Diabetic Retinopathy Detection - GUI based Python code - main.py

import copy
import os
import pickle
import sys
from itertools import chain
from subprocess import call

import cv2
import keras
# from skimage import feature
# import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from keras import backend as K
from keras.layers.core import Dense, Dropout
from keras.models import Model
from keras.preprocessing.image import array_to_img, img_to_array, load_img
from PIL import Image
from PyQt5 import QtCore
from PyQt5.QtCore import QCoreApplication, QTimer, pyqtSlot
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
from PyQt5.uic import loadUi
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.neighbors import KNeighborsClassifier

prognosis = ["1. Get a comprehensive dilated eye exam once a year.\n2. Do exercise to maintain health\n3. Do not smoke\n4. Maintain normal blood pressure",
             "1. Get a comprehensive dilated eye exam in 2 to 3 months.\n2. Control Blood Sugar levels.\n3. Maintain normal blood pressure\n4. Do exercise to maintain health.\n5. 95% chances of vision Restoration.",
             "1. This stage is curable, 90% vision restoration possible.\n2. Regular screening in 20-30 days.\n3. Maintain blood Sugar levels & cholestrol.\n 4. Stop smoking if you are.\n 5. Consult doctor ASAP",
             "1. Patient has abnormal growth of new blood vessels & scar tissue, bleeding in form of clear, jelly-like substance i.e. viterous humour.\n2. 60%-70% chances of vision restoration.\n3. Regular screening 5-10 days.\n3. Control blood Pressure and Sugar levels",
             "Prognosis for Proliferative DR"
             ]

DISEASE_CLASSES = {
                      0: 'Normal',
                      1: 'Mild NPDR',
                      2: 'Moderate NPDR',
                      3: 'Severe NPDR',
                      4: 'Proliferative DR'
                    }


class DiabeticRetinopathyMobilenet(QDialog):
    def __init__(self):
        super(DiabeticRetinopathyMobilenet, self).__init__()
        loadUi('gui.ui', self)

        # self.label.setText("Browse Image to test !")
        self.TrainButton.clicked.connect(self.trainingClicked)
        self.BrowseButton.clicked.connect(self.browseClicked)
        self.DetectRecogniseButton.clicked.connect(self.detectRecogniseClicked)
        self.ExitButton.clicked.connect(self.exitClicked)
        # print('Training Data Loaded !\n')
        self.font = cv2.FONT_HERSHEY_PLAIN
        # store the number of points and radius
        self.numPoints = 24
        self.radius = 8
        # https: // www.penwatch.net / cms / pyqt5_qplaintextedit /
        # self.plainTextEdit.document().setPlainText("HELLO WORLD !")

    @pyqtSlot()
    def trainingClicked(self):
        call(["python", "model_training.py"])

    @pyqtSlot()
    def browseClicked(self):
        fname, filter = QFileDialog.getOpenFileName(self, 'Open File', '.\\', "Image Files (*.*)")
        if fname:
            self.LoadImageFunction(fname)
        else:
            print("Invalid Image")

    def LoadImageFunction(self, fname):
        self.image = cv2.imread(fname)
        self.DisplayImage(self.image, 1)

    def DisplayImage(self, img, window=1):
        qformat = QImage.Format_Indexed8
        if len(img.shape) == 3:
            if (img.shape[2]) == 4:
                qformat = QImage.Format_RGBA8888
            else:
                qformat = QImage.Format_RGB888

        outImg = QImage(img, img.shape[1], img.shape[0], img.strides[0], qformat)

        outImg = outImg.rgbSwapped()

        if window == 1:
            self.imgLabel.setPixmap(QPixmap.fromImage(outImg))
            self.imgLabel.setAlignment(QtCore.Qt.AlignCenter | QtCore.Qt.AlignVCenter)
            self.imgLabel.setScaledContents(True)

    def DetectRetinopathy(self, img):

        model = self.Load_Training_Model()

