Metadata-Version: 2.1
Name: kerfex
Version: 0.0.1
Summary: Generic feature extraction using keras pre-built CNN's with imagenet weights.
Home-page: https://github.com/caiocarneloz/kerfex
Author: Caio Carneloz
Author-email: caiocarneloz@gmail.com
License: MIT License
Description: # kerfex
        Generic feature extraction using [keras pre-built CNN's](https://keras.io/api/applications/) with imagenet weights.
        
        ## Getting Started
        #### Dependencies
        You need Python 3.7 or later to run **kerfex**. You can find it at [python.org](https://www.python.org/).
        
        You aso need pandas, numpy, keras and tensorflow packages, which is available from [PyPI](https://pypi.org). If you have PyPI, run:
        ```
        pip install pandas numpy keras tensorflow
        ```
        #### Installation
        Clone this repo to your local machine using:
        ```
        git clone https://github.com/caiocarneloz/kerfex.git
        ```
        Or install it using pip:
        ```
        pip install kerfex
        ```
        ### Usage
        The [demo.py](https://github.com/caiocarneloz/kerfex/blob/main/demo.py) file shows a simple example using VGG16 with three [Unsplash](https://unsplash.com/) images from the authors [@mybibimbaplife](https://unsplash.com/@mybibimbaplife), [@davidbraud](https://unsplash.com/@davidbraud), and [@analoglugunler](https://unsplash.com/@analoglugunler). The "extract" function requires:
        
        - **CNN instance itself**
        - **CNN pre-processing module**
        - **List of images**
        - **Images shape**
        
        As return, the function will send a pandas Dataframe containing the numerical features extracted from every image, where each line represents a single image and each column represents a single feature:
        ```
                        0          1          2   ...      15599     15600     15601
        	
        0  	 0.000000   0.000000   0.000000   ...   0.000000  0.000000  3.754401
        1  	 0.000000  15.284859  37.369953   ...  22.756908  6.398854  0.000000
        2  	12.172541   0.000000   0.000000   ...   0.000000  0.000000  0.000000
        ```
        
Keywords: keras,feature-extraction,CNN,Imagenet
Platform: UNKNOWN
Description-Content-Type: text/markdown
