Improved Tracker Samples #26202
Relates to #25006
This sample has been rewritten to track a selected target in a video or camera stream. It combines VIT tracker, Nano tracker and Dasiamrpn tracker into one tracker sample
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Fix gpt2 sample #26573
This PR adds dynamic input support for `gpt2_inference.py` sample.
Fixes#26518Fixes#26517
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Code Fixes and changed post processing based on models.yml in Object Detection Sample #26486
## Major Changes
1. Changes to add findModel support for config file in models like yolov4, yolov4-tiny, yolov3, ssd_caffe, tiny-yolo-voc, ssd_tf and faster_rcnn_tf.
2. Added new model and config download links for ssd_caffe, as previous links were not working.
3. Switched to DNN ENGINE_CLASSIC for non-cpu convig as new engine does not support it.
4. Fixes in python sample related to yolov5 usage.
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Modify DNN Samples to use ENGINE_CLASSIC for Non-Default Back-end or Target #26334
PR resolves#26325 regarding fall-back to ENGINE_CLASSIC if non-default back-end or target is passed by user.
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Updated segmentation sample as per current update usage and engine #26347
This PR updates segmentation sample as per new usage, and change their DNN engines. It adds usage of findModel, dynamic fontsize, etc. in segmentation sample.
The PR makes the sample consistent with other dnn samples
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[BUG FIX] Object detection sample preprocessing #26326
PR resloves #26315 related to incorrect preprocessing for 'Image2BlobParams' in object detection sample.
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[BUG FIX] Fix issues in Person ReID C++ sample #26336
This PR fixes multiple issues in the Person ReID C++ sample that were causing incorrect outputs. It addresses improper matrix initialization, adds a missing return statement, and ensures that vectors are properly cleared before reuse. These changes correct the output of the sample.
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Improved person reid cpp and python sample #25667#25006
This sample has been rewritten to track a selected target in a video or camera stream. Person detection has been integrated using yolov8 and the user can provide a target image via command line or interactively select the target at start of the execution
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Add support for downloading DNN config files in download_models.py #26186
PR resloves #26160 related to downloading DNN config files using download_models.py
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Merged yolo_detector and object detection sample #25710
Relates to #25006
This pull request merges the yolo_detector.cpp sample with the object_detector.cpp sample. It also beautifies the bounding box display on the output images
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Added and tested yolov5l model. #26154
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Improved and refactored text detection sample in dnn module #25326
Clean up samples: #25006
This pull requests merges and simplifies different text detection samples in dnn module of opencv in to one file. An option has been provided to choose the detection model from EAST or DB
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#25006#25314
This pull request removes hed_pretrained caffe model to the SOTA dexined onnx model for edge detection. Usage of conventional methods like canny has also been added
The obsolete cpp and python sample has been removed
TODO:
- [ ] Remove temporary hack for quantized models. Refer issue https://github.com/opencv/opencv_zoo/issues/273
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Diffusion Inpainting Sample #25950
This PR adds inpaiting sample that is based on [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/pdf/2112.10752) paper (reference github [repository](https://github.com/CompVis/latent-diffusion)).
Steps to run the model:
1. Firstly needs ONNX graph of the Latent Diffusion Model. You can get it in two different ways.
> a. Generate the using this [repo](https://github.com/Abdurrahheem/latent-diffusion/tree/ash/export2onnx) and follow instructions below
```bash
git clone https://github.com/Abdurrahheem/latent-diffusion.git
cd latent-diffusion
conda env create -f environment.yaml
conda activate ldm
wget -O models/ldm/inpainting_big/last.ckpt https://heibox.uni-heidelberg.de/f/4d9ac7ea40c64582b7c9/?dl=1
python -m scripts.inpaint.py --indir data/inpainting_examples/ --outdir outputs/inpainting_results --export=True
```
> b. Download the ONNX graph (there 3 fiels) using this link: TODO make a link
2. Build opencv (preferebly with CUDA support enabled
3. Run the script
```bash
cd opencv/samples/dnn
python ldm_inpainting.py
python ldm_inpainting.py -e=<path-to-InpaintEncoder.onnx file> -d=<path-to-InpaintDecoder.onnx file> -df=<path-to-LatenDiffusion.onnx file> -i=<path-to-image>
```
Right after the last command you will be prompted with image. You can click on left mouse bottom and starting selection a region you would like to be inpainted (deleted). Once you finish marking the region, click on left mouse botton again and press esc button on your keyboard. The inpainting proccess will start.
