raspberry_ver.py

#Loading all the needed frameworks, libraries and packages.
import cv2
import imageio as iio
import sys
import shutil
import threading
sys.path.insert(0, '/home/pi/caffe/python')
import os
from os.path import isfile, join
from os import path
import caffe
import numpy as np
from sklearn.metrics.pairwise import euclidean_distances
import time
import PySimpleGUI as sg

'''
Loading caffe models! 
'''
#Caffe model formats:: prototxt: description for the structure of the neuron
#network/ caffemodel: the model after training and learning.

proto = "/home/pi/Project-pygui-rasp/Models/mobilenet_v2_deploy.prototxt"
model = "/home/pi/Project-pygui-rasp/Models/mobilenet_v2.caffemodel"
caffe.set_mode_cpu()
#Load the net, net: is the set of layers that the model is composed of.
net = caffe.Net(proto, model, caffe.TEST)
#Intialiaze the transformer for data pre-processing, it is required when there is a prototxt file.
#The conventional blob dimensions for batches of image data are number (batch size) N x no. of channels K x height H x width W.
#net.blobs: for input data and its propagation in the layers / net.blobs['data']: contains input data, an array of shape (1,3,244,244).
#The (:) is used to differentiate between the key and the value. 
#Specifically 'data' is the key and the tuple returned by 'net.blobs['data'].data.shape' (whose value for this particular example is 
#(1, 3, 244, 244) ) is the value. So basically the 'inputs' field of the transformer object is initialized with the key-value pair 
#'data', '(1, 3, 244, 244)'.
transformer = caffe.io.Transformer({'data':net.blobs['data'].data.shape})
#set_transpose(input data , new order we want the dimensions in)
#Swaps your image dimensions. Normally when an image library loads an image the dimensions of the loaded array are H x W x C 
#(where H is height, W is width and C is/are number of channels), but since caffe expects input in C x H x W, we transpose the data. 
#transpose('data, (2, 0, 1)) means transposing data such that 0th dimension is replaced by 2nd (height with channels), 1st by 
#0th (widht by height) and so on.
transformer.set_transpose('data',(2, 0, 1))
# Swap channels for the model as it accpets BGR format instead of RGB
transformer.set_channel_swap('data', (2, 1, 0))
# Makes the network perform its model calculations expecting images in the grayscale range 0-255 instead of the default 0-1.
transformer.set_raw_scale('data', 255)

print ("Caffe Model for Feature Extraction Loaded")
#########################################################################
proto1 = "/home/pi/Project-pygui-rasp/Models/deploy.prototxt"
model1 = "/home/pi/Project-pygui-rasp/Models/memnet.caffemodel"
net1 = caffe.Net(proto1, model1, caffe.TEST)

transformer1 = caffe.io.Transformer({'data':net1.blobs['data'].data.shape})
transformer1.set_transpose('data',(2, 0, 1))
transformer1.set_channel_swap('data', (2, 1, 0))
transformer1.set_raw_scale('data', 255)
#sets the data part of the blob in the fashion (batch size, channel value, height, width). The batch size is the no. of concurrent 
#images (or any data) that can be used for classification.
net1.blobs['data'].reshape(1, 3, 227, 227)

print ("Caffe Model for Memorability Prediction Loaded")
########################################################################           
def WebCam_SUMM(window,record_path,output_folder,active,key_output):
    print ("Inside WebCam function")
    # A check if the window is not opened anymore while the algorithm is running, it will close the window and the system.
    if active is False:
        sys.exit()
    #Loading the input video.    
    capture = cv2.VideoCapture(record_path)
    #getting total number of frames for the video.
    total_frames = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
    #getting FPS of the input video.
    fps = int(capture.get(cv2.CAP_PROP_FPS))
    print ("FPS = " , fps, "\t total frames = ",total_frames)
    #An array to save the memorability scores in.
    m_scores = []
    m_scores.append([])
    m_scores.append([])
    #Reading first frame in the video, then we get its the properties of the frame.
    ret, frame1 = capture.read()
    height,width,channels = frame1.shape
    size = (width,height)
    frame1 = cv2.resize(frame1,(352,288))
    counter = 1
    pb = 0
    #that line allows the gui event loop and the implemented threading not to interfere
    #with each other causing errors, which helped to make the gui interactive and summarizing more than one video
    #at the same time. 
    window.write_event_value('PROGRESS_TEXT2', 'progress')
    #Start algorithm main loop.
    while(True):
        ret2, frame2 = capture.read()
        if ret2 is True:
            frame2 = cv2.resize(frame2,(352,288))
            distt = segment(frame1,frame2)
            print ('Processing frames ... ')
            #parameter for the progress bar of the recording duration.
            pb+=(total_frames/5)
            window.write_event_value('PROGRESS2', 'loader')
            #different images means the segment of events ended.
            if distt >= 8000:
                m_scores = np.array(m_scores)
                [rows,cols] = m_scores.shape
                #get the keyframe from the shot, the one with the maximum memorability score
                if cols > 0:
                    max_index = m_scores[0].argmax()
                    keyframe_number = int(m_scores[1][max_index])
                    keyframe = capture.set(0,keyframe_number)
                    #print (keyframe_number , " \t########")
                    temp, keyframe = capture.read()
                    if temp:
                        pathh = output_folder+ str(keyframe_number) + '.jpg'
                        print ("############## \t 'Writing key frame at" , pathh, "'\t##############", "\a")
                        cv2.imwrite(pathh,keyframe)
                        
