semvec/pypi-packages huggingface dataset:
ochoneycomb 0.0.1
OpenCensus exporter for Honeycomb

# OpenCensus Python exporter for Honeycomb

A simple exporter to translate OpenCensus span data into Honeycomb traces.

## Example

```python
import time
import os

from opencensus.trace import tracer as tracer_module
from ochoneycomb import HoneycombExporter

exporter = HoneycombExporter(writekey=os.getenv("HONEYCOMB_WRITEKEY"), dataset=os.getenv("HONEYCOMB_DATASET"), service_name="test-app")

# exporter = file_exporter.FileExporter(file_name='traces')

# Initialize a tracer, by default using the `PrintExporter`
tracer = tracer_module.Tracer(exporter=exporter)

def do_something_to_trace():
    time.sleep(1)

# Example for creating nested spans
with tracer.span(name='span1') as span1:
    do_something_to_trace()
    with tracer.span(name='span1_child1') as span1_child1:
        span1_child1.add_annotation("something")
        do_something_to_trace()
    with tracer.span(name='span1_child2') as span1_child2:
        do_something_to_trace()
with tracer.span(name='span2') as span2:
    do_something_to_trace()

```

## Install

```bash
pip install ochoneycomb
```

## Requirements

```bash
pip install opencensus
pip install libhoney
```
frapi 0.1.6
Package for Face Recognition API

# FaceRec

簡單易懂，高精準度的人臉辨識技術封裝


# Papers

深度學習人臉辨識技術

0. 基礎: 機器學習/深度學習/圖形處理器技術

1. "DeepFace: Closing the Gap to Human-Level Performance in Face Verification"

*https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf?spm=5176.100239.blogcont55892.18.pm8zm1&file=Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf

最早的深度學習人臉辨識, 已有 metric learning 的觀念 (使用 siamese network)

但, 無權值共享的 CNN 帶來過多的參數, 3D alignment 也顯得過度複雜


2. "Deep Face Recognition" 

*http://cis.csuohio.edu/~sschung/CIS660/DeepFaceRecognition_parkhi15.pdf

著名的 VGG Face, 整套流程包含 face dataset 的建立


3. "FaceNet: A Unified Embedding for Face Recognition and Clustering"

*https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Schroff_FaceNet_A_Unified_2015_CVPR_paper.pdf

用 triplet loss 產生 128 維的 FaceNet embeddings (此向量空間內的距離代表人臉的相似程度), LFW 準確度超過 99%

網路結構:

101. (A) "Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations"

*https://arxiv.org/pdf/1409.1556/

經典的 VGG Network, 包含 VGG16, VGG19

102. "Going Deeper With Convolutions"

http://openaccess.thecvf.com/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf

GoogLeNet, 使用 3x3, 1x1 convolution 構成 inception 網路模組

103. "Deep residual learning for image recognition"

http://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf

residual network, 解決梯度消失問題, 讓訓練 100 (甚至1000) 層以上的深度學習變得容易

104. "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"

https://arxiv.org/abs/1704.04861

mobile net, 小而快的網路， 但犧牲準確度， 

A. "Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments"

http://cs.brown.edu/courses/cs143/2011/proj4/papers/lfw.pdf

*著名的 lfw 人臉辨識準確率測試資料集

# Results
99%
*https://github.com/BIG-CHENG/FaceRec/blob/master/fr_lfw_prec_recall_all.png
![LFW precision-recall ](https://github.com/BIG-CHENG/FaceRec/blob/master/fr_lfw_prec_recall_all.png)
*https://github.com/BIG-CHENG/FaceRec/blob/master/fr_lfw_roc_all.png
![LFW ROC ](https://github.com/BIG-CHENG/FaceRec/blob/master/fr_lfw_roc_all.png)
picoraster 0.0.2
Lazy raster band processing
Requires Python: >3.3

# picoraster

Small Python library for processing large raster images.

Currently a work in progress.

## Example usage

```python
source = AWSLandsat8Source("LC08_L1TP_139045_20170304_20170316_01_T1", band="8")

# Lazily create a band and build a description of processing steps
band = Band(source) \
    .and_then(Resize(extents)) \
    .and_then(HistogramAdjust()) \
    .and_then(Reproject(crs))

# Forces computation
array = band.render_to_array()

band.render_to_file("output.tif")
```

## Installation

Installing GDAL is the most challenging part. Installing directly from PyPI is historically unlikely to work.

First, install numpy:
`pip install numpy`

Then, choose one of the following:

- install with a system package manager
    - Ubuntu: `sudo apt install libgdal-dev`
    - MacOS: `brew install gdal`
- install from conda-forge: `conda install -c conda-forge gdal`
- [compile manually](http://trac.osgeo.org/gdal/wiki/BuildHints)

Afterwards, the correct Python bindings can be installed with
```bash
pip install GDAL==$(gdal-config --version) --global-option=build_ext --global-option="-I/usr/include/gdal" 
```

Finally,
`pip install picoraster`

## Running tests

`python -m src.tests`
zipit 0.0.2
Very thin wrapper around zipapp that let's you package a python module and dependencies.


![PyPI](https://img.shields.io/pypi/v/zipit.svg)

# zipit

Very thin wrapper around zipapp that let's you package a python module and dependencies.

### Example
We'll use our demo "app" to showcase zipit.

First, let's take a look at what our app contains:
```
$ cd demo
$ ls app
__main__.py  requirements.txt
```

Our "app" contains two files:
1. `__main__.py`: This is the entrypoint to our app.
2. `requirements.txt`: This is a classic requirements file as consumed by pip.

#### Getting things set up
First we need to install the dependencies for our app. zipit doesn't much care how dependencies are installed. We just need to keep track of where they're installed.

Let's use pip:
```
mkdir deps
$ python3 -m pip install -r app/requirements.txt --target deps
```

#### zipit
Once the dependencies are installed, we can let zipit do it's work:
```
cd ..
$ python3 -m zipit demo/app -d demo/deps
```

This will produce `.pyz` file runable with python.
```
$ python3 app.pyz
Hello, World!
```
SDGraph 0.0.5
A simple jedi based python3 dependency analysis tool


# Table of Contents

1.  [What is this tool for?](#org560c33d)
2.  [Usage](#org4fa6ae3)
    1.  [Requirement](#org52604c3)
    2.  [Install the tool](#orgbf52507)
    3.  [use case](#org23288c1)



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# What is this tool for?

  This is just a simple python static dependency generate tool, It can help you generate the dependency graph
find circular import.


<a id="org4fa6ae3"></a>

# Usage


<a id="org52604c3"></a>

## Requirement

You will need the git and graphviz for the tool

    apt-get update
    apt-get install git graphviz graphviz-dev


<a id="orgbf52507"></a>

## Install the tool

    pip install SDGraph


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## use case

    SDGraph -f a.py -o dot -n output_name