What is Image Segmentation ?

Image Segmentation is the process by which a digital image is partitioned into various subgroups (of pixels) called Image Objects, which can reduce the complexity of the image, and thus analyzing the image becomes simpler.

Usage of Segmentation in real world applications

One of the distinct and famous applications can be seen in Cancer cell detection systems where Image Segmentation proved pivotal in faster detection of disease tissues and cells from the imagery and hence enabling the Doctors in providing timely treatment.

The manufacturing industry now highly relies on image recognition techniques to detect anomalies which usually escape human eyes, hence increasing the efficiency of the products.

Image segmentation Techniques

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The objective of this tutorial is to use open cv Haar feature-based classifiers to detect the number plates from the images. This will be a very simple machine learning approach to object detection in OpenCV. To learn how Haar cascade object detection works before you begin this notebook, check out this site: https://docs.opencv.org/3.4/db/d28/tutorial_cascade_classifier.html.

Import Required Libraries

QR code is a type of matrix barcode that is machine readable optical label which contains information about the item to which it is attached. In practice, QR codes often contain data for a locator, identifier, or tracker that points to a website or application, etc.

Problem Statement :

Generate and read QR codes in Python using qrcode and OpenCV libraries

Installing required dependencies:

pyqrcode module is a QR code generator. The module automates most of the building process for creating QR codes. This module attempts to follow the QR code standard as closely as possible. …

A Spectrogram is a visual representation of the frequencies of a signal as it varies with time.

Now, sound classification or audio tagging have various applications.

• Content-based multimedia indexing and retrieval
• Assisting deaf individuals in their daily activities
• Smart home use cases such as 360-degree safety and security capabilities
• Industrial uses such as predictive maintenance

Detecting sentiments of a quote

Categorize quotes that are uploaded during Pride Month on the basis of its sentiment —

• positive,
• negative, and
• random.

Task

To build an engine that combines the concepts of OCR and NLP that accepts a .jpg file as input, extracts the text, if any, and classifies sentiment as positive or negative. If the text sentiment is neutral or an image file does not have any text, then it is classified as random.

Dataset

The dataset consists of quotes that are uploaded during Pride Month.

Install TextHero for automatic NLP

!pip install texthero

Install Tesseract for reading text from Images

!sudo apt install tesseract-ocr
!pip install pytesseract

Import corresponding Libraries

import pytesseract
import shutil
import os
import random

Sample text processing from Images

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Deep Learning involving images can be a fascinating field to work with. And most probably the project involves working with Convolutional Neural Networks. Whether it is an image classification or image recognition based project, there is always one common factor, a lot of images. And most of the time you need lots of them to carry out the process of deep learning properly.

We neither want you model to overfit nor underfit. You also don’t want that your model should recognize images wrongly. Well, there is only one way out of it. Get a lot of image data. But sometimes…

Dataset :

Introduction

Streamlit is an open-source Python library that makes it easy to build beautiful custom web-apps for machine learning and data science.

Streamlit is an awesome new tool that allows engineers to quickly build highly interactive web applications around their data, machine learning models, and pretty much anything.

The best thing about Streamlit is it doesn’t require any knowledge of web development. If you know Python, you’re good to go!

Follow these steps and to get a sample app running in less than 5 minutes.

1. You need to have Python 3.6 or greater installed
2. Install streamlit