                print ("Different images = " , distt , "\t" , counter)
                #Empty the score array for the next shot.
                m_scores = []
                m_scores.append([])
                m_scores.append([])
                
            #same Images ==> compute image memorability and store it in the scores array.           
            else:
                m_value = mem_calculation((frame1))
                m_scores[0].append(m_value)
                m_scores[1].append(counter)
            counter = counter + (fps-14)
            frame1 = frame2
        #The video ended here, now we check what form of the output the user chose
        #act accordingly.   
        else:
            if key_output == 'CB23':
                convert_frames_to_video(record_path,output_folder,output_folder,fps)
                print('Done!')
                window.write_event_value('DONE2', 'program done')
                break
            if key_output == 'CB21':
                remove_video(record_path ,output_folder)
                print('Done!')
                window.write_event_value('DONE2', 'program done')
                break
            if key_output == 'CB22':
                convert_frames_to_video(record_path,output_folder,output_folder,fps)
                remove_frames(output_folder)
                print('Done!')
                window.write_event_value('DONE2', 'program done')
                break
    
    capture.release()
    window.write_event_value('-THREAD2-', '*** The thread says.... "I am finished" ***')   
#####################################################################################################################
def record(event,window,record_path,duration):
    #Recording function.
    #Load camera information.
    camera = iio.get_reader("<video0>")
    meta = camera.get_meta_data()
    #get number of frames
    num_frames = duration * int(meta["fps"])
    delay = 1/meta["fps"]
    dt = 0

    buffer = list()
    if event == sg.WIN_CLOSED:
        sys.exit()
    #recording loop    
    for frame_counter in range(num_frames):
        frame = camera.get_next_data()
        buffer.append(frame)
        dt +=1
        recording_bar(event,window,dt,num_frames)
        time.sleep(delay)

    camera.close()
    #Saving the recorded video in the given path, and naming the video uniquely.
    i = 0
    new_record_path = ''
    while True:
        new_record_path += record_path +'/' + 'webcam' + str(i) + '.avi'
        if path.exists(new_record_path):
            new_record_path = ''
            i+=1
        else:
            break
       
    iio.mimwrite(new_record_path, buffer, macro_block_size=8, fps=meta["fps"])
    return new_record_path
#######################################################################################
#An event update function for the recording bar gui.
def recording_bar(event,window,i, duration):
    event, values = window.read(timeout=1)
    if event == sg.WIN_CLOSED:
        sys.exit()
    else:  
        window['RECORDING'].update_bar(i, duration)
#######################################################################################
#Same steps happening as WebCam_SUMM function but getting input video and output video path.
def Recorded_Video_SUMM(window,input_video,output_folder,active,key_output):
    print ("Inside Recorded_Video function")
    if active is False:
        sys.exit()
        
    capture = cv2.VideoCapture(input_video)
    total_frames = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
    fps = int(capture.get(cv2.CAP_PROP_FPS))
    print ("FPS = " , fps, "\t total frames = ",total_frames)
    m_scores = []
    m_scores.append([])
    m_scores.append([])
    ret, frame1 = capture.read()
    height,width,channels = frame1.shape
    size = (width,height)
    frame1 = cv2.resize(frame1,(352,288))
    counter = 1
    #pb = 0
    window.write_event_value('PROGRESS_TEXT', 'progress')    
    while(True):
        ret2, frame2 = capture.read()
        if ret2 is True:
            frame2 = cv2.resize(frame2,(352,288))
            distt = segment(frame1,frame2)
            print ('Processing frames ... ')
            #pb+=(total_frames/5)
            window.write_event_value('PROGRESS', 'loader')
            if distt >= 8000:#different images 
                m_scores = np.array(m_scores)
                [rows,cols] = m_scores.shape
                
                if cols > 0:
                    max_index = m_scores[0].argmax()
                    keyframe_number = int(m_scores[1][max_index])
                    keyframe = capture.set(0,keyframe_number)
                    #print (keyframe_number , " \t########")
                    temp, keyframe = capture.read()
                    if temp:
                        pathh = output_folder+ str(keyframe_number) + '.jpg'
                        print ("############## \t 'Writing key frame at" , pathh, "'\t##############", "\a")
                        cv2.imwrite(pathh,keyframe)
                        
