semvec/pypi-packages huggingface dataset:
PySkript 0.0.1
Simple scriptinglanguage.
License: MIT
Keywords: script,programming,programming language,scripting language

Simple opensource scripting language. 
Website: pyscript.godaddysites.com


Examples:

PRINT("HI")
WHILE TRUE THEN PRINT("HI")
VAR A = 9
IF A == 9 THEN PRINT(A)

Change Log
==========

0.0.1 (19/04/2020)
-------------------
- First Release
- Bug Fixes
slicevision 0.1.0
A vision library for performing sliced inference on large images/small objects
License: MIT
Requires Python: >=3.6

# SliceVision: A vision library for performing sliced inference on large images/small objects

<img width="700" alt="teaser" src="./demo/sliced_inference.gif">

## Overview

Object detection and instance segmentation are by far the most important fields of applications in Computer Vision. However, detection of small objects and inference on large images are still major issues in practical usage. Here comes the SliceVision to help developers overcome these real-world problems.

## Getting started

### Blogpost

Check the [official SliceVision blog post](https://medium.com/@fcakyon/slicevision-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80).


### Installation

- Install slicevision using conda for Linux, Mac and Windows (preferred):

```console
conda install -c obss slicevision
```

- Install slicevision using pip for Linux and Mac:

```console
pip install slicevision
```

- Install your desired version of pytorch and torchvision:
```console
pip install torch torchvision
```

- Install your desired detection framework (such as mmdet):
```console
pip install mmdet
```

## Usage

- Sliced inference:
```python
result = get_sliced_prediction(
    image,
    detection_model,
    slice_height = 256,
    slice_width = 256,
    overlap_height_ratio = 0.2,
    overlap_width_ratio = 0.2
)

```
Refer to [inference notebook](demo/inference.ipynb) for detailed usage.

- Slice an image:
```python
from slicevision.slicing import slice_image

slice_image_result, num_total_invalid_segmentation = slice_image(
    image=image_path,
    output_file_name=output_file_name,
    output_dir=output_dir,
    slice_height=256,
    slice_width=256,
    overlap_height_ratio=0.2,
    overlap_width_ratio=0.2,
)
```

- Slice a coco formatted dataset:
```python
from slicevision.slicing import slice_coco

coco_dict, coco_path = slice_coco(
    coco_annotation_file_path=coco_annotation_file_path,
    image_dir=image_dir,
    slice_height=256,
    slice_width=256,
    overlap_height_ratio=0.2,
    overlap_width_ratio=0.2,
)
```

## Adding new detection framework support

SliceVision library currently only supports [MMDetection models](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md). However it is easy to add new frameworks.

All you need to do is, creating a new class in [model.py](slicevision/model.py) that implements [DetectionModel class](https://dev.obss.com.tr/bitbucket/projects/ML/repos/ml/browse/coco-slicing/cocoslicing/model.py?at=refs%2Fheads%2Fcoco-slicing#10). You can take the [MMDetection wrapper](https://dev.obss.com.tr/bitbucket/projects/ML/repos/ml/browse/coco-slicing/cocoslicing/model.py?at=refs%2Fheads%2Fcoco-slicing#10) as a reference.