semvec/pypi-packages huggingface dataset:
VMData 1.0
Extract metadata from video.
Requires Python: >=3.5

# Extract meta data from video file
Metadata is text inside the video file that references the video. The Meta information of a video shows that what is included in the video and what type of content is available in the video.

Before getting into extraction, first you need to install ffmpeg.
## - Ubuntu
*sudo apt-get install ffmpeg*
## - Windows
Download ffmpeg software from [FFMPEG official website](https://www.ffmpeg.org/download.html#build-windows).
Unzip the file and add the bin folder to **environment path**.

### Using MediaInfo class
```
	media_obj = Mediainfo()
	or
	media_obj = Mediainfo(VIDEO_PATH) # VIDEO_PATH must be string
```
> Set video path if not passed
```
	media_obj.video_path = 'video_path'
```

> Get video path
```
	VIDEO_PATH = media_obj.video_path
```

> Extract metadata
```
	metadata = media_obj.getVideoDetail()
```

## Sample of Metadata of video
```
{'DISPOSITION': {'default': '1', 'dub': '0', 'original': '0', 'comment': '0', 'lyrics': '0', 'karaoke': '0', 'forced': '0', 'hearing_impaired': '0', 'visual_impaired': '0', 'clean_effects': '0', 'attached_pic': '0', 'timed_thumbnails': '0'}, 'TAG': {'rotate': '90', 'creation_time': '2020-01-15T10:35:59.000000Z', 'language': 'eng', 'handler_name': 'VideoHandle'}, 'index': '0', 'codec_name': 'h264', 'codec_long_name': 'H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10', 'profile': 'High', 'codec_type': 'video', 'codec_time_base': '133393/3870000', 'codec_tag_string': 'avc1', 'codec_tag': '0x31637661', 'width': '1440', 'height': '1080', 'coded_width': '1440', 'coded_height': '1088', 'has_b_frames': '0', 'sample_aspect_ratio': '1:1', 'display_aspect_ratio': '4:3', 'pix_fmt': 'yuv420p', 'level': '40', 'color_range': 'tv', 'color_space': 'bt709', 'color_transfer': 'bt709', 'color_primaries': 'bt709', 'chroma_location': 'left', 'field_order': 'unknown', 'timecode': 'N/A', 'refs': '1', 'is_avc': 'true', 'nal_length_size': '4', 'id': 'N/A', 'r_frame_rate': '30/1', 'avg_frame_rate': '1935000/133393', 'time_base': '1/90000', 'start_pts': '0', 'start_time': '0.000000', 'duration_ts': '266786', 'duration': '2.964289', 'bit_rate': '11845320', 'max_bit_rate': 'N/A', 'bits_per_raw_sample': '8', 'nb_frames': '43', 'nb_read_frames': 'N/A', 'nb_read_packets': 'N/A', 'side_data_type': 'Display Matrix', 'displaymatrix': '', 'rotation': '-90'}
```

## Extract frame from video
```
	frame = Extractframes(VIDEO_PATH)
	frame.extract_frames() # frame_path='', index=0 as optional arguments.
```
nanologger 1.0.3
A tiny logger tool for Python

#  nanologger
nanologger is a tiny logger made with Python.

It includes basic functionality such as
1. A basic log
2. Warning logs
3. Fatal logs
4. Success logs

This tiny package also includes time format editing and enabling/disabling colors

***Warning***: Windows does not support color in console, so it is automatically disabled

##  Example code:
```python
import nanologger as log

print("The first four logs will be printed, and will not exit on Fatal call")
log.Log("Database service is up")
log.Warning("File not found, using default directory")
log.Fatal("Cannot connect to server", exit = False)
log.Success("Operation Complete\n")

print("Next three four will be printed without color")
log.hasColor = False
log.Log("Database service is up")
log.Warning("File not found, using default directory")
log.Fatal("Cannot connect to server", exit = False)
log.Success("Operation Complete\n")

print("Next three four will be printed without timestamp")
log.hasColor = True
log.hasTimestamp = False
log.Log("Database service is up")
log.Warning("File not found, using default directory")
log.Fatal("Cannot connect to server", exit = False)
log.Success("Operation Complete\n")

print("Next three logs will be printed in a different time format, and exit on Fatal call")
log.hasTimestamp = True
log.hasColor = True
log.timeFormat = "%Y-%m-%d"
log.Log("Database service is up")
log.Warning("File not found, using default directory")
log.Fatal("Cannot connect to server")
log.Success("Operation Complete")
```
##  Methods:
1.  `Log` prints a log with a timestamp
2.  `Warning` prints a warning with timestamp
3.  `Fatal` prints a fatal log (terminates program if appropeiate value set)
4.  `Success` prints a success log

##  Variables:
1. `timeFormat` is a string, supports regular Python timestamps **default: "%d-%m-%Y %H:%M:%S"**
2. `hasColor` is a boolean that sets/removes color from the logs **default: True**
3. `hasTimestamp` is a boolean that sets/removes the timestamp **default: True**

##  Installation
Install the package  by running
```bash
pip install nanologger
```
Import it with
```python
import nanologger
```
bullattack 0.8
Website or Location Hacker Tool

<!--
https://pypi.org/project/bullattack/
https://pypi.org/project/bullattack/
-->

[![](https://img.shields.io/pypi/pyversions/readme-generator.svg?longCache=True)](https://pypi.org/project/readme-generator/)

#### Installation
```bash
$ pip install bullattack
```

#### Features
+   **`bullattack`**, **Website or IP Location Hacker**


#### How it works
```
$ bullattack

Created by Parixit Sutariya
Github : github.com/Bhai4You
Youtube : youtube.com/BullAnonymous

```

<p align="center">
    <a href="https://pypi.org/project/bullattack/">Bull-Attack Tool</a>
</p>
ipattack 0.4
Website or IP Attack Tool

<!--
https://pypi.org/project/ipattack/
https://pypi.org/project/ipattack/
-->

[![](https://img.shields.io/pypi/pyversions/readme-generator.svg?longCache=True)]

#### Installation
```bash
$ pip install ipattack
```

#### Features
+   **`ipattack`**, **Website or IP Address Attack Tool**


#### How it works
```
$ ipattack

Created by Parixit Sutariya
Github : github.com/Bhai4You
Youtube : youtube.com/BullAnonymous

```

<p align="center">
    <a href="https://pypi.org/project/ipattack/">IP Attack Tool</a>
</p>
simpleocr 0.0.23
A ocr tool for traditional chinese
Requires Python: >=3.6

# Simpleocr library
Simpleocr is a traditional chinese OCR python package that based on deep learning method.

The library consists of text localization and text recognition.

## Text localization
The model is a reimplementation of CRAFT(Character-Region Awareness For Text detection) by tensorflow.

[paper](https://arxiv.org/abs/1904.01941) | [github](https://github.com/clovaai/CRAFT-pytorch)
 
## Text recognition
The reimplementation is based on CRNN model that RNN layer is replaced with self-attention layer.

##### CRNN
[paper](https://arxiv.org/abs/1707.03985)

##### Self attention

[paper](https://arxiv.org/abs/1706.03762)

# Installation
```
$ pip install simpleocr
```
or 
```
$ git clone https://github.com/xianyuntang/simpleocr
$ cd simpleocr
$ python setup.py install
```
# Usage
```
from simpleocr import ocr
ocr.get_text(['image.jpg'])
```



# TODO
1. English support
2. GPU support