                print ("Different images = " , distt , "\t" , counter)  
                m_scores = []
                m_scores.append([])
                m_scores.append([])
                       
            else:#same images
                #print ("Similar images= " , distt , "\t" , counter)
                m_value = mem_calculation((frame1))
                #scores[index][0] = m_value
                m_scores[0].append(m_value)
                m_scores[1].append(counter)
            counter = counter + (fps-14)
            frame1 = frame2
        
        else:
            if key_output == 'CB3':
                convert_frames_to_video(input_video,output_folder,output_folder,fps)
                print('Done!')
                window.write_event_value('DONE', 'program done')
                break
            if key_output == 'CB1':
                remove_video(input_video ,output_folder)
                print('Done!')
                window.write_event_value('DONE', 'program done')
                break
            if key_output == 'CB2':
                convert_frames_to_video(input_video,output_folder,output_folder,fps)
                remove_frames(output_folder)
                print('Done!')
                window.write_event_value('DONE', 'program done')
                break
    
    capture.release()
    window.write_event_value('-THREAD-', '*** The thread says.... "I am finished" ***')
##############################################################################################################      
def segment(frame1,frame2):
    #print ("Inside segment function")
    
    #preporcessing frames by transforming them for the model.
    resized_image1 = caffe.io.resize_image(frame1,[224,224])
    resized_image2 = caffe.io.resize_image(frame2,[224,224])
    
    net.blobs['data'].reshape(1, 3, 224, 224)
    #Execute the transformation.
    net.blobs['data'].data[...] = transformer.preprocess('data', resized_image1)
    #Compute the output of the layer.
    net.forward()
    #Extracting features of the first frame from the output layer FC7.
    features1 = net.blobs['fc7'].data[0].reshape(1,1000)
    features1 = np.array(features1)
    net.blobs['data'].data[...] = transformer.preprocess('data', resized_image2)
    net.forward()
    #Extracting features of the second frame from the output layer FC7.
    features2 = net.blobs['fc7'].data[0].reshape(1,1000)
    features2 = np.array(features2)
    return euclidean_distances(features1,features2)

#####################################################################################################################    
def mem_calculation(frame1):
    #print ("Inside mem_calculation function")
    #preprocessing image for the model.
    resized_image = caffe.io.resize_image(frame1,[227,227])
    #Execute the transformation.
    net1.blobs['data'].data[...] = transformer1.preprocess('data', resized_image)
    #Getting the output score from the output layer FC8.
    value = net1.forward()
    value = value['fc8-euclidean']
    return value[0][0]
######################################################################################################################
#This is the video for converting frames to video incase the user chose so.
def convert_frames_to_video(input_video_path, key_frames_path, pathOut, fps):
    print('Inside convert')
    frame_array = []
    
    try:
        #The frames are numbered and sorted in the output directory, so we load them
        #in the frame array but sorted as well so that we can combine them into a video
        #in orderly manner.
        files_in_path = os.listdir(key_frames_path)
        files = [file for file in files_in_path if file.endswith(".jpg")]
        #for sorting the file names properly
        sorted_files = sorted(files,key=lambda x: int(os.path.splitext(x)[0]))
        
        for i in range(len(files)):
            filename=key_frames_path + sorted_files[i]
            #reading each files
            img = cv2.imread(filename)
            height, width, layers = img.shape
            size = (width,height)
            print(filename)
            #inserting the frames into an image array
            frame_array.append(img)
        #Deciding the output video format based on the input video format.
        video_name_format = os.path.split(input_video_path)[1]
        video_format = video_name_format.split(".")[-1]
        video_name = video_name_format.split(".")[-2]
        #print(video_name , video_format)
        out_video = ''
        out_video = out_video + video_name + '_summary' + '.' + video_format
        pathOut += out_video
        print(pathOut)
        if video_format == 'avi':
            out = cv2.VideoWriter(pathOut,cv2.VideoWriter_fourcc(*'DIVX'), fps, size)
        else:
            out = cv2.VideoWriter(pathOut,cv2.VideoWriter_fourcc(*'mp4v'), fps, size)

        for i in range(len(frame_array)):
            #writing to a image array
            out.write(frame_array[i])
        out.release()
        
