OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. It provides a wide range of algorithms and functions that enable developers to perform various image and video processing tasks. OpenCV is written in C++ and has interfaces for various programming languages, including Python.
# Performing basic image processing operations like resizing, cropping, rotating, and flipping images.
# Cross platform Supports Many Language such as C++, Java, Python
# Image, Video, Camera related Computer Vision, exist in Open CV
# Computer Vision Applications
# Applying various image filters and enhancements, such as blurring, sharpening, and adjusting brightness/contrast ( HPF, LPF).
# Detecting and recognizing objects, faces, and text in images and videos.
# Extracting features from images, such as edges, corners, and keypoints ( Feature Extraction).
# Performing image segmentation and finding contours.
# Calibrating cameras and working with camera parameters.
# Pattern recognition - color, pixels value - object can be recognize
# Photogrammetry - measurements - weights, height, volume
# Open CV AR, VR# 3 types of image - Read by Open CV
# Color image, BW image
# Loads image RGB alpha channel
# Image Filtering
# Find out the content Features
# Filter, High Pass Filter (HPF) , Low Pass Filter (LPF)
# Object can be identified
# Low pass filter is noise such as you can see when click photos during night
HPF helps in finding the edges in an image
# You have to create matrix and apply convolution
# Convolution - Feature Extraction - Dimensional Reduction
# Also called Kernel ( also LPF, HPF )
# After then It is called Convoluted image
# Pixels values change so we can detect by our eyes
# Eg. Black Hole image is created using pixels since it can't be captured and seen
# 3 by 3 matrix, 5 by 5 which can be larger, 2 by 2 can be smaller
# Normalize 
Harr-cascade
Based on opencv principle another application is made which is called harr-cascade
# Mainly focused on face
# Viola-Jones algorithm two scientist
# Later it is widely used for object detection
# If you want light application then you can use harr-cascade, but for accurate result deep learning is necessary
# In present, Feature loss problem is a major problem
# If you want light application then you can still use this
# For accurate result deep learning is necessity
# Harr-cascade operates on Black n White image
# Lighting issue on phot could not recognize the person
# Could not recognize the black people 
# Code
import cv2
image = cv2.imread('/content/salon_rai.jpg')
image
--> array([[[194, 194, 182],
[195, 195, 183],
[194, 194, 182],
...,
[ 57, 46, 38],
[ 12, 6, 1],
[ 6, 1, 0]],
[[197, 197, 185],
[198, 198, 186],
[197, 197, 185],
Color images consist of 3 dimension