[](https://badge.fury.io/py/asone) [](https://badge.fury.io/py/asone) [](https://drive.google.com/file/d/1xy5P9WGI19-PzRH3ceOmoCgp63K6J_Ls/view?usp=sharing) [](https://github.com/augmentedstartups/AS-One) [](https://opensource.org/licenses/)
](https://www.youtube.com/watch?v=K-VcpPwcM8k)
Watch the step-by-step tutorial 🤝
## 💻 Install
🔥 Prerequisites
- Make sure to install `GPU` drivers in your system if you want to use `GPU` . Follow [driver installation](asone/linux/Instructions/Driver-Installations.md) for further instructions. - Make sure you have [MS Build tools](https://aka.ms/vs/17/release/vs_BuildTools.exe) installed in system if using windows. - [Download git for windows](https://git-scm.com/download/win) if not installed.👉 Install from Source
### 💾 Clone the Repository Navigate to an empty folder of your choice. `git clone https://github.com/augmentedstartups/AS-One.git` Change Directory to AS-One `cd AS-One`👉 For Linux
```shell python3 -m venv .env source .env/bin/activate pip install -r requirements.txt # for CPU pip install torch torchvision # for GPU pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113 ```👉 For Windows 10/11
```shell python -m venv .env .env\Scripts\activate pip install numpy Cython pip install lap pip install -e git+https://github.com/samson-wang/cython_bbox.git#egg=cython-bbox pip install asone onnxruntime-gpu==1.12.1 pip install typing_extensions==4.7.1 pip install super-gradients==3.1.3 # for CPU pip install torch torchvision # for GPU pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113 or pip install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio===0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html ```👉 For MacOS
```shell python3 -m venv .env source .env/bin/activate pip install -r requirements.txt # for CPU pip install torch torchvision ```
## Sample Code Snippets 📃
6.1 👉 Object Detection
```python import asone from asone import ASOne model = ASOne(detector=asone.YOLOV9_C, use_cuda=True) # Set use_cuda to False for cpu vid = model.read_video('data/sample_videos/test.mp4') for img in vid: detection = model.detecter(img) annotations = ASOne.draw(detection, img=img, display=True) ``` Run the `asone/demo_detector.py` to test detector. ```shell # run on gpu python -m asone.demo_detector data/sample_videos/test.mp4 # run on cpu python -m asone.demo_detector data/sample_videos/test.mp4 --cpu ```6.1.1 👉 Use Custom Trained Weights for Detector
Use your custom weights of a detector model trained on custom data by simply providing path of the weights file. ```python import asone from asone import ASOne model = ASOne(detector=asone.YOLOV9_C, weights='data/custom_weights/yolov7_custom.pt', use_cuda=True) # Set use_cuda to False for cpu vid = model.read_video('data/sample_videos/license_video.mp4') for img in vid: detection = model.detecter(img) annotations = ASOne.draw(detection, img=img, display=True, class_names=['license_plate']) ```6.1.2 👉 Changing Detector Models
Change detector by simply changing detector flag. The flags are provided in [benchmark](asone/linux/Instructions/Benchmarking.md) tables. - Our library now supports YOLOv5, YOLOv7, and YOLOv8 on macOS. ```python # Change detector model = ASOne(detector=asone.YOLOX_S_PYTORCH, use_cuda=True) # For macOs # YOLO5 model = ASOne(detector=asone.YOLOV5X_MLMODEL) # YOLO7 model = ASOne(detector=asone.YOLOV7_MLMODEL) # YOLO8 model = ASOne(detector=asone.YOLOV8L_MLMODEL) ```6.2 👉 Object Tracking
