Yolov5-opencv-cpp-python - Example of using ultralytics YOLO V5 with OpenCV 4.5.4, C++ and Python

Last update: Jan 09, 2023

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Overview

yolov5-opencv-cpp-python

Example of performing inference with ultralytics YOLO V5, OpenCV 4.5.4 DNN, C++ and Python

Looking for YOLO V4 OpenCV C++/Python inference? Check this repository

Prerequisites

Make sure you have already on your system:

Any modern Linux OS (tested on Ubuntu 20.04)
OpenCV 4.5.4+
Python 3.7+ (only if you are intended to run the python program)
GCC 9.0+ (only if you are intended to run the C++ program)

IMPORTANT!!! Note that OpenCV versions prior to 4.5.4 will not work at all.

Running the python script

The python code is here.

git clone https://github.com/doleron/yolov5-opencv-cpp-python.git
cd yolov5-opencv-cpp-python
python python/yolo.py

If your machine/OpenCV install are CUDA capable you can try out running using the GPU:

git clone https://github.com/doleron/yolov5-opencv-cpp-python.git
cd yolov5-opencv-cpp-python
python python/yolo.py cuda

Running the C++ program

The C++ code is here.

git clone https://github.com/doleron/yolov5-opencv-cpp-python.git
cd yolov5-opencv-cpp-python
g++ -O3 cpp/yolo.cpp -o yolo_example `pkg-config --cflags --libs opencv4`
./yolo_example

Or using CUDA if available:

git clone https://github.com/doleron/yolov5-opencv-cpp-python.git
cd yolov5-opencv-cpp-python
g++ -O3 cpp/yolo.cpp -o yolo_example `pkg-config --cflags --libs opencv4`
./yolo_example cuda

PS.: Video sample from https://www.youtube.com/watch?v=NyLF8nHIquM

Which YOLO version should I use?

This repository uses YOLO V5 but it is not the only YOLO version out there. You can read this article to learn more about YOLO versions and choose the more suitable one for you.

Exporting yolo v5 models to .onnx format

Check here: https://github.com/ultralytics/yolov5/issues/251

My commands were:

git clone https://github.com/ultralytics/yolov5
cd yolov5
pip install -r requirements.txt

And then to convert the model:

$ python3 export.py --weights yolov5n.pt --img 640 --include onnx
export: data=data/coco128.yaml, weights=['yolov5n.pt'], imgsz=[640], batch_size=1, device=cpu, half=False, inplace=False, train=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=12, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['onnx']
YOLOv5 🚀 v6.0-192-g436ffc4 torch 1.10.1+cu102 CPU

Fusing layers... 
Model Summary: 213 layers, 1867405 parameters, 0 gradients

PyTorch: starting from yolov5n.pt (4.0 MB)

ONNX: starting export with onnx 1.10.2...
/home/user/workspace/smartcam/yolov5/models/yolo.py:57: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
ONNX: export success, saved as yolov5n.onnx (7.9 MB)

Export complete (1.33s)
Results saved to /home/doleron/workspace/smartcam/yolov5
Visualize with https://netron.app
Detect with `python detect.py --weights yolov5n.onnx` or `model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5n.onnx')
Validate with `python val.py --weights yolov5n.onnx`
$

throubleshooting

First time I got a error with protobuf version:

"AttributeError: module 'google.protobuf.descriptor' has no attribute '_internal_create_key"?

I fixed it by running:

pip install --upgrade protobuf

Yolov5-opencv-cpp-python - Example of using ultralytics YOLO V5 with OpenCV 4.5.4, C++ and Python

Related tags

Overview

yolov5-opencv-cpp-python

Prerequisites

Running the python script

Running the C++ program

Which YOLO version should I use?

Exporting yolo v5 models to .onnx format

throubleshooting

References

Owner

ICLR 2021 i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning

Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark

Learning from Synthetic Shadows for Shadow Detection and Removal [Inoue+, IEEE TCSVT 2020].

CNN Based Meta-Learning for Noisy Image Classification and Template Matching

Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study.

Datasets for new state-of-the-art challenge in disentanglement learning

PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation

A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python

A library for optimization on Riemannian manifolds

Self-Supervised Image Denoising via Iterative Data Refinement

Visyerres sgdf woob - Modules Woob pour l'intranet et autres sites Scouts et Guides de France

Semantic Segmentation in Pytorch. Network include: FCN、FCN_ResNet、SegNet、UNet、BiSeNet、BiSeNetV2、PSPNet、DeepLabv3_plus、 HRNet、DDRNet

Keras-tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation（Unfinished）

PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+

Code for our NeurIPS 2021 paper Mining the Benefits of Two-stage and One-stage HOI Detection

An algorithm study of the 6th iOS 10 set of Boost Camp Web Mobile

Machine learning Bot detection technique, based on United States election dataset

Official Pytorch implementation for 2021 ICCV paper "Learning Motion Priors for 4D Human Body Capture in 3D Scenes" and trained models / data

Analyses of the individual electric field magnitudes with Roast.

Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation