Metadata-Version: 2.1
Name: pyshine
Version: 0.0.9
Summary: This library contains various Audio and Video Signal Processing utilities
Home-page: https://github.com/py2ai/pyshine_streamer.git
Author: PyShine
Author-email: python2ai@gmail.com
License: MIT
Description: # pyshine
        
        A collection of simply yet high level utilities for Python.
        
        ## Installation
        
        ### Installing dependencies
        
        Provided the below python packages are installed, pyshine is completely pip installable.
        
        
        
        ### Installing pyshine
        
        `pip install pyshine`
        
        To upgrade to the newest version
        `pip install --upgrade pyshine`
        
        
        ### pyshine.putBText()
        
        putBText(): Put Background Box with Text
        
        ```
        Inputs:
        img: cv2 image img
        text_offset_x, text_offset_x: X,Y location of text start
        vspace, hspace: Vertical and Horizontal space between text and box boundries
        font_scale: Font size
        background_RGB: Background R,G,B color
        text_RGB: Text R,G,B color
        font: Font Style e.g. cv2.FONT_HERSHEY_DUPLEX,cv2.FONT_HERSHEY_SIMPLEX,cv2.FONT_HERSHEY_PLAIN,cv2.FONT_HERSHEY_COMPLEX
              cv2.FONT_HERSHEY_TRIPLEX, etc
        thickness: Thickness of the text font
        alpha: Opacity 0~1 of the box around text
        gamma: 0 by default
        
        Output:
        img: CV2 image with text and background
        ```
        
        ### usage
        
        
        
        ```python3
        import pyshine as ps
        import cv2
        image = cv2.imread('lena.jpg')
        text  =  'HELLO WORLD!'
        image =  ps.putBText(image,text,text_offset_x=20,text_offset_y=20,vspace=10,hspace=10, font_scale=1.0,background_RGB=(228,225,222),text_RGB=(1,1,1))
        cv2.imshow('Output', image)
        cv2.waitKey(0)
        ```
        
        
        
        ### pyshine.audioCapture()
        
        audioCapture(): Send or Get the Audio from pc Microphone
        
        ```
        Inputs:
        mode: 'send' to send the audio chunk data or 'get' to receive the audio data
        
        Output:
        audio: Audio data, which can be accessed using audio.get() or send using audio.put()
        ```
        
        ### usage
        
        ```python3
        import pyshine as ps
        mode =  'send'
        audio=audioCapture(mode)
        
        ```
        
        ### pyshine.showPlot()
        
        showPlot(): Plots the live data 
        
        ```
        Inputs:
        audio: audio data obtained 
        name: 'Tile of the plot'
        length defult 8 times 1024
        xmin: default 0 along the x axis
        ymin: default -0.5 along the x axis
        xmax: default 8*1024 along the y axis
        ymax: default 0.5 along the y axis
        color: Color of the plot (0,1,0.29)
        
        
        Output:
        show the plot()
        ```
        
        ### usage
        
        ```python3
        import pyshine as ps
        mode =  'send'
        audio,context=ps.audioCapture(mode=mode)
        name =  'pyshine.com'
        ps.showPlot(context,name=name)
        while True:
        	frame = audio.get()
        
        
        
        ```
        
        ### pyshine.RPSNET
        
        A CNN model for the Keras library, incorporating Rock, Paper, Scissor learnining Network.
        
        ```python
        import pyshine as ps
        from keras.optimizers import Adam
        
        # WIDTH : width of image about 80 pixels
        # HEIGHT : height of image about 80 pixels
        # DEPTH : dimensions of image such as 3
        # NUM_CLASSES : number of classes to classify as output
        model =ps.RPSNET.build(width=WIDTH, height=HEIGHT, depth=DEPTH, classes=NUM_CLASSES)
        ```
        
        ```python
        INIT_LR = 1e-3
        EPOCHS = 1000
        OPT = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
        model.compile(
        		optimizer=OPT,
        		loss='categorical_crossentropy',
        		metrics=['accuracy']
        		)
        # data: numpy image array containing data samples
        # labels: corresponding labels per data
        model.fit(np.array(data), np.array(labels),epochs=EPOCHS)
        model.save("RPS-model.h5")
        pred = model.predict(np.array([image]))
        
        ```
        
        ### pyshine.Streamer
        
        Low latency video streamer for webcams and raspberry pi camera.
        
        ​		
        
        
        
        
        
        
        
        
        
        
        
        
        
        ## License
        
        © 2020 PyShine
        
        This repository is licensed under the MIT license. See LICENSE for details.
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
