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

codes for IKM (arXiv2021, Submitted to IEEE Trans)

Official repository of PanoAVQA: Grounded Audio-Visual Question Answering in 360° Videos (ICCV 2021)

Convert scikit-learn models to PyTorch modules

Official PyTorch implementation of N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras (ICCV 2021)

PyTorch Implementation of PIXOR: Real-time 3D Object Detection from Point Clouds

[ ICCV 2021 Oral ] Our method can estimate camera poses and neural radiance fields jointly when the cameras are initialized at random poses in complex scenarios (outside-in scenes, even with less texture or intense noise )

Pytorch Implementation of rpautrat/SuperPoint

Code related to the manuscript "Averting A Crisis In Simulation-Based Inference"

Food Drinks and groceries Images Multi Lingual (FooDI-ML) dataset.

code for "Self-supervised edge features for improved Graph Neural Network training",

CVPR 2021 Official Pytorch Code for UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training

Code of paper Interact, Embed, and EnlargE (IEEE): Boosting Modality-specific Representations for Multi-Modal Person Re-identification.

A lightweight deep network for fast and accurate optical flow estimation.

A dataset for online Arabic calligraphy

An easier way to build neural search on the cloud

Code for visualizing the loss landscape of neural nets

To prepare an image processing model to classify the type of disaster based on the image dataset

Learnable Motion Coherence for Correspondence Pruning

A Tensorflow implementation of BicycleGAN.

Source code for The Power of Many: A Physarum Swarm Steiner Tree Algorithm