odn 0.0.1

A python library for object detection neural networks.

About

We provide a set of object detection neural network training and evaluation functions.
We currently support Faster-RCNN. An infant funuds image object detection application is provided.
The Faster-RCNN module is based on https://github.com/kentaroy47/frcnn-from-scratch-with-keras. We have updated the code according to the latest keras API change (K..image_dim_ordering () -> K.image_data_format()).
We also uses the 3rd-party data augmentation package (https://github.com/Paperspace/DataAugmentationForObjectDetection).

Install

pip install tensorflow-gpu == 1.14.0
pip install odn

How to use

1. Output images with annotations

   from odn.fundus import dataset

   dataset.synthesize_anno(label_file = '../data/fundus/all_labels.csv', dir_images = '../data/fundus/images/', dir_output = '../data/fundus/ground_truth/',
   verbose = True, display = 5 )

   The labe file should be in this format:

    seq,class,cx,cy,filename,height,laterality,width,xmax,xmin,ymax,ymin
    259,Macula,384,293,00569c080bf51c7f182cbe4c76f1823a.jpg,480,L002,720,436,332,345,241
    668,OpticDisk,191,275,00569c080bf51c7f182cbe4c76f1823a.jpg,480,L002,720,224,158,308,242
   

   The generated image is like this:
   

2. Split dataset

   dataset.split(label_file = '../data/fundus/all_labels.csv', dir_images = '../data/fundus/images/', train_output = '../data/fundus/train.txt', test_output = '../data/fundus/test.txt', test_size = 0.2, verbose = True)

3. Train a new model by transfer learning uing pre-trained PRN weigths

   cmd and cd to the the odn folder:

   python -m odn.train_frcnn -o simple -p ../../data/fundus/train.txt --network vgg16 --rpn ./models/rpn/pretrained_rpn_vgg_model.36-1.42.hdf5 --hf true --num_epochs 20

   or if using github source,

   python train_frcnn.py -o simple -p ../../data/fundus/train.txt --network vgg16 --rpn ./models/rpn/pretrained_rpn_vgg_model.36-1.42.hdf5 --hf true --num_epochs 20

   from odn import utils

   utils.plot_training_curves(input_file = '../src/odn/training_log.txt', output_file = '../src/odn/training_curves.png')

   

4. Test

   python -m odn.test_frcnn --network vgg16 -p ../../data/fundus/test_public.txt --load models/vgg16/19e.hdf5 --num_rois 32 --write

   or if using github source,

   python test_frcnn.py --network vgg16 -p ../../data/fundus/test_public.txt --load models/vgg16/19e.hdf5 --num_rois 32 --write

   A candidate_rois.txt with all candidate ROIs will be generated.

5. ROI filtering

   from odn.fundus import annotation, dataset

   annotation.rule_filter_rois(input_file = '../src/odn/candidate_rois.txt', output_file = '../src/odn/rois.txt', verbose = True)

   dataset.synthesize_anno('../src/odn/rois.txt', dir_images = '../data/fundus/images_public/', dir_output = '../data/fundus/odn_19e/',
   verbose = True, display = 5 )

6. Evaluation

   6.1 Output a single comparison plot

   from odn import metrics

   metrics.fundus_compare_metrics(gt = '../data/fundus/all_labels.csv', pred = '../src/odn/rois.txt', output_file = './comparison_with_metrics.jpg', image_dirs = [ '../data/fundus/ground_truth_public', '../data/fundus/odn_19e_raw',
   '../data/fundus/odn_19e_naive', '../data/fundus/odn_19e'], verbose = True )

   

   6.2 Output image-wise comparison plot

   fundus_compare_metrics_separate(input_file = '../src/odn/rois.txt', output_dir = './comparison_separate/', image_dirs = [ '../data/fundus/ground_truth_public', '../data/fundus/odn_19e_raw', '../data/fundus/odn_19e'], verbose = True )

   6.3 Only output comparison results that are differents between different detection algorithms

   fundus_compare_metrics_html(gt = '../data/fundus/all_labels.csv', pred = '../src/odn/rois_naive.txt')

   fundus_compare_metrics(gt = '../data/fundus/all_labels.csv', pred = '../src/odn/rois.txt', output_file = './comparison_with_metrics_diff.jpg', image_dirs = [ '../data/fundus/ground_truth_public', '../data/fundus/odn_19e_raw',
   '../data/fundus/odn_19e_naive', '../data/fundus/odn_19e'], image_subset = image_subset, verbose = True )

   

Jupyter notebooks

Under /notebooks, we provide two examples for fundus image object detection.

Deployment

After training, you will get a keras h5 model file. You can further convert it to tflite format, or tfjs format.
Then you can deploy on mobile device or browser-based apps.