odn 0.2.9

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, SSD and YOLO(todo). An infant funuds image object detection application is provided.

Credit

The following 3rd-party packages are used:

1. 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()).
2. Data augmentation package (https://github.com/Paperspace/DataAugmentationForObjectDetection).
3. SSD (Single Shot MultiBox Detector) by tensorflow. We took lots of efforts to integrate tensorflow's objection detection and slim source, mainly revising the relative importing. https://github.com/tensorflow/models/research/object_detection and https://github.com/tensorflow/models/research/slim
4. The darkflow framework (https://github.com/thtrieu/darkflow)
5. The pytorch_yolov5 project (https://github.com/ultralytics/yolov5)

Install

pip install tensorflow-gpu == 1.14.0  
pip install tf-slim 
pip install odn

For tf_ssd, we need to add odn\tf_ssd\protoc-3.4.0-win32\bin to PATH, then run: cd tf_ssd
protoc object_detection/protos/*.proto --python_out=.

How to use - FRCNN

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/', drawzones = True, # set true if you want to draw ROP zones
   verbose = True, display = 5 )

   or output annotated images in place:

   dataset.synthesize_anno('../src/odn/rois.txt', dir_images = '../data/fundus/images_public/', dir_output = 'inplace', drawzones = True, verbose = True, suffix = '_FRCNN', # inplace image name suffix 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 )

   

How to use - TF-SSD

1. Follow our notebook (8.4 Object Detection - Fundus - TF-SSD ) to build the tfrecord.

2. Training

   cmd > cd to '/odn/tf_ssd' > run:

   python train.py --logtostderr --train_dir=training --pipeline_config_path=ssd_mobilenet_v1_fundus.config

   To use a pretrained model, revise ssd_mobilenet_v1_fundus.config:

    fine_tune_checkpoint: "../tf/export/model.ckpt"
   

   If the training runs correctly, should output something like:

    INFO:tensorflow:global step 1906: loss = 4.3498 (3.848 sec/step)
    I0610 11:49:51.364413 13848 learning.py:512] global step 1906: loss = 4.3498 (3.848 sec/step)
    INFO:tensorflow:global step 1907: loss = 3.9057 (3.903 sec/step)
    I0610 11:49:55.267028 13848 learning.py:512] global step 1907: loss = 3.9057 (3.903 sec/step)
    INFO:tensorflow:global step 1908: loss = 3.4026 (3.856 sec/step)

3. Export model as tf graph

   python export_inference_graph.py --input_type image_tensor --pipeline_config_path ssd_mobilenet_v1_fundus.config --trained_checkpoint_prefix training/model.ckpt-1945 --output_directory ./export2002206

4. Prediction

    from odn.fundus import annotation 

    # get image file list to predict
    FILES = utils.get_all_images_in_dir(folder = '../data/fundus/images_public/') 

    # load tf graph model
    detection_graph, category_index = annotation.load_tf_graph(ckpt_path = '../src/odn/tf_ssd/export/frozen_inference_graph.pb',
                    label_path = '../src/odn/tf_ssd/fundus_label_map.pbtxt', 
                    num_classes = 2)

    # object detection
    annotation.tf_batch_object_detection(detection_graph, category_index, FILES, 
                                    '../data/fundus/ssd/', 
                                    '../data/fundus/ssd_202206.txt', 
                                    new_img_width = 300, fontsize = 12)


    Or output annotated images in place:

    annotation.tf_batch_object_detection(detection_graph, category_index, FILES, 
                                  'inplace', 
                                  '../data/fundus/ssd_202206.txt',                        
                                  new_img_width = 900, fontsize = None, suffix = '_SSD')

The annotated images will be generated in the ssd folder. A sample image is as follows,

We draw ROP Zone I, Posterior Zone II and Zone II.

How to use - Darflow Yolo

1. Prerequisie: Install darkflow from source

    src\odn\darkflow_yolo>pip install -e .
        Obtaining file:///C:/Users/eleve/Documents/codex/py/keras/8.%20Object%20Detection/github/src/odn/darkflow_yolo
        Installing collected packages: darkflow
        Running setup.py develop for darkflow
        Successfully installed darkflow

2. Load yolo model and make prediction

    from odn import yolo​

    tfnet = yolo.load_tfnet(options = {
            'model': '../src/odn/darkflow_yolo/cfg/yolo.cfg', # 'bin/yolov1.cfg',
            'load': '../src/odn/darkflow_yolo/bin/yolov2.weights', # 'bin/yolov1.weights',
            'config': '../src/odn/darkflow_yolo/cfg/',
            'threshold': 0.3,
            'gpu': 0.7
        })

    yolo.predict(tfnet, '../data/generic/image2.jpg')

How to use - pytorch YOLO

1. Train by transfer learning

   cd to src\odn\torch_yolo, run (download the yolov5m pretrained model first):

   python train.py --data data/fundus.yaml --cfg models/yolov5m.yaml --weights models/yolov5m.pt --batch-size 16 --epochs 30 --img 640

   The training results and models are saved to runs/train/exp/

2. Predict

   python detect.py --source ../../../data/fundus/test --weights runs/train/exp/weights/best.pt

How to use - Camera App

from odn import camera

camera.realtime_object_detection(ckpt_path = '../src/odn/models/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb',
                label_path = '../src/odn/models/mscoco_label_map.pbtxt', 
                num_classes = 90)

Jupyter notebooks

Under /notebooks, we provide several examples for both general-purposed and fundus image object detection.

Dataset

data/fundus/images_public : a fundus image set in courtesy of SZEH (Shenzhen Eye Hospital)
data/fundus/ground_truth_public : images with ground truth annotations
data/fundus/frcnn_19e_zones : object detection results by FRCNN (trained 19 epochs)
data/fundus/ssd : object detection results by SSD (Single Shot MultiBox Detector)

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.