    except:
        sg.popup_error('Provided path does not exist anymore, please refresh and provide paths again!', auto_close_duration = 20)
#####################################################################################################################
#This function is used to remove the frames incase the user chose to have the videos only in the output directory.
def remove_frames(output_path):
    print('Inside remove')
    #Load output path.
    files_in_path = os.listdir(output_path)
    #print('all files', files_in_path)
    #Get all the files with .jpg extension.
    filtered_files = [file for file in files_in_path if file.endswith(".jpg")]
    #print('frames files', filtered_files)
    #Loop on all the files, and remove them from the directory.
    for file in filtered_files:
        path_to_file = os.path.join(output_path, file)
        os.remove(path_to_file)   
#####################################################################################################################
#This function is used to remove the video incase the user chose to have frames only in the output directory.
def remove_video(input_video , output_folder):
    # Finding the output video in its output directory.
    video_name_format = os.path.split(input_video)[1]
    video_format = video_name_format.split(".")[-1]
    video_name = video_name_format.split(".")[-2]
    output_folder = output_folder + '/' + video_name
    new_video_name_format = ''
    new_video_name_format += '_summary' + '.' + video_format
    video = output_folder + new_video_name_format
    print(video)
    #remove the the video if found.
    if os.path.isfile(video):
        os.remove(video)         
#####################################################################################################################
#This is the function the automatically creates the output folder when the input video is provided.
def create_output_folder(input_video , output_folder):
    #Get information about the input video.
    video_name_format = os.path.split(input_video)[1]
    video_format = video_name_format.split(".")[-1]
    video_name = video_name_format.split(".")[-2]
    #Create the output directory.
    output_folder = output_folder + '/' + video_name + '/'
    video = output_folder + video_name_format
    #If the output directory already exists it will raise error, however with using shutil it will ignore the error and overwrite the existing directory.
    shutil.rmtree(output_folder, ignore_errors=True)
    #remove the video if something old is in that directory.
    if os.path.isfile(video):
        os.remove(video)
        
    os.makedirs(output_folder)
    return output_folder
#####################################################################################################################
#Function that links GUI front-end with the back-end algorithm.
def program_start():
    #Loading general theme.
    sg.theme('DarkPurple1')
    #Loading the images used in the form of 64-bit codec.
    gif = b'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'    
    back_button = b'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'
    next_button = b'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'
    home_page = b'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'
    home_buttons = b'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'
    refresh = b'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'
    browser = b'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'
    taskbar_icon = b'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'
    start = b'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'
    FewFrames = b'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'
    frames = b'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'
# Layouts are the different pages of the GUI.
    layout1 = [[sg.Image(data = frames),sg.Text('Welcome to Video Summarizer Tool!', justification='center' , text_color = 'cyan',font = ('Courier',15)),sg.Image(data = FewFrames)]
               ,[sg.Text('Click on one of the following:',  pad = ((0,0),(100,0)), justification='center' , text_color = 'cyan',font = ('Courier',15))]
               ,[sg.Button('Webcam', key = 'WEBCAM', image_data=home_buttons, button_color=(sg.theme_background_color(), sg.theme_background_color()), border_width=0, font='Any 15'),sg.Button('Recorded video', key = 'RECORDEDVIDEO', image_data=home_buttons, button_color=(sg.theme_background_color(), sg.theme_background_color()), border_width=0, font='Any 15')]]

    layout2 = [[sg.Button('', key = 'NEXTBUTTON', pad = ((0,10),(0,0)), image_data=next_button, button_color=(sg.theme_background_color(), sg.theme_background_color()), border_width=0, font='Any 15'),sg.Button('', key = 'HOMEPAGE2', pad = ((0,300),(0,0)), image_data=home_page, tooltip = 'return to homepage', button_color=(sg.theme_background_color(), sg.theme_background_color()), border_width=0, font='Any 15')
               ,sg.Button('Refresh', tooltip = 'Refresh page', key = 'REFRESH2' , size = (20,20), pad = ((350,0),(0,0)), image_data = refresh, image_subsample = 2, button_color=(sg.theme_background_color(), sg.theme_background_color()), border_width=0, font='Any 15')],
               [sg.Text("Time of record in seconds", key = 'TIMETEXT', justification='center', pad = ((0,60),(0,0)), text_color = 'cyan', font = ('Courier',15)),sg.Input(key='-TIMEINPUT-', pad = ((0,60),(0,0)),size=(35,0.7),background_color = 'MediumOrchid')],
               [sg.Text("Select the recorded video path", key = 'INPUTTEXT2', justification='center', pad = ((0,0),(0,0)), text_color = 'cyan', font = ('Courier',15)),sg.Input(key='-INPUT2-',size=(35,0.7),background_color = 'MediumOrchid'), sg.Button('', key = '-BROWSE VIDEO12-' , tooltip = 'Browse', image_data = browser, button_color = (sg.theme_background_color(), sg.theme_background_color()) , border_width=0, font='Any 15')],
               [sg.Button('Start Recording', key = '-RECORD-' ,  tooltip = 'Start the record' , image_data = home_buttons , button_color=(sg.theme_background_color(), sg.theme_background_color()), border_width=0, font='Any 15')],
               [sg.Text('Recording Video...!', key = 'RECORDINGVIDEO', pad = ((30,0),(30,0)), text_color = 'cyan', font = ('Courier',15), visible = False) ],
               [sg.ProgressBar(1, key = 'RECORDING', orientation='h', pad = ((10,0),(0,0)),  size=(40, 20), bar_color = ('Red','MediumOrchid') , visible = False)]]
             
