改进版的yolov5+双目测距

资源文件列表(大概)

文件名

大小

yolov5_stereo_Pro/

yolov5_stereo_Pro/LICENSE

34.3KB

yolov5_stereo_Pro/detect_and_stereo_video_030.py

28.78KB

yolov5_stereo_Pro/detect_and_stereo_video_033.py

30.95KB

yolov5_stereo_Pro/cuda_test.py

1.02KB

yolov5_stereo_Pro/README.md

10.55KB

yolov5_stereo_Pro/train.py

31.51KB

yolov5_stereo_Pro/test.py

16.14KB

yolov5_stereo_Pro/tutorial.ipynb

384.14KB

yolov5_stereo_Pro/Dockerfile

1.68KB

yolov5_stereo_Pro/detect.py

8.03KB

yolov5_stereo_Pro/hubconf.py

5.15KB

yolov5_stereo_Pro/code.txt

103B

yolov5_stereo_Pro/requirements.txt

610B

yolov5_stereo_Pro/models/

yolov5_stereo_Pro/models/yolov5s.yaml

1.33KB

yolov5_stereo_Pro/models/experimental.py

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yolov5_stereo_Pro/models/__init__.py

yolov5_stereo_Pro/models/yolov5m.yaml

1.33KB

yolov5_stereo_Pro/models/yolo.py

11.78KB

yolov5_stereo_Pro/models/export.py

4.32KB

yolov5_stereo_Pro/models/common.py

12.69KB

yolov5_stereo_Pro/models/yolov5l.yaml

1.33KB

yolov5_stereo_Pro/models/yolov5x.yaml

1.33KB

yolov5_stereo_Pro/models/__pycache__/

yolov5_stereo_Pro/models/__pycache__/yolo.cpython-36.pyc

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yolov5_stereo_Pro/models/__pycache__/experimental.cpython-37.pyc

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yolov5_stereo_Pro/models/__pycache__/common.cpython-36.pyc

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yolov5_stereo_Pro/models/__pycache__/__init__.cpython-38.pyc

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yolov5_stereo_Pro/models/__pycache__/experimental.cpython-36.pyc

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yolov5_stereo_Pro/models/__pycache__/__init__.cpython-37.pyc

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yolov5_stereo_Pro/models/__pycache__/experimental.cpython-38.pyc

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yolov5_stereo_Pro/models/__pycache__/yolo.cpython-38.pyc

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yolov5_stereo_Pro/models/__pycache__/yolo.cpython-37.pyc

9.78KB

yolov5_stereo_Pro/models/__pycache__/common.cpython-37.pyc

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yolov5_stereo_Pro/models/__pycache__/common.cpython-38.pyc

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yolov5_stereo_Pro/models/__pycache__/__init__.cpython-36.pyc

