semvec/pypi-packages huggingface dataset:
sytd 1.0.10
Simple YouTube Downloader
License: MIT
Requires Python: >=3.5
Keywords: gui,downloader,youtube,simple

![PyPI - License](https://img.shields.io/pypi/l/sytd)
![PyPI](https://img.shields.io/pypi/v/sytd)
![PyPI - Status](https://img.shields.io/pypi/status/sytd)

# Simple YouTube Downloader
![](images/demo-small.gif)

## Installation
* Currently there is only support for **Windows** as I don't have access to other machines.
* **Chrome** is required for the application to run as intended. ([Install Chrome](https://www.google.com/chrome/))
```
pip install sytd
# or
easy_install sytd
```

## Usage
```
python -m sytd
# or
simple-youtube-downloader
# or
sytd
```

## Contact
If you have any suggestions of improvement or want to report bugs [join my discord](https://discord.gg/DHnBsRZ) or message me directly: Neo#3385
openpy 0.0.2
Open files for humans
License: MIT

# openpy

author: Oren Ovadia

## Overview

Just open files, local filesystem, s3 or online.
Decompresses any format automatically (currently only `gzip` is supported��)

## Installation
```
pip install openpy
```

or: 

```
pip install git+git://github.com/orenovadia/openpy.git#egg=master
```

## Examples
```python
from openpy import read

# local files, uncompressed:
with read('data.gz') as f:
    print(f.read())

# s3:
with read('s3://bucket/path/to/file.gz') as f:
    print(f.read())
 
```

## Upload to pypi:
```
./publish.sh
```
DISTS-pytorch 0.1
Deep Image Structure and Texture Similarity (DISTS) Metric
License: MIT
Keywords: pytorch,similarity,VGG,texture,structure,metric

# Deep Image Structure and Texture Similarity (DISTS) Metric

This is the repository of paper [Image Quality Assessment: Unifying Structure and Texture Similarity](https://arxiv.org/abs/2004.07728).


### Highlights:
1. A full-reference IQA model correlated well with human perception of image quality.
2. It is robust to texture variance (e.g., evaluating the images generated by GANs) and mild geometric transformations (e.g., evaluating the image pair that are not strictly point-by-point aligned).
3. It can be employed as the objective function in various optimization problems. <!-- (The best practice: pretrain with L1/L2 loss, then finetune with DISTS + GAN loss) -->


**Three implementation versions:** 
1. Pytorch [```DISTS_pt.py```](/DISTS_pytorch/DISTS_pt.py) (recommend)
2. Tensorflow [```DISTS_tf.py```](/DISTS_tensorflow/DISTS_tf.py) 
3. Matlab [```DISTS.m```](/DISTS_matlab/DISTS.m). 

### ====== Pytorch ======
**Installation:** 
- ```pip install dists-pytorch```

**Requirements:** 
- Python>=3.6
- Pytorch>=1.0

**Usage:** 
```python
from DISTS_pytorch import DISTS
D = DISTS()
# calculate DISTS between X, Y (a batch of RGB images, data range: 0~1)
# X: (N,3,H,W) 
# Y: (N,3,H,W) 
dists_value = D(X, Y)
# set 'require_grad=True, batch_average=True' to get a scalar value as loss.
dists_loss = D(X, Y, require_grad=True, batch_average=True) 
dists_loss.backward()
```
or

```bash
git clone https://github.com/dingkeyan93/DISTS
cd DISTS_pytorch
python DISTS_pt.py --ref <ref_path> --dist <dist_path>
```


### ====== Tensorflow ======

**Requirements:** 
- Python>=3.6
- Tensorflow>=1.15

**Usage:** 
```bash
git clone https://github.com/dingkeyan93/DISTS
cd DISTS_tensorflow
python DISTS_tf.py --ref <ref_path> --dist <dist_path>
```

### ====== Matlab ======

**Requirements:** 
- Matlab>=2019b

**Usage:** 
```bash
git clone https://github.com/dingkeyan93/DISTS
run demo.m
help DISTS
```

### Citation
```
@article{ding2020iqa,
  title={Image Quality Assessment: Unifying Structure and Texture Similarity},
  author={Ding, Keyan and Ma, Kede and Wang, Shiqi and Simoncelli, Eero P.},
  journal = {CoRR},
  volume = {abs/2004.07728},
  year={2020},
  url = {https://arxiv.org/abs/2004.07728}
}
```