Extracting slides from "Inheritance is the base class of evil"

check_gray.py

# First run `ffmpeg -i movie.mp4 -vf crop=953:704:162:11 cropped.mp4`
# Then run `ffmpeg -i cropped.mp4 'frames/%05d.png'` and run `python3 check_gray.py frames/*.png`.
# The script deletes frames where the upper left corner 10x10 box is neither white nor dark gray.

from PIL import Image
import numpy as np
import sys
import os

for f in sys.argv[1:]:
    # Load the image
    image = Image.open(f)

    # Crop the 10x10 region from the top-left (0,0)
    cropped = image.crop((0, 0, 10, 10))

    # Convert to NumPy array for processing
    pixels = np.array(cropped)

    # Compute the average color in the region
    avg_color = pixels.mean(axis=(0, 1))[:3]  # Ignore alpha channel if present

    # Convert to integer RGB values
    r, g, b = map(int, avg_color)

    # Reference color (Hex #485858 -> RGB 72, 88, 88)
    r_ref, g_ref, b_ref = 72, 88, 88
    r_ref_white, g_ref_white, b_ref_white = 248, 248, 248

    # Compute Euclidean distance
    distance = np.sqrt((r - r_ref)**2 + (g - g_ref)**2 + (b - b_ref)**2)
    distance_white = np.sqrt((r - r_ref_white)**2 + (g - g_ref_white)**2 + (b - b_ref_white)**2)


    # Define a threshold for approximate match
    threshold = 20

    image.close()

    # Check if the color is approximately equal
    if distance >= threshold and distance_white >= threshold:
        os.remove(f)

remove_duplicate_frames.py

# removes adjacent frames that are too similar. To be run as `python3 remove_duplicate_frames.py`
import cv2
import os
import numpy as np

def are_frames_similar(frame1, frame2, threshold=0.99):
    """
    Compares two frames using template matching and returns True if they are similar.

    Args:
        frame1: The first frame (numpy array).
        frame2: The second frame (numpy array).
        threshold: The similarity threshold (correlation value).

    Returns:
        True if the frames are similar, False otherwise.
    """
    result = cv2.matchTemplate(frame1, frame2, cv2.TM_CCORR_NORMED)
    _, correlation, _, _ = cv2.minMaxLoc(result)
    return correlation >= threshold

def remove_duplicate_frames(directory):
    """
    Loops through PNG frames in a directory, compares adjacent frames, and removes duplicates.

    Args:
        directory: The directory containing the PNG frames.
    """
    frames = sorted([f for f in os.listdir(directory) if f.endswith(".png")])
    if not frames:
        print(f"No PNG frames found in directory: {directory}")
        return

    previous_frame = None
    frames_to_remove = []

    for i, frame_file in enumerate(frames):
        frame_path = os.path.join(directory, frame_file)
        current_frame = cv2.imread(frame_path)

        if current_frame is None:
            print(f"Error: Could not read frame {frame_file}")
            continue

        if previous_frame is not None:
            if are_frames_similar(previous_frame, current_frame):
                frames_to_remove.append(frame_file)
            else:
                previous_frame = current_frame #Only store the current frame as the previous frame, if they are not similar.
        else:
            previous_frame = current_frame # First frame, set as previous frame.

    for frame_file in frames_to_remove:
        frame_path = os.path.join(directory, frame_file)
        os.remove(frame_path)
        print(f"Removed duplicate frame: {frame_file}")

if __name__ == "__main__":
    directory = "frames"  # Replace with your directory
    remove_duplicate_frames(directory)