162B

yolov5_stereo_Pro/models/hub/

yolov5_stereo_Pro/models/hub/yolov3-tiny.yaml

1.17KB

yolov5_stereo_Pro/models/hub/yolov5s6.yaml

1.93KB

yolov5_stereo_Pro/models/hub/yolov5-panet.yaml

1.42KB

yolov5_stereo_Pro/models/hub/yolov5-fpn.yaml

1.22KB

yolov5_stereo_Pro/models/hub/yolov5x6.yaml

1.93KB

yolov5_stereo_Pro/models/hub/yolov5-p7.yaml

2.18KB

yolov5_stereo_Pro/models/hub/anchors.yaml

3.28KB

yolov5_stereo_Pro/models/hub/yolov3-spp.yaml

1.5KB

yolov5_stereo_Pro/models/hub/yolov5m6.yaml

1.93KB

yolov5_stereo_Pro/models/hub/yolov5l6.yaml

1.93KB

yolov5_stereo_Pro/models/hub/yolov3.yaml

1.49KB

yolov5_stereo_Pro/models/hub/yolov5-p6.yaml

1.77KB

yolov5_stereo_Pro/models/hub/yolov5-p2.yaml

1.7KB

yolov5_stereo_Pro/stereo/

yolov5_stereo_Pro/stereo/dianyuntu_yolo.py

8.64KB

yolov5_stereo_Pro/stereo/stereoconfig_040_2.py

1.55KB

yolov5_stereo_Pro/stereo/stereo.py

12.4KB

yolov5_stereo_Pro/stereo/dianyuntu.py

8.59KB

yolov5_stereo_Pro/stereo/yolo/

yolov5_stereo_Pro/stereo/__pycache__/

yolov5_stereo_Pro/stereo/__pycache__/stereo.cpython-36.pyc

4.49KB

yolov5_stereo_Pro/stereo/__pycache__/stereoconfig_Bud.cpython-36.pyc

1.12KB

yolov5_stereo_Pro/stereo/__pycache__/stereoconfig_040_2.cpython-36.pyc

1.15KB

yolov5_stereo_Pro/stereo/__pycache__/dianyuntu_yolo.cpython-36.pyc

4.44KB

yolov5_stereo_Pro/data/

yolov5_stereo_Pro/data/hyp.finetune.yaml

846B

yolov5_stereo_Pro/data/coco128.yaml

1.51KB

yolov5_stereo_Pro/data/argoverse_hd.yaml

849B

yolov5_stereo_Pro/data/coco.yaml

1.7KB

yolov5_stereo_Pro/data/voc.yaml

738B

yolov5_stereo_Pro/data/hyp.scratch.yaml

1.53KB

yolov5_stereo_Pro/data/video/

yolov5_stereo_Pro/data/video/gym_001.mov

31.95MB

yolov5_stereo_Pro/data/scripts/

yolov5_stereo_Pro/data/scripts/get_voc.sh

4.33KB

yolov5_stereo_Pro/data/scripts/get_argoverse_hd.sh

1.97KB

yolov5_stereo_Pro/data/scripts/get_coco.sh

963B

yolov5_stereo_Pro/data/images/

yolov5_stereo_Pro/data/images/zidane.jpg

164.99KB

yolov5_stereo_Pro/data/images/bus.jpg

476.01KB

yolov5_stereo_Pro/__pycache__/

yolov5_stereo_Pro/__pycache__/test.cpython-36.pyc

10.63KB

yolov5_stereo_Pro/utils/

yolov5_stereo_Pro/utils/general.py

23.35KB

yolov5_stereo_Pro/utils/autoanchor.py

6.78KB

yolov5_stereo_Pro/utils/activations.py

2.2KB

yolov5_stereo_Pro/utils/__init__.py

yolov5_stereo_Pro/utils/torch_utils.py

11.68KB

yolov5_stereo_Pro/utils/loss.py

9.18KB

yolov5_stereo_Pro/utils/google_utils.py

4.76KB

yolov5_stereo_Pro/utils/metrics.py

8.76KB

yolov5_stereo_Pro/utils/datasets.py

43.14KB

yolov5_stereo_Pro/utils/plots.py

17.7KB

yolov5_stereo_Pro/utils/aws/

yolov5_stereo_Pro/utils/aws/mime.sh

780B

yolov5_stereo_Pro/utils/aws/__init__.py

yolov5_stereo_Pro/utils/aws/resume.py

1.09KB

yolov5_stereo_Pro/utils/aws/userdata.sh

1.21KB

yolov5_stereo_Pro/utils/google_app_engine/

yolov5_stereo_Pro/utils/google_app_engine/Dockerfile

821B

yolov5_stereo_Pro/utils/google_app_engine/app.yaml

173B

yolov5_stereo_Pro/utils/google_app_engine/additional_requirements.txt

105B

yolov5_stereo_Pro/utils/__pycache__/

yolov5_stereo_Pro/utils/__pycache__/autoanchor.cpython-36.pyc

5.94KB

yolov5_stereo_Pro/utils/__pycache__/__init__.cpython-36.pyc

161B

yolov5_stereo_Pro/utils/__pycache__/general.cpython-36.pyc

18.76KB

yolov5_stereo_Pro/utils/__pycache__/torch_utils.cpython-36.pyc

10.74KB

yolov5_stereo_Pro/utils/__pycache__/datasets.cpython-37.pyc

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yolov5_stereo_Pro/utils/__pycache__/metrics.cpython-37.pyc

