基于GAN深度学习生成对抗网络进行行人重识别python源码+实验报告+答辩PPT.zip

# DCGAN in Tensorflow ## Prerequisites - Python 2.7 or Python 3.3+ - [Tensorflow 0.12.1](https://github.com/tensorflow/tensorflow/tree/r0.12) (Notice that it is not the latest version) - [SciPy](http://www.scipy.org/install.html) - [pillow](https://github.com/python-pillow/Pillow) - CUDA 8.0 Add Cuda Path to bashrc first ```bash export LD_LIBRARY_PATH="/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH" ``` We recommend you to install anaconda. Here we write a simple script for you to install the dependence by anaconda. ```python # install env (especially for old version Tensorflow) conda env create -f dcgan.yml # activate env, then you can run code in this env without downgrading the outside Tensorflow. source activate dcgan ``` ### Let's start ### 1.Train ```bash mkdir data copy your dataset(market) to data folder python main.py --dataset market --options 1 ``` `market` is the dir path which contains images. You can change it to your dataset path. ### 2.Test ```bash python main.py --dataset market --options 5 --output_path gen_market --sample_size 24000 python resizeImage.py ``` It will use your trained model and generate 24000 images for the following semi-supervised training. the generated images are stored in gen_market folder, then run resizeImage.py, the generated images will be resized into 126*64 stored in gen_0000 market. after that, add gen_0000 to the training set of market, so the generated images will be used as training image to help model training.