OpenCV Revision

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 
High Pass Low Pass Filter
LPF helps in removing noise, blurring the image
HPF helps in finding the edges in an image
Low pass filter
Types of Filtering: Averaging, Gaussian, Median, Bilateral Filtering

# 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 
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 
Haar cascade
# Code 
import cv2
image  = cv2.imread('/content/salon_rai.jpg')

--> 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

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