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import gradio as gr
import os
import glob
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
def process(
video_path,
bg_type,
frame_buffer_history,
hash_size,
hash_func,
hash_queue_len,
sim_threshold,
):
output_dir_path = "output_results"
output_dir_path = create_output_directory(video_path, output_dir_path, bg_type)
if bg_type.lower() == "Frame Diff":
capture_slides_frame_diff(video_path, output_dir_path)
else:
if bg_type.lower() == "gmg":
thresh = DEC_THRESH
elif bg_type.lower() == "knn":
thresh = DIST_THRESH
capture_slides_bg_modeling(
video_path,
output_dir_path,
type_bgsub=bg_type,
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.
hash_func = HASH_FUNC_DICT.get(hash_func.lower())
diff_threshold = int(hash_size * hash_size * (100 - sim_threshold) / 100)
remove_duplicates(
output_dir_path, hash_size, hash_func, hash_queue_len, diff_threshold
)
pdf_path = convert_slides_to_pdf(video_path, output_dir_path)
# Remove unneccessary files
os.remove(video_path)
for image_path in glob.glob(f"{output_dir_path}/*.jpg"):
os.remove(image_path)
return pdf_path
def process_file(
file_obj,
bg_type,
frame_buffer_history,
hash_size,
hash_func,
hash_queue_len,
sim_threshold,
):
return process(
file_obj.name,
bg_type,
frame_buffer_history,
hash_size,
hash_func,
hash_queue_len,
sim_threshold,
)
def process_via_url(
url,
bg_type,
frame_buffer_history,
hash_size,
hash_func,
hash_queue_len,
sim_threshold,
):
if validators.url(url):
video_path = download_video(url)
if video_path is None:
raise gr.Error("An error occurred while downloading the video, please try again later")
return process(
video_path,
bg_type,
frame_buffer_history,
hash_size,
hash_func,
hash_queue_len,
sim_threshold,
)
else:
raise gr.Error("Please enter a valid video URL")
with gr.Blocks(css="style.css") as demo:
with gr.Row(elem_classes=["container"]):
gr.Markdown(
"""
# Video 2 Slides Converter
Convert your video presentation into PDF slides with one click.
You can browse your video from the local file system, or enter a video URL/YouTube video link to start processing.
**Note**:
- It will take a bit of time to complete (~40% of the original video length), so stay tuned!
- Remember to press Enter if you are using an external URL
""",
elem_id="container",
)
with gr.Row(elem_classes=["container"]):
with gr.Column(scale=1):
with gr.Accordion("Advanced parameters"):
bg_type = gr.Dropdown(
["Frame Diff", "GMG", "KNN"],
value="GMG",
label="Background subtraction",
info="Type of background subtraction to be used",
)
frame_buffer_history = gr.Slider(
minimum=5,
maximum=20,
value=FRAME_BUFFER_HISTORY,
step=5,
label="Frame buffer history",
info="Length of the frame buffer history to model background.",
)
# Post process
hash_func = gr.Dropdown(
["Difference hashing", "Perceptual hashing", "Average hashing"],
value="Difference hashing",
label="Background subtraction",
info="Hash function to use for image hashing",
)
hash_size = gr.Slider(
minimum=8,
maximum=16,
value=HASH_SIZE,
step=2,
label="Hash size",
info="Hash size to use for image hashing",
)
hash_queue_len = gr.Slider(
minimum=5,
maximum=15,
value=HASH_BUFFER_HISTORY,
step=5,
label="Hash queue len",
info="Number of history images used to find out duplicate image",
)
sim_threshold = gr.Slider(
minimum=90,
maximum=100,
value=SIM_THRESHOLD,
step=1,
label="Similarity threshold",
info="Minimum similarity threshold (in percent) to consider 2 images to be similar",
)
with gr.Column(scale=2):
with gr.Row(elem_id="row-flex"):
with gr.Column(scale=3):
file_url = gr.Textbox(
value="",
label="Upload your file",
placeholder="Enter a video url or YouTube link",
show_label=False,
)
with gr.Column(scale=1, min_width=160):
upload_button = gr.UploadButton("Browse File", file_types=["video"])
file_output = gr.File(file_types=[".pdf"], label="Output PDF")
gr.Examples(
[
[
"https://www.youtube.com/watch?v=bfmFfD2RIcg",
"output_results/Neural Network In 5 Minutes.pdf",
],
[
"https://www.youtube.com/watch?v=EEo10bgsh0k",
"output_results/react-in-5-minutes.pdf",
],
],
[file_url, file_output],
)
file_url.submit(
process_via_url,
[
file_url,
bg_type,
frame_buffer_history,
hash_size,
hash_func,
hash_queue_len,
sim_threshold,
],
file_output,
)
upload_button.upload(
process_file,
[
upload_button,
bg_type,
frame_buffer_history,
hash_size,
hash_func,
hash_queue_len,
sim_threshold,
],
file_output,
)
demo.queue(concurrency_count=4).launch()
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