytics
+```
+
+> **Tip:** Use a virtual environment like `venv` or [`conda`](https://docs.ultralytics.com/guides/conda-quickstart/) (recommended) to manage dependencies.
+
+> **GPU Support:** Install PyTorch based on your system and CUDA version by following the official guide: [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/)
+
+## 🚀 Quickstart
+
+### Step 1: Download, Convert, or Specify Model
+
+- For pre-trained Ultralytics YOLO [models](https://docs.ultralytics.com/models/) (e.g., `yolo11s.pt` or `yolov8s.pt`), simply specify the model name in the script parameters (`model_file`). These models will be automatically downloaded and cached. You can also manually download them from [Ultralytics Assets Releases](https://github.com/ultralytics/assets/releases) and place them in the project folder.
+- If you're using a custom-trained YOLO model, ensure the model file is in the project folder or provide its relative path.
+- For CPU-only devices, export your chosen model (e.g., `yolov8n.pt`) to the [NCNN format](https://docs.ultralytics.com/integrations/ncnn/) using the Ultralytics `export` mode.
+
+- **Supported Formats:**
+  - `yolo11s.pt` (for GPU with PyTorch)
+  - `yolov8n_ncnn_model` (directory containing `.param` and `.bin` files for CPU with NCNN)
+
+### Step 2: Configure the Script
+
+Edit the global parameters at the top of `interactive_tracker.py`:
+
+```python
+# --- Configuration ---
+enable_gpu = False  # Set True if running with CUDA and PyTorch model
+model_file = "yolo11s.pt"  # Path to model file (.pt for GPU, _ncnn_model dir for CPU)
+show_fps = True  # Display current FPS in the top-left corner
+show_conf = False  # Display confidence score for each detection
+save_video = False  # Set True to save the output video stream
+video_output_path = "interactive_tracker_output.avi"  # Output video file name
+
+# --- Detection & Tracking Parameters ---
+conf = 0.3  # Minimum confidence threshold for object detection
+iou = 0.3  # IoU threshold for Non-Maximum Suppression (NMS)
+max_det = 20  # Maximum number of objects to detect per frame
+
+tracker = "bytetrack.yaml"  # Tracker configuration: 'bytetrack.yaml' or 'botsort.yaml'
+track_args = {
+    "persist": True,  # Keep track history across frames
+    "verbose": False,  # Suppress detailed tracker debug output
+}
+
+window_name = "Ultralytics YOLO Interactive Tracking"  # Name for the OpenCV display window
+# --- End Configuration ---
+```
+
+- **`enable_gpu`**: Set to `True` if you have a CUDA-compatible GPU and are using a `.pt` model. Keep `False` for NCNN models or CPU-only execution.
+- **`model_file`**: Ensure this points to the correct model file or directory based on `enable_gpu`.
+- **`conf`**: Adjust the [confidence](https://www.ultralytics.com/glossary/confidence) threshold. Lower values detect more objects but may increase false positives.
+- **`iou`**: Set the [Intersection over Union (IoU)](https://www.ultralytics.com/glossary/intersection-over-union-iou) threshold for [Non-Maximum Suppression (NMS)](https://www.ultralytics.com/glossary/non-maximum-suppression-nms). Higher values allow more overlapping boxes.
+- **`tracker`**: Choose between available tracker configuration files ([ByteTrack](https://docs.ultralytics.com/reference/trackers/byte_tracker/), [BoT-SORT](https://docs.ultralytics.com/reference/trackers/bot_sort/)).
+
+### Step 3: Run the Object Tracking
+
+Execute the script from your terminal:
+
+```bash
+python interactive_tracker.py
+```
+
+### Controls
+
+- 🖱️ **Left-click** on a detected object's bounding box to start tracking it.
+- 🔄 Press the **`c`** key to cancel the current tracking and select a new object.
+- ❌ Press the **`q`** key to quit the application.
+
+### Saving Output Video (Optional)
+
+If you want to record the tracking session, enable the `save_video` option in the configuration:
+
+```python
+save_video = True  # Enables video recording
+video_output_path = "output.avi"  # Customize your output file name (e.g., .mp4, .avi)
+```
+
+The video file will be saved in the project's working directory when you quit the application by pressing `q`.
+
+## 👤 Author
+
+- **Alireza**
+- [Connect on LinkedIn](https://www.linkedin.com/in/alireza787b)
+- Published: 2025-04-01
+
+## 📜 License & Disclaimer
+
+This project is released under the [AGPL-3.0 license](https://www.ultralytics.com/legal/agpl-3-0-software-license). For full licensing details, please refer to the [Ultralytics Licensing page](https://www.ultralytics.com/license).
+
+This software is provided "as is" for educational and demonstration purposes. Use it responsibly and at your own risk. The author assumes no liability for misuse or unintended consequences.
+
+## 🤝 Contributing
+
+Contributions, feedback, and bug reports are welcome! Feel free to open an issue or submit a pull request on the original repository if you have improvements or suggestions.