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It seeeeeez!
Computer Vision with OpenCV
Marcel Neidinger
Department of Mathematics and Computer Science,
University of Basel
April 27, 2017
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Derivation of Gradient Form for Images
Introduce transformation V : R3 → R2
(
3
x
)
yt →
(
2
x − v1t
)
y − v2t (1)
Rewrite I(x, y, t) = ˜
I(V(x, y, t)) and use chain-rule for
derivative
gradI(x, y, t) = grad˜
I(x − v1t, y − v2t) ·
(
1 0 −v1
0 1 −v2
)
(2)
We search for ∂I
∂t
→ third component
∂I
∂t
= −
(
v1
·
∂I
∂x
+ v2
∂I
∂y
)
= −(grad˜
I ·⃗
v) (3)
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Derivation of Gradient Form for Images
Introduce transformation V : R3 → R2
(
3
x
)
yt →
(
2
x − v1t
)
y − v2t (1)
Rewrite I(x, y, t) = ˜
I(V(x, y, t)) and use chain-rule for
derivative
gradI(x, y, t) = grad˜
I(x − v1t, y − v2t) ·
(
1 0 −v1
0 1 −v2
)
(2)
We search for ∂I
∂t
→ third component
∂I
∂t
= −
(
v1
·
∂I
∂x
+ v2
∂I
∂y
)
= −(grad˜
I ·⃗
v) (3)
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OpenCV is practical
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OpenCV: History
Started at Intel Research
Cross-plattform library for CV
Today: More then a CV Library (machine learning)
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Programming OpenCV
+ =
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Programming OpenCV
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Programming OpenCV
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Programming OpenCV
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Programming OpenCV
OpenCV is polygamous.
- Florian Spiess
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Programming OpenCV
OpenCV is polygamous.
- Florian Spiess
We will still use C++ in this workshop.
Why?
It’s the root language of OpenCV(fastest)
Most examples are in C++(followed by python)
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Programming OpenCV
OpenCV is polygamous.
- Florian Spiess
We will still use C++ in this workshop.
Why?
It’s the root language of OpenCV(fastest)
Most examples are in C++(followed by python)
I’m a c++ fanboy and this is my workshop
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Loading an image
#include
int main(int argc, char** argv)
{
cv::Mat img;
img = cv::imread(argv[1]);
}
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Displaying the image
[...]
cv::namedWindow(”debug”, cv::CV_WINDOW_AUTOSIZE);
imshow(”debug”, img);
while( true )
{
if( cv::waitKey(0) != −1 )
{
break;
}
}
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Theory: Segmenting images
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Segementation in OpenCV
// Create new image to store greyscale
cv::Mat imgGray, imgThresh;
cv::cvtColor( img, imgGray, cv::CV_BGR2GRAY );
// Threshold
int value = 20;
int maxval = 255;
cv::threshold( imgGray, imgThresh, value, maxval,
cv::THRESH_BINARY);
dst(x, y) =
{
maxval, if src(x, y) > value
0, else
(4)
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OpenCV: Finding contours
std::vector > contours;
std::vector hierarchy;
cv::findContours( imgThresh, contours, hierarchy,
CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, cv::
Point(0,0) );
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OpenCV: Practical example
Enough examples.
Let’s write a tennis ball
tracker
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OpenCV: And moving images?
VideoCapture cap(0);
if( !cap.isOpened() )
{
return −1;
}
for(;;)
{
Mat frame;
cap >> frame;
// Treat like normal picture
}
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Beyond the scope
This barely scratched the surface of what OpenCV can do
Advanced object detection
Advanced feature selection
Advanced tracking and object recognition methods
Video analysis capabilities
Machine Learning Capabilities(Boosting, Cascaders, k-nearest
Neighbor …)
Support for OpenCL and CUDA
Integration into e.g. TensorFlow
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Beyond the scope
This barely scratched the surface of what OpenCV can do
Advanced object detection
Advanced feature selection
Advanced tracking and object recognition methods
Video analysis capabilities
Machine Learning Capabilities(Boosting, Cascaders, k-nearest
Neighbor …)
Support for OpenCL and CUDA
Integration into e.g. TensorFlow
Theorem
Rule of Thumb: If there is an algorithm in computer vision,
OpenCV has a highly optimized implementation readily available.
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Questions?