init

.gitignore

+# Data dir
+data/imgs/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
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+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
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+
+# Translations
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+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# IPython Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# dotenv
+.env
+
+# virtualenv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+
+# Rope project settings
+.ropeproject
+
+# hdf5
+*.h5
+*.hdf5

.idea/.gitignore

+# Default ignored files
+/workspace.xml
\ No newline at end of file

.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Remote Python 3.6.9 Docker (diofor/tensorflow:no_gpu)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+</module>
\ No newline at end of file

.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="JavaScriptSettings">
+    <option name="languageLevel" value="ES6" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Remote Python 3.6.9 Docker (diofor/tensorflow:no_gpu)" project-jdk-type="Python SDK" />
+  <component name="PythonCompatibilityInspectionAdvertiser">
+    <option name="version" value="3" />
+  </component>
+</project>
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.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/keras-frcnn-master.iml" filepath="$PROJECT_DIR$/.idea/keras-frcnn-master.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

.idea/other.xml

+<?xml version="1.0" encoding="UTF-8"?>
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+    <option name="PY_SCI_VIEW_SUGGESTED" value="true" />
+  </component>
+</project>
\ No newline at end of file

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+<project version="4">
+  <component name="VcsDirectoryMappings">
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\ No newline at end of file

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README.md

+# keras-frcnn
+Keras implementation of Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.
+cloned from https://github.com/yhenon/keras-frcnn/
+
+Please note that I currently am quite busy with other projects and unfortunately dont have a lot of time to spend on this maintaining this repository, but any contributions are welcome!
+
+
+USAGE:
+- Both theano and tensorflow backends are supported. However compile times are very high in theano, and tensorflow is highly recommended.
+- `train_frcnn.py` can be used to train a model. To train on Pascal VOC data, simply do:
+`python train_frcnn.py -p /path/to/pascalvoc/`. 
+- the Pascal VOC data set (images and annotations for bounding boxes around the classified objects) can be obtained from: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
+- simple_parser.py provides an alternative way to input data, using a text file. Simply provide a text file, with each
+line containing:
+
+    `filepath,x1,y1,x2,y2,class_name`
+
+    For example:
+
+    /data/imgs/img_001.jpg,837,346,981,456,cow
+    
+    /data/imgs/img_002.jpg,215,312,279,391,cat
+
+    The classes will be inferred from the file. To use the simple parser instead of the default pascal voc style parser,
+    use the command line option `-o simple`. For example `python train_frcnn.py -o simple -p my_data.txt`.
+
+- Running `train_frcnn.py` will write weights to disk to an hdf5 file, as well as all the setting of the training run to a `pickle` file. These
+settings can then be loaded by `test_frcnn.py` for any testing.
+
+- test_frcnn.py can be used to perform inference, given pretrained weights and a config file. Specify a path to the folder containing
+images:
+    `python test_frcnn.py -p /path/to/test_data/`
+- Data augmentation can be applied by specifying `--hf` for horizontal flips, `--vf` for vertical flips and `--rot` for 90 degree rotations
+
+
+
+NOTES:
+- config.py contains all settings for the train or test run. The default settings match those in the original Faster-RCNN
+paper. The anchor box sizes are [128, 256, 512] and the ratios are [1:1, 1:2, 2:1].
+- The theano backend by default uses a 7x7 pooling region, instead of 14x14 as in the frcnn paper. This cuts down compiling time slightly.
+- The tensorflow backend performs a resize on the pooling region, instead of max pooling. This is much more efficient and has little impact on results.
+
+
+Example output:
+
+![ex1](http://i.imgur.com/7Lmb2RC.png)
+![ex2](http://i.imgur.com/h58kCIV.png)
+![ex3](http://i.imgur.com/EbvGBaG.png)
+![ex4](http://i.imgur.com/i5UAgLb.png)
+
+ISSUES:
+
+- If you get this error:
+`ValueError: There is a negative shape in the graph!`    
+    than update keras to the newest version
+
+- This repo was developed using `python2`. `python3` should work thanks to the contribution of a number of users.
+
+- If you run out of memory, try reducing the number of ROIs that are processed simultaneously. Try passing a lower `-n` to `train_frcnn.py`. Alternatively, try reducing the image size from the default value of 600 (this setting is found in `config.py`.

