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fix 3.4 links

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This commit is contained in:
Alexander Alekhin
2021-12-22 12:38:21 +00:00
parent 54c180092d
commit b1a57c4cb2
19 changed files with 30 additions and 28 deletions
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samples/dnn/README.md
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@@ -7,7 +7,7 @@ Check [a wiki](https://github.com/opencv/opencv/wiki/Deep-Learning-in-OpenCV) fo
If OpenCV is built with [Intel's Inference Engine support](https://github.com/opencv/opencv/wiki/Intel%27s-Deep-Learning-Inference-Engine-backend) you can use [Intel's pre-trained](https://github.com/opencv/open_model_zoo) models.
There are different preprocessing parameters such mean subtraction or scale factors for different models.
You may check the most popular models and their parameters at [models.yml](https://github.com/opencv/opencv/blob/master/samples/dnn/models.yml) configuration file. It might be also used for aliasing samples parameters. In example,
You may check the most popular models and their parameters at [models.yml](https://github.com/opencv/opencv/blob/3.4/samples/dnn/models.yml) configuration file. It might be also used for aliasing samples parameters. In example,
```bash
python object_detection.py opencv_fd --model /path/to/caffemodel --config /path/to/prototxt
@@ -27,7 +27,7 @@ You can download sample models using ```download_models.py```. For example, the
python download_models.py --save_dir FaceDetector opencv_fd
```
You can use default configuration files adopted for OpenCV from [here](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn).
You can use default configuration files adopted for OpenCV from [here](https://github.com/opencv/opencv_extra/tree/3.4/testdata/dnn).
You also can use the script to download necessary files from your code. Assume you have the following code inside ```your_script.py```:
@@ -79,6 +79,6 @@ AR @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.528 | 0.528 |
## References
* [Models downloading script](https://github.com/opencv/opencv/samples/dnn/download_models.py)
* [Configuration files adopted for OpenCV](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn)
* [Configuration files adopted for OpenCV](https://github.com/opencv/opencv_extra/tree/3.4/testdata/dnn)
* [How to import models from TensorFlow Object Detection API](https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API)
* [Names of classes from different datasets](https://github.com/opencv/opencv/tree/3.4/samples/data/dnn)
samples/dnn/openpose.cpp
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@@ -3,11 +3,11 @@
//
// it can be used for body pose detection, using either the COCO model(18 parts):
// http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel
// https://raw.githubusercontent.com/opencv/opencv_extra/master/testdata/dnn/openpose_pose_coco.prototxt
// https://raw.githubusercontent.com/opencv/opencv_extra/3.4/testdata/dnn/openpose_pose_coco.prototxt
//
// or the MPI model(16 parts):
// http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/mpi/pose_iter_160000.caffemodel
// https://raw.githubusercontent.com/opencv/opencv_extra/master/testdata/dnn/openpose_pose_mpi_faster_4_stages.prototxt
// https://raw.githubusercontent.com/opencv/opencv_extra/3.4/testdata/dnn/openpose_pose_mpi_faster_4_stages.prototxt
//
// (to simplify this sample, the body models are restricted to a single person.)
//