added readme and minor change in main_train.py

README.md

@@ -1,2 +1,41 @@
 # Crack_detection_part_metal_python2
 Python 2 implementation for android app development
+  A python flask app running on local host.
+  Due to computation limitation, images if trimmed to 512x512 pixels.
+
+## Pre-requisite nstall requirements
+  `pip install -r requirements.txt`
+
+## Run the program
+  ### Some preprocessing to image
+  #### Structure of folders :
+  * Download the dataset from [DataSet](https://drive.google.com/open?id=168I7Gg0AMEZ_ne6mU3wx4puUxMs4TFse) and extract the contents to the folder "YE358311_Fender_apron"
+  * Dataset Directory containing "normal" and "defect" folders
+    * dataset ---> "/YE358311_defects/YE358311_Crack_and_Wrinkle_defect/"
+    * dataset ---> "/YE358311_Healthy/"
+  * Destination Directory containing "train" and "test" folders
+    * data ----> train ----> {"normal", "defect"} subfolders
+    * data ----> test ----> {"normal", "defect"} subfolders
+
+  ### Training a simple CNN classifier (3 Conv + 1 FC)
+  * `python2 main_train.py` --- Start the training application server(local)
+  * `http://127.0.0.1:5000/crack_detection_train` --- Train the model
+  * `python2 main_predict.py` --- Start the testing application server(local)
+  * `http://127.0.0.1:5000/crack_detection_test` --- Opens an hmtl to upload the image and predict if Defective or Healthy
+
+## Accuracy Metrics
+  Validation accuracy and loss
+  * Training loss : 0.1695
+  * **Training accuracy** : 97%
+  * Validation/Test loss : 0.2475
+  * **Validation/Test accuracy** : 92.4%
+
+## Need for Improvements
+* Next commit is to submit an andriod one page app to interact with the python flask API, showing upload, train and test functionality just like the webapp above.
+* Image Preprocssing/Data Preparation
+  * Since cracks are of less area as compared to image and noise, will introduce dropouts to improve accuracy
+  * Preparing a object extraction module (For removing the rest of noise to improve accuracy)
+  * Preparing a background color update module (For easy extraction of metal part after grayscale conversion)
+* Trainng Improvements
+  * Using a pretrained model such as VGG16 trained on ImageNet
+  * Using model ensembles such as CNN+SVM (rbf kernel), Gauss filter+LBP+SVM(rbf kernel) etc that have proved improving accuracy

main_train.py

@@ -1,6 +1,5 @@
 from flask import Flask, render_template, request
 from werkzeug import secure_filename
-from urllib.request import urlopen
 from PIL import Image
 from keras.models import Sequential
 from keras.layers import Conv2D, MaxPooling2D, BatchNormalization
@@ -10,7 +9,6 @@
 
 import numpy as np
 import cv2
-import urllib
 import webcolors
 import time, os
 import sys, h5py
@@ -51,4 +49,4 @@ def train_cnn():
 
 
 if __name__ == '__main__':
-   app.run(debug=False)
+   app.run(debug=True)