data/convert_json_to_txt.py

+from random import shuffle
+from math import ceil
+from PIL import Image
+import json
+import io
+import os
+import tensorflow as tf
+
+DEBUG = False
+annotation_file = "caries_dataset_annotation.json"
+out_file = 'annotations.txt'
+img_path = 'imgs/'
+dictionary = {}
+
+def get_filenames_list_and_dict():
+    caries = []
+    no_caries = []
+    print(os.getcwd())
+    with open(annotation_file) as file:
+        json_data = json.load(file)
+        for picture in json_data:
+            current_name = picture['External ID']
+            if not os.path.isfile(img_path+current_name):
+                print('File ”{}” not found. Therefore it\'s not part of the record file.'.format(current_name))
+                continue
+            if 'CARIES' in picture['Label']:
+                caries.append(current_name)
+                dictionary[current_name] = picture['Label']['CARIES']
+            else:
+                no_caries.append(current_name)
+    return caries, no_caries
+
+def print_information(caries, no_caries):
+    print('Caries', len(filenames_caries))
+    print(caries[:5])
+    print()
+    print('No Caries', len(filenames_no_caries))
+    print(no_caries[:5])
+
+def get_caries_entries_as_list(filename):
+    entries = []
+    if filename in dictionary.keys():
+        for bbox in dictionary[filename]:
+            if DEBUG: print(bbox)
+            x, y = [], []
+            for point in bbox['geometry']:
+                x.append(point['x'])
+                y.append(point['y'])
+            if DEBUG: print(min(x), max(x), min(y), max(y))
+            if DEBUG: print()
+            entries.append('data/'+img_path+filename+','+str(min(x))+','+str(min(y))+','+str(max(x))+','+str(max(y))+','+'caries\n')
+    return entries
+
+if __name__ == '__main__':
+    filenames_caries, filenames_no_caries = get_filenames_list_and_dict()
+
+    # reduce amount of data only for testing purpose
+    # remove for real use
+    #filenames_caries = filenames_caries[:len(filenames_no_caries)]
+
+    print_information(filenames_caries, filenames_no_caries)
+
+    shuffle(filenames_caries)
+    shuffle(filenames_no_caries)
+
+    test = 0.2 # quota of test examples from the whole dataset.
+    caries_splitting_element = ceil(len(filenames_caries) * test)
+    no_caries_splitting_element = ceil(len(filenames_no_caries) * test)
+    print(caries_splitting_element)
+    print(no_caries_splitting_element)
+
+    filenames = {}
+    filenames['test'] = filenames_caries[:caries_splitting_element]
+    filenames['test'].extend(filenames_no_caries[:no_caries_splitting_element])
+
+    filenames['train'] = filenames_caries[caries_splitting_element:]
+    filenames['train'].extend(filenames_no_caries[no_caries_splitting_element:])
+
+    '''for kind in filenames.keys():
+        writer = tf.io.TFRecordWriter('../data/'+kind +'.record')
+        for filename in filenames[kind]:
+            tf_example = create_tf_example(filename)
+            writer.write(tf_example.SerializeToString())
+        writer.close()
+        print('Successfully created the ' + kind + ' TFRecords')'''
+
+    with open(out_file, 'w') as file:
+        for filename in filenames['train']:
+            file.writelines(get_caries_entries_as_list(filename))

data/remove_everything_other_then_training_examples_from_test.py

+import os
+
+test_path = 'imgs/train_only_annotated/'
+annotation_file = 'annotations.txt'
+
+def removeImage(filename):
+    if os.path.isfile(test_path+filename):
+        os.remove(test_path+filename)
+        print("File {} removed.".format(filename))
+    else:
+        print("File {} does not exist.".format(filename))
+
+def load_names_of_train_images():
+    filenames = set()
+    with open(annotation_file) as file:
+        for line in file.readlines():
+            filenames.add(line.split(',')[0].split('/')[-1])
+    return list(filenames)
+
+
+if __name__ == '__main__':
+    filenames = load_names_of_train_images()
+    for idx, img_name in enumerate(sorted(os.listdir(test_path))):
+        if not img_name.lower().endswith(('.bmp', '.jpeg', '.jpg', '.png', '.tif', '.tiff')):
+            continue
+        if not img_name in filenames:
+            removeImage(img_name)

data/remove_training_examples_from_test.py

+import os
+
+test_path = 'imgs/test/'
+annotation_file = 'annotations.txt'
+
+def removeImage(filename):
+    if os.path.isfile(test_path+filename):
+        os.remove(test_path+filename)
+        print("File {} removed.".format(filename))
+    else:
+        print("File {} does not exist.".format(filename))
+
+def load_names_of_train_images():
+    filenames = set()
+    with open(annotation_file) as file:
+        for line in file.readlines():
+            filenames.add(line.split(',')[0].split('/')[-1])
+    return list(filenames)
+
+
+if __name__ == '__main__':
+    filenames = load_names_of_train_images()
+    for training_image in filenames:
+        removeImage(training_image)
\ No newline at end of file

data/tempCodeRunnerFile.py

+filenames_caries
\ No newline at end of file

keras_frcnn/FixedBatchNormalization.py

+from keras.engine import Layer, InputSpec
+from keras import initializers, regularizers
+from keras import backend as K
+
+
+class FixedBatchNormalization(Layer):
+
+    def __init__(self, epsilon=1e-3, axis=-1,
+                 weights=None, beta_init='zero', gamma_init='one',
+                 gamma_regularizer=None, beta_regularizer=None, **kwargs):
+
+        self.supports_masking = True
+        self.beta_init = initializers.get(beta_init)
+        self.gamma_init = initializers.get(gamma_init)
+        self.epsilon = epsilon
+        self.axis = axis
+        self.gamma_regularizer = regularizers.get(gamma_regularizer)
+        self.beta_regularizer = regularizers.get(beta_regularizer)
+        self.initial_weights = weights
+        super(FixedBatchNormalization, self).__init__(**kwargs)
+
+    def build(self, input_shape):
+        self.input_spec = [InputSpec(shape=input_shape)]
+        shape = (input_shape[self.axis],)
+
+        self.gamma = self.add_weight(shape,
+                                     initializer=self.gamma_init,
+                                     regularizer=self.gamma_regularizer,
+                                     name='{}_gamma'.format(self.name),
+                                     trainable=False)
+        self.beta = self.add_weight(shape,
+                                    initializer=self.beta_init,
+                                    regularizer=self.beta_regularizer,
+                                    name='{}_beta'.format(self.name),
+                                    trainable=False)
+        self.running_mean = self.add_weight(shape, initializer='zero',
+                                            name='{}_running_mean'.format(self.name),
+                                            trainable=False)
+        self.running_std = self.add_weight(shape, initializer='one',
+                                           name='{}_running_std'.format(self.name),
+                                           trainable=False)
+
+        if self.initial_weights is not None:
+            self.set_weights(self.initial_weights)
+            del self.initial_weights
+