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

Last update: Jan 09, 2023

Related tags

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

Garbage classification using structure data.

Adversarial Framework for (non-) Parametric Image Stylisation Mosaics

A Low Complexity Speech Enhancement Framework for Full-Band Audio (48kHz) based on Deep Filtering.

A Python reference implementation of the CF data model

Can we learn gradients by Hamiltonian Neural Networks?

Human Pose Detection on EdgeTPU

Hypersearch weight debugging and losses tutorial

PyTorch implementation of the Deep SLDA method from our CVPRW-2020 paper "Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis"

PyTorch code for the ICCV'21 paper: "Always Be Dreaming: A New Approach for Class-Incremental Learning"

Multistream CNN for Robust Acoustic Modeling

The AugNet Python module contains functions for the fast computation of image similarity.

Original code for "Zero-Shot Domain Adaptation with a Physics Prior"

This is the repository of our article published on MDPI Entropy "Feature Selection for Recommender Systems with Quantum Computing".

Buffon’s needle: one of the oldest problems in geometric probability

Pytorch implementation of MLP-Mixer with loading pre-trained models.

Official code repository for Continual Learning In Environments With Polynomial Mixing Times

Some methods for comparing network representations in deep learning and neuroscience.

This is the code for our paper "Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text"

Evaluation toolkit of the informative tracking benchmark comprising 9 scenarios, 180 diverse videos, and new challenges.

Gym environment for FLIPIT: The Game of "Stealthy Takeover"