self.warper=cv.PyRotationWarper(self.warper_type,scale)# setScale not working: https://docs.opencv.org/master/d5/d76/classcv_1_1PyRotationWarper.html#a90b000bb75f95294f9b0b6ec9859eb55
self.warper=cv.PyRotationWarper(self.warper_type,scale)# setScale not working: https://docs.opencv.org/4.x/d5/d76/classcv_1_1PyRotationWarper.html#a90b000bb75f95294f9b0b6ec9859eb55
- Make sure the files `aarcascade_frontalface_default.xml` and `haarcascade_eye.xml` are present in project's directory. They can be obtained from [OpenCV sources](https://github.com/opencv/opencv/tree/master/data/haarcascades).
- Make sure the files `aarcascade_frontalface_default.xml` and `haarcascade_eye.xml` are present in project's directory. They can be obtained from [OpenCV sources](https://github.com/opencv/opencv/tree/4.x/data/haarcascades).
- Make sure a sample image file `lena.jpg` exists in project's directory. It should display people's faces for this example to make sense. The following image is known to work:
@ -4,7 +4,9 @@ Using OpenCV.js {#tutorial_js_usage}
Steps
-----
In this tutorial, you will learn how to include and start to use `opencv.js` inside a web page. You can get a copy of `opencv.js` from `opencv-{VERSION_NUMBER}-docs.zip` in each [release](https://github.com/opencv/opencv/releases), or simply download the prebuilt script from the online documentations at "https://docs.opencv.org/{VERSION_NUMBER}/opencv.js" (For example, [https://docs.opencv.org/3.4.0/opencv.js](https://docs.opencv.org/3.4.0/opencv.js). Use `master` if you want the latest build). You can also build your own copy by following the tutorial on Build Opencv.js.
In this tutorial, you will learn how to include and start to use `opencv.js` inside a web page.
You can get a copy of `opencv.js` from `opencv-{VERSION_NUMBER}-docs.zip` in each [release](https://github.com/opencv/opencv/releases), or simply download the prebuilt script from the online documentations at "https://docs.opencv.org/{VERSION_NUMBER}/opencv.js" (For example, [https://docs.opencv.org/4.5.0/opencv.js](https://docs.opencv.org/4.5.0/opencv.js). Use `4.x` if you want the latest build).
You can also build your own copy by following the tutorial @ref tutorial_js_setup.
@ -36,7 +36,7 @@ gives us a feature vector containing 64 values. This is the feature vector we us
Finally, as in the previous case, we start by splitting our big dataset into individual cells. For
every digit, 250 cells are reserved for training data and remaining 250 data is reserved for
testing. Full code is given below, you also can download it from [here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/ml/py_svm_opencv/hogsvm.py):
testing. Full code is given below, you also can download it from [here](https://github.com/opencv/opencv/tree/4.x/samples/python/tutorial_code/ml/py_svm_opencv/hogsvm.py):
You may also find the source code in the `samples/cpp/tutorial_code/calib3d/camera_calibration/`
folder of the OpenCV source library or [download it from here
](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/calib3d/camera_calibration/camera_calibration.cpp). For the usage of the program, run it with `-h` argument. The program has an
](https://github.com/opencv/opencv/tree/4.x/samples/cpp/tutorial_code/calib3d/camera_calibration/camera_calibration.cpp). For the usage of the program, run it with `-h` argument. The program has an
essential argument: the name of its configuration file. If none is given then it will try to open the
one named "default.xml". [Here's a sample configuration file
](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/calib3d/camera_calibration/in_VID5.xml) in XML format. In the
](https://github.com/opencv/opencv/tree/4.x/samples/cpp/tutorial_code/calib3d/camera_calibration/in_VID5.xml) in XML format. In the
configuration file you may choose to use camera as an input, a video file or an image list. If you
opt for the last one, you will need to create a configuration file where you enumerate the images to
use. Here's [an example of this ](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/calib3d/camera_calibration/VID5.xml).
use. Here's [an example of this ](https://github.com/opencv/opencv/tree/4.x/samples/cpp/tutorial_code/calib3d/camera_calibration/VID5.xml).
