Streamlit vision demo

 

	import streamlit as st
	import cv2
	from PIL import Image,ImageEnhance
	import numpy as np
	import os
	import PIL.ImageOps
	imagefile = st.file_uploader("Upload Image",type=['jpg','png','jpeg'])
	if imagefile is not None:
	image = Image.open(imagefile)
	opencvImage = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
	st.text("Original Image")
	st.image(image)
	#Create Gray Scale Image
	st.markdown('**Grey Image Genration:**')
	greyimagetask = st.button('Generate grayscale image')
	if greyimagetask:
	gray = cv2.cvtColor(opencvImage, cv2.COLOR_BGR2GRAY)
	st.image(gray)
	st.markdown('**Negative Image Genration:**')
	#Create Negative Image
	negativeimagetask = st.button('Generate negative image')
	if negativeimagetask:
	inverted_image = PIL.ImageOps.invert(image)
	st.image(inverted_image)

view raw visionexample.py hosted with ❤ by GitHub

	conda create --name visionexperiment
	conda activate visionexperiment
	conda install pip
	pip install -r requirements.txt

view raw environmentcreation.txt hosted with ❤ by GitHub

	anaconda-client
	dlib==19.21.1
	imutils==0.5.4
	numpy
	opencv-python==4.5.1.48
	scikit-image==0.18.1
	scipy==1.6.3
	tensorboard
	tensorboard-data-server
	tensorboard-plugin-wit
	tensorflow
	tensorflow-estimator
	streamlit

view raw requirements.txt hosted with ❤ by GitHub

Happy Learning!!!!