    layout22 = [[sg.Button('', key = 'BACKBUTTON', pad = ((0,10),(0,0)), image_data=back_button, button_color=(sg.theme_background_color(), sg.theme_background_color()), border_width=0, font='Any 15'),sg.Button('', key = 'HOMEPAGE22', pad = ((0,300),(0,0)), tooltip = 'return to homepage', image_data=home_page, button_color=(sg.theme_background_color(), sg.theme_background_color()), border_width=0, font='Any 15')
               ,sg.Button('Refresh', tooltip = 'Refresh page', key = 'REFRESH22' , size = (20,20), pad = ((350,0),(0,0)), image_data = refresh, image_subsample = 2, button_color=(sg.theme_background_color(), sg.theme_background_color()), border_width=0, font='Any 15')],
               [sg.Text("Select the summarized video path:", key = 'OUTPUTTEXT2', justification='center' , pad = ((0,0),(0,0)), text_color = 'cyan', font = ('Courier',15)),sg.Input(key='-OUTPUT2-', size=(35,0.7),background_color = 'MediumOrchid'), sg.Button('', key = '-BROWSE VIDEO22-' , tooltip = 'Browse', image_data = browser, button_color = (sg.theme_background_color(), sg.theme_background_color()) , border_width=0, font='Any 15')],
               [sg.Text('Select your Output form!',pad = ((20,0),(40,0)), justification='center', text_color = 'cyan', font = ('Courier',15))],
               [sg.Checkbox('Frames only', key = 'CB21', text_color = 'cyan',font = ('Courier',12), enable_events = True),sg.Checkbox('Video only', key = 'CB22', text_color = 'cyan',font = ('Courier',12) , enable_events = True),sg.Checkbox('Both', key = 'CB23', text_color = 'cyan',font = ('Courier',12), enable_events = True)],
               [sg.Button('', key = '-START-' ,  tooltip = 'Start Processing' , image_data = start , button_color=(sg.theme_background_color(), sg.theme_background_color()), border_width=0, font='Any 15')],
               [sg.Text('processing....!',key = 'PROGRESS_TEXT2', pad = ((30,0),(30,0)), text_color = 'cyan', font = ('Courier',15), visible = False)],
               [sg.Image(data=gif, enable_events=True, visible = False, key='-IMAGE22-', right_click_menu=['UNUSED', ['Exit']])]]

    layout3 = [[sg.Button('', key = 'HOMEPAGE3', pad = ((0,350),(0,0)), tooltip = 'return to homepage', image_data=home_page, button_color=(sg.theme_background_color(), sg.theme_background_color()), border_width=0, font='Any 15'),
               sg.Button('Refresh', tooltip = 'Refresh page', key = 'REFRESH' , size = (20,20), pad = ((350,0),(0,0)), image_data = refresh, image_subsample = 2, button_color=(sg.theme_background_color(), sg.theme_background_color()), border_width=0, font='Any 15')],
              [sg.Text("Select the input video path", justification='center', pad = ((0,60),(0,0)), text_color = 'cyan', font = ('Courier',15)),sg.Input(key='-INPUT-',size=(35,0.7),background_color = 'MediumOrchid'), sg.Button('', key = '-BROWSE VIDEO1-' , tooltip = 'Browse', image_data = browser, button_color = (sg.theme_background_color(), sg.theme_background_color()) , border_width=0, font='Any 15')],
              [sg.Text("Select the summarized video path", justification='center', pad = ((0,0),(0,0)), text_color = 'cyan', font = ('Courier',15)),sg.Input(key='-OUTPUT-',size=(35,0.7),background_color = 'MediumOrchid'), sg.Button('', key = '-BROWSE VIDEO2-' , tooltip = 'Browse', image_data = browser, button_color = (sg.theme_background_color(), sg.theme_background_color()) , border_width=0, font='Any 15')],
              [sg.Text('Select your Output form!',pad = ((20,0),(20,0)), justification='center', text_color = 'cyan', font = ('Courier',15))],
              [sg.Checkbox('Frames only', key = 'CB1', text_color = 'cyan',font = ('Courier',12), enable_events = True),sg.Checkbox('Video only', key = 'CB2', text_color = 'cyan',font = ('Courier',12) , enable_events = True),sg.Checkbox('Both', key = 'CB3', text_color = 'cyan',font = ('Courier',12), enable_events = True)],
              [sg.Button('', key = '-SUBMIT FILE-' ,  tooltip = 'Start Processing' , image_data = start , button_color=(sg.theme_background_color(), sg.theme_background_color()), border_width=0, font='Any 15')],
              [sg.Text('Processing...!', key = 'PROGRESS_TEXT', pad = ((30,0),(30,0)), text_color = 'cyan', font = ('Courier',15), visible = False)],
              [sg.Image(data=gif, enable_events=True, visible = False, key='-IMAGE-', right_click_menu=['UNUSED', ['Exit']])]]

