YouTube Transcript:
OpenCV Tutorial in 5 minutes - All Modules Overview

opencv is an open source Library
consisting of hundreds of computer
vision algorithms while the library was
mainly developed in C and C plus it can
be used in other languages such as Java
JavaScript R and of course python opencv
python is a collection of python
bindings which allow data scientists and
Engineers to use opencv using a python
interface the core operations module
contains the basic building blocks of
opencv such as data structures and
procedures specific to computer vision
applications such as pixel editing
geomatic Transformations and code
optimization the core functionality
includes reading and editing individual
pixels or regions of interest within
images splitting and merging channels
padding images matte operations and
images such as addition blending
operations or bitwise operations and
last but not least functionality to
measure performance and optimize opencv
code image processing with opencv can be
used to change the color space of an
image for example from blue green red to
grayscale or HSV Hue saturation value
color model and to track a colored
object in an image or in a video to
perform geometric Transformations on
images such as scaling translation
rotation a fine transformation and
perspective transformation convert
images to Binary images using image
thresholding to smoothen blur and filter
images to apply morphological
Transformations on the shapes of images
like erosion dilatation opening closing
morphological gradient top hat or black
hat to find image gradients using Sobel
and laplacian derivatives to perform
Edge detection using the Kani Edge
detection algorithm to create image
pyramids on multiple levels as well as
using pyramids for image blending to be
able to find and draw Contours to be
able to find image histograms to plot
image histograms as well as to analyze
image histograms to use the Fourier
transform of an image in order to
perform Edge detection to be able to use
the template matching method to detect
either one or multiple matching objects
in an image to use the whole line
transform in order to detect lines in an
image or the whole circle transform to
detect circles in an image using the
Watershed algorithm in order to segment
images using the grab cut algorithm to
extract the foreground of an image
feature detection functionalities in
opencv help us to better understand the
features of a given image to use the
Harris Corner detection algorithm to
detect Corners in images or the shy
tomasi Corner detector for tracking
Corners as well as low scale invariant
feature transform detector for Corners
in images with changing scales as well
as its faster Alternatives the speeded
up robust features detector the fast
detector for real-time applications the
brief detector outputting binary
descriptors the free and unpatented orb
detector the feature detection module in
opencv also has functionality to match
features across different images video
analysis and opencv includes techniques
such as main shift cam shift and Optical
flow minship finds the area of Maximum
pixel density in an image by placing a
window at a random position over an
image and then Shifting the window
during repeated iterations until it
converges to the area of Maximum density
mean shift uses a fixed size window
regardless of image which can be an
issue for some applications camshaft
relies on applying mean shift first then
it fits a scaled rotated ellipse
detected area after which minshift is
run again using the new ellipse window
Optical flow is the pattern of apparent
motion of image objects between the
consecutive frames of a video and can be
detected using the Lucas can 8 method
implemented in opencv camera calibration
and 3D reconstruction can be performed
as well in opencv opencv helps you
identify distortions caused by cameras
in images such as radial distortions
when straight lines appear curved or
tangential Distortion when some areas of
the image look nearer than expected
opencv can help to detect the object or
image Corner points and use them as
input for the camera calibration
procedure to underscore or the original
image finally opencv can also help to
estimate the reprojection error after
calibration opencv can be used for pose
estimation to understand how an object
is situated in space and once that is
achieved we can render 3D objects within
the image itself opencv can also
estimate depth between images using
epipolar geometry as well as stereo
images Machine learning in opencv is
implemented using several models such as
K nearest neighbors and support Vector
machines for classification and
handwritten data extraction as well as
k-means clustering for applications such
as color quantization that is reducing
the number of colors in an image in
computational photography techniques in
opencv include image denoising to remove
noise using non-local means denoising
image in painting to restore old
degraded images affected by black spots
and strokes and lastly to generate and
display High dynamic range images or HDR
images from multiple exposed images and
use the exposure Fusion technique to
merge our exposure sequence object
detection is one of the main
applications of computer vision and it's
implemented in opencv using the Cascade
classifier opencv provides the Cascade
classifiers using the har feature-based
object detection which can detect eyes
faces or other objects in images or in
video streams opencv bindings allow data
scientists to leverage and extend the
opencv C plus modules with python if you
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