7.48KB

yolov5_stereo_Pro/utils/__pycache__/datasets.cpython-38.pyc

32.47KB

yolov5_stereo_Pro/utils/__pycache__/metrics.cpython-38.pyc

7.42KB

yolov5_stereo_Pro/utils/__pycache__/activations.cpython-36.pyc

3.36KB

yolov5_stereo_Pro/utils/__pycache__/activations.cpython-37.pyc

3.37KB

yolov5_stereo_Pro/utils/__pycache__/plots.cpython-37.pyc

15.53KB

yolov5_stereo_Pro/utils/__pycache__/plots.cpython-38.pyc

15.34KB

yolov5_stereo_Pro/utils/__pycache__/activations.cpython-38.pyc

3.33KB

yolov5_stereo_Pro/utils/__pycache__/google_utils.cpython-37.pyc

3.15KB

yolov5_stereo_Pro/utils/__pycache__/metrics.cpython-36.pyc

7.51KB

yolov5_stereo_Pro/utils/__pycache__/google_utils.cpython-38.pyc

3.19KB

yolov5_stereo_Pro/utils/__pycache__/torch_utils.cpython-38.pyc

10.73KB

yolov5_stereo_Pro/utils/__pycache__/__init__.cpython-38.pyc

141B

yolov5_stereo_Pro/utils/__pycache__/general.cpython-38.pyc

18.73KB

yolov5_stereo_Pro/utils/__pycache__/general.cpython-37.pyc

18.68KB

yolov5_stereo_Pro/utils/__pycache__/google_utils.cpython-36.pyc

3.19KB

yolov5_stereo_Pro/utils/__pycache__/datasets.cpython-36.pyc

32.76KB

yolov5_stereo_Pro/utils/__pycache__/__init__.cpython-37.pyc

137B

yolov5_stereo_Pro/utils/__pycache__/torch_utils.cpython-37.pyc

10.69KB

yolov5_stereo_Pro/utils/__pycache__/plots.cpython-36.pyc

15.63KB

yolov5_stereo_Pro/utils/__pycache__/loss.cpython-36.pyc

6.37KB

yolov5_stereo_Pro/utils/__pycache__/autoanchor.cpython-38.pyc

5.84KB

yolov5_stereo_Pro/utils/__pycache__/autoanchor.cpython-37.pyc

5.88KB

yolov5_stereo_Pro/utils/wandb_logging/

yolov5_stereo_Pro/utils/wandb_logging/wandb_utils.py

6.73KB

yolov5_stereo_Pro/utils/wandb_logging/__init__.py

yolov5_stereo_Pro/utils/wandb_logging/log_dataset.py

1.71KB

yolov5_stereo_Pro/weights/

yolov5_stereo_Pro/weights/download_weights.sh

277B

yolov5_stereo_Pro/weights/yolov5s/

yolov5_stereo_Pro/weights/yolov5s/yolov5s.pt

14.11MB

yolov5_stereo_Pro/weights/person/

yolov5_stereo_Pro/weights/person/last_person_1000.pt

13.73MB

yolov5_stereo_Pro/weights/person/last_person_300.pt

13.72MB

yolov5_stereo_Pro/runs/

yolov5_stereo_Pro/runs/detect/

资源内容介绍

新版本代码特点：（注意目前只适用于2560*720分辨率的双目，其他分辨率需要修改)1、替换“回”字形查找改为“米”字形查找，可以设置存储像素点的个数20可修改，然后取有效像素点的中位数（个人觉得比平均值更有代表性）。2、每10帧（约1/3秒）双目匹配一次，提升代码的运行速度。3、可以进行实时检测，运行速度与机器的性能有关。

<a href="https://apps.apple.com/app/id1452689527" target="_blank"><img src="https://user-images.githubusercontent.com/26833433/98699617-a1595a00-2377-11eb-8145-fc674eb9b1a7.jpg" width="1000"></a>&nbsp<a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk.<img src="https://user-images.githubusercontent.com/26833433/103594689-455e0e00-4eae-11eb-9cdf-7d753e2ceeeb.png" width="1000">** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.- **January 5, 2021**: [v4.0 release](https://github.com/ultralytics/yolov5/releases/tag/v4.0): nn.SiLU() activations, [Weights & Biases](https://wandb.ai/) logging, [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/) integration.- **August 13, 2020**: [v3.0 release](https://github.com/ultralytics/yolov5/releases/tag/v3.0): nn.Hardswish() activations, data autodownload, native AMP.- **July 23, 2020**: [v2.0 release](https://github.com/ultralytics/yolov5/releases/tag/v2.0): improved model definition, training and mAP.- **June 22, 2020**: [PANet](https://arxiv.org/abs/1803.01534) updates: new heads, reduced parameters, improved speed and mAP [364fcfd](https://github.com/ultralytics/yolov5/commit/364fcfd7dba53f46edd4f04c037a039c0a287972).- **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145).## Pretrained Checkpoints| Model | size | APval | APtest | AP50 | SpeedV100 | FPSV100 || params | GFLOPS ||---------- |------ |------ |------ |------ | -------- | ------| ------ |------ | :------: || [YOLOv5s](https://github.com/ultralytics/yolov5/releases) |640 |36.8 |36.8 |55.6 |**2.2ms** |**455** ||7.3M |17.0| [YOLOv5m](https://github.com/ultralytics/yolov5/releases) |640 |44.5 |44.5 |63.1 |2.9ms |345 ||21.4M |51.3| [YOLOv5l](https://github.com/ultralytics/yolov5/releases) |640 |48.1 |48.1 |66.4 |3.8ms |264 ||47.0M |115.4| [YOLOv5x](https://github.com/ultralytics/yolov5/releases) |640 |**50.1** |**50.1** |**68.7** |6.0ms |167 ||87.7M |218.8| | | | | | | || || [YOLOv5x](https://github.com/ultralytics/yolov5/releases) + TTA |832 |**51.9** |**51.9** |**69.6** |24.9ms |40 ||87.7M |1005.3** APtest denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy. ** All AP numbers are for single-model single-scale without ensemble or TTA. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` ** SpeedGPU averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes image preprocessing, FP16 inference, postprocessing and NMS. NMS is 1-2ms/img. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45` ** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). ** Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) runs at 3 image sizes. **Reproduce TTA** by `python test.py --data coco.yaml --img 832 --iou 0.65 --augment` ## RequirementsPython 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run:```bash$ pip install -r requirements.txt```## Tutorials* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)  ð RECOMMENDED* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)  ð NEW* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)  â NEW* [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251)* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)  â NEW* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)## EnvironmentsYOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):- **Google Colab and Kaggle** notebooks with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>## Inferencedetect.py runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.```bash$ python detect.py --source 0 # webcam file.jpg # image file.mp4 # video path/ # directory path/*.jpg # glob rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream rtmp://192.168.1.105/live/test # rtmp stream http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream```To run inference on example images in `data/images`:```bash$ python detect.py --source data/images --weights yolov5s.pt --conf 0.25Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt'])YOLOv5 v4.0-96-g83dc1b4 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB

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改进版的yolov5+双目测距

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