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crack-detector

A nice crack detector :)

How to run:

WebApp

  • python predict.py --meta_file="./alg1_output/model_complete.meta" --CP_dir="./alg1_output" --start_as_server=True
  • python predict.py --meta_file="./alg2_output/model_complete.meta" --CP_dir="./alg2_output" --start_as_server=True --port=8081 --model_number=2
  • open WebApp/index.html in a browser disabling CORS, here is how to do it for chrome: https://alfilatov.com/posts/run-chrome-without-cors/

Project 1:

Based on https://www.researchgate.net/publication/315613676_Deep_Learning-Based_Crack_Damage_Detection_Using_Convolutional_Neural_Networks?fbclid=IwAR1NqL8c7qwKNCFxAW7E9BAW6c98DKCSEfdgoYAB0WYY5iaQVSTqxBMVqCY

Requirements:

  • tensorflow: 1.15.0
  • matplotlib: 3.1.2
  • opencv-python: 4.1.2.30

How to run:

  • Change directory to CracksDetectionApp (cd CracksDetectionApp)
  • python trainAlg1.py
  • python trainAlg2.py

How to test:

  • Change directory to CracksDetectionApp (cd CracksDetectionApp)
  • python predict.py --meta_file="./alg1_output/model_complete.meta" --CP_dir="./alg1_output"
  • look into folder CracksDetectionApp/results for the resulting images

In order to test the other algorithm run:

  • python predict.py --meta_file="./alg2_output/model_complete.meta" --CP_dir="./alg2_output"

To start the predictor as servers for each algorithm run the following commands:

  • python predict.py --meta_file="./alg1_output/model_complete.meta" --CP_dir="./alg1_output" --start_as_server=True
  • python predict.py --meta_file="./alg2_output/model_complete.meta" --CP_dir="./alg2_output" --start_as_server=True --port=8081 --model_number=2

Project 2 Unet:

Based on https://towardsdatascience.com/understanding-semantic-segmentation-with-unet-6be4f42d4b47

Requirements:

  • numpy 1.17.4
  • pandas 0.25.3
  • matplotlib 3.1.2
  • Pillow 6.2.1
  • tqdm 4.40.0
  • scikit-image 0.16.2
  • tensorflow 2.0.0

How to run:

  • cd CommandLineApp/CrackDetection/
  • python ImageProcessorUsingUNET.py

Project 3 V2:

Requirements:

  • tensorflow 2.0.0
  • matplotlib 3.1.2
  • aspectlib 1.4.2

How to run:

  • python CommandLineApp/CrackDetection/ImageProcessorV2.py

How to test:

  • python CommandLineApp/CrackDetection/predictionV2.py

technical report link : https://docs.google.com/document/d/1XFI4-NG3JoQ1_NO4GCPsOfUy0MokdtFchDorpWL-vLI/edit?fbclid=IwAR0nZoZapOnE3ei6PkDeUQHi26dwDNDQne-qzYXeWncD7e6qYNYw2_YZ4h8

google docs link : https://docs.google.com/document/d/1HZ3XukJdGuqtobCRUvc7KHZCh3Oun3Pu6DqvPCUoquE/edit?usp=sharing

presentation link : https://docs.google.com/presentation/d/1DjbLFpMnE6zBXJQ2NxbrZQuKOJR_Lwlu5tKPKM8E8As/edit?fbclid=IwAR3YqGYsOe-zBtoFLWWVQR8RJFR6HVrujhF05Yt-fmvpuGBRf9jjZQ8cPVc#slide=id.p2

trello link : https://trello.com/b/60KTWa0j/crack-detector

Coordinators: Associate Professor, PhD Adrian Iftene PhD Anca Ignat

Members: