init

.gitignore

@@ -0,0 +1,3 @@
+flagged
+frames
+videos

License

@@ -0,0 +1,51 @@
+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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+END OF TERMS AND CONDITIONS

README.md

@@ -11,3 +11,4 @@ license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Adapted from https://huggingface.co/spaces/YiYiXu/it-happened-one-frame-2

app.py

@@ -0,0 +1,217 @@
+import torch
+import clip
+import cv2, youtube_dl
+from PIL import Image,ImageDraw, ImageFont
+import wget
+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 = youtube_dl.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 youtube_dl.YoutubeDL(ydl_opts) as ydl:
+        try:
+          ydl.cache.remove()
+          meta = ydl.extract_info(url)
+          thumb_url = meta['thumbnail']
+          thumb_location = f"videos/{meta['id']}.{thumb_url.split('.')[-1]}"
+          wget.download(thumb_url, out=thumb_location)
+          save_location = 'videos/' + meta['id'] + '.' + meta['ext']
+        except youtube_dl.DownloadError as error:
+          print(f'error with download_video function: {error}')
+          save_location = None
+          thumb_location = None
+          meta = 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, thumb_location, meta = None, None, None, None
+    return(fps, save_location, thumb_location, meta['title'])
+
+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, thumb, title = 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(title, thumb, 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, bs=526):
+    title, thumb, skip_frames, path_frames= vid2frames(url,sampling_interval)
+    print(f"searching {title}")
+    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')
+      thumb = Image.open(thumb).convert("RGB")
+      batch_thumb = torch.tensor(np.stack([preprocess(thumb)])).to(device)
+      with torch.no_grad():
+          thumb_features = model.encode_image(batch_thumb).to(dtype=torch.float32)
+          thumb_features /= thumb_features.norm(dim=-1, keepdim=True)
+    
+      similarity = (100.0 * image_features @ thumb_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,title, times,2)
+      print('task complete')
+    else:
+      title = "not able to download video"
+      thumb = 0
+      image_output = None
+
+    return(title, thumb, 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)')]
+outputs = [
+    gr.outputs.HTML(label=""),  # To be used as title
+    gr.outputs.Image(label="Original thumbnail"),
+    gr.outputs.Image(label="Top matches in the video"),
+]
+
+article = "Adapted from [It Happened One Frame](https://huggingface.co/spaces/YiYiXu/it-happened-one-frame-2)."
+
+gr.Interface(
+    run_inference,
+    inputs=inputs,
+    outputs=outputs,
+    title="Locate YouTube Thumbnail In Video",
+    description='A CLIP-based app that find the YouTube video frame that matches its thumbnail.',
+    article = article,
+    examples=[
+        ['https://youtu.be/v1rkzUIL8oc', 1],
+        ['https://youtu.be/k4R5wZs8cxI', 1],
+        ['https://youtu.be/0diCvgWv_ng', 1]
+    ]
+).launch(debug=True,enable_queue=True,share=True)
+

packages.txt

@@ -0,0 +1,2 @@
+ffmpeg
+fonts-liberation2

requirements.txt

@@ -0,0 +1,5 @@
+git+https://github.com/openai/CLIP.git
+torch
+youtube_dl
+opencv-python-headless
+wget