TrafficSignalDetection

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TrafficSignalDetection

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A Traffic Signal Detection Android/iOS App made by using Object Detection API from TensorFlow

TrafficSignalDetection 최신버젼 다운로드

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// repository documentation

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# TrafficSignalsDetection
A Traffic Signal Detection Android/iOS App made by using Object Detection API from TensorFlow Sistemi Digitali M at UNIBO ![alt text](https://github.com/nicobargit/TrafficSignalDetection/blob/master/images/logo.png)
# Downloads
Dataset: https://drive.google.com/file/d/1TNdAzz5U5sh1AtGJNtSHtfqfHDR_FYzZ/view?usp=sharing 
Models trained on COCO: https://drive.google.com/file/d/1SY9x8EA3pxPa2J7ntXLbt7Qb_uTQ_8_H/view?usp=sharing 
Fine Tuned models on the Dataset: https://drive.google.com/open?id=1pIYxxg0VLJd-JzMOrVqv5X6sJGPW5UOy
# Setup
0. Set up Anaconda Environment For Python and install TensorFlow 1.15 and other dependencies needed. 
1. Clone this repo. 
2. Clone TensorFlow Model Repo (https://github.com/tensorflow/models). 
3. Copy and overwrite TrafficSignalDetection content to research/object_detection folder. 
4. Copy the content of Dataset .zip archive into research/object_detection folder. 
5. Copy the content of Models .zip archive into research/object_detection folder. 
# Train
1. In order to train your model you need to download the dataset we used or generate your own tfrecord files of your own dataset. 
You can use generate_tfrecord.py script (in Dataset.zip) after modifying class bindings:
```
python generate_tfrecord.py --csv_input=train_labels.csv --image_dir=train --output_path=train.record
```
You need to provide a label file in the right format in .csv. 
Few example lines of annotation are below (train_labels.csv from Dataset.zip):
``` 
filename,width,height,class,xmin,ymin,xmax,ymax,
00000.png,1360,800,id11,774,411,815,446, 
....
....
....
00XXX.png,1360,800,idX,AAA,BBB,CCC,DDD,
```
Put the generated or the downloaded train.record and test.record in research/object_detection. 
2. Pick one model's pipeline.config from Models .zip (SSD_Mobilenet_v2 gave us best results), open it and modify all PATH_TO_BE_CONFIGURED to match your system. 
3. Copy the label43signals.pbtxt (you can find it into Dataset.zip) and pipeline.config in research/object_detection/training folder. 
4. Run train.py script to train:
```
python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/pipeline.config
```
5. When train is done you can export ssd_tflite_graph and convert to .tflite. 
Exporting ssd_tflite_graph:
```
python export_tflite_ssd_graph.py 
--pipeline_config_path training/pipeline.config 
--trained_checkpoint_prefix training/model.ckpt-XXXXX --output_directory exported_inference_graph_tflite 
```
Converting to .tflite by using convert_to_tflite3.py inside research/object_detection/trained_models_tflite:
```
python trained_models_tflite/convert_to_tflite3.py
```
6. Now you can use .tflite model in Android or iOS app. 
Note: you can't fully quantize the model as custom operation aren't supported yet. 
Note2: use TensorFlow 2.0.0 to run this script. 
Note3: remember to change PATH_TO_BE_CONFIGURED inside pipeline.config file to match your system.
# Android and iOS Apps
<img src="https://github.com/nicobargit/TrafficSignalDetection/blob/master/images/ios.PNG" width="300" height="660">
<img src="https://github.com/nicobargit/TrafficSignalDetection/blob/master/images/android.jpeg" width="350" height="660">

# Apps Usage 
Android and iOS apps projects are inside mobile_app_projects folder. Open them to Android Studio or xCode to sideload the app on your phone. 
Note: You need a real phone with a working mobile camera. 
Note2: You need to copy a .tflite in Model folder for xCode or Assets Folder on Android Studio to make the app work.
# Contributors
Nicolò Bartelucci @nicobargit Milo Marchetti @imRaazy

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