Use tracker on sample video. ```python import asone from asone import ASOne # Instantiate Asone object model = ASOne(tracker=asone.BYTETRACK, detector=asone.YOLOV9_C, use_cuda=True) #set use_cuda=False to use cpu tracks = model.video_tracker('data/sample_videos/test.mp4', filter_classes=['car']) # Loop over track to retrieve outputs of each frame for model_output in tracks: annotations = ASOne.draw(model_output, display=True) # Do anything with bboxes here ``` [Note] Use can use custom weights for a detector model by simply providing path of the weights file. in `ASOne` class.6.2.1 👉 Changing Detector and Tracking Models
Change Tracker by simply changing the tracker flag. The flags are provided in [benchmark](asone/linux/Instructions/Benchmarking.md) tables. ```python model = ASOne(tracker=asone.BYTETRACK, detector=asone.YOLOV9_C, use_cuda=True) # Change tracker model = ASOne(tracker=asone.DEEPSORT, detector=asone.YOLOV9_C, use_cuda=True) ``` ```python # Change Detector model = ASOne(tracker=asone.DEEPSORT, detector=asone.YOLOX_S_PYTORCH, use_cuda=True) ```6.3 👉 Segmentation
```python import asone from asone import ASOne model = ASOne(detector=asone.YOLOV9_C, segmentor=asone.SAM, use_cuda=True) #set use_cuda=False to use cpu tracks = model.video_detecter('data/sample_videos/test.mp4', filter_classes=['car']) for model_output in tracks: annotations = ASOne.draw_masks(model_output, display=True) # Draw masks ```6.4 👉 Text Detection
Sample code to detect text on an image ```python # Detect and recognize text import asone from asone import ASOne, utils import cv2 model = ASOne(detector=asone.CRAFT, recognizer=asone.EASYOCR, use_cuda=True) # Set use_cuda to False for cpu img = cv2.imread('data/sample_imgs/sample_text.jpeg') results = model.detect_text(img) annotations = utils.draw_text(img, results, display=True) ``` Use Tracker on Text ```python import asone from asone import ASOne # Instantiate Asone object model = ASOne(tracker=asone.DEEPSORT, detector=asone.CRAFT, recognizer=asone.EASYOCR, use_cuda=True) #set use_cuda=False to use cpu tracks = model.video_tracker('data/sample_videos/GTA_5-Unique_License_Plate.mp4') # Loop over track to retrieve outputs of each frame for model_output in tracks: annotations = ASOne.draw(model_output, display=True) # Do anything with bboxes here ``` Run the `asone/demo_ocr.py` to test ocr. ```shell # run on gpu python -m asone.demo_ocr data/sample_videos/GTA_5-Unique_License_Plate.mp4 # run on cpu python -m asone.demo_ocr data/sample_videos/GTA_5-Unique_License_Plate.mp4 --cpu ```6.5 👉 Pose Estimation
