Commit Graph

374 Commits

Author SHA1 Message Date
Gursimar Singh
073488896e
Merge pull request #25326 from gursimarsingh:improved_text_detection_sample
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

### 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.
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2024-09-09 17:43:15 +03:00
Gursimar Singh
f8fb3a7f55
Merge pull request #25515 from gursimarsingh:improved_edge_detection_sample
#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

### Pull Request Readiness Checklist

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2024-09-06 12:47:04 +03:00
Abduragim Shtanchaev
050085c996
Merge pull request #25950 from Abdurrahheem:ash/add-inpainting-sample
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

### Pull Request Readiness Checklist

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2024-08-21 14:48:37 +03:00
Gursimar Singh
35eba9ca90
Merge pull request #25519 from gursimarsingh:improved_classification_sample
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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2024-08-06 09:16:11 +03:00
Abduragim Shtanchaev
88f05e49be
Merge pull request #25868 from Abdurrahheem:ash/add-gpt2-sample
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. 

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.
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      Patch to opencv_extra has the same branch name.
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2024-07-18 16:47:12 +03:00
Alexander Smorkalov
fc9208cff5 Merge branch 4.x 2024-07-17 10:08:16 +03:00
Gursimar Singh
96a8e6d76c
Merge pull request #25756 from gursimarsingh:bug_fix/segmentation_sample
[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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2024-07-03 14:03:12 +03:00
Abduragim Shtanchaev
a8d1373919
Merge pull request #25794 from Abdurrahheem:ash/yolov10-support
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>
```

### Pull Request Readiness Checklist

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2024-07-02 18:26:34 +03:00
Alexander Smorkalov
3abd9f2a28 Merge branch 4.x 2024-07-01 15:59:43 +03:00
Alexander Smorkalov
4500eb937f Drop redundant dependency from download_models.py 2024-06-28 10:45:52 +03:00
Maksim Shabunin
26ea34c4cb Merge branch '4.x' into '5.x' 2024-06-26 19:01:34 +03:00
richard28039
11c69bb171
Merge pull request #25775 from richard28039:4.x
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.
![image](https://github.com/opencv/opencv/assets/89371302/7a7b0090-ef4c-478d-8f24-7d99260fe0c9)
2024-06-24 10:28:38 +03:00
Yuantao Feng
e3884a9ea8
Merge pull request #25771 from fengyuentau:vittrack_black_input
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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2024-06-18 12:48:28 +03:00
Gursimar Singh
48c31bddc4
Merge pull request #25559 from gursimarsingh:improved_segmentation_sample
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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2024-05-15 09:39:34 +03:00
Wanli
b637e3a66e
Merge pull request #25463 from WanliZhong:ocvface2YuNet
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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2024-05-08 15:49:10 +03:00
NlSEMONO
c999cb3c5e
Update text_detection.cpp 2024-05-02 11:38:18 -04:00
Wanli
096ccd410b change js_face_recognition sample with yunet 2024-04-22 15:59:54 +08:00
Gursimar Singh
448375d1e7
Merge pull request #25433 from gursimarsingh:colorization_onnx_sample
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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2024-04-18 18:15:05 +03:00
Alexander Smorkalov
282c762ead Merge branch 4.x 2024-04-10 11:27:47 +03:00
Alessandro de Oliveira Faria (A.K.A.CABELO)
953581a92a
Merge pull request #25357 from cabelo:yolov8m