Note: If you are running it on CPU it might take a large chank of time. Also make sure to have about 15GB of RAM to make process faster (other wise swapping will click in and everything will be slower)
Current challenges:
1. Diffusion process is slow (many layers fallback to CPU with running with CUDA backend)
2. The diffusion result is does exactly mach that of the original torch pipeline
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Improved classification sample #25519#25006#25314
This pull requests replaces the caffe model for classification with onnx versions. It also adds resnet in model.yml.
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Add sample for GPT2 inference #25868
### Pull Request Readiness Checklist
This PR adds sample for inferencing GPT-2 model. More specificly implementation of GPT-2 from [this repository](https://github.com/karpathy/build-nanogpt). Currently inference in OpenCV is only possible to do with fixed window size due to not supported dynamic shapes.
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[BUG FIX] Segmentation sample u2netp model results #25756
PR resloves #25753 related to incorrect output from u2netp model in segmentation sample
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Add sample support of YOLOv9 and YOLOv10 in OpenCV #25794
This PR adds sample support of [`YOLOv9`](https://github.com/WongKinYiu/yolov9) and [`YOLOv10`](https://github.com/THU-MIG/yolov10/tree/main)) in OpenCV. Models for this test are located in this [PR](https://github.com/opencv/opencv_extra/pull/1186).
**Running YOLOv10 using OpenCV.**
1. In oder to run `YOLOv10` one needs to cut off postporcessing with dynamic shapes from torch and then convert it to ONNX. If someone is looking for ready solution, there is [this forked branch](https://github.com/Abdurrahheem/yolov10/tree/ash/opencv-export) from official YOLOv10. Particularty follow this proceduce.
```bash
git clone git@github.com:Abdurrahheem/yolov10.git
conda create -n yolov10 python=3.9
conda activate yolov10
pip install -r requirements.txt
python export_opencv.py --model=<model-name> --imgsz=<input-img-size>
```
By default `model="yolov10s"` and `imgsz=(480,640)`. This will generate file `yolov10s.onnx`, which can be use for inference in OpenCV
2. For inference part on OpenCV. one can use `yolo_detector.cpp` [sample](https://github.com/opencv/opencv/blob/4.x/samples/dnn/yolo_detector.cpp). If you have followed above exporting procedure, then you can use following command to run the model.
``` bash
build opencv from source
cd build
./bin/example_dnn_yolo_detector --model=<path-to-yolov10s.onnx-file> --yolo=yolov10 --width=640 --height=480 --input=<path-to-image> --scale=0.003921568627 --padvalue=114
```
If you do not specify `--input` argument, OpenCV will grab first camera that is avaliable on your platform.
For more deatils on how to run the `yolo_detector.cpp` file see this [guide](https://docs.opencv.org/4.x/da/d9d/tutorial_dnn_yolo.html#autotoc_md443)
**Running YOLOv9 using OpenCV**
1. Export model following [official guide](https://github.com/WongKinYiu/yolov9)of the YOLOv9 repository. Particularly you can do following for converting.
```bash
git clone https://github.com/WongKinYiu/yolov9.git
cd yolov9
conda create -n yolov9 python=3.9
conda activate yolov9
pip install -r requirements.txt
wget https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-t-converted.pt
python export.py --weights=./yolov9-t-converted.pt --include=onnx --img-size=(480,640)
```
This will generate <yolov9-t-converted.onnx> file.
