app.py

import torch
import clip
import cv2, youtube_dl
from PIL import Image,ImageDraw, ImageFont
import os
from functools import partial
from multiprocessing.pool import Pool
import shutil
from pathlib import Path
import numpy as np
import datetime
import gradio as gr


# load model and preprocess
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/32")

def select_video_format(url, format_note='480p', ext='mp4'):
    defaults = ['480p', '360p','240p','144p']
    ydl_opts = {}
    ydl = youtube_dl.YoutubeDL(ydl_opts)
    info_dict = ydl.extract_info(url, download=False)
    formats = info_dict.get('formats', None)
    available_format_notes = set([f['format_note'] for f in formats])
    if format_note not in available_format_notes:
        format_note = [d for d in defaults if d in available_format_notes][0]
    formats = [f for f in formats if f['format_note'] == format_note and f['ext'] == ext]
    format = formats[0]
    format_id = format.get('format_id', None)
    fps = format.get('fps', None)
    print(f'format selected: {format}')
    return(format_id, fps)

def download_video(url,format_id):
    ydl_opts = {
      'format':format_id,
      'outtmpl': "%(id)s.%(ext)s"}
    meta = youtube_dl.YoutubeDL(ydl_opts).extract_info(url)
    save_location = meta['id'] + '.' + meta['ext']
    return(save_location)

def read_frames(dest_path):
  original_images = []
  images = []
  for filename in sorted(dest_path.glob('*.jpg'),key=lambda p: int(p.stem)):
    image = Image.open(filename).convert("RGB")
    original_images.append(image)
    images.append(preprocess(image))
  return original_images, images

def process_video_parallel(video, skip_frames, dest_path, num_processes, process_number):
    cap = cv2.VideoCapture(video)
    chunks_per_process = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) // (num_processes * skip_frames)
    count =  skip_frames * chunks_per_process * process_number
    print(f"worker: {process_number}, process frames {count} ~ {skip_frames * chunks_per_process * (process_number + 1)} \n total number of frames: {cap.get(cv2.CAP_PROP_FRAME_COUNT)} \n video: {video}; isOpen? : {cap.isOpened()}")
    while count < skip_frames * chunks_per_process * (process_number + 1) :
        if skip_frames > 1: 
            cap.set(cv2.CAP_PROP_POS_FRAMES, count)
        ret, frame = cap.read()
        if not ret:
            break
        filename =f"{dest_path}/{count}.jpg"
        cv2.imwrite(filename, frame)
        print(f"saved {filename}")
        count += skip_frames  # Skip 300 frames i.e. 10 seconds for 30 fps
    cap.release()


def vid2frames(url, sampling_interval=1, ext='mp4'):
  # create folder for extracted frames - if folder exists, delete and create a new one
    dest_path = Path('frames')
    try:
        dest_path.mkdir(parents=True)
    except FileExistsError:
        shutil.rmtree(dest_path)
        dest_path.mkdir(parents=True)
    # figure out the format for download,
    # by default select 480p, if not available, choose the best format available
    # mp4
    format_id, fps = select_video_format(url, format_note='480p', ext='mp4')
    # download the video 
    video = download_video(url,format_id)
    # calculate skip_frames
    try:
        skip_frames = int(fps * sampling_interval)
    except:
        skip_frames = int(30 * sampling_interval)

    # testing
    skip_frames = 1
    print(f'video saved at: {video}, fps:{fps}, skip_frames: {skip_frames}')
    # extract video frames at given sampling interval with multiprocessing - 
    print('extracting frames...')
    n_workers = min(os.cpu_count(), 1)
   # testing..
    cap = cv2.VideoCapture(video)
    print(f'video: {video}; isOpen? : {cap.isOpened()}')
    print(f'n_workers: {n_workers}')
    with Pool(n_workers) as pool:
        pool.map(partial(process_video_parallel, video, skip_frames, dest_path, n_workers), range(n_workers))
    return dest_path


def captioned_strip(images, caption=None, times=None, rows=1):
    increased_h = 0 if caption is None else 30
    w, h = images[0].size[0], images[0].size[1]
    img = Image.new("RGB", (len(images) * w // rows, h * rows + increased_h))
    for i, img_ in enumerate(images):
        img.paste(img_, (i // rows * w, increased_h + (i % rows) * h))
    if caption is not None:
        draw = ImageDraw.Draw(img)
        font = ImageFont.truetype(
            "/usr/share/fonts/truetype/liberation2/LiberationMono-Bold.ttf", 16
        )
        font_small = ImageFont.truetype(
            "/usr/share/fonts/truetype/liberation2/LiberationMono-Bold.ttf", 12
        )
        draw.text((20, 3), caption, (255, 255, 255), font=font)
        for i,ts in enumerate(times):
          draw.text((
              (i % rows) * w + 40 , #column poistion
               i // rows * h  + 33) # row position
          , ts, 
          (255, 255, 255), font=font_small)
    return img

def run_inference(url, sampling_interval, search_query):
    path_frames = vid2frames(url,sampling_interval)
    original_images, images = read_frames(path_frames)
    image_input = torch.tensor(np.stack(images)).to(device)
    with torch.no_grad():
        image_features = model.encode_image(image_input)
        text_features = model.encode_text(clip.tokenize(search_query).to(device))
  
    image_features /= image_features.norm(dim=-1, keepdim=True)
    text_features /= text_features.norm(dim=-1, keepdim=True)
  
    similarity = (100.0 * image_features @ text_features.T)
    values, indices = similarity.topk(4, dim=0)
  
    best_frames = [original_images[ind] for ind in indices]
    times = [f'{datetime.timedelta(seconds = ind[0].item() * sampling_interval)}' for ind in indices]
    image_output = captioned_strip(best_frames,search_query, times,2)
    title = search_query
    return(title, image_output)

inputs = [gr.inputs.Textbox(label="Give us the link to your youtube video!"),
          gr.Number(5,label='sampling interval (seconds)'),
          gr.inputs.Textbox(label="What do you want to search?")]
outputs = [
    gr.outputs.HTML(label=""),  # To be used as title
    gr.outputs.Image(label=""),
]

gr.Interface(
    run_inference,
    inputs=inputs,
    outputs=outputs,
    title="It Happened One Frame",
    description='A CLIP-based app that search video frame based on text',
    examples=[
        ['https://youtu.be/v1rkzUIL8oc', 1, "James Cagney dancing down the stairs"],
        ['https://youtu.be/k4R5wZs8cxI', 1, "James Cagney smashes a grapefruit into Mae Clarke's face"]
    ]
).launch(debug=True,enable_queue=True)