Somayeh-Komeylian-Computer-Vision-Digital-Memorial-Albums-with-Python-Codes

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Somayeh-Komeylian-Computer-Vision-Digital-Memorial-Albums-with-Python-Codes

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Computer Vision-Digital Memorial Albums with Python Codes

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# Provided by Somayeh Komeylian: PhD Student at UCSD & SDSU #
# Computer Vision - Digital Memorial Albums with Python Codes #
# Please save all files (a Jupyter file, the images and a movie) in the given Zip code here first in the same folder, then run the second cell of the Jupyter file #

import tkinter as tk
from PIL import Image, ImageTk
import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk

# --- Configuration --- #
# Use forward slashes for cross-platform compatibility
image_files = [
    r'C:/Users/komey/OneDrive/Desktop/Summer_classes/Computer Vision/Final Project/image1.jpg',
    r'C:/Users/komey/OneDrive/Desktop/Summer_classes/Computer Vision/Final Project/image2.jpg',
    r'C:/Users/komey/OneDrive/Desktop/Summer_classes/Computer Vision/Final Project/image3.jpg',
    r'C:/Users/komey/OneDrive/Desktop/Summer_classes/Computer Vision/Final Project/image4.jpg',
    r'C:/Users/komey/OneDrive/Desktop/Summer_classes/Computer Vision/Final Project/image5.jpg',
    r'C:/Users/komey/OneDrive/Desktop/Summer_classes/Computer Vision/Final Project/image6.jpg',
    r'C:/Users/komey/OneDrive/Desktop/Summer_classes/Computer Vision/Final Project/image7.jpg',
    r'C:/Users/komey/OneDrive/Desktop/Summer_classes/Computer Vision/Final Project/image8.jpg',
    r'C:/Users/komey/OneDrive/Desktop/Summer_classes/Computer Vision/Final Project/image9.jpg'
]

# Path to the loading movie file
loading_movie_path = r'C:/Users/komey/OneDrive/Desktop/Summer_classes/Computer Vision/Final Project/loading.mp4'

# Set the delay in milliseconds for 5 seconds for the image slideshow
delay_milliseconds = 5 * 1000  # 5 seconds = 5000 milliseconds

# --- Global variables to manage the slideshow state and image references ---
current_image_index = 0
image_labels = {}  # Dictionary to hold the labels for each channel

# --- Matplotlib setup ---
# Create a Matplotlib figure for the histogram plot
fig, ax = plt.subplots(figsize=(6, 4), tight_layout=True)
ax.set_title("BGR Histogram")
ax.set_xlabel("Pixel Value")
ax.set_ylabel("Frequency")
ax.grid(True)

# --- Tkinter setup ---
root = tk.Tk()
root.title("Loading Movie and Image Slideshow with Histograms")

# --- Function to handle key presses ---
def key_handler(event):
    """Handles keyboard events, primarily for quitting the application."""
    if event.char == 'q':
        # Release the video capture object before destroying the window
        if 'video_cap' in globals() and video_cap.isOpened():
            video_cap.release()
        root.destroy()

# --- Function to update and display the next image ---
def update_image():
    """Loads the next image, splits it into channels, updates the labels, and plots the histogram."""
    global current_image_index
    global image_labels
    if not image_files:
        print("Error: No image files specified.")
        return

image_path = image_files[current_image_index]
    try:
        img = cv2.imread(image_path)
        if img is None:
            print(f"Error: Could not read image from path {image_path}")
            current_image_index = (current_image_index + 1) % len(image_files)
            root.after(delay_milliseconds, update_image)
            return
    except Exception as e:
        print(f"An error occurred while loading image {image_path}: {e}")
        current_image_index = (current_image_index + 1) % len(image_files)
        root.after(delay_milliseconds, update_image)
        return

# Resize the image for display
    display_width = 400
    display_height = 300
    resized_img = cv2.resize(img, (display_width, display_height), interpolation=cv2.INTER_AREA)

