import torch import clip import cv2, yt_dlp 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, ydl_opts={}, format_note='240p', ext='mp4', max_size = 500000000): defaults = ['480p', '360p','240p','144p'] ydl_opts = ydl_opts ydl = yt_dlp.YoutubeDL(ydl_opts) info_dict = ydl.extract_info(url, download=False) formats = info_dict.get('formats', None) # filter out formats we can't process formats = [f for f in formats if f['ext'] == ext and f['vcodec'].split('.')[0] != 'av01' and f['filesize'] is not None and f['filesize'] <= max_size] 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] format = formats[0] format_id = format.get('format_id', None) fps = format.get('fps', None) print(f'format selected: {format}') return(format, format_id, fps) def download_video(url): # create "videos" foder for saved videos path_videos = Path('videos') try: path_videos.mkdir(parents=True) except FileExistsError: pass # clear the "videos" folder videos_to_keep = ['v1rkzUIL8oc', 'k4R5wZs8cxI','0diCvgWv_ng'] if len(list(path_videos.glob('*'))) > 10: for path_video in path_videos.glob('*'): if path_video.stem not in set(videos_to_keep): path_video.unlink() print(f'removed video {path_video}') # select format to download for given video # by default select 240p and .mp4 try: format, format_id, fps = select_video_format(url) ydl_opts = { 'format':format_id, 'outtmpl': "videos/%(id)s.%(ext)s"} with yt_dlp.YoutubeDL(ydl_opts) as ydl: try: ydl.cache.remove() meta = ydl.extract_info(url) save_location = 'videos/' + meta['id'] + '.' + meta['ext'] except yt_dlp.DownloadError as error: print(f'error with download_video function: {error}') save_location = None except IndexError as err: print(f"can't find suitable video formats. we are not able to process video larger than 95 Mib at the moment") fps, save_location = None, None return(fps, save_location) def process_video_parallel(video, skip_frames, dest_path, num_processes, process_number): cap = cv2.VideoCapture(video) frames_per_process = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) // (num_processes) count = frames_per_process * process_number cap.set(cv2.CAP_PROP_POS_FRAMES, count) print(f"worker: {process_number}, process frames {count} ~ {frames_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 < frames_per_process * (process_number + 1) : ret, frame = cap.read() if not ret: break if count % skip_frames ==0: filename =f"{dest_path}/{count}.jpg" cv2.imwrite(filename, frame) count += 1 cap.release() def vid2frames(url, sampling_interval=1): # create folder for extracted frames - if folder exists, delete and create a new one path_frames = Path('frames') try: path_frames.mkdir(parents=True) except FileExistsError: shutil.rmtree(path_frames) path_frames.mkdir(parents=True) # download the video fps, video = download_video(url) if video is not None: if fps is None: fps = 30 skip_frames = int(fps * sampling_interval) print(f'video saved at: {video}, fps:{fps}, skip_frames: {skip_frames}') # extract video frames at given sampling interval with multiprocessing - n_workers = min(os.cpu_count(), 12) print(f'now extracting frames with {n_workers} process...') with Pool(n_workers) as pool: pool.map(partial(process_video_parallel, video, skip_frames, path_frames, n_workers), range(n_workers)) else: skip_frames, path_frames = None, None return(skip_frames, path_frames) 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((60, 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, bs=526): print(f"search for : {search_query}") skip_frames, path_frames= vid2frames(url,sampling_interval) if path_frames is not None: filenames = sorted(path_frames.glob('*.jpg'),key=lambda p: int(p.stem)) n_frames = len(filenames) bs = min(n_frames,bs) print(f"extracted {n_frames} frames, now encoding images") # encoding images one batch at a time, combine all batch outputs -> image_features, size n_frames x 512 image_features = torch.empty(size=(n_frames, 512),dtype=torch.float32).to(device) print(f"encoding images, batch size :{bs} ; number of batches: {len(range(0, n_frames,bs))}") for b in range(0, n_frames,bs): images = [] # loop through all frames in the batch -> create batch_image_input, size bs x 3 x 224 x 224 for filename in filenames[b:b+bs]: image = Image.open(filename).convert("RGB") images.append(preprocess(image)) batch_image_input = torch.tensor(np.stack(images)).to(device) # encoding batch_image_input -> batch_image_features with torch.no_grad(): batch_image_features = model.encode_image(batch_image_input) batch_image_features /= batch_image_features.norm(dim=-1, keepdim=True) # add encoded image embedding to image_features image_features[b:b+bs] = batch_image_features # encoding search query print(f'encoding search query') with torch.no_grad(): text_features = model.encode_text(clip.tokenize(search_query).to(device)).to(dtype=torch.float32) text_features /= text_features.norm(dim=-1, keepdim=True) similarity = (100.0 * image_features @ text_features.T) values, indices = similarity.topk(4, dim=0) print(f"indices for best matches{indices}") print(f"filenames for best matches {[filenames[i]for i in indices]}") best_frames = [Image.open(filenames[ind]).convert("RGB") for ind in indices] times = [f'{datetime.timedelta(seconds = round(ind[0].item() * sampling_interval,2))}' for ind in indices] image_output = captioned_strip(best_frames,search_query, times,2) title = search_query print('task complete') else: title = "not able to download video" image_output = None return(title, image_output) inputs = [gr.inputs.Textbox(label="Give us the link to your youtube video! (maximum size 50 MB)"), gr.Number(1,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=""), ] article = "Check out [this blogpost](https://yiyixuxu.github.io/2022/06/12/It-Happened-One-Frame.html) about this app." gr.Interface( run_inference, inputs=inputs, outputs=outputs, title="It Happened One Frame", description='A CLIP-based app that search YouTube video frame based on text', article = article, 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"], ['https://youtu.be/0diCvgWv_ng', 1, "little Deborah practicing her ballet while wearing a tutu in empty restaurant"] ] ).launch(debug=True,enable_queue=True,share=True)