Amodal Completion for Estimating Centers of Grapes

This is an implementation of Mask R-CNN and U-Net on Python 3, Keras, TensorFlow and PyTorch. The framework performs instance segmentation, followed by amodal semantic segmentation on the masks from instance segmentation. We used the implementation of Mask R-CNN from Matterport. Please refer to the official github page for more information on the Mask R-CNN implementation. (Note we use an outdated implementation in our current repository.)

You can also check my paper here!

amodal_completion_results

Framework

Before running any of the commands below, make sure to follow the instructions in the Installation section.

Running the grape code will store all the results images in the results folder.

If you want to crop the all resulting images in the folder, you can run the crop_results.py script to only keep the image and discard the blank around the image saved from Matplotlib:

python crop_results.py

1. Mask R-CNN for instance segmentation

Possible argument to pass to the command:

Training on grape dataset

python -m samples.coco.grape train --model=coco --dataset=./data/fastgrape/grape/train/

Evaluating on grape dataset

python -m samples.coco.grape evaluate --model=last --dataset=./data/fastgrape/grape/val/

Evaluating on one image

python -m samples.coco.grape evaluate --model=last --image=./data/fastgrape/grape/val/fuji/before_fuji234_05_rgb.JPG

For more details on the use of Mask R-CNN for instance segmentation only, please refer to the instructions written by 尾形 亮輔.

2. U-Net for amodal completion

Possible argument to pass to the command:

Training on synthetic grape dataset

python -m unet.main -train -exp [name_of_your_experiment]

The experiments will be saved in the output folder, created in unet folder under the name:

Unet-{mmddyy}-lr-{learning rate value}-b{batch size value}_{name of your experiment}

Once trained, your folder should looks like something like this:

.
├── data/
├── mrcnn/
├── samples/
└── unet
    ├── ...
    └── output
        ├── ...
        └── Unet-{date}-lr-{learning rate value}-b{batch size value}_{name of your experiment}
            ├── eval
            └── train
                ├── logs
                └── models

During the training, the logs for tensorboard are stored in the logs subfolder, and the models in the models subfolder. The best model is saved under the name best_model.pth, which can be renamed (but not the extension) in the model.py file (line 357). Plus, every one fifth of the total number of epoch, the model is save, in case of a crash or power outage.

Evaluating on synthetic grape dataset

Here you must specitfy the path to the folder of your experiment from the root of the directory:

python -m unet.main -eval -exp ./unet/output/your_experiment_folder/

The results will be stored as images in the eval subfolder in the experiment folder (see above for folder structure).

Data augmentation for amodal completion

With the parameter --augmentation or -aug, you can enable data augmentation during the training of amodal completion model. All the implemented augmentation are grouped in the data_aug.py file. The augmentation are seperated into classes and all the augmentation are passed in a list to our personalized data augmentation class, which calls the transformations and apply shuffling or a random choice of augmentation. All the augmentations used in our project are from torchvision.

Create your own augmentation

You can add your own augmentation and use data augmentation operations from other packages. However, as we perform online data augmentation to make sure the format is preserved when loading data, you should create a class which performs the application of the desired augmentation. To create your own class, you can refer to this simple template below:

import torchvision.transforms.functional as tff

class MyAugmentation(object):

    def __call__(self, sample):
        sample['image'] = my_augmentation(sample['image'])
        sample['target'] = my_augmentation(sample['target'])
        return sample

You have to apply the transformation on the image and its target if your augmentation affects the structure of the image. As our sample is a python dictionary, it must be done separately.

3. Combine Mask R-CNN and U-Net

To perform amodal completion on the results of instance segmentation, you just need to add two more arguments to the commands of Mask R-CNN because they’re not set by default:

Once a model is trained with U-Net, use the best_model.pth model from the unet/output/[experiment_folder]/train/models/ folder to perform amodal completion with Mask R-CNN. The file must be put in the root of unet folder. To make them more explicit, you can also rename them, like we did for our baseline model:

.
├── data/
├── mrcnn/
├── samples/
└── unet
    ├── ...
    ├── baseline.pth
    └── your_model.pth

Feel free to change the amodal model by the one you trained. You just have to specify the name of the .pth file through the --amodal_model parameter.

Perform amodal completion on grape dataset

python -m samples.coco.grape evaluate --model=last --dataset=./data/fastgrape/grape/val/ --amodal --amodal_model=baseline

Perform amodal completion on one grape image

python -m samples.coco.grape evaluate --model=last --image=/path/to/image --amodal --amodal_model=baseline

Requirements

Python 3.4+, TensorFlow 1.3, Keras 2.0.8, Torch 1.4 and other common packages listed in requirements.txt.

Installation

  1. (Optional) If not existing, create a virtual environment in the directory and activate it: 
     virtualenv -p python3 my_venv
 source venv_amodalCompletion/bin/activate

    Otherwise just activate the existing virtual environment:

     source venv_amodalCompletion/bin/activate
  2. Install dependencies: 
    pip install -r requirements.txt
  3. Clone this repository.
  4. Create empty data and results folders in the root of the repository. (All the data can be found on the IMP server to the following path: /disk020/usrs/julian/amodal_completion/data/.)
  5. Download pre-trained COCO weights (mask_rcnn_coco.h5) from the releases page and save the file in the mrcnn directory (only if you want to train Mask-RCNN from scratch again). (Note: Make sure to also retrieve the logs folder of Mask R-CNN here: /disk020/usrs/julian/amodal/completion/mrcnn/.)
  6. If not already existing, add the grape and synthetic grape datasets to the data folder and run the following command: 
     python ./unet/data_to_json.py -f ./data/synthethic_grape/

At the end of the installation, the structure of your directory should be something like this:

.
├── data
│   ├── fastgrape/
│   └── synthetic_grape/
│       ├── grapes/
│       ├── imgs/
│       └── fname_dataset.json
├── mrcnn
|   ├── ...
|   ├── logs
|   └── mask_rcnn_coco.h5
├── samples
│   └── coco/
└── unet/

The last command will create the fname_dataset.json file, used to generate train, val and test set for amodal completion on the synthetic dataset.