#Main Layout that include the sub-layouts.
    layout = [[sg.Column(layout1, element_justification = 'center', key='-COL1-'),
               sg.Column(layout2, element_justification = 'center', visible=False, key='-COL2-'),
               sg.Column(layout22, element_justification = 'center', visible=False, key='-COL22-'),
               sg.Column(layout3, element_justification = 'center', visible=False, key='-COL3-')]]
    #Loading the GUI window.
    window = sg.Window('Video Summarizer Tool', layout ,  icon = taskbar_icon,  resizable = False , element_justification='center' , size = (888,500))
    #GUI Event Loop             
    while True:
        event, values = window.read(timeout=10)
        #print(event, values)
        #Several event checks to see which layout should be loaded and which should disappear when a button is clicked.
        if event == sg.WIN_CLOSED:
            sys.exit()
        if event == 'HOMEPAGE2':
            window['-COL2-'].update(visible=False)
            window['-COL1-'].update(visible=True)
            
        if event == 'HOMEPAGE22':
            window['-COL22-'].update(visible=False)
            window['-COL1-'].update(visible=True)
            
        if event == 'BACKBUTTON':
            window['-COL22-'].update(visible=False)
            window['-COL2-'].update(visible=True)
            
        if event == 'NEXTBUTTON':
            window['-COL2-'].update(visible=False)
            window['-COL22-'].update(visible=True)
            
        if event == 'HOMEPAGE3':
            window['-COL3-'].update(visible=False)
            window['-COL1-'].update(visible=True)
            
        if event == 'WEBCAM':
            window['-COL1-'].update(visible=False)
            window['-COL2-'].update(visible=True)
            
        if event == 'RECORDEDVIDEO':
            window['-COL1-'].update(visible=False)
            window['-COL3-'].update(visible=True)
        #.............................................. Recorded Video layout events..............................................#
            
        #Validation checks on the provided inputs.
        if event == '-BROWSE VIDEO1-' and values['-INPUT-'] == '':
            input_video = sg.popup_get_file('',title = 'Choose input Video',  initial_folder ='/home/pi', icon = taskbar_icon, file_types = (('AVI Video File', '*.avi*'),("MP4 Video File","*.mp4"),("FLV Video File", "*.flv"),("All Files","*.*"),))
            if not path.exists(str(input_video)):
                sg.popup_quick_message('Please provide a valid path!', text_color = 'cyan', font = ('Courier',15) ,location = (470,150))
            else:
                window['-INPUT-'].update(input_video)
            
        if event == '-BROWSE VIDEO2-' and values['-OUTPUT-'] == '' and values['-INPUT-'] != '':
            output_folder = sg.popup_get_folder('',title = 'Choose output path', initial_folder ='/home/pi', icon = taskbar_icon)
            if not path.exists(str(output_folder)):
                sg.popup_quick_message('Please provide valid paths!', text_color = 'cyan', font = ('Courier',15) ,location = (470,150))
            else:
                if output_folder != None and len(output_folder) != 0:
                    new_output_folder = create_output_folder(input_video,output_folder)
                    window['-OUTPUT-'].update(new_output_folder)
                else:
                    sg.popup_quick_message('Invalid input path!', text_color = 'cyan', font = ('Courier',15) ,location = (470,150))
                    
                        
        if event == '-BROWSE VIDEO2-' and values['-INPUT-'] == '':
            sg.popup_quick_message('Choose the Input path first!', text_color = 'cyan', font = ('Courier',15) ,location = (470,150))
            
        if event == '-SUBMIT FILE-' and (not path.exists(values['-INPUT-']) or not path.exists(values['-OUTPUT-'])):
            sg.popup_quick_message('Please provide valid paths!', text_color = 'cyan', font = ('Courier',15) ,location = (470,150))
        #Starting the algorithm in a thread, which in turn helps the gui to be interactive while the algorithm is running.
        #The several checks are for which check box is chosen.
        if event == '-SUBMIT FILE-' and values['-INPUT-'] != '' and values['-OUTPUT-'] != '' and path.exists(values['-INPUT-']) and path.exists(values['-OUTPUT-']) and values['CB1'] == True:
            thread = threading.Thread(target=Recorded_Video_SUMM, args=(window, input_video , new_output_folder , True , 'CB1'), daemon=True)
            thread.start()           
            
