使用Keras+Tkinter進行深度學習瑕疵檢測GUI應用程式開發（Google Colab）

Industrial Desktop Deep Learning Application with Keras and Tkinter

參考資料：

An Industrial Case study on Deep learning image classification

Colab連結

defect_detection_vgg16.ipynb

取得資料集

本次範例的資料來自於Kaggle上，可透過該文章學習如何直接在Colab上下載資料

https://www.kaggle.com/ravirajsinh45/real-life-industrial-dataset-of-casting-product/download

串連Colab和Google Drive

from google.colab import drive
drive.mount('/content/drive',force_remount=True)
#此時會跳出需要授權，請透過連結網址得到授權碼

訓練並下載模型

採用了VGG16來訓練鑄件瑕疵檢測模型

請根據自己dataset的路徑位置作路徑修改，並且把完成訓練後的模型下載到本地當中，要與下個步驟的GUI程式搭配

from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
input_shape = (300, 300, 3)
# Initialising the CNN
model = Sequential([
    Conv2D(64, (3, 3), input_shape=input_shape, padding='valid',activation='relu'),
    Conv2D(64, (3, 3), activation='relu', padding='same'),
    MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
    Conv2D(128, (3, 3), activation='relu', padding='same'),
    Conv2D(128, (3, 3), activation='relu', padding='same',),
    MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
    Conv2D(256, (3, 3), activation='relu', padding='same',),
    Conv2D(256, (3, 3), activation='relu', padding='same',),
    Conv2D(256, (3, 3), activation='relu', padding='same',),
    MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
    Conv2D(512, (3, 3), activation='relu', padding='same',),
    Conv2D(512, (3, 3), activation='relu', padding='same',),
    Conv2D(512, (3, 3), activation='relu', padding='same',),
    MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
    Conv2D(512, (3, 3), activation='relu', padding='same',),
    Conv2D(512, (3, 3), activation='relu', padding='same',),
    Conv2D(512, (3, 3), activation='relu', padding='same',),
    MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
    Flatten(),
    Dense(4096, activation='relu'),
    Dropout(0.5),
    Dense(4096, activation='relu'),
    Dropout(0.5),
    Dense(1000, activation='softmax'),])
# Compiling the CNN
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Part 2 - Fitting the CNN to the images
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('/content/drive/My Drive/defect_detection_training/casting_data/casting_data/train', target_size = (300, 300), batch_size = 32, class_mode = 'binary')
test_set = test_datagen.flow_from_directory('/content/drive/My Drive/defect_detection_training/casting_data/casting_data/test', target_size = (300, 300),batch_size = 32,class_mode = 'binary')
model = classifier.fit_generator(training_set,
                                 steps_per_epoch = 1000,
                                 epochs = 5,
                                 validation_data = test_set,        validation_steps = 1000)
classifier.save("vggmodel.h5")
print("Saved model to disk")

將Model整合到GUI程式

程式碼參考自murali

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 13 10:31:19 2020
@author: murali
"""
from tkinter import *
from keras.preprocessing import image
import numpy as np
from keras.models import load_model
# loading Python Imaging Library 
from PIL import ImageTk, Image   
# To get the dialog box to open when required  
from tkinter import filedialog
model = load_model('vggmodel.h5')
print('Model Loaded Sucessfully')
     
def open_img(): 
    # Select the Imagename  from a folder  
    x = openfilename() 
    # opens the image 
    img = Image.open(x) 
    im1 = img.save("casting.jpeg")
    img = ImageTk.PhotoImage(img) 
    # create a label 
    panel = Label(root, image = img)   
    # set the image as img  
    panel.image = img 
    panel.place(bordermode=OUTSIDE, x=50, y=50)
       
    
def openfilename(): 
  
    # open file dialog box to select image 
    # The dialogue box has a title "Open" 
    filename = filedialog.askopenfilename(title ='Select Image') 
    return filename
def prediction():
    img ="casting.jpeg"    
    test_image = image.load_img(img, target_size = (300, 300))
    test_image = image.img_to_array(test_image)
    test_image = np.expand_dims(test_image, axis = 0)
    result = model.predict(test_image)
    
    if result[0][0] == 1:
        prediction = 'CASTING IS OK         '
        
    else:
        prediction = 'CASTING IS DEFECTIVE'
        
    result = Label(root, text = prediction)   
    # set the image as img  
    result.place(bordermode=OUTSIDE, x=400, y=120)
# Create a window 
root = Tk()   
# Set Title as Image Loader 
root.title("CASTING INSPECTOR")   
# Set the resolution of window 
root.geometry("700x450")   
# Do't Allow Window to be resizable 
root.resizable(width = False, height = False)   
# Create a button and place it into the window using place layout 
btn_open_image = Button(root, text ='Open image', command = open_img).place( 
                                        x = 10, y= 400) 
btn_predict = Button(root, text ='Predict', command = prediction).place( 
                                        x = 200, y= 400) 
result_hd = Label(root, text = "RESULT")
result_hd.place(bordermode=OUTSIDE, x=400, y=90)
root.mainloop()