Added and tested yolov8m model. #25357

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Below is evidence of the test:
![yolov8m](https://github.com/opencv/opencv/assets/675645/f9bfe2c6-fe4a-42fc-93a6-17e4da5c9bb5)
2024-04-09 16:56:07 +03:00
Alexander Smorkalov
cb6d295f15 Merge branch 4.x 2024-04-02 16:39:54 +03:00
Alessandro de Oliveira Faria (A.K.A.CABELO)
4c86b287fd
Merge pull request #25176 from cabelo:4.x
Added and tested yolov8s and yolov8n model #25176

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Below is evidence of the test:
![yolos-n](https://github.com/opencv/opencv/assets/675645/f3bd19ae-85a4-4747-9fa9-f6e31257d2d5)
2024-03-25 10:02:17 +03:00
Alexander Smorkalov
a22130fbfa Merge branch 4.x 2024-02-28 18:49:05 +03:00
Alessandro de Oliveira Faria (A.K.A.CABELO)
0b3232a160
Merge pull request #25095 from cabelo:yolov8x
Added and tested yolov8x model #25095

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Below is evidence of the test:
![opencv](https://github.com/opencv/opencv/assets/675645/40e81951-a8fd-410b-9dfc-c08254f99bdc)
2024-02-27 15:34:15 +03:00
Alexander Smorkalov
3a55f50133 Merge branch 4.x 2024-02-12 14:20:35 +03:00
Abduragim Shtanchaev
372b36c1d3
Merge pull request #24898 from Abdurrahheem:ash/yolo_ducumentation
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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2024-01-31 09:46:58 +03:00
uday
03994163b5
Merge pull request #24913 from usyntest:optical-flow-sample-raft
Raft support added in this sample code #24913

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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>
2024-01-29 17:37:52 +03:00
Alexander Smorkalov
decf6538a2 Merge branch 4.x 2024-01-23 17:06:52 +03:00
Alexander Smorkalov
c739117a7c Merge branch 4.x 2024-01-19 17:32:22 +03:00
Sean McBride
e64857c561
Merge pull request #23736 from seanm:c++11-simplifications
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>
2024-01-19 16:53:08 +03:00
Chia-Hsiang Tsai
83d70b0f36
Merge pull request #24396 from Tsai-chia-hsiang:yolov8cv
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.

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2023-11-16 13:40:00 +03:00
Alessandro de Oliveira Faria (A.K.A.CABELO)
4a69877eaa
Merge pull request #24496 from cabelo:yolov3
Add weights yolov3 in models.yml #24496

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- [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.
2023-11-14 09:06:36 +03:00
richard28039
e95c0055af
Merge pull request #24397 from richard28039:add_fcnresnet101_to_dnn_sample
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
2023-11-03 15:42:43 +03:00
alexlyulkov
b71be65f57
Merge pull request #24294 from alexlyulkov:al/remove-torch7-from-dnn
Remove torch (old torch7) from dnn in 5.x #24294

Merge with https://github.com/opencv/opencv_extra/pull/1097

Completely removed torch (old torch7) from dnn:
- removed modules/dnn/src/torch directory that contained torch7 model parser
- removed readNetFromTorch() and readTorchBlob() public functions
- removed torch7 references from comments and help texts
- replaced links to t7 models by links to similar onnx models in js_style_transfer turtorial (similar to https://github.com/opencv/opencv/pull/24245/files)
2023-10-26 11:27:56 +03:00
Alexander Smorkalov
97620c053f Merge branch 4.x 2023-10-23 11:53:04 +03:00
Yuantao Feng
d789cb459c
Merge pull request #24231 from fengyuentau:halide_cleanup_5.x
dnn: cleanup of halide backend for 5.x #24231

Merge with https://github.com/opencv/opencv_extra/pull/1092.

### 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
2023-10-13 16:53:18 +03:00
Alexander Smorkalov
163d544ecf Merge branch 4.x 2023-10-02 10:17:23 +03:00
Emmanuel Ferdman
8a8c0d285e
fix: update location to samples/dnn/download_models.py 2023-09-29 12:30:46 +03:00
lpylpy0514
70d7e83dca
Merge pull request #24201 from lpylpy0514:4.x
