pybx 0.4.1
A simple python module to generate anchor boxes for object detection tasks.
License: MIT License
Requires Python: >=3.7
Keywords: python computer-vision deep-learning fast-rcnn object-detection bounding-boxes rcnn multibox single-shot-multibox-detector single-shot-detection anchor-box rcnn-model multi-box single-shot-detector anchor-boxes multibox-detector

PyBx
================

<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

### Installation

``` shell
pip install pybx
```

### Usage

To calculate the anchor boxes for a single feature size and aspect
ratio, given the image size:

``` python
from pybx import anchor, ops

image_sz = (256, 256)
feature_sz = (10, 10)
asp_ratio = 1/2.

coords, labels = anchor.bx(image_sz, feature_sz, asp_ratio)
```

100 anchor boxes of `asp_ratio` 0.5 is generated along with [unique
labels](../data/README.md):

``` python
len(coords), len(labels)
```

    (100, 100)

The anchor box labels are especially useful, since they are pretty
descriptive:

``` python
coords[-1], labels[-1]
```

    ([234, 225, 252, 256], 'a_10x10_0.5_99')

To calculate anchor boxes for **multiple** feature sizes and aspect
ratios, we use `anchor.bxs` instead:

``` python
feature_szs = [(10, 10), (8, 8)]
asp_ratios = [1., 1/2., 2.]

coords, labels = anchor.bxs(image_sz, feature_szs, asp_ratios)
```

All anchor boxes are returned as `ndarrays` of shape `(N,4)` where N is
the number of boxes.

The box labels are even more important now, since they help you uniquely
identify to which feature map size or aspect ratios they belong to.

``` python
coords[101], labels[101]
```

    (array([29,  0, 47, 30]), 'a_10x10_0.5_1')

``` python
coords[-1], labels[-1]
```

    (array([217, 228, 256, 251]), 'a_8x8_2.0_63')

#### [`MultiBx`](https://thatgeeman.github.io/pybx/basics.html#multibx) methods

Box coordinates (with/without labels) in any format (usually `ndarray`,
`list`, `json`, `dict`) can be instantialized as a
[`MultiBx`](https://thatgeeman.github.io/pybx/basics.html#multibx),
exposing many useful methods and attributes of
[`MultiBx`](https://thatgeeman.github.io/pybx/basics.html#multibx). For
example to calculate the area of each box iteratively:

``` python
from pybx.basics import * 
# passing anchor boxes and labels from anchor.bxs()
print(coords.shape)

boxes = mbx(coords, labels)
type(boxes)
```

    (492, 4)

    pybx.basics.MultiBx

``` python
len(boxes)
```

    492

``` python
areas = [b.area for b in boxes]
```

Each annotation in the
[`MultiBx`](https://thatgeeman.github.io/pybx/basics.html#multibx)
object `boxes` is also a
[`BaseBx`](https://thatgeeman.github.io/pybx/basics.html#basebx) with
its own set of methods and properties.

``` python
boxes[-1]
```

    BaseBx(coords=[[217, 228, 256, 251]], label=['a_8x8_2.0_63'])

``` python
boxes[-1].coords, boxes[-1].label
```

    ([[217, 228, 256, 251]], (#1) ['a_8x8_2.0_63'])

[`MultiBx`](https://thatgeeman.github.io/pybx/basics.html#multibx)
objects can also be “added” which stacks them vertically to create a new
[`MultiBx`](https://thatgeeman.github.io/pybx/basics.html#multibx)
object:

``` python
boxes_true = mbx(coords_json)    # annotation as json records
len(boxes_true)
```

    2

``` python
boxes_anchor = mbx(coords_numpy) # annotation as ndarray
len(boxes_anchor)
```

    492

``` python
boxes_true
```

    MultiBx(coords=[[130, 63, 225, 180], [13, 158, 90, 213]], label=['clock', 'frame'])

``` python
boxes = boxes_true + boxes_anchor + boxes_true
```

``` python
len(boxes)
```

    496

# Use ground truth boxes for model training

``` python
from pybx.anchor import get_gt_thresh_iou, get_gt_max_iou
from pybx.vis import VisBx
```

``` python
image_sz
```

    (256, 256)

``` python
boxes_true
```

    MultiBx(coords=[[130, 63, 225, 180], [13, 158, 90, 213]], label=['clock', 'frame'])

Calculate candidate anchor boxes for many aspect ratios and scales.

``` python
feature_szs = [(10, 10), (3, 3), (2, 2)]
asp_ratios = [0.3, 1/2., 2.]

anchors, labels = anchor.bxs(image_sz, feature_szs, asp_ratios)
```

Wrap using pybx methods. This step is not necessary but convenient.

``` python
boxes_anchor = get_bx(anchors, labels) 
len(boxes_anchor)
```

    341

The following function returns two positive ground truth anchors with
largest IOU for each class in the label bounding boxes passed.

``` python
gt_anchors, gt_ious, gt_masks = get_gt_max_iou( 
    true_annots=boxes_true, 
    anchor_boxes=boxes_anchor,  # if plain numpy, pass anchor_boxes and anchor_labels 
    update_labels=False,  # whether to replace ground truth labels with true labels
    positive_boxes=1,  # can request extra boxes 
)
```

``` python
gt_anchors
```

    {'clock': BaseBx(coords=[[156, 0, 227, 180]], label=['a_2x2_0.3_1']),
     'frame': BaseBx(coords=[[12, 152, 72, 256]], label=['a_3x3_0.5_6'])}

``` python
all_gt_anchors = gt_anchors['clock'] + gt_anchors['frame']
all_gt_anchors
```

    /mnt/data/projects/pybx/pybx/basics.py:464: BxViolation: Change of object type imminent if trying to add <class 'pybx.basics.BaseBx'>+<class 'pybx.basics.BaseBx'>. Use <class 'pybx.basics.BaseBx'>+<class 'pybx.basics.BaseBx'> instead or basics.stack_bxs().
      f"Change of object type imminent if trying to add "

    MultiBx(coords=[[156, 0, 227, 180], [12, 152, 72, 256]], label=['a_2x2_0.3_1', 'a_3x3_0.5_6'])

``` python
v = VisBx(pth='../data/', img_fn='image.jpg', image_sz=image_sz)
v.show(all_gt_anchors, color={'a_2x2_0.3_1':'red', 'a_3x3_0.5_6': 'red'})
```

    <AxesSubplot:>

![](index_files/figure-commonmark/cell-26-output-2.png)

More exploratory stuff in the [walkthrough
notebook](https://github.com/thatgeeman/pybx/blob/master/examples/pybx_walkthrough_0.4.ipynb)
or [![Open In
Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/thatgeeman/pybx/blob/master/examples/pybx_walkthrough_0.4.ipynb)