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feat: Bump to version v0.1.3, face alignment feature added

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yakhyo
2024-11-21 02:34:01 +00:00
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README.md
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@@ -17,8 +17,10 @@
---
## Features
- [ ] Age and gender detection (Planned).
- [ ] Face recognition (Planned).
- [x] Face Alignment (Added: 2024-11-21).
- [x] High-speed face detection using ONNX models (Added: 2024-11-20).
- [x] Accurate facial landmark localization (e.g., eyes, nose, and mouth) (Added: 2024-11-20).
- [x] Easy-to-use API for inference and visualization (Added: 2024-11-20).
@@ -27,24 +29,17 @@
## Installation
### Using pip
The easiest way to install **UniFace** is via [PyPI](https://pypi.org/project/uniface/). This will automatically install the library along with its prerequisites.
```bash
pip install uniface
```
### Local installation using pip
**Clone the repository**
To work with the latest version of **UniFace**, which may not yet be released on PyPI, you can install it directly from the repository:
```bash
git clone https://github.com/yakhyo/uniface.git
cd uniface
```
**Install using pip**
```bash
pip install .
```
@@ -147,6 +142,7 @@ cv2.destroyAllWindows()
### `RetinaFace` Class
#### Initialization
```python
RetinaFace(
model: str,
@@ -158,17 +154,19 @@ RetinaFace(
```
**Parameters**:
- `model` *(str)*: Name of the model to use. Supported models:
- `model` _(str)_: Name of the model to use. Supported models:
- `retinaface_mnet025`, `retinaface_mnet050`, `retinaface_mnet_v1`, `retinaface_mnet_v2`
- `retinaface_r18`, `retinaface_r34`
- `conf_thresh` *(float, default=0.5)*: Minimum confidence score for detections.
- `pre_nms_topk` *(int, default=5000)*: Max detections to keep before NMS.
- `nms_thresh` *(float, default=0.4)*: IoU threshold for Non-Maximum Suppression.
- `post_nms_topk` *(int, default=750)*: Max detections to keep after NMS.
- `conf_thresh` _(float, default=0.5)_: Minimum confidence score for detections.
- `pre_nms_topk` _(int, default=5000)_: Max detections to keep before NMS.
- `nms_thresh` _(float, default=0.4)_: IoU threshold for Non-Maximum Suppression.
- `post_nms_topk` _(int, default=750)_: Max detections to keep after NMS.
---
### `detect` Method
```python
detect(
image: np.ndarray,
@@ -182,22 +180,25 @@ detect(
Detects faces in the given image and returns bounding boxes and landmarks.
**Parameters**:
- `image` *(np.ndarray)*: Input image in BGR format.
- `max_num` *(int, default=0)*: Maximum number of faces to return. `0` means return all.
- `metric` *(str, default="default")*: Metric for prioritizing detections:
- `image` _(np.ndarray)_: Input image in BGR format.
- `max_num` _(int, default=0)_: Maximum number of faces to return. `0` means return all.
- `metric` _(str, default="default")_: Metric for prioritizing detections:
- `"default"`: Prioritize detections closer to the image center.
- `"max"`: Prioritize larger bounding box areas.
- `center_weight` *(float, default=2.0)*: Weight for prioritizing center-aligned faces.
- `center_weight` _(float, default=2.0)_: Weight for prioritizing center-aligned faces.
**Returns**:
- `bounding_boxes` *(np.ndarray)*: Array of detections as `[x_min, y_min, x_max, y_max, confidence]`.
- `landmarks` *(np.ndarray)*: Array of landmarks as `[(x1, y1), ..., (x5, y5)]`.
- `bounding_boxes` _(np.ndarray)_: Array of detections as `[x_min, y_min, x_max, y_max, confidence]`.
- `landmarks` _(np.ndarray)_: Array of landmarks as `[(x1, y1), ..., (x5, y5)]`.
---
### Visualization Utilities
#### `draw_detections`
```python
draw_detections(
image: np.ndarray,
@@ -210,9 +211,10 @@ draw_detections(
Draws bounding boxes and landmarks on the given image.
**Parameters**:
- `image` *(np.ndarray)*: The input image in BGR format.
- `detections` *(Tuple[np.ndarray, np.ndarray])*: A tuple of bounding boxes and landmarks.
- `vis_threshold` *(float)*: Minimum confidence score for visualization.
- `image` _(np.ndarray)_: The input image in BGR format.
- `detections` _(Tuple[np.ndarray, np.ndarray])_: A tuple of bounding boxes and landmarks.
- `vis_threshold` _(float)_: Minimum confidence score for visualization.
---