The yolov code by MIT has been used for object detection with modifications.
PLease note:
- Download the pretrained weights of Yolov from the link below and place it in the yolov3-coco folder:
wget https://pjreddie.com/media/files/yolov3.weights
- Install darknet:
git clone https://github.com/pjreddie/darknet
cd darknet
make
Go to this link for further customizations : https://pjreddie.com/darknet/yolo/
- Open CV version must be 3.3 or higher (I am currently using version 4.1) To upgrade : pip install --upgrade opencv-python
Install numpy and other required dependencies. You are now ready to go!
This project implements an image and video object detection classifier using pretrained yolov3 models. The yolov3 models are taken from the official yolov3 paper which was released in 2018. The yolov3 implementation is from darknet. Also, this project implements an option to perform classification real-time using the webcam.
- Clone the repository
git clone https://github.com/Sanskar777/elmart.git
- Move to the directory
cd elmart
- To infer on an image that is stored on your local machine
python3 yolo.py --image-path='/path/to/image/'
- To infer on a video that is stored on your local machine
python3 yolo.py --video-path='/path/to/video/'
- To infer real-time on webcam
python3 yolo.py
Note: This works considering you have the weights and config files at the yolov3-coco directory.
If the files are located somewhere else then mention the path while calling the yolov3.py. For more details
yolo.py --help
Click on the image to Play the video on YouTube
Click on the image to Play the video on YouTube
The code in this project is distributed under the MIT License.