video_2_slides.py

import argparse
import os
import validators
from config import *
from download_video import download_video
from bg_modeling import capture_slides_bg_modeling
from frame_differencing import capture_slides_frame_diff
from post_process import remove_duplicates
from utils import create_output_directory, convert_slides_to_pdf


if __name__ == "__main__":
 parser = argparse.ArgumentParser(
 description="This script is used to convert video frames into slide PDFs."
 )
 parser.add_argument(
 "-v", "--video_path", help="Path to the video file, video url, or YouTube video link", type=str
 )
 parser.add_argument(
 "-o",
 "--out_dir",
 default="output_results",
 help="Path to the output directory",
 type=str,
 )
 parser.add_argument(
 "--type",
 help="type of background subtraction to be used",
 default="GMG",
 choices=["Frame_Diff", "GMG", "KNN"],
 type=str,
 )
 parser.add_argument(
 "-hf",
 "--hash-func",
 help="Hash function to use for image hashing. Only effective if post-processing is enabled",
 default=HASH_FUNC,
 choices=["dhash", "phash", "ahash"],
 type=str,
 )
 parser.add_argument(
 "-hs",
 "--hash-size",
 help="Hash size to use for image hashing. Only effective if post-processing is enabled",
 default=HASH_SIZE,
 choices=[8, 12, 16],
 type=int,
 )
 parser.add_argument(
 "--threshold",
 help="Minimum similarity threshold (in percent) to consider 2 images to be similar. Only effective if post-processing is enabled",
 default=SIM_THRESHOLD,
 choices=range(90, 101),
 type=int,
 )
 parser.add_argument(
 "-q",
 "--queue-len",
 help="Number of history images used to find out duplicate image. Only effective if post-processing is enabled",
 default=HASH_BUFFER_HISTORY,
 type=int,
 )
 parser.add_argument(
 "--no_post_process",
 action="store_true",
 default=False,
 help="flag to apply post processing or not",
 )
 parser.add_argument(
 "--convert_to_pdf",
 action="store_true",
 default=False,
 help="flag to convert the entire image set to pdf or not",
 )
 args = parser.parse_args()

 queue_len = args.queue_len
 if queue_len <= 0:
 print(
 f"Warnings: queue_len argument must be positive. Fallback to {HASH_BUFFER_HISTORY}"
 )
 queue_len = HASH_BUFFER_HISTORY

 video_path = args.video_path
 output_dir_path = args.out_dir
 type_bg_sub = args.type
 temp_file = False

 if validators.url(video_path):
 video_path = download_video(video_path)
 temp_file = True
 if video_path is None:
 exit(1)
 elif not os.path.exists(video_path):
 raise ValueError(
 "The video doesn't exist or isn't a valid URL. Please check your video path again"
 )

 output_dir_path = create_output_directory(video_path, output_dir_path, type_bg_sub)

 if type_bg_sub.lower() == "frame_diff":
 capture_slides_frame_diff(video_path, output_dir_path)
 else:
 if type_bg_sub.lower() == "gmg":
 thresh = DEC_THRESH
 elif type_bg_sub.lower() == "knn":
 thresh = DIST_THRESH

 capture_slides_bg_modeling(
 video_path,
 output_dir_path,
 type_bgsub=type_bg_sub,
 history=FRAME_BUFFER_HISTORY,
 threshold=thresh,
 MIN_PERCENT_THRESH=MIN_PERCENT,
 MAX_PERCENT_THRESH=MAX_PERCENT,
 )

 # Perform post-processing using difference hashing technique to remove duplicate slides.
 if not args.no_post_process:
 hash_size = args.hash_size
 hash_func = HASH_FUNC_DICT.get(args.hash_func)
 sim_threshold = args.threshold

 diff_threshold = int(hash_size * hash_size * (100 - sim_threshold) / 100)
 remove_duplicates(
 output_dir_path, hash_size, hash_func, queue_len, diff_threshold
 )

 if args.convert_to_pdf:
 convert_slides_to_pdf(output_dir_path)

 # if temp_file:
 # os.remove(video_path)