| Name | imgalz JSON |
| Version |
0.0.7.3
JSON |
| download |
| home_page | None |
| Summary | onnx pipline |
| upload_time | 2025-07-16 09:25:38 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
onnx
yolov5
yolov8
mmpose
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# imgalz: A Modular Library for Image Analysis by onnx
## Usage
See below for quickstart installation and usage examples.
All guidance can refer to full [imgalz docs](https://pleb631.github.io/imgalz)
### Installation
```bash
pip install .[all]
# or
pip install imgalz[all]
```
### example
```python
import cv2
import numpy as np
import imgalz
from imgalz.models.detector import YOLOv5
# Use local path if available, otherwise download from Hugging Face
model = YOLOv5(model_path = "yolov5n.onnx")
# model = YOLOv5("yolov6n.onnx")
im = imgalz.imread("resources/bus.jpg",1)
bboxes = model.detect(im, aug=True)
# plot box on img
for box in bboxes:
cv2.rectangle(
im, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 0, 255), 2
)
imgalz.cv_imshow("yolov5-det", im)
```
You can refer to the specific usage by [demo](https://github.com/pleb631/imgalz/tree/main/demo)
## Optional Models
### Detector
- YOLOv5/6
- YOLOv8/11
- YOLOv8pose
- YOLOv8seg
### Tracker
- ByteTrack
- Motpy
- NorFair
- OCSort
### Pose
- ViT-Pose
## todo
- add mmpose inference code
- add more tool for image processing
- add more tracker and refact code
## Weights
The ONNX model in the example is exported directly from the official code and can be obtained from the [huggingface](https://huggingface.co/pleb631/onnxmodel).
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"description": "# imgalz: A Modular Library for Image Analysis by onnx\n\n\n\n## Usage\n\nSee below for quickstart installation and usage examples.\nAll guidance can refer to full [imgalz docs](https://pleb631.github.io/imgalz)\n\n### Installation\n\n```bash\npip install .[all]\n# or\npip install imgalz[all]\n```\n\n### example\n\n```python\nimport cv2\nimport numpy as np\n\nimport imgalz\nfrom imgalz.models.detector import YOLOv5\n\n# Use local path if available, otherwise download from Hugging Face\nmodel = YOLOv5(model_path = \"yolov5n.onnx\")\n# model = YOLOv5(\"yolov6n.onnx\")\nim = imgalz.imread(\"resources/bus.jpg\",1)\nbboxes = model.detect(im, aug=True)\n# plot box on img\nfor box in bboxes:\n cv2.rectangle(\n im, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 0, 255), 2\n )\n\nimgalz.cv_imshow(\"yolov5-det\", im)\n\n```\n\nYou can refer to the specific usage by [demo](https://github.com/pleb631/imgalz/tree/main/demo)\n\n## Optional Models\n\n### Detector\n\n- YOLOv5/6\n- YOLOv8/11\n- YOLOv8pose\n- YOLOv8seg\n\n### Tracker\n\n- ByteTrack\n- Motpy\n- NorFair\n- OCSort\n\n### Pose\n\n- ViT-Pose\n\n## todo\n\n- add mmpose inference code\n- add more tool for image processing\n- add more tracker and refact code\n\n## Weights\n\nThe ONNX model in the example is exported directly from the official code and can be obtained from the [huggingface](https://huggingface.co/pleb631/onnxmodel).\n",
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