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

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

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

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.

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)

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]))

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License

© 2020 PyShine

This repository is licensed under the MIT license. See LICENSE for details.