        # pre-process the image for classification
        image = cv2.resize(img, (224, 224))
        image = image.astype("float") / 255.0
        image = img_to_array(image)
        image = np.expand_dims(image, axis=0)

        predictions = model.predict(image)      # [23,41,5,45,1]

        print(predictions)

        final_prediction = predictions.argmax(axis=1)[0]

        print(final_prediction)

        disease_name = DISEASE_CLASSES[final_prediction]

        print(disease_name)

        print("Disease Detected is: ", disease_name)
        self.label.setText(disease_name)
        self.Show_Bar_Chart(predictions)
        self.draw_barchart(final_prediction)
        

    def Load_Training_Model(self):
        # Create a MobileNet model
        mobile = keras.applications.mobilenet.MobileNet()
        x = mobile.layers[-6].output
        x = Dropout(0.25)(x)
        predictions = Dense(5, activation='softmax')(x)
        model = Model(inputs=mobile.input, outputs=predictions)

        for layer in model.layers[:-23]:
            layer.trainable = False

        model.load_weights('model.h5')

        return model

    def draw_barchart(self, index):
        xdata = ["No DR", "Mild", "Moderate", "Severe", "Proliferative DR"]
        ydata = [[1, 0, 0, 0, 0],
                 [0, 1, 0, 0, 0],
                 [0, 0, 1, 0, 0],
                 [0, 0, 0, 1, 0],
                 [0, 0, 0, 0, 1]
                 ]
        barlabels = [0, 1, 2, 3, 4]

        x = np.arange(len(barlabels))  # the label locations
        width = 0.35  # the width of the bars
        fig, ax = plt.subplots()
        rects1 = ax.bar(x, ydata[index], width, label='Disease Detected', color=['black', 'red', 'green', 'blue', 'cyan'])

        # Add some text for labels, title and custom x-axis tick labels, etc.
        ax.set_ylabel('Scores')
        ax.set_title('Diabetic Retinopathy Detection')
        ax.set_xticks(x)
        ax.set_xticklabels(xdata)
        ax.legend()

        fig.tight_layout()
        plt.savefig("barplot.png")
        # plt.show()
        # barimage = cv2.imread("barplot.png")
        barimage = cv2.imread("barplot_probabilities.png")
        self.DisplayBarChart(barimage, 1)
        QMessageBox.information(self, 'Details', prognosis[index], QMessageBox.Ok,
                                QMessageBox.Ok)
        self.plainTextEdit.document().setPlainText(prognosis[index])

    def DisplayBarChart(self, img, window=1):
        qformat = QImage.Format_Indexed8
        if len(img.shape) == 3:
            if (img.shape[2]) == 4:
                qformat = QImage.Format_RGBA8888
            else:
                qformat = QImage.Format_RGB888

        outImg = QImage(img, img.shape[1], img.shape[0], img.strides[0], qformat)

        outImg = outImg.rgbSwapped()

        if window == 1:
            self.barimgLabel.setPixmap(QPixmap.fromImage(outImg))
            self.barimgLabel.setAlignment(QtCore.Qt.AlignCenter | QtCore.Qt.AlignVCenter)
            self.barimgLabel.setScaledContents(True)
            
    def Show_Bar_Chart(self, predictions_list):
        
        objects = ('normal', 'Mild_NPDR','Moderate_NPDR', 'Severe_NPDR', 'PDR')
        y_pos = np.arange(len(objects))
        
        flatten_predictions_list = list(chain.from_iterable(predictions_list))
        flatten_predictions_percentages = map(lambda x: round(x * 100, 2), flatten_predictions_list)   
            
        performance = list(flatten_predictions_percentages)
        print(performance)
        
        plt.bar(y_pos, performance, align='center', alpha=0.5, color=['black', 'red', 'green', 'blue', 'cyan'])
        
        for index,data in enumerate(performance):
            plt.text(x=index , y =data+1 , s=f"{data}" , fontdict=dict(fontsize=10))
            
        plt.xticks(y_pos, objects)
        plt.ylabel('Percentage Probabilities')
        plt.title('Disease Predictions')
        plt.savefig("barplot_probabilities.png")
        # plt.show()
    

    @pyqtSlot()
    def detectRecogniseClicked(self):
        self.DetectRetinopathy(self.image)

    @pyqtSlot()
    def exitClicked(self):
        QApplication.instance().quit()


if __name__ == '__main__':

    app = QApplication(sys.argv)
    window = DiabeticRetinopathyMobilenet()
    window.setWindowTitle('Diabetic Retinopathy Detection using Mobilenet CNN')
    window.show()
    sys.exit(app.exec_())