# realsense-D455-YOLOV5
## 1.利用realsense深度相机实现yolov5目标检测的同时测出距离
## 2.你可以将其他版本的yolo v5 的版本应用到这个上面,因为我只更改了detect.py为realsensedetect.py
## 3.运行的代码为:python realsensedetect.py
![image](https://github.com/wenyishengkingkong/realsense-D455-YOLOV5/blob/master/0210901110009.jpg)
<a href="https://apps.apple.com/app/id1452689527" target="_blank">
<img src="https://user-images.githubusercontent.com/26833433/82944393-f7644d80-9f4f-11ea-8b87-1a5b04f555f1.jpg" width="1000"></a>
 
![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)
This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. **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/26833433/90187293-6773ba00-dd6e-11ea-8f90-cd94afc0427f.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.
- **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).
- **June 9, 2020**: [CSP](https://github.com/WongKinYiu/CrossStagePartialNetworks) updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
- **May 27, 2020**: Public release. YOLOv5 models are SOTA among all known YOLO implementations.
- **April 1, 2020**: Start development of future compound-scaled [YOLOv3](https://github.com/ultralytics/yolov3)/[YOLOv4](https://github.com/AlexeyAB/darknet)-based PyTorch models.
## Pretrained Checkpoints
| Model | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | FLOPS |
|---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: |
| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/tag/v3.0) | 37.0 | 37.0 | 56.2 | **2.4ms** | **416** || 7.5M | 13.2B
| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/tag/v3.0) | 44.3 | 44.3 | 63.2 | 3.4ms | 294 || 21.8M | 39.4B
| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/tag/v3.0) | 47.7 | 47.7 | 66.5 | 4.4ms | 227 || 47.8M | 88.1B
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/tag/v3.0) | **49.2** | **49.2** | **67.7** | 6.9ms | 145 || 89.0M | 166.4B
| | | | | | || |
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/tag/v3.0) + TTA|**50.8**| **50.8** | **68.9** | 25.5ms | 39 || 89.0M | 354.3B
| | | | | | || |
| [YOLOv3-SPP](https://github.com/ultralytics/yolov5/releases/tag/v3.0) | 45.6 | 45.5 | 65.2 | 4.5ms | 222 || 63.0M | 118.0B
** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy.
** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. **Reproduce** by `python test.py --data coco.yaml --img 640 --conf 0.001`
** Speed<sub>GPU</sub> measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. **Reproduce** by `python test.py --data coco.yaml --img 640 --conf 0.1`
** 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** by `python test.py --data coco.yaml --img 832 --augment`
## Requirements
Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.6`. To install run:
```bash
$ pip install -r requirements.txt
```
## Tutorials
* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)
* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)
* [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)
* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)
## Environments
YOLOv5 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 Notebook** 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>
- **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov5](https://www.kaggle.com/ultralytics/yolov5)
- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
- **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker)
## Inference
detect.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 `inference/output`.
```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 `inference/images`:
```bash
$ python detect.py --source inference/images --weights yolov5s.pt --conf 0.25
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thre
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基于yolov5+realsense-D455的单目测距系统源码.zip
共47个文件
py:18个
pyc:9个
yaml:8个
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基于yolov5+realsense-D455的单目测距系统源码,运行的代码为:python realsensedetect.py,注意这个是yolov5-3.1源码实现,因此训练自己模型要用yolov5-3.1源码,yolov5-5.0及其以上版本源码训练的模型不能调用。
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基于yolov5+realsense-D455的单目测距系统源码.zip (47个子文件)
code
inference
output
car.mp4 885KB
使用前必读.txt 110B
weights
yolov5s.pt 14.48MB
LICENSE 34KB
newdetect.py 12KB
hubconf.py 4KB
utils
evolve.sh 747B
__init__.py 0B
google_utils.py 5KB
general.py 53KB
activations.py 2KB
google_app_engine
Dockerfile 821B
app.yaml 173B
additional_requirements.txt 105B
datasets.py 38KB
__pycache__
torch_utils.cpython-38.pyc 9KB
datasets.cpython-38.pyc 27KB
google_utils.cpython-38.pyc 3KB
general.cpython-38.pyc 41KB
__init__.cpython-38.pyc 144B
torch_utils.py 9KB
sotabench.py 14KB
Dockerfile 2KB
realsensedetect.py 8KB
requirements.txt 566B
0210901110009.jpg 1.11MB
models
hub
yolov5-panet.yaml 1KB
yolov3-spp.yaml 1KB
yolov5-fpn.yaml 1KB
__init__.py 0B
export.py 4KB
yolov5m.yaml 1KB
yolov5s.yaml 1KB
yolov5l.yaml 1KB
common.py 7KB
experimental.py 6KB
__pycache__
experimental.cpython-38.pyc 6KB
common.cpython-38.pyc 9KB
yolo.cpython-38.pyc 10KB
__init__.cpython-38.pyc 145B
yolov5x.yaml 1KB
yolo.py 12KB
detect.py 8KB
train.py 28KB
test.py 14KB
README.md 10KB
tutorial.ipynb 3.12MB
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