potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-29 23:33:05 +04:00
Code Packages Releases Wiki Activity

Merge pull request #29220 from omrope79:doc_optimizations_v4

Browse Source 
[FOLLOW UP] : Documentation optimizations for the new Sphinx structure #29220

### Pull Request Readiness Checklist

This PR serves as a follow-up to the new documentation system introduced in [#29206](https://github.com/opencv/opencv/pull/29206)
Co-authored by: @abhishek-gola @kirtijindal14 @Akansha-977 @Prasadayus @varun-jaiswal17

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
omrope79
2026-06-05 16:48:27 +05:30
committed by GitHub
parent c9e7878a1d
commit 04aee009aa
43 changed files with 3468 additions and 650 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/tutorials/dnn/dnn_custom_layers/dnn_custom_layers.md
+6 -37
View File
@@ -96,38 +96,9 @@ After network was initialized only `forward` method is called for every network'
reallocate all the internal memory. That leads to efficiency gaps. Try to initialize
and deploy models using a fixed batch size and image's dimensions.
## Example: custom layer from Caffe
Let's create a custom layer `Interp` from https://github.com/cdmh/deeplab-public.
It's just a simple resize that takes an input blob of size `N x C x Hi x Wi` and returns
an output blob of size `N x C x Ho x Wo` where `N` is a batch size, `C` is a number of channels,
`Hi x Wi` and `Ho x Wo` are input and output `height x width` correspondingly.
This layer has no trainable weights but it has hyper-parameters to specify an output size.
In example,
~~~~~~~~~~~~~
layer {
name: "output"
type: "Interp"
bottom: "input"
top: "output"
interp_param {
height: 9
width: 8
}
}
~~~~~~~~~~~~~
This way our implementation can look like:
@snippet dnn/custom_layers.hpp InterpLayer
Next we need to register a new layer type and try to import the model.
@snippet dnn/custom_layers.hpp Register InterpLayer
## Example: custom layer from TensorFlow
This is an example of how to import a network with [tf.image.resize_bilinear](https://www.tensorflow.org/versions/master/api_docs/python/tf/image/resize_bilinear)
operation. This is also a resize but with an implementation different from OpenCV's or `Interp` above.
operation. This is also a resize but with an implementation different from OpenCV's built-in resize.
Let's create a single layer network:
~~~~~~~~~~~~~{.py}
@@ -235,13 +206,11 @@ in the opencv_extra repository.
## Define a custom layer in Python
The following example shows how to customize OpenCV's layers in Python.
Let's consider [Holistically-Nested Edge Detection](https://arxiv.org/abs/1504.06375)
deep learning model. That was trained with one and only difference comparing to
a current version of [Caffe framework](http://caffe.berkeleyvision.org/). `Crop`
layers that receive two input blobs and crop the first one to match spatial dimensions
of the second one used to crop from the center. Nowadays Caffe's layer does it
from the top-left corner. So using the latest version of Caffe or OpenCV you will
get shifted results with filled borders.
Let's consider the [Holistically-Nested Edge Detection](https://arxiv.org/abs/1504.06375)
model. Its `Crop` layers receive two input blobs and crop the first one to match the
spatial dimensions of the second. OpenCV's built-in `Crop` layer trims from the
top-left corner, whereas this model expects cropping from the center, so using the
built-in behaviour directly would produce shifted results with filled borders.
Next we're going to replace OpenCV's `Crop` layer that makes top-left cropping by
a centric one.
doc/tutorials/introduction/android_binary_package/android_dnn_intro.markdown
+4 -4
View File
@@ -26,9 +26,9 @@ Tutorial was written for Android Studio 2022.2.1.
- Get the latest pre-built OpenCV for Android release from https://github.com/opencv/opencv/releases
and unpack it (for example, `opencv-4.X.Y-android-sdk.zip`, minimum version 4.9 is required).
- Download MobileNet object detection model from https://github.com/chuanqi305/MobileNet-SSD.
Configuration file `MobileNetSSD_deploy.prototxt` and model weights `MobileNetSSD_deploy.caffemodel`
are required.
- Download the MobileNet-SSD object detection model in ONNX format and save it as
`mobilenet.onnx`. This single file contains both the network topology and its
trained weights.
## Create an empty Android Studio project and add OpenCV dependency
@@ -59,7 +59,7 @@ a correct screen orientation and allow to use a camera.
@snippet android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java mobilenet_tutorial_package
@snippet android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java mobilenet_tutorial
- Put downloaded `deploy.prototxt` and `mobilenet_iter_73000.caffemodel`
- Put the downloaded `mobilenet.onnx`
into `app/src/main/res/raw` folder. OpenCV DNN model is mainly designed to load ML and DNN models
from file. Modern Android does not allow it without extra permissions, but provides Java API to load
bytes from resources. The sample uses alternative DNN API that initializes a model from in-memory
doc/tutorials/introduction/config_reference/config_reference.markdown
+1 -1
View File
@@ -522,7 +522,7 @@ OpenCV have own DNN inference module which have own build-in engine, but can als
| `OPENCV_DNN_OPENCL` | _ON_ | Enable built-in OpenCL inference backend. |
| `WITH_INF_ENGINE` | _OFF_ | **Deprecated since OpenVINO 2022.1** Enables [Intel Inference Engine (IE)](https://github.com/openvinotoolkit/openvino) backend. Allows to execute networks in IE format (.xml + .bin). Inference Engine must be installed either as part of [OpenVINO toolkit](https://en.wikipedia.org/wiki/OpenVINO), either as a standalone library built from sources. |
| `INF_ENGINE_RELEASE` | _2020040000_ | **Deprecated since OpenVINO 2022.1** Defines version of Inference Engine library which is tied to OpenVINO toolkit version. Must be a 10-digit string, e.g. _2020040000_ for OpenVINO 2020.4. |
| `WITH_NGRAPH` | _OFF_ | **Deprecated since OpenVINO 2022.1** Enables Intel NGraph library support. This library is part of Inference Engine backend which allows executing arbitrary networks read from files in multiple formats supported by OpenCV: Caffe, TensorFlow, PyTorch, Darknet, etc.. NGraph library must be installed, it is included into Inference Engine. |
| `WITH_NGRAPH` | _OFF_ | **Deprecated since OpenVINO 2022.1** Enables Intel NGraph library support. This library is part of Inference Engine backend which allows executing arbitrary networks read from files in multiple formats supported by OpenCV: ONNX, TensorFlow, PyTorch, etc.. NGraph library must be installed, it is included into Inference Engine. |
| `WITH_OPENVINO` | _OFF_ | Enable Intel OpenVINO Toolkit support. Should be used for OpenVINO>=2022.1 instead of `WITH_INF_ENGINE` and `WITH_NGRAPH`. |
| `WITH_ONNXRUNTIME` | _OFF_ | Enable Microsoft ONNX Runtime backend support for OpenCV DNN. |
| `DOWNLOAD_ONNXRUNTIME` | _OFF_ | Download official ONNX Runtime prebuilt binaries when enabled (or when ONNX Runtime is not available in system paths). |