Sample code to estimate pose on an image ```python # Pose Estimation import asone from asone import PoseEstimator, utils import cv2 model = PoseEstimator(estimator_flag=asone.YOLOV8M_POSE, use_cuda=True) #set use_cuda=False to use cpu img = cv2.imread('data/sample_imgs/test2.jpg') kpts = model.estimate_image(img) annotations = utils.draw_kpts(kpts, image=img, display=True) ``` - Now you can use Yolov8 and Yolov7-w6 for pose estimation. The flags are provided in [benchmark](asone/linux/Instructions/Benchmarking.md) tables. ```python # Pose Estimation on video import asone from asone import PoseEstimator, utils model = PoseEstimator(estimator_flag=asone.YOLOV7_W6_POSE, use_cuda=True) #set use_cuda=False to use cpu estimator = model.video_estimator('data/sample_videos/football1.mp4') for model_output in estimator: annotations = utils.draw_kpts(model_output) # Do anything with kpts here ``` Run the `asone/demo_pose_estimator.py` to test Pose estimation. ```shell # run on gpu python -m asone.demo_pose_estimator data/sample_videos/football1.mp4 # run on cpu python -m asone.demo_pose_estimator data/sample_videos/football1.mp4 --cpu ```| Model Name / Model Flag | FPS-GPU | FPS-CPU |
|---|---|---|
| YOLOV5X6_PYTORCH | 20.8 | 3.69 |
| YOLOV5S_PYTORCH | 57.25 | 25.4 |
| YOLOV5N_PYTORCH | 68 | 45 |
| YOLOV5M_PYTORCH | 54 | 14 |
| YOLOV5L_PYTORCH | 40.06 | 8.28 |
| YOLOV5X_PYTORCH | 28.8 | 4.32 |
| YOLOV5N6_PYTORCH | 63.5 | 39 |
| YOLOV5S6_PYTORCH | 58 | 23 |
| YOLOV5M6_PYTORCH | 49 | 10 |
| YOLOV5L6_PYTORCH | 33 | 6.5 |
| Model Name / Model Flag | FPS-GPU | FPS-CPU |
|---|---|---|
| YOLOV5X6_ONNX | 2.58 | 2.46 |
| YOLOV5S_ONNX | 17 | 16.35 |
| YOLOV5N_ONNX | 57.25 | 35.23 |
| YOLOV5M_ONNX | 45.8 | 11.17 |
| YOLOV5L_ONNX | 4.07 | 4.36 |
| YOLOV5X_ONNX | 2.32 | 2.6 |
| YOLOV5N6_ONNX | 28.6 | 32.7 |
| YOLOV5S6_ONNX | 17 | 16.35 |
| YOLOV5M6_ONNX | 7.5 | 7.6 |
| YOLOV5L6_ONNX | 3.7 | 3.98 |
| Model Name / Model Flag |
|---|
| YOLOV5X6_MLMODEL |
| YOLOV5S_MLMODEL |
| YOLOV5N_MLMODEL |
| YOLOV5M_MLMODEL |
| YOLOV5L_MLMODEL |
| YOLOV5X_MLMODEL |
| YOLOV5N6_MLMODEL |
| YOLOV5S6_MLMODEL |
| YOLOV5M6_MLMODEL |
| YOLOV5L6_MLMODEL |
| Model Name / Model Flag | FPS-GPU | FPS-CPU |
|---|---|---|
| YOLOV6N_PYTORCH | 65.4 | 35.32 |
| YOLOV6T_PYTORCH | 63 | 15.21 |
| YOLOV6S_PYTORCH | 49.24 | 20 |
| YOLOV6M_PYTORCH | 35 | 9.96 |
| YOLOV6L_PYTORCH | 31 | 6.2 |
| YOLOV6L_RELU_PYTORCH | 27 | 6.3 |
| YOLOV6S_REPOPT_PYTORCH | 63.5 | 39 |
| Model Name / Model Flag | FPS-GPU | FPS-CPU |
|---|---|---|
| YOLOV6N_ONNX | 50 | 30 |
| YOLOV6T_ONNX | 45.8 | 16 |
| YOLOV6S_ONNX | 41 | 13.8 |
| YOLOV6M_ONNX | 25 | 6.07 |
| YOLOV6L_ONNNX | 17.7 | 3.32 |
| YOLOV6L_RELU_ONNX | 19.15 | 4.36 |
| YOLOV6S_REPOPT_ONNX | 63.5 | 39 |
| Model Name / Model Flag | FPS-GPU | FPS-CPU |
|---|---|---|
| YOLOV7_TINY_PYTORCH | 53 | 19 |
| YOLOV7_PYTORCH | 38 | 6.83 |
| YOLOV7_X_PYTORCH | 28 | 4.36 |
| YOLOV7_W6_PYTORCH | 32.7 | 7.26 |
| YOLOV7_E6_PYTORCH | 15.26 | 3.07 |
| YOLOV7_D6_PYTORCH | 21 | 3.78 |
| YOLOV7_E6E_PYTORCH | 24 | 3.36 |
| Model Name / Model Flag | FPS-GPU | FPS-CPU |
|---|---|---|
| YOLOV7_TINY_ONNX | 41.6 | 22 |
| YOLOV7_ONNX | 26 | 3.78 |
| YOLOV7_X_ONNX | 19.08 | 2.35 |
| YOLOV7_W6_ONNX | 28.6 | 5.2 |
| YOLOV7_E6_ONNX | 14.3 | 2.97 |
| YOLOV7_D6_ONNX | 18.32 | 2.58 |
| YOLOV7_E6E_ONNX | 15.26 | 2.09 |
| Model Name / Model Flag |
|---|
| YOLOV7_TINY_MLMODEL |
| YOLOV7_MLMODEL |
| YOLOV7_X_MLMODEL |
| YOLOV7_W6_MLMODEL |
| YOLOV7_E6_MLMODEL |
| YOLOV7_D6_MLMODEL |
| YOLOV7_E6E_MLMODEL |
| Model Name / Model Flag |
|---|
| --- |
| --- |
| --- |
| YOLOV7_W6_POSE |
| --- |
| --- |
| --- |
| Model Name / Model Flag | FPS-GPU | FPS-CPU |
|---|---|---|
| YOLOR_CSP_X_PYTORCH | 28.6 | 1.83 |
| YOLOR_CSP_X_STAR_PYTORCH | 30 | 1.76 |