The important part to remember is that the images need to be specified using the absolute path or
the relative one from your application's working directory. You may find all this in the samples
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/4.x/samples/data/LinuxLogo.jpg) and [WindowsLogo.jpg](https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/WindowsLogo.jpg)
@warning Since we are *adding**src1* and *src2*, they both have to be of the same size
@ -291,15 +291,15 @@ and are not intended to be used as a replacement of a raster graphics editor!**
### Code
@add_toggle_cpp
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/4.x/samples/cpp/tutorial_code/ImgProc/changing_contrast_brightness_image/changing_contrast_brightness_image.cpp).
@end_toggle
@add_toggle_java
Code for the tutorial is [here](https://github.com/opencv/opencv/blob/master/samples/java/tutorial_code/ImgProc/changing_contrast_brightness_image/ChangingContrastBrightnessImageDemo.java).
Code for the tutorial is [here](https://github.com/opencv/opencv/blob/4.x/samples/java/tutorial_code/ImgProc/changing_contrast_brightness_image/ChangingContrastBrightnessImageDemo.java).
@end_toggle
@add_toggle_python
Code for the tutorial is [here](https://github.com/opencv/opencv/blob/master/samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/changing_contrast_brightness_image.py).
Code for the tutorial is [here](https://github.com/opencv/opencv/blob/4.x/samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/changing_contrast_brightness_image.py).
](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/4.x/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.
@ -34,7 +34,7 @@ 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/4.x/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.
@ -42,7 +42,7 @@ 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/4.x/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.
@ -229,7 +229,7 @@ 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/4.x/samples/data/imageTextN.png) and [this rotated](https://raw.githubusercontent.com/opencv/opencv/4.x/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/4.x/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.
@ -183,7 +183,7 @@ 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/4.x/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.
@ -245,7 +245,7 @@ In the following section, we will vectorize a simple convolution function for si
You may learn more about convolution from the previous tutorial. We use the same naive implementation from the previous tutorial and compare it to the vectorized version.
The full tutorial code is [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/univ_intrin/univ_intrin.cpp).
The full tutorial code is [here](https://github.com/opencv/opencv/tree/4.x/samples/cpp/tutorial_code/univ_intrin/univ_intrin.cpp).
@ -23,7 +23,7 @@ In this tutorial, we first introduce how to obtain the custom OCR model, then ho
After completing the model training, please use [transform_to_onnx.py](https://github.com/zihaomu/deep-text-recognition-benchmark/blob/master/transform_to_onnx.py) to convert the model into onnx format.
#### Execute in webcam
The Python version example code can be found at [here](https://github.com/opencv/opencv/blob/master/samples/dnn/text_detection.py).
The Python version example code can be found at [here](https://github.com/opencv/opencv/blob/4.x/samples/dnn/text_detection.py).
@ -22,7 +22,7 @@ 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/4.x/samples/dnn/classification.cpp).
@include dnn/classification.cpp
@ -30,11 +30,11 @@ Explanation
-----------
-# Firstly, download GoogLeNet model files:
[bvlc_googlenet.prototxt ](https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/bvlc_googlenet.prototxt) and
[bvlc_googlenet.prototxt ](https://github.com/opencv/opencv_extra/blob/4.x/testdata/dnn/bvlc_googlenet.prototxt) and
For OpenCV-Python building from source, follow the corresponding instructions from the @ref tutorial_py_table_of_contents_setup.
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/4.x/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
The below line initiates requirements installation into the previously activated virtual environment:
```console
@ -140,7 +140,7 @@ class CommonConfig:
Thus, the converted ResNet-50 will be saved in ``dnn_model_runner/dnn_conversion/models``.
### Inference Pipeline
Now we can use ```models/resnet50.onnx``` for the inference pipeline using OpenCV C/C++ API. The implemented pipeline can be found in [samples/dnn/classification.cpp](https://github.com/opencv/opencv/blob/master/samples/dnn/classification.cpp).
Now we can use ```models/resnet50.onnx``` for the inference pipeline using OpenCV C/C++ API. The implemented pipeline can be found in [samples/dnn/classification.cpp](https://github.com/opencv/opencv/blob/4.x/samples/dnn/classification.cpp).
After the build of samples (``BUILD_EXAMPLES`` flag value should be ``ON``), the appropriate ``example_dnn_classification`` executable file will be provided.
To provide model inference we will use the below [squirrel photo](https://www.pexels.com/photo/brown-squirrel-eating-1564292) (under [CC0](https://www.pexels.com/terms-of-service/) license) corresponding to ImageNet class ID 335:
For OpenCV-Python building from source, follow the corresponding instructions from the @ref tutorial_py_table_of_contents_setup.
