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

Learning Compatible Embeddings, ICCV 2021

[ICCV 2021] Code release for "Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks"

Efficient 6-DoF Grasp Generation in Cluttered Scenes

Code for "Learning Canonical Representations for Scene Graph to Image Generation", Herzig & Bar et al., ECCV2020

Advancing mathematics by guiding human intuition with AI

PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training”

ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing

Migration of Edge-based Distributed Federated Learning

A map update dataset and benchmark

Unadversarial Examples: Designing Objects for Robust Vision

Official implementation for Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos

AfriBERTa: Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages

Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022)

Supervised Contrastive Learning for Downstream Optimized Sequence Representations

A novel pipeline framework for multi-hop complex KGQA task. About the paper title: Improving Multi-hop Embedded Knowledge Graph Question Answering by Introducing Relational Chain Reasoning

Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes

Code and data for paper "Deep Photo Style Transfer"

Fully convolutional networks for semantic segmentation

PyTorch reimplementation of the Smooth ReLU activation function proposed in the paper "Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations" [arXiv 2022].