        if event == '-SUBMIT FILE-' and values['-INPUT-'] != '' and values['-OUTPUT-'] != '' and path.exists(values['-INPUT-']) and path.exists(values['-OUTPUT-']) and values['CB2'] == True:
            thread = threading.Thread(target=Recorded_Video_SUMM, args=(window, input_video , new_output_folder , True , 'CB2'), daemon=True)
            thread.start()
            
        if event == '-SUBMIT FILE-' and values['-INPUT-'] != '' and values['-OUTPUT-'] != '' and path.exists(values['-INPUT-']) and path.exists(values['-OUTPUT-']) and values['CB3'] == True:
            thread = threading.Thread(target=Recorded_Video_SUMM, args=(window, input_video , new_output_folder , True , 'CB3'), daemon=True)
            thread.start()
            
        if event == '-SUBMIT FILE-' and values['CB1'] == False and values['CB2'] == False and values['CB3'] == False :
            sg.popup_quick_message('Please check one of the boxes!', text_color = 'cyan', font = ('Courier',15) ,location = (470,300))
            
        #checking what the user chose for the output form.    
        if values['CB1'] == True and values['CB2'] == False and values['CB3'] == False:
            window['CB2'].update(disabled = True)
            window['CB3'].update(disabled = True)
            
        if values['CB2'] == True and values['CB1'] == False and values['CB3'] == False:
            window['CB1'].update(disabled = True)
            window['CB3'].update(disabled = True)
            
        if values['CB3'] == True and values['CB1'] == False and values['CB2'] == False:
            window['CB1'].update(disabled = True)
            window['CB2'].update(disabled = True)
            
        if values['CB1'] == False and values['CB2'] == False and values['CB3'] == False:
            window['CB1'].update(disabled = False)
            window['CB2'].update(disabled = False)
            window['CB3'].update(disabled = False)
            
        #Refresh button functionality.    
        if event == 'REFRESH':
            window['-OUTPUT-'].update('')
            window['-INPUT-'].update('')
            window['CB1'].update(value = False, disabled = False)
            window['CB2'].update(value = False, disabled = False)
            window['CB3'].update(value = False, disabled = False)
            
        #Processing icon and text.    
        if event == 'PROGRESS':
            window['PROGRESS_TEXT'].update(visible = True)
            window['-IMAGE-'].update(visible = True)
            window['-IMAGE-'].update_animation(gif,  time_between_frames=25)
        #Message will appear when the algorithm is done. 
        if event == 'DONE':
            window['PROGRESS_TEXT'].update(visible = False)
            window['-IMAGE-'].update(visible = False)
            sg.popup_quick_message('A video is summarized, please check your output folders!', text_color = 'cyan', font = ('Courier',15) ,location = (300,400), auto_close_duration = 3)   
        #An indicator that the thread finished its job.    
        if event == '-THREAD-':            
            thread.join(timeout=0)
            print('Thread finished')
            
     #.............................................. webcam Video layout events..................................................#
        #Validation checks on the provided inputs.
        if event == '-BROWSE VIDEO12-' and values['-INPUT2-'] == '':
            input_path = sg.popup_get_folder('',title = 'Choose input path', initial_folder ='/home/pi', icon = taskbar_icon)
            if not path.exists(str(input_path)):
                sg.popup_quick_message('Please provide a valid path!', text_color = 'cyan', font = ('Courier',15) ,location = (470,150))
            else:
                window['-INPUT2-'].update(input_path)
                     
        if event == '-RECORD-' and (values['-TIMEINPUT-'] != '0' or values['-TIMEINPUT-'] != '') and values['-INPUT2-'] == '' :
            try:
                int(values['-TIMEINPUT-'])
            except:
                sg.popup_quick_message('Invalid input format!', text_color = 'cyan', font = ('Courier',15) ,location = (470,150)) 
            
