| import gradio as gr
|
| import os
|
| import validators
|
| from imutils import paths
|
| 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,
|
| )
|
|
|
|
|
| hash_func = HASH_FUNC_DICT.get(hash_func.lower())
|
|
|
| diff_threshold = int(hash_size * hash_size * (100 - sim_threshold) / 100)
|
| remove_duplicates(
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| output_dir_path, hash_size, hash_func, hash_queue_len, diff_threshold
|
| )
|
|
|
| pdf_path = convert_slides_to_pdf(output_dir_path)
|
|
|
|
|
| os.remove(video_path)
|
| for image_path in paths.list_images(output_dir_path):
|
| 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 some time to complete (~ half of the original video length), so stay tuned!
|
| - If the YouTube video link doesn't work, you can try again later or download video to your computer and then upload it to the app
|
| - 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.",
|
| )
|
|
|
| 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 video 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],
|
| )
|
|
|
| with gr.Row(elem_classes=["container"]):
|
| gr.HTML(
|
| """<br><br><br><center>You can duplicate this Space to skip the queue:<a href="https://huggingface.co/spaces/dragonSwing/video2slide?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a><br>
|
| <p><img src="https://visitor-badge.glitch.me/badge?page_id=dragonswing.video2slide" alt="visitors"></p></center>"""
|
| )
|
|
|
| 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()
|
|
|
