This page is dedicated to OpenCV projects using Python.
What about giving your python programs eyes? Yes with the OpenCV module your python have the power to process images(so the videos).
Computer Vision algorithms enables computers to capture images and process the captured data to obtain useful results. The capturing is done usually through cameras interfaced to the processing devise(Computer).
"Today, images and video are everywhere. Online photo-sharing sites and social net-
works have them in the billions. Search engines will produce images of just about any conceivable query. Practically all phones and computers come with built-in cameras.It is not uncommon for people to have many gigabytes of photos and videos on their devices.
Programming a computer and designing algorithms for understanding what is in these images is the field of computer vision. Computer vision powers applications like image search, robot navigation, medical image analysis, photo management, and many more."(from Programming Computer Vision using Python-Jan Erik Solem)
• Optical character recognition (OCR): reading handwritten postal codes on letters and automatic number plate recognition (ANPR)
• Machine inspection: rapid parts inspection for quality assurance using stereo vision
with specialized illumination to measure tolerances on aircraft wings or auto body parts or looking for defects in steel castings using X-ray vision
• Retail: object recognition for automated checkout lanes
• 3D model building (photogrammetry): fully automated construction of 3D models
from aerial photographs used in systems such as Bing Maps
• Medical imaging: registering pre-operative and intra-operative imagery
or performing long-term studies of people’s brain morphology as they age
• Automotive safety: detecting unexpected obstacles such as pedestrians on the street,under conditions where active vision techniques such as radar or lidar do not work well
• Match move: merging computer-generated imagery (CGI) with live action footage by tracking feature points in the source video to estimate the 3D camera motion and shape of the environment. Such techniques are widely used in Hollywood (e.g., in movies such as Jurassic Park) they also require the use of precise matting to insert new elements between foreground and background elements
• Motion capture (mocap): using retro-reflective markers viewed from multiple cam-
eras or other vision-based techniques to capture actors for computer animation
• Surveillance: monitoring for intruders, analyzing highway traffic, and
monitoring pools for drowning victims
• Fingerprint recognition and biometrics: for automatic access authentication as well
as forensic applications.
• Stitching: turning overlapping photos into a single seamlessly stitched panorama
• Exposure bracketing: merging multiple exposures taken under challenging lighting
conditions (strong sunlight and shadows) into a single perfectly exposed image
• Morphing: turning a picture of one of your friends into another, using a seamless
morph transition
• 3D modeling: converting one or more snapshots into a 3D model of the object or
person you are photographing
• Video match move and stabilization: inserting 2D pictures or 3D models into your
videos by automatically tracking nearby reference points or using motion estimates to remove shake from your videos
• Photo-based walkthroughs: navigating a large collection of photographs, such as the
interior of your house, by flying between different photos in 3D
• Face detection: for improved camera focusing as well as more relevant image searching
• Visual authentication: automatically logging family members onto your home computer as they sit down in front of the webcam
So I hope this convinces you enough to learn OpenCV.Let's Hit the road by installing OpenCV on your computer.
What about giving your python programs eyes? Yes with the OpenCV module your python have the power to process images(so the videos).
What is OpenCV ?
OpenCV is a library of commonly used building block algorithms for image processing.Open CV stands for Open Source Computer VisionWhat is Computer Vision?
Computer Vision algorithms enables computers to capture images and process the captured data to obtain useful results. The capturing is done usually through cameras interfaced to the processing devise(Computer).
"Today, images and video are everywhere. Online photo-sharing sites and social net-
works have them in the billions. Search engines will produce images of just about any conceivable query. Practically all phones and computers come with built-in cameras.It is not uncommon for people to have many gigabytes of photos and videos on their devices.
Programming a computer and designing algorithms for understanding what is in these images is the field of computer vision. Computer vision powers applications like image search, robot navigation, medical image analysis, photo management, and many more."(from Programming Computer Vision using Python-Jan Erik Solem)
Application of Computer Vision:
(courtesy:Computer Vision:Algorithms and Applications Richard Szeliski)• Optical character recognition (OCR): reading handwritten postal codes on letters and automatic number plate recognition (ANPR)
• Machine inspection: rapid parts inspection for quality assurance using stereo vision
with specialized illumination to measure tolerances on aircraft wings or auto body parts or looking for defects in steel castings using X-ray vision
• Retail: object recognition for automated checkout lanes
• 3D model building (photogrammetry): fully automated construction of 3D models
from aerial photographs used in systems such as Bing Maps
• Medical imaging: registering pre-operative and intra-operative imagery
or performing long-term studies of people’s brain morphology as they age
• Automotive safety: detecting unexpected obstacles such as pedestrians on the street,under conditions where active vision techniques such as radar or lidar do not work well
• Match move: merging computer-generated imagery (CGI) with live action footage by tracking feature points in the source video to estimate the 3D camera motion and shape of the environment. Such techniques are widely used in Hollywood (e.g., in movies such as Jurassic Park) they also require the use of precise matting to insert new elements between foreground and background elements
• Motion capture (mocap): using retro-reflective markers viewed from multiple cam-
eras or other vision-based techniques to capture actors for computer animation
• Surveillance: monitoring for intruders, analyzing highway traffic, and
monitoring pools for drowning victims
• Fingerprint recognition and biometrics: for automatic access authentication as well
as forensic applications.
• Stitching: turning overlapping photos into a single seamlessly stitched panorama
• Exposure bracketing: merging multiple exposures taken under challenging lighting
conditions (strong sunlight and shadows) into a single perfectly exposed image
• Morphing: turning a picture of one of your friends into another, using a seamless
morph transition
• 3D modeling: converting one or more snapshots into a 3D model of the object or
person you are photographing
• Video match move and stabilization: inserting 2D pictures or 3D models into your
videos by automatically tracking nearby reference points or using motion estimates to remove shake from your videos
• Photo-based walkthroughs: navigating a large collection of photographs, such as the
interior of your house, by flying between different photos in 3D
• Face detection: for improved camera focusing as well as more relevant image searching
• Visual authentication: automatically logging family members onto your home computer as they sit down in front of the webcam
So I hope this convinces you enough to learn OpenCV.Let's Hit the road by installing OpenCV on your computer.
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