This code allows you to create a synthetic data-set, for Instance Segmentation or Object Detection. The data_generation.py script outputs data in LabelMe format, which can also be converted to other formats like the COCO JSON format.
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Clone the repository
git clone https://github.com/TannerGilbert/Object-Detection-Synthetic-Data-Generation -
Install dependencies
cd Object-Detection-Synthetic-Data-Generation pip3 install -r requirements.txt
Before you can start generating your synthetic data-set, you'll have to get some input images. You'll need to kinds of input images:
- Foreground images
- Background images
For background images, you can take some pictures or download them from the internet (no additional processing needed). For the foreground images, you'll have to take pictures of the object and then remove the image's background.
I recommend labeling a few images with LabelMe and then run my create_input_images_from_labelme.py script to extract the objects. For more information check out create_input_images_with_labelme.md.
You can also make use of a tool look Photoshop or GIMP.
To generate images, run the data_generation.py script.
usage: data_generation.py [-h] --input_dir INPUT_DIR --output_dir OUTPUT_DIR
[--augmentation_path AUGMENTATION_PATH]
--image_number IMAGE_NUMBER
[--max_objects_per_image MAX_OBJECTS_PER_IMAGE]
[--image_width IMAGE_WIDTH]
[--image_height IMAGE_HEIGHT]
Synthetic Image Generator
optional arguments:
-h, --help show this help message and exit
--input_dir INPUT_DIR
Path to the input directory. It must contain a
backgrounds directory and a foregrounds directory
--output_dir OUTPUT_DIR
The directory where images and label files will be
placed
--augmentation_path AUGMENTATION_PATH
Path to albumentations augmentation pipeline file
--image_number IMAGE_NUMBER
Number of images to create
--max_objects_per_image MAX_OBJECTS_PER_IMAGE
Maximum number of objects per images
--image_width IMAGE_WIDTH
Width of the output images
--image_height IMAGE_HEIGHT
Height of the output images
Example:
python data_generation.py --input_dir input/ --output_dir output/ --image_number 50
If the LabelMe format doesn't work for you, you can convert the data into another format.
You can convert the JSON files created by labelme to COCO using the labelme2coco.py file created by Github user Tony607.
