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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_file_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(video_path, output_dir_path)
# if temp_file:
# os.remove(video_path)
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