We used the following images: [LinuxLogo.jpg](https://raw.githubusercontent.com/opencv/opencv/master/samples/data/LinuxLogo.jpg) and [WindowsLogo.jpg](https://raw.githubusercontent.com/opencv/opencv/master/samples/data/WindowsLogo.jpg)
We used the following images: [LinuxLogo.jpg](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/data/LinuxLogo.jpg) and [WindowsLogo.jpg](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/data/WindowsLogo.jpg)
@warning Since we are *adding**src1* and *src2*, they both have to be of the same size
@ -185,7 +185,7 @@ and are not intended to be used as a replacement of a raster graphics editor!**
### Code
Code for the tutorial is [here](https://github.com/opencv/opencv/blob/master/samples/cpp/tutorial_code/ImgProc/changing_contrast_brightness_image/changing_contrast_brightness_image.cpp).
Code for the tutorial is [here](https://github.com/opencv/opencv/blob/3.4/samples/cpp/tutorial_code/ImgProc/changing_contrast_brightness_image/changing_contrast_brightness_image.cpp).
](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp) or
](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp) or
find it in the
`samples/cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp` of the
OpenCV source code library.
@ -27,7 +27,7 @@ OpenCV source code library.
@add_toggle_java
You can [download this from here
](https://raw.githubusercontent.com/opencv/opencv/master/samples/java/tutorial_code/core/discrete_fourier_transform/DiscreteFourierTransform.java) or
](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/java/tutorial_code/core/discrete_fourier_transform/DiscreteFourierTransform.java) or
find it in the
`samples/java/tutorial_code/core/discrete_fourier_transform/DiscreteFourierTransform.java` of the
OpenCV source code library.
@ -35,7 +35,7 @@ OpenCV source code library.
@add_toggle_python
You can [download this from here
](https://raw.githubusercontent.com/opencv/opencv/master/samples/python/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.py) or
](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/python/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.py) or
find it in the
`samples/python/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.py` of the
OpenCV source code library.
@ -222,7 +222,7 @@ An application idea would be to determine the geometrical orientation present in
example, let us find out if a text is horizontal or not? Looking at some text you'll notice that the
text lines sort of form also horizontal lines and the letters form sort of vertical lines. These two
main components of a text snippet may be also seen in case of the Fourier transform. Let us use
[this horizontal ](https://raw.githubusercontent.com/opencv/opencv/master/samples/data/imageTextN.png) and [this rotated](https://raw.githubusercontent.com/opencv/opencv/master/samples/data/imageTextR.png)
[this horizontal ](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/data/imageTextN.png) and [this rotated](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/data/imageTextR.png)
The goal of this tutorial is to show you how to use the OpenCV `parallel_for_` framework to easily
parallelize your code. To illustrate the concept, we will write a program to draw a Mandelbrot set
exploiting almost all the CPU load available.
The full tutorial code is [here](https://github.com/opencv/opencv/blob/master/samples/cpp/tutorial_code/core/how_to_use_OpenCV_parallel_for_/how_to_use_OpenCV_parallel_for_.cpp).
The full tutorial code is [here](https://github.com/opencv/opencv/blob/3.4/samples/cpp/tutorial_code/core/how_to_use_OpenCV_parallel_for_/how_to_use_OpenCV_parallel_for_.cpp).
If you want more information about multithreading, you will have to refer to a reference book or course as this tutorial is intended
to remain simple.
@ -175,7 +175,7 @@ C++ 11 standard allows to simplify the parallel implementation by get rid of the
Results
-----------
You can find the full tutorial code [here](https://github.com/opencv/opencv/blob/master/samples/cpp/tutorial_code/core/how_to_use_OpenCV_parallel_for_/how_to_use_OpenCV_parallel_for_.cpp).
You can find the full tutorial code [here](https://github.com/opencv/opencv/blob/3.4/samples/cpp/tutorial_code/core/how_to_use_OpenCV_parallel_for_/how_to_use_OpenCV_parallel_for_.cpp).
The performance of the parallel implementation depends of the type of CPU you have. For instance, on 4 cores / 8 threads
CPU, you can expect a speed-up of around 6.9X. There are many factors to explain why we do not achieve a speed-up of almost 8X.
@ -13,7 +13,7 @@ We will demonstrate results of this example on the following picture.
Source Code
-----------
We will be using snippets from the example application, that can be downloaded [here](https://github.com/opencv/opencv/blob/master/samples/dnn/classification.cpp).
We will be using snippets from the example application, that can be downloaded [here](https://github.com/opencv/opencv/blob/3.4/samples/dnn/classification.cpp).
You can also download it from [here](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.cpp).
You can also download it from [here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/cpp/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.cpp).
You can also download it from [here](https://raw.githubusercontent.com/opencv/opencv/master/samples/java/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.java).
You can also download it from [here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/java/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.java).
You can also download it from [here](https://raw.githubusercontent.com/opencv/opencv/master/samples/python/tutorial_code/imgProc/morph_lines_detection/morph_lines_detection.py).
You can also download it from [here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/python/tutorial_code/imgProc/morph_lines_detection/morph_lines_detection.py).
var proto = 'https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt';
var proto = 'https://raw.githubusercontent.com/opencv/opencv/3.4/samples/dnn/face_detector/deploy.prototxt';
var weights = 'https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel';
var recognModel = 'https://raw.githubusercontent.com/pyannote/pyannote-data/master/openface.nn4.small2.v1.t7';
utils.createFileFromUrl('face_detector.prototxt', proto, () => {