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/4.x/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
The below line initiates requirements installation into the previously activated virtual environment:
Chosen from the list classification model will be read into OpenCV cv.dnn.Net object. Evaluation results of PyTorch and OpenCV models (accuracy, inference time, L1) will be written into the log file. Inference time values will be also depicted in a chart to generalize the obtained model information.
Necessary evaluation configurations are defined in the [test_config.py](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) and can be modified in accordance with actual paths of data location:
Necessary evaluation configurations are defined in the [test_config.py](https://github.com/opencv/opencv/tree/4.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) and can be modified in accordance with actual paths of data location:
Here ``default_img_preprocess`` key defines whether you'd like to parametrize the model test process with some particular values or use the default values, for example, ``scale``, ``mean`` or ``std``.
Test configuration is represented in [test_config.py](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestClsModuleConfig`` class:
Test configuration is represented in [test_config.py](https://github.com/opencv/opencv/tree/4.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestClsModuleConfig`` class:
The default image preprocessing options are defined in [default_preprocess_config.py](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/default_preprocess_config.py). For instance:
The default image preprocessing options are defined in [default_preprocess_config.py](https://github.com/opencv/opencv/tree/4.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/default_preprocess_config.py). For instance:
```python
BASE_IMG_SCALE_FACTOR = 1 / 255.0
@ -349,7 +349,7 @@ pytorch_resize_input_blob = {
}
```
The basis of the model testing is represented in [samples/dnn/classification.py](https://github.com/opencv/opencv/blob/master/samples/dnn/classification.py). ``classification.py`` can be executed autonomously with provided converted model in ``--input`` and populated parameters for cv.dnn.blobFromImage.
The basis of the model testing is represented in [samples/dnn/classification.py](https://github.com/opencv/opencv/blob/4.x/samples/dnn/classification.py). ``classification.py`` can be executed autonomously with provided converted model in ``--input`` and populated parameters for cv.dnn.blobFromImage.
To reproduce from scratch the described in "Model Conversion Pipeline" OpenCV steps with ``dnn_model_runner`` execute the below line:
For OpenCV-Python building from source, follow the corresponding instructions from the @ref tutorial_py_table_of_contents_setup.
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/4.x/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
The below line initiates requirements installation into the previously activated virtual environment:
Chosen from the list classification model will be read into OpenCV ``cv.dnn_Net`` object. Evaluation results of TF and OpenCV models (accuracy, inference time, L1) will be written into the log file. Inference time values will be also depicted in a chart to generalize the obtained model information.
Necessary evaluation configurations are defined in the [test_config.py](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) and can be modified in accordance with actual paths of data location::
Necessary evaluation configurations are defined in the [test_config.py](https://github.com/opencv/opencv/tree/4.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) and can be modified in accordance with actual paths of data location::
Here ``default_img_preprocess`` key defines whether you'd like to parametrize the model test process with some particular values or use the default values, for example, ``scale``, ``mean`` or ``std``.
Test configuration is represented in [test_config.py](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestClsModuleConfig`` class:
Test configuration is represented in [test_config.py](https://github.com/opencv/opencv/tree/4.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestClsModuleConfig`` class:
```python
@dataclass
@ -347,7 +347,7 @@ tf_input_blob = {
}
```
The basis of the model testing is represented in [samples/dnn/classification.py](https://github.com/opencv/opencv/blob/master/samples/dnn/classification.py). ``classification.py`` can be executed autonomously with provided converted model in ``--input`` and populated parameters for cv.dnn.blobFromImage.
The basis of the model testing is represented in [samples/dnn/classification.py](https://github.com/opencv/opencv/blob/4.x/samples/dnn/classification.py). ``classification.py`` can be executed autonomously with provided converted model in ``--input`` and populated parameters for cv.dnn.blobFromImage.
To reproduce from scratch the described in "Model Conversion Pipeline" OpenCV steps with ``dnn_model_runner`` execute the below line:
For OpenCV-Python building from source, follow the corresponding instructions from the @ref tutorial_py_table_of_contents_setup.
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/4.x/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
The below line initiates requirements installation into the previously activated virtual environment:
```console
@ -98,7 +98,7 @@ To provide model inference we will use the below [double-decker bus photo](https
To initiate the test process we need to provide an appropriate model configuration. We will use [``ssd_mobilenet_v1_coco.config``](https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_mobilenet_v1_coco.config) from [TensorFlow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection#tensorflow-object-detection-api).
