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 and f['vcodec'].split('.')[0] != 'av01'] 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) # to-do: delete saved videos # testing aria2c def download_video(url,format_id, n_keep=10): ydl_opts = { 'format':format_id, 'external_downloader' : 'aria2c', 'external_downloader_args' :['--max-connection-per-server=16','--dir=videos'], 'outtmpl': "videos/%(id)s.%(ext)s"} # create a directory for saved videos video_path = Path('videos') try: video_path.mkdir(parents=True) except FileExistsError: pass with youtube_dl.YoutubeDL(ydl_opts) as ydl: try: ydl.cache.remove() meta = ydl.extract_info(url) save_location = 'videos/' + meta['id'] + '.' + meta['ext'] except youtube_dl.DownloadError as error: print(f'error with download_video function: {error}') return(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) #print(f"saved {filename}") count += 1 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 and .mp4 format, 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) 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, dest_path, n_workers), range(n_workers)) return(skip_frames, 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((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): skip_frames, path_frames= vid2frames(url,sampling_interval) 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)).to(device) print(f"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 with torch.no_grad(): text_features = model.encode_text(clip.tokenize(search_query).to(device)) text_features /= text_features.norm(dim=-1, keepdim=True) print(image_features.dtype, text_features.dtype) similarity = (100.0 * image_features @ text_features.T) values, indices = similarity.topk(4, dim=0) best_frames = [Image.open(filenames[ind]).convert("RGB") 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"], ['https://youtu.be/0diCvgWv_ng', 1, "little Deborah practicing her ballet while wearing a tutu in empty restaurant"] ] ).launch(debug=True,enable_queue=True)