| YOLOR_CSP_STAR_PYTORCH | 38.1 | 2.86 |
| YOLOR_CSP_PYTORCH | 38 | 2.77 |
| YOLOR_P6_PYTORCH | 20 | 1.57 |
| Model Name / Model Flag | FPS-GPU | FPS-CPU |
|---|---|---|
| YOLOR_CSP_X_ONNX | 15.7 | 2.53 |
| YOLOR_CSP_X_STAR_ONNX | 15.79 | 2.05 |
| YOLOR_CSP_STAR_ONNX | 18.32 | 3.34 |
| YOLOR_CSP_ONNX | 15.7 | 2.53 |
| YOLOR_P6_ONNX | 25.4 | 5.58 |
| Model Name / Model Flag | FPS-GPU | FPS-CPU |
|---|---|---|
| YOLOX_L_PYTORCH | 2.58 | 2.31 |
| YOLOX_NANO_PYTORCH | 35 | 32 |
| YOLOX_TINY_PYTORCH | 25.4 | 25.4 |
| YOLOX_DARKNET_PYTORCH | 2 | 1.94 |
| YOLOX_S_PYTORCH | 9.54 | 9.7 |
| YOLOX_M_PYTORCH | 4.4 | 4.36 |
| YOLOX_X_PYTORCH | 15.64 | 1.39 |
| Model Name / Model Flag | FPS-GPU | FPS-CPU |
|---|---|---|
| YOLOX_L_ONNX | 22.9 | 3.07 |
| YOLOX_NANO_ONNX | 59 | 54 |
| YOLOX_TINY_ONNX | 60 | 35 |
| YOLOX_DARKNET_ONNX | 24 | 3.36 |
| YOLOX_S_ONNX | 45 | 13.8 |
| YOLOX_M_ONNX | 32 | 6.54 |
| YOLOX_X_ONNX | 15.79 | 2.03 |
| Model Name / Model Flag | FPS-GPU | FPS-CPU |
|---|---|---|
| YOLOV8N_PYTORCH | 26.7 | 17.0 |
| YOLOV8S_PYTORCH | 26.4 | 12.3 |
| YOLOV8M_PYTORCH | 25.1 | 6.8 |
| YOLOV8L_PYTORCH | 23.6 | 4.0 |
| YOLOV8X_PYTORCH | 20.7 | 2.8 |
| Model Name / Model Flag | FPS-GPU | FPS-CPU |
|---|---|---|
| YOLOV8N_ONNX | 25.1 | 10.5 |
| YOLOV8S_ONNX | 24.5 | 7.5 |
| YOLOV8M_ONNX | 22.9 | 4.7 |
| YOLOV8l_ONNX | 20.4 | 2.9 |
| YOLOV8X_ONNX | 19.0 | 2.0 |
| Model Name / Model Flag |
|---|
| YOLOV8N_MLMODEL |
| YOLOV8S_MLMODEL |
| YOLOV8M_MLMODEL |
| YOLOV8L_MLMODEL |
| YOLOV8X_MLMODEL |
| Model Name / Model Flag |
|---|
| YOLOV8N_POSE |
| YOLOV8S_POSE |
| YOLOV8M_POSE |
| YOLOV8L_POSE |
| YOLOV8X_POSE |
Type **winver** in RUN to check the version of the installed windows. - Windows 11 64-bit: Home or Pro version 21H2 or higher,\ or Enterprise or Education version 21H2 or higher - Windows 10 64-bit: Home or Pro 21H1 (build 19043) or higher,\ or Enterprise or Education 20H2 (build 19042) or higher - Enable the WSL 2 feature on Windows. For detailed instructions,\ refer to the [wsl installation](https://docs.microsoft.com/en-us/windows/wsl/install) **Hardware Requirements to run WSL 2 on Windows 10 or Windows 11**
- 64-bit processor with Second Level Address Translation (SLAT) - 4GB system RAM - BIOS-level hardware virtualization support must be enabled in the \ BIOS settings. For more information, see [Virtualization](https://docs.docker.com/desktop/troubleshoot/topics/) ## Installation with Batch and Configuring Devices 1. Download the [enable_feature.bat](enable_feature.bat) and run it as administrator. - Reboot your system. 2. Download the [installation.bat](installation.bat) and run it as administrator. - Again Reboot your system. 3. Open XLaunch and select Multiple windows - Select the option Start no client - In Extra Settings, select the option 1. Clipboard 2. Primary Selection 3. Native opengl 4. Disable access control - Save configuration file for later use 4. Open [cam2ip.exe](cam2ip-1.6-64bit-cv/cam2ip.exe) see "Listening on: 56000" - IP stream will be on `http://localhost:56000/mjpeg` You can now go back to [Installation Page](../README.md). ================================================ FILE: asone/windows/cam2ip-1.6-64bit-cv/AUTHORS ================================================ Milan Nikolic