# Split the image into B, G, R channels
    B, G, R = cv2.split(resized_img)
    zeros = np.zeros_like(G)
    blue = cv2.merge([B, zeros, zeros])
    green = cv2.merge([zeros, G, zeros])
    red = cv2.merge([zeros, zeros, R])

def to_photoimage(cv2_image):
        img_rgb = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2RGB)
        pil_image = Image.fromarray(img_rgb)
        return ImageTk.PhotoImage(pil_image)

photos = {
        'Original': to_photoimage(resized_img),
        'Blue': to_photoimage(blue),
        'Green': to_photoimage(green),
        'Red': to_photoimage(red)
    }

if not image_labels:
        for widget in root.winfo_children():
            widget.destroy()
        
        main_frame = tk.Frame(root)
        main_frame.pack(padx=10, pady=10)

titles = ['Original', 'Blue', 'Green', 'Red']
        for i, title in enumerate(titles):
            row = i // 2
            col = i % 2
            label_frame = tk.Frame(main_frame)
            label_frame.grid(row=row, column=col, padx=5, pady=5)
            image_label = tk.Label(label_frame, image=photos[title])
            image_label.pack()
            title_label = tk.Label(label_frame, text=title)
            title_label.pack()
            image_labels[title] = image_label
            
        # Create a new frame for the histogram plot
        histogram_frame = tk.Frame(root)
        histogram_frame.pack(padx=10, pady=10)
        
        # Embed the matplotlib figure into the Tkinter frame
        canvas = FigureCanvasTkAgg(fig, master=histogram_frame)
        canvas.draw()
        canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=1)

# Optional: Add a toolbar for the plot
        toolbar = NavigationToolbar2Tk(canvas, histogram_frame)
        toolbar.update()
        canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=1)
        
    else:
        for title in ['Original', 'Blue', 'Green', 'Red']:
            image_labels[title].config(image=photos[title])
            image_labels[title].image = photos[title]
            
    # Calculate and plot the histogram for each channel
    ax.clear()  # Clear the previous plot
    colors = ('b', 'g', 'r')
    for i, col in enumerate(colors):
        hist = cv2.calcHist([resized_img], [i], None, [256], [0, 256])
        ax.plot(hist, color=col)
    
    ax.set_title("BGR Histogram")
    ax.set_xlabel("Pixel Value")
    ax.set_ylabel("Frequency")
    ax.grid(True)
    
    fig.canvas.draw()
    
    current_image_index = (current_image_index + 1) % len(image_files)
    root.after(delay_milliseconds, update_image)

# --- Functions for playing the loading video ---
def play_video():
    """Starts playing the loading movie."""
    global video_cap, video_label, loading_label
    # Create a frame to hold both the video and the loading text
    loading_frame = tk.Frame(root)
    loading_frame.pack(fill="both", expand=True)

# Create and pack the loading label
    loading_label = tk.Label(loading_frame, text="Loading Digital Memorial Albums\n Computer Vision in Tehran Institute of Technology" , font=("Helvetica", 24), fg="white", bg="green")
    loading_label.pack(pady=20)

# Create and pack the video label within the same frame
    video_label = tk.Label(loading_frame)
    video_label.pack()

video_cap = cv2.VideoCapture(loading_movie_path)
    if not video_cap.isOpened():
        print(f"Error: Could not open video file {loading_movie_path}. Starting slideshow immediately.")
        loading_frame.destroy()
        update_image()
        return
    update_frame()

def update_frame():
    """Reads a new frame and updates the video label."""
    global video_cap, video_label
    ret, frame = video_cap.read()
    if ret:
        # Resize frame to a fixed size to avoid resizing issues
        display_width = 640
        display_height = 480
        resized_frame = cv2.resize(frame, (display_width, display_height))
        frame_rgb = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2RGB)
        pil_img = Image.fromarray(frame_rgb)
        tk_img = ImageTk.PhotoImage(image=pil_img)
        video_label.config(image=tk_img)
        video_label.image = tk_img
        fps = video_cap.get(cv2.CAP_PROP_FPS)
        if fps > 0:
            frame_delay = int(1000 / fps)
        else:
            frame_delay = 30
        root.after(frame_delay, update_frame)
    else:
        video_cap.release()
        print("Video finished. Starting image slideshow.")
        # Destroy the loading frame and its contents (including the label)
        for widget in root.winfo_children():
            widget.destroy()
        update_image()

# --- Main execution ---
if __name__ == '__main__':
    root.bind('<Key>', key_handler)
    play_video()
    root.mainloop()

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