Simple tool to create a dataset from a collections of images using darknet supported networks for object detection.
git clone https://github.com/mive93/datasetGen
cd datasetGen
git submodule update --init --recursive
cd darknet/
Open the Makefile and set the following values to 1:
GPU=1
CUDNN=1
OPENCV=1
LIBSO=1
Then
make -j4
cd ..
cp darknet/libdarknet.so .
Then open cfg/coco.data and change names to:
names = darknet/data/coco.names
Finally, download the weights of the used network, e.g. Yolov4
wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights
The genImages script takes in input a stream or a video and extract images from it each s seconds.
It stores those images in a given folder.
python genImages.py
Arguments:
--input_stream INPUT_STREAM: RTSP stream or video path from wich to extract images
--image_dir IMAGE_DIR: folder where to store the images
--s S: interval of seconds to store images
The genDataset script will open all the images in a given folder and run a darknet model for object detection on that. The images will be displayed to screen with the given labels and the user will be able to:
- remove the undesired labels with a click (mouse left click) on the unwanted label.
- restore all the original labels in case of mistake (mouse right click).
The script will create both a folder with a txt file for each image in the darknet format and a txt file containing all the image paths (dataset.txt).
python genDataset.py
Arguments:
--dataset_location DATASET_LOCATION: image to annotate folder path--outdir OUTDIR: folder where to write the labels--weights WEIGHTS: yolo weights path--config_file CONFIG_FILE: path to config file--data_file DATA_FILE: path to data file (e.g. coco.data)--thresh THRESH: remove detections with lower confidence
checkLabels is a script to check the correctness of darknet format labels.
python checkLabels.py
Arguments:
--images_file IMAGES_FILE: path to a txt file with all the paths of the images to check--labels_dir LABELS_DIR: folder where to find the labels
Given the demo video
python genImages.py --s 10
The following images are saved:
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On that we select the labels we think are correct. The generated labels can be found in img folder.
python genDataset.py
Finally we check that eveything is in order.
python checkLabels.py
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