2. Inference on OpenCV.
```bash
build opencv from source
cd build
./bin/example_dnn_yolo_detector --model=<path-to-yolov9-t-converted.onnx> --yolo=yolov9 --width=640 --height=480 --scale=0.003921568627 --padvalue=114 --path=<path-to-image>
```
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Add yolov8l.onnx to samples #25775
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Hello, I noticed that the /samples/dnn/models.yml said it should be used for all yolov8 models, but the YOLOv8l is not included in the file, so I added it to the file, thanks.
video: fix vittrack in the case where crop size grows until out-of-memory when the input is black #25771
Fixes https://github.com/opencv/opencv/issues/25760
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Improved segmentation sample #25559#25006
This pull request replaces caffe models with onnx for the dnn segmentation sample in cpp and python
fcnresnet-50 and fcnresnet-101 has been replaced
u2netp (foreground-background) segmentation onnx model has been added [U2NET](https://github.com/xuebinqin/U-2-Net)
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Change opencv_face_detector related tests and samples from caffe to onnx #25463
Part of https://github.com/opencv/opencv/issues/25314
This PR aims to change the tests related to opencv_face_detector from caffe framework to onnx. Tests in `test_int8_layer.cpp` and `test_caffe_importer.cpp` will be removed in https://github.com/opencv/opencv/pull/25323
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Replaced caffe model with onnx for colorization sample #25433#25006
Improved sample for colorization with onnx model in cpp and python. Added a demo image in data folder for testing
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Added and tested yolov8m model. #25357
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Added and tested yolov8s and yolov8n model #25176
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Added and tested yolov8x model #25095
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Documentation for Yolo usage in Opencv #24898
This PR introduces documentation for the usage of yolo detection model family in open CV. This is not to be merge before #24691, as the sample will need to be changed.
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Raft support added in this sample code #24913
### Pull Request Readiness Checklist
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- [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
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Patch to opencv_extra has the same branch name.
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fix: https://github.com/opencv/opencv/issues/24424 Update DNN Optical Flow sample with RAFT model
I implemented both RAFT and FlowNet v2 leaving it to the user which one he wants to use to estimate the optical flow.
Co-authored-by: Uday Sharma <uday@192.168.1.35>
Removed all pre-C++11 code, workarounds, and branches #23736
This removes a bunch of pre-C++11 workrarounds that are no longer necessary as C++11 is now required.
It is a nice clean up and simplification.
* No longer unconditionally #include <array> in cvdef.h, include explicitly where needed
* Removed deprecated CV_NODISCARD, already unused in the codebase
* Removed some pre-C++11 workarounds, and simplified some backwards compat defines
* Removed CV_CXX_STD_ARRAY
* Removed CV_CXX_MOVE_SEMANTICS and CV_CXX_MOVE
* Removed all tests of CV_CXX11, now assume it's always true. This allowed removing a lot of dead code.
* Updated some documentation consequently.
* Removed all tests of CV_CXX11, now assume it's always true
* Fixed links.
---------
Co-authored-by: Maksim Shabunin <maksim.shabunin@gmail.com>
Co-authored-by: Alexander Smorkalov <alexander.smorkalov@xperience.ai>
Using cv2 dnn interface to run yolov8 model #24396
This is a sample code for using opencv dnn interface to run ultralytics yolov8 model for object detection.
### Pull Request Readiness Checklist
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
- [] There is a reference to the original bug report and related work
- [] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [] The feature is well documented and sample code can be built with the project CMake
Add weights yolov3 in models.yml #24496
### Pull Request Readiness Checklist
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
I don't know if this action is necessary, or the previous PR scale for the brach master.
Thanks.
Added PyTorch fcnresnet101 segmentation conversion cases #24397
We write a sample code about transforming Pytorch fcnresnet101 to ONNX running on OpenCV.
The input source image was shooted by ourself.
### Pull Request Readiness Checklist
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
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
Gursimar Singh
Abduragim Shtanchaev
Alexander Smorkalov
Alexander Smorkalov
Alessandro de Oliveira Faria (A.K.A.CABELO)
Maksim Shabunin
richard28039
Yuantao Feng
Wanli
NlSEMONO
uday
Sean McBride
Chia-Hsiang Tsai