Finding Wally using Object Detection and Neural Networks
- Python 3.7
- pip >= 19
First clone the project repository to get the source
git clone https://github.com/giulic3/WALL-Y.gitRun the following to initialize a copy of the Tensorflow Object Detection API project
git submodule init
git submodule update(Carefully) follow the instructions here to install the API.
Create a virtualenv and install the required packages to run WALL-Y
virtualenv -p /path/to/python3.7 venv
source venv/bin/activate
pip install -r requirements.txt
data/: contains the original dataset, the dataset with cropped images and the annotations (xml and csv)utils/: contains the script used to reduce the original images in size (crop_images.py) and other helper functions to be used during inferencenetworks/: contains model configuration and checkpointsrc/: contains Python scripts for training (model_main.py), performing object detection (find_wally_.py,inference.py) and graph freeze for inference
Every command supposes the current working directory is the project root and the venv is activated.
python src/model_main.py --logtostderr --model_dir="OUTPUT_DIR" --pipeline_config_path=networks/faster_rcnn_resnet101_coco.config --num_train_steps=3000
Replace "OUTPUT_DIR" with the directory where trained model and checkpoints will be saved; num_train_steps states total number of training steps. Be careful when increasing this number as overfitting problems may arise.
python src/model_main.py --run_once --pipeline_config_path=networks/faster_rcnn_resnet101_coco.config --checkpoint_dir="TRAINED_MODEL_DIR" --model_dir="OUTPUT_DIR"
Replace "OUTPUT_DIR" with the path you want to save Tensorboard data to and also replace "TRAINED_MODEL_DIR" pointing to the path containing the trained model.
NB: "TRAINED_MODEL_DIR" MUST point to the directory containing checkpoints and the last one will be automatically loaded; don't put names or suffixes (such as model.ckpt).
python src/export_inference_graph.py --input_type=image_tensor --pipeline_config_path=networks/faster_rcnn_resnet101_coco.config --trained_checkpoint_prefix="TRAINED_MODEL_PREFIX" --output_directory="OUTPUT_DIR"
In order to use the trained model for inference, it has to be frozen with this command.
Replace "TRAINED_MODEL_PREFIX" with the path of the trained model, including model name prefix (such as model.ckpt-1000); replace also "OUTPUT_DIR" to state output directory for the frozen graph.
python src/inference.py --label_map=data/mscoco_label_map.pbtxt --model_path="TRAINED_MODEL_DIR"/frozen_inference_graph.pb --image_dir=data/dataset_cropped/inference/ --filename=lake.jpg
Replace "TRAINED_MODEL_DIR" pointing to the path containing the trained model. The resulting image will be saved in image_dir/tmp/.