This repository is organized as follows:
┤ LICENCE the MIT License file
│ README.md this README file
│ REPORT.pdf the project report
│
├───DATASET
│ │ create_xml.py a script creating XML annotation files from the
│ │ original 'gt.txt' annotation file
│ │ gt.txt ground truth information containing locations
│ │ and classes of all traffic signs in the dataset
│ │ ReadMe.txt the dataset's README file
│ │ test.txt a list of identifiers of all test images
│ │ train.txt a list of identifiers of all test images
│ │
│ └───Annotations
│ 00000.xml – 00899.xml ground truth information containing locations
│ and classes of all traffic signs in each image
│
├───RESOURCES
│ Helvetica.ttf a font neccessary to create annotations in
│ images, cf. image files in 'RESULTS'
│ label_map.json a JSON file containing the traffic sign class
│ numbers and corresponding names
│ TEST_images.json a JSON file containing the absolute paths of
│ all test image files
│ TEST_objects.json a JSON file containing ground truth information
│ containing locations and classes of all traffic
│ signs in the test images
│ train.log a log file created during training, listing
│ epochs and loss
│ trained.pth.tar the weights of the model trained as described
│ in the report
│ TRAIN_images.json a JSON file containing the absolute paths of
│ all training image files
│ TRAIN_objects.json a JSON file containing ground truth information
│ containing locations and classes of all traffic
│ signs in the training images
│
├───RESULTS
│ 00601.png – 00899.png images annotated by the detector with a
│ bounding box, the class name and the detection
│ score per detection. Images without detections
│ are skipped.
│
└───SRC
area_under_curve.py a code file to calculate precision and recall
for multiple threshold values to create a
precision-recall curve
create_data_lists.py a code file to (re)create the JSON files to be
processed by the PyTorch Dataset from the XML
annotation files
datasets.py a code file containing a PyTorch Dataset class
for the GTSDB dataset
model.py a code file containing the SSD model
my_eval.py a code file to calculate precision and recall
for one default threshold value and create the
annotated image files located in 'RESULTS'
train.py a code file to train the model
utils.py a code file containing several utility functions
Please download the full GTSDB dataset (available via https://doi.org/10.17894/ucph.358970eb-0474-4d8f-90b5-3f124d9f9bc6) to your computer and place all *****.ppm image files from its root directory into the DATASET folder. All other files neccessary for detection, such as the XML annotation files, have been created using the information from the original gt.txt file (cf. create_xml.py).
- Python (we are using Python 3.7.6)
- PyTorch (we are using PyTorch 1.5.0) with torchvision
- Pillow
- tqdm
- Navigate into the
SRCsubdirectory. - Run
python create_data_lists.pyto create the JSON files to be processed by the data loader from the XML annotation files. - Run
python train.pyto train the model. The weights are saved in the fileRESOURCES\checkpoint.pth.tar. - Run
python my_eval.pyto calculate precision and recall for one default threshold value (min_value=0.45, declared in the definition of themy_evaluate(…)function) and create the annotated image files located inRESULTS. Weights from the fileRESOURCES\trained.pth.tarare used. - Run
python area_under_curve.pyto calculate precision and recall for multiple threshold values to create a precision-recall curve. The area under the curve can then be computed e.g. using a spreadsheet software of your choice.
Portions of the software in this repository utilize the following copyrighted material, the use of which is hereby acknowledged.
MIT License
Copyright (c) 2019 Sagar Vinodababu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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