        #Recording page validation checks, and if the inputs are valid, it will start recording.    
        if event == '-RECORD-' and (values['-TIMEINPUT-'] != '0' or values['-TIMEINPUT-'] != '') and path.exists(values['-INPUT2-']):
            try:
                int(values['-TIMEINPUT-'])
                window['RECORDINGVIDEO'].update(visible = True)
                window['RECORDING'].update(0,visible = True)
                record_path = record(event, window, values['-INPUT2-'] , int(values['-TIMEINPUT-']))        
                sg.popup_quick_message('The video is recorded!', text_color = 'cyan', font = ('Courier',15) ,location = (470,150))
                window['-COL2-'].update(visible=False)
                window['-COL22-'].update(visible=True)                    
            except:
                sg.popup_quick_message('Invalid input, please provide a valid path and time format!', text_color = 'cyan', font = ('Courier',15) ,location = (470,150)) 
        #Validation checks on the provided inputs.    
        if event == '-BROWSE VIDEO22-' and values['-OUTPUT2-'] == '' :
            try:
                output_folder2 = sg.popup_get_folder('',title = 'Choose output path', initial_folder ='/home/pi', icon = taskbar_icon)
                if not path.exists(str(output_folder2)):
                    sg.popup_quick_message('Please provide a valid path!', text_color = 'cyan', font = ('Courier',15) ,location = (470,150))
                else:
                    if output_folder2 != None and len(output_folder2) != 0:
                        new_output_folder2 = create_output_folder(record_path,output_folder2)
                        window['-OUTPUT2-'].update(new_output_folder2)
            except:
                sg.popup_quick_message('Record a video first!', text_color = 'cyan', font = ('Courier',15) ,location = (470,150))

        
        if event == '-START-' and values['-OUTPUT2-'] == '' :
            sg.popup_quick_message('Please select output path!', text_color = 'cyan', font = ('Courier',15) ,location = (470,150))
        
        if event == '-START-' and values['CB21'] == False and values['CB22'] == False and values['CB23'] == False:
            sg.popup_quick_message('Please check one of the boxes!', text_color = 'cyan', font = ('Courier',15) ,location = (470,300))
        
        if event == '-START-' and not path.exists(values['-OUTPUT2-']):
            sg.popup_quick_message('Please provide a valid path!', text_color = 'cyan', font = ('Courier',15) ,location = (470,200))
            
        #Starting the algorithm in a thread, which in turn helps the gui to be interactive while the algorithm is running.
        #The several checks are for which check box is chosen.    
        if event == '-START-' and path.exists(values['-OUTPUT2-']) and values['CB21'] == True :
            thread2 = threading.Thread(target=WebCam_SUMM, args=(window, record_path, values['-OUTPUT2-'], True , 'CB21'), daemon=True)
            thread2.start()
            
        if event == '-START-' and path.exists(values['-OUTPUT2-']) and values['CB23'] == True:
            thread2 = threading.Thread(target=WebCam_SUMM, args=(window, record_path, values['-OUTPUT2-'], True , 'CB23'), daemon=True)
            thread2.start()
            
        if event == '-START-' and path.exists(values['-OUTPUT2-']) and values['CB22'] == True:
            thread2 = threading.Thread(target=WebCam_SUMM, args=(window, record_path, values['-OUTPUT2-'], True , 'CB22'), daemon=True)
            thread2.start()
        #checking what the user chose for the output form.  
        if event == 'PROGRESS2':
            window['PROGRESS_TEXT2'].update(visible = True)
            window['-IMAGE22-'].update(visible = True)
            window['-IMAGE22-'].update_animation(gif,  time_between_frames=25)
        #Message will appear when the algorithm is done.  
        if event == 'DONE2':
            window['PROGRESS_TEXT2'].update(visible = False)
            window['-IMAGE22-'].update(visible = False)
            sg.popup_quick_message('A recorded video is summarized, please check your output folders!', text_color = 'cyan', font = ('Courier',15) ,location = (300,400), auto_close_duration = 3)
        #An indicator that the thread finished its job.    
        if event == '-THREAD2-':            
            thread2.join(timeout=0)
            print('Thread2 finished')
        #checking what the user chose for the output form in layout22.     
        if values['CB21'] == True and values['CB22'] == False and values['CB23'] == False:
            window['CB22'].update(disabled = True)
            window['CB23'].update(disabled = True)
            
        if values['CB22'] == True and values['CB21'] == False and values['CB23'] == False:
            window['CB21'].update(disabled = True)
            window['CB23'].update(disabled = True)
            
        if values['CB23'] == True and values['CB21'] == False and values['CB22'] == False:
            window['CB21'].update(disabled = True)
            window['CB22'].update(disabled = True)
            
        if values['CB21'] == False and values['CB22'] == False and values['CB23'] == False:
            window['CB21'].update(disabled = False)
            window['CB22'].update(disabled = False)
            window['CB23'].update(disabled = False)
        #Refresh button functionality in layout2.    
        if event == 'REFRESH2':
            window['-TIMEINPUT-'].update('')
            window['-INPUT2-'].update('')
            window['RECORDINGVIDEO'].update(visible = False)
            window['RECORDING'].update(0,visible = False) 
        #Refresh button functionality in layout22.    
        if event == 'REFRESH22':
            window['-OUTPUT2-'].update('')
            window['CB21'].update(value = False, disabled = False)
            window['CB22'].update(value = False, disabled = False)
            window['CB23'].update(value = False, disabled = False)
                       
    window.close()

###################################################################################################
###################################################################################################

if __name__ == '__main__':
    program_start()