TensorFlow Object Detection API framework contains helpful mechanisms for object detection model manipulations.
We will use this configuration to provide a text graph representation. To generate ``.pbtxt`` we will use the corresponding [``samples/dnn/tf_text_graph_ssd.py``](https://github.com/opencv/opencv/blob/master/samples/dnn/tf_text_graph_ssd.py) script:
We will use this configuration to provide a text graph representation. To generate ``.pbtxt`` we will use the corresponding [``samples/dnn/tf_text_graph_ssd.py``](https://github.com/opencv/opencv/blob/4.x/samples/dnn/tf_text_graph_ssd.py) script:
After successful execution ``ssd_mobilenet_v1_coco_2017_11_17.pbtxt`` will be created.
Before we run ``object_detection.py``, let's have a look at the default values for the SSD MobileNetV1 test process configuration. They are located in [``models.yml``](https://github.com/opencv/opencv/blob/master/samples/dnn/models.yml):
Before we run ``object_detection.py``, let's have a look at the default values for the SSD MobileNetV1 test process configuration. They are located in [``models.yml``](https://github.com/opencv/opencv/blob/4.x/samples/dnn/models.yml):
Chosen from the list segmentation model will be read into OpenCV ``cv.dnn_Net`` object. Evaluation results of PyTorch and OpenCV models (pixel accuracy, mean IoU, inference time) will be written into the log file. Inference time values will be also depicted in a chart to generalize the obtained model information.
Necessary evaluation configurations are defined in the [``test_config.py``](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py):
Necessary evaluation configurations are defined in the [``test_config.py``](https://github.com/opencv/opencv/tree/4.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py):
Here ``default_img_preprocess`` key defines whether you'd like to parametrize the model test process with some particular values or use the default values, for example, ``scale``, ``mean`` or ``std``.
Test configuration is represented in [``test_config.py``](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestSegmModuleConfig`` class:
Test configuration is represented in [``test_config.py``](https://github.com/opencv/opencv/tree/4.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestSegmModuleConfig`` class:
The model will be read into OpenCV ``cv.dnn_Net`` object. Evaluation results of TF and OpenCV models (pixel accuracy, mean IoU, inference time) will be written into the log file. Inference time values will be also depicted in a chart to generalize the obtained model information.
Necessary evaluation configurations are defined in the [``test_config.py``](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py):
Necessary evaluation configurations are defined in the [``test_config.py``](https://github.com/opencv/opencv/tree/4.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py):
```python
@dataclass
@ -364,7 +364,7 @@ python -m dnn_model_runner.dnn_conversion.tf.segmentation.py_to_py_segm --test T
Here ``default_img_preprocess`` key defines whether you'd like to parametrize the model test process with some particular values or use the default values, for example, ``scale``, ``mean`` or ``std``.
Test configuration is represented in [``test_config.py``](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestSegmModuleConfig`` class:
Test configuration is represented in [``test_config.py``](https://github.com/opencv/opencv/tree/4.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestSegmModuleConfig`` class:
You may also find the source code and the video file in the
`samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity` directory of the OpenCV
source library or download it from [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity.cpp).
source library or download it from [here](https://github.com/opencv/opencv/tree/4.x/samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity.cpp).
The full source code is quite long (due to the controlling of the application via the command line
arguments and performance measurement). Therefore, to avoid cluttering up these sections with those
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/4.x/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/4.x/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/4.x/samples/python/tutorial_code/imgProc/morph_lines_detection/morph_lines_detection.py).
@ -339,7 +339,7 @@ Integration with [FFmpeg](https://en.wikipedia.org/wiki/FFmpeg) library for deco
- _swscale_
- _avresample_ (optional)
Exception is Windows platform where a prebuilt [plugin library containing FFmpeg](https://github.com/opencv/opencv_3rdparty/tree/ffmpeg/master) will be downloaded during a configuration stage and copied to the `bin` folder with all produced libraries.
Exception is Windows platform where a prebuilt [plugin library containing FFmpeg](https://github.com/opencv/opencv_3rdparty/tree/ffmpeg/4.x) will be downloaded during a configuration stage and copied to the `bin` folder with all produced libraries.
@note [Libav](https://en.wikipedia.org/wiki/Libav) library can be used instead of FFmpeg, but this combination is not actively supported.
Alexander Alekhin
3 years ago