VIT track(gsoc realtime object tracking model) #24201

Vit tracker(vision transformer tracker) is a much better model for real-time object tracking. Vit tracker can achieve speeds exceeding nanotrack by 20% in single-threaded mode with ARM chip, and the advantage becomes even more pronounced in multi-threaded mode. In addition, on the dataset, vit tracker demonstrates better performance compared to nanotrack. Moreover, vit trackerprovides confidence values during the tracking process, which can be used to determine if the tracking is currently lost.
opencv_zoo: https://github.com/opencv/opencv_zoo/pull/194
opencv_extra: [https://github.com/opencv/opencv_extra/pull/1088](https://github.com/opencv/opencv_extra/pull/1088)

# Performance comparison is as follows:
NOTE: The speed below is tested by **onnxruntime** because opencv has poor support for the transformer architecture for now.

ONNX speed test on ARM platform(apple M2)(ms):
| thread nums | 1| 2| 3| 4|
|--------|--------|--------|--------|--------|
| nanotrack| 5.25| 4.86| 4.72| 4.49|
| vit tracker| 4.18| 2.41| 1.97| **1.46 (3X)**|

ONNX speed test on x86 platform(intel i3 10105)(ms):
| thread nums | 1| 2| 3| 4|
|--------|--------|--------|--------|--------|
| nanotrack|3.20|2.75|2.46|2.55|
| vit tracker|3.84|2.37|2.10|2.01|

opencv speed test on x86 platform(intel i3 10105)(ms):
| thread nums | 1| 2| 3| 4|
|--------|--------|--------|--------|--------|
| vit tracker|31.3|31.4|31.4|31.4|

preformance test on lasot dataset(AUC is the most important data. Higher AUC means better tracker):

|LASOT | AUC| P| Pnorm|
|--------|--------|--------|--------|
| nanotrack| 46.8| 45.0| 43.3|
| vit tracker| 48.6| 44.8| 54.7|

[https://youtu.be/MJiPnu1ZQRI](https://youtu.be/MJiPnu1ZQRI)
 In target tracking tasks, the score is an important indicator that can indicate whether the current target is lost. In the video, vit tracker can track the target and display the current score in the upper left corner of the video. When the target is lost, the score drops significantly. While nanotrack will only return 0.9 score in any situation, so that we cannot determine whether the target is lost.

### 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
- [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.
- [ ] The feature is well documented and sample code can be built with the project CMake
2023-09-19 15:36:38 +03:00
Alexander Smorkalov
fdab565711 Merge branch 4.x 2023-09-13 14:49:25 +03:00
Alexander Smorkalov
1a8d37d19e
Merge pull request #24245 from alexlyulkov/al/update-fast-neural-style-dnn-sample
Replaced torch7 model by ONNX model in fast-neural-style dnn sample
2023-09-08 16:33:18 +03:00
alexlyulkov
91cf0d1843
Merge pull request #24244 from alexlyulkov:al/update-dnn-js-face-recognition-sample
Replaced torch7 by onnx model in js_face_recognition dnn sample #24244

Changed face recognition model in js_face_recognition dnn sample: replaced torch7 model from https://github.com/pyannote/pyannote-data by ONNX model from https://github.com/opencv/opencv_zoo/tree/main/models/face_recognition_sface
2023-09-08 15:36:01 +03:00
Alexander Lyulkov
910db5c9b7 changed readNetFromONNX to readNet 2023-09-08 18:36:13 +07:00
Alexander Lyulkov
ceeb01dce5 Replaced torch7 by onnx model in fast-neural-style dnn sample 2023-09-08 12:44:22 +07:00
Alexander Smorkalov
1a3523d2d8 Drop Python2 support. 2023-07-14 15:06:53 +03:00
Alexander Smorkalov
5af40a0269 Merge branch 4.x 2023-07-05 15:51:10 +03:00
Alexander Smorkalov
c75d0b8ca3
Merge pull request #23684 from LaurentBerger:I23683
solve Issue I23683
2023-05-26 12:01:50 +03:00
unknown
45dcd156b7 solve Issue 23685 2023-05-25 21:34:51 +02:00
unknown
e6282b8ace solve Issue I23683 2023-05-25 19:42:01 +02:00
Alessandro de Oliveira Faria (A.K.A. CABELO)
7867da47cf ADD weights yolov4-tiny in models.yml 2023-04-17 02:55:56 -03:00