A demonstration of fast adversarial example generation applied to GoogLeNet (Szegedy et al., 2014a) on ImageNet. By adding an imperceptibly small vector whose elements are equal to the sign of the elements of the gradient of the cost function with respect to the input, we can change GoogLeNet’s classification of the image.
## Preparation Prepare real-world licence plate dataset and put them in path ./data/B, since we gonna use pix2pix model and we need to extract the outlines of your plate images, the scripts are provided in `./scripts/extractOutline.py` ```bash python3 extractOutline.py --A_path path/where/you/want/to/store/outlines --B_path path/stored/original/car-plate/images ``` after running the above line you could get two datasets, the outline of the license plate(**folder A**) and the original image of the plate(**folder B**). Then you have to combine the outline and original image into ONE SINGLE image. To do that, you should run the following lines: ```bash python3 pix2pixModel/datasets/combine_A_and_B.py --fola_A /path/to/your/outline --fola_B /path/to/your/original_image --fold_AB /path/to/store/combined/image ```
one training set image will be composed with the original image and its corresponding outline
From the image shown above, we could find that the dataset is filled with many blur and noisy images... I hope you have better dataset. By now, you have already preprocessed the dataset, then you need to divide your dataset into three parts: train, val, test. The dataset path will be denoted as data_path in the following. ## Training This project uses [pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) model, I have already integrated them into this repository so you do not need to clone the original model. ```bash git clone https://github.com/mingbocui/Generate-LicensePlate-with-GAN.git ``` To train the model, you need specify the data_path as shown below: ```bash python train.py --dataroot data_path --name carPlate_pix2pix --model pix2pix --direction AtoB ``` After training with enough epochs, you will get a model which could generate realistic licence plates from the outline of scratch. ## Usage Now we have a model which could transfer outline to realistic data, so what we need to do is to prepare the outlines, which could be divided into 2 steps: **step 1**: run genplate.py to generate perfect license plate images. ```bash python3 scripts/genPlate.py ```
Original artificial car-plate image
**step 2**: run extractOutline.py to extact the outlines of these generated images. ```bash python3 extractOutline.py --A_path path/where/you/want/to/store/outlines --B_path path/of/artificial/images ```
After extracting outlines
Finally, we could deploy the trained model on the extracted outlines by running the following code: ```bash python3 pix2pixModel/test.py --dataroot path/of/outlines --name carPlate_pix2pix --model test --netG unet_256 --direction AtoB --dataset_mode single --norm batch ```
Images generated by feeding the outlines to GAN
## Problem The generated images are very blur, but this is because the original training set is very blur and noisy. And the distribution of the generated dataset is also similar to the real dataset. To improve the quality of images, maybe we need a more clean dataset.