Build Vector Embeddings for Video via Python Notebook and OpenAI CLIP

Delve into AI's capabilities to analyze video data and how vector embeddings, created with Python and OpenAI CLIP, can help interpret and analyze video content.

Akmal Chaudhri

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Sep. 23, 24 · Tutorial

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As AI continues to impact many types of data processing, vector embeddings have also emerged as a powerful tool for video analysis. This article delves into some of the capabilities of AI in analyzing video data. We'll explore how vector embeddings, created using Python and OpenAI CLIP, can be used to interpret and analyze video content. Discuss the significance of vector embeddings in video analysis, offering a step-by-step guide to building these embeddings using a simple example.

The notebook file used in this article is available on GitHub.

Tutorial

1. Create a SingleStore Cloud Account

A previous article showed the steps to create a free SingleStore Cloud account. We'll use the Free Shared Tier and take the default names for the Workspace and Database.

2. Import the Notebook

We'll download the notebook from GitHub (linked in the article introduction).

From the left navigation pane in the SingleStore cloud portal, we'll select DEVELOP > Data Studio.

In the top right of the web page, we'll select New Notebook > Import From File. We'll use the wizard to locate and import the notebook we downloaded from GitHub.

3. Run the Notebook

After checking that we are connected to our SingleStore workspace, we'll run the cells one by one.

We'll start by downloading an example video from GitHub and then playing the short video directly in the notebook. The example video is 142 seconds long.

Contrastive Language-Image Pretraining (CLIP) is a model by OpenAI that understands both images and text by associating them in a shared embedding space. We'll load it as follows:

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

We'll break down a video into its individual picture frames, as follows:

    Python
   
 

   def extract_frames(video_path):
    frames = []
    cap = cv2.VideoCapture(video_path)
    frame_rate = cap.get(cv2.CAP_PROP_FPS)
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    total_seconds = total_frames / frame_rate
    target_frame_count = int(total_seconds)
    target_frame_index = 0
    for i in range(target_frame_count):
        cap.set(cv2.CAP_PROP_POS_FRAMES, target_frame_index)
        ret, frame = cap.read()
        if not ret:
            break
        frames.append(frame)
        target_frame_index += int(frame_rate)
    cap.release()
    return frames
  

Next, we'll summarise what's happening in a picture in a simpler form:

    Python
   
 

   def generate_embedding(frame):
    frame_tensor = preprocess(PILImage.fromarray(frame)).unsqueeze(0).to(device)
    with torch.no_grad():
        embedding = model.encode_image(frame_tensor).cpu().numpy()
    return embedding[0]
  

We'll now extract and summarise visual information from a video into a structured format for further analysis:

    Python
   
 

   def store_frame_embedding_and_image(video_path):
    frames = extract_frames(video_path)
    data = [
        (i+1, generate_embedding(frame), frame)
        for i, frame in enumerate(tqdm(
            frames,
            desc = "Processing frames",
            bar_format = "{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]")
        )
    ]
    return pd.DataFrame(data, columns = ["frame_number", "embedding_data", "frame_data"])
  

Let's examine the size characteristics of the data stored in the DataFrame:

    Python
   
 

   embedding_lengths = df["embedding_data"].str.len()
frame_lengths = df["frame_data"].str.len()

# Calculate min and max lengths for embeddings and frames
min_embedding_length, max_embedding_length = embedding_lengths.min(), embedding_lengths.max()
min_frame_length, max_frame_length = frame_lengths.min(), frame_lengths.max()

# Print results
print(f"Min length of embedding vectors: {min_embedding_length}")
print(f"Max length of embedding vectors: {max_embedding_length}")
print(f"Min length of frame data vectors: {min_frame_length}")
print(f"Max length of frame data vectors: {max_frame_length}")
  

Example output:

    Plain Text
   
   Min length of embedding vectors: 512
Max length of embedding vectors: 512
Min length of frame data vectors: 1080
Max length of frame data vectors: 1080

Now, let's quantify how similar the query embedding is to each frame's embedding in the DataFrame, providing a measure of similarity between a query and the frames:

    Python
   
 

   def calculate_similarity(query_embedding, df):
    # Convert the query embedding to a tensor
    query_tensor = torch.tensor(query_embedding, dtype = torch.float32).to(device)

    # Convert the list of embeddings to a numpy array
    embeddings_np = np.array(df["embedding_data"].tolist())

    # Create a tensor from the numpy array
    embeddings_tensor = torch.tensor(embeddings_np, dtype = torch.float32).to(device)

    # Compute similarities using matrix multiplication
    similarities = torch.mm(embeddings_tensor, query_tensor.unsqueeze(1)).squeeze().tolist()
    return similarities
  

Now, we'll summarize the meaning of a text query in a simpler numerical form:

    Python
   
 

   def encode_text_query(query):
    # Tokenize the query text
    tokens = clip.tokenize([query]).to(device)
    
    # Compute text features using the pretrained model
    with torch.no_grad():
        text_features = model.encode_text(tokens)
    
    # Convert the tensor to a NumPy array and return it
    return text_features.cpu().numpy().flatten()
  

And enter the query string "Ultra-Fast Ingestion" when prompted:

    Python
   
   query = input("Enter your query: ")
text_query_embedding = encode_text_query(query)
text_similarities = calculate_similarity(text_query_embedding, df)
df["text_similarity"] = text_similarities

We'll now get the top 5 best text matches:

    Python
   
   # Retrieve the top 5 text matches based on similarity
top_text_matches = df.nlargest(5, "text_similarity")

print("Top 5 best matches:")
print(top_text_matches[["frame_number", "text_similarity"]].to_string(index = False))

Example output:

    Plain Text
   
 

   Top 5 best matches:
 frame_number  text_similarity
           40        36.456184
           39        36.081161
           43        33.295975
           42        32.423229
           45        31.931164
  

We can also plot the frames:

    Python
   
 

   def plot_frames(frames, frame_numbers):
    num_frames = len(frames)
    fig, axes = plt.subplots(1, num_frames, figsize = (15, 5))
    
    for ax, frame_data, frame_number in zip(axes, frames, frame_numbers):
        ax.imshow(frame_data)
        ax.set_title(f"Frame {frame_number}")
        ax.axis("off")
    
    plt.tight_layout()
    plt.show()

# Collect frame data and numbers for the top text matches
top_text_matches_indices = top_text_matches.index.tolist()
frames = [df.at[index, "frame_data"] for index in top_text_matches_indices]
frame_numbers = [df.at[index, "frame_number"] for index in top_text_matches_indices]

# Plot the frames
plot_frames(frames, frame_numbers)
  

Now, we'll summarise an image query in a simpler numerical form:

    Python
   
 

   def encode_image_query(image):
    # Preprocess the image and add batch dimension
    image_tensor = preprocess(image).unsqueeze(0).to(device)
    
    # Extract features using the model
    with torch.no_grad():
        image_features = model.encode_image(image_tensor)
    
    # Convert features to NumPy array and flatten
    return image_features.cpu().numpy().flatten()
  

Download an example image to use for a query:

    Python
   
 

   image_url = "https://github.com/VeryFatBoy/clip-demo/raw/main/thumbnails/1_what_makes_singlestore_unique.png"

response = requests.get(image_url)

if response.status_code == 200:
    display(Image(url = image_url))
    image_file = PILImage.open(BytesIO(response.content))

    image_query_embedding = encode_image_query(image_file)
    image_similarities = calculate_similarity(image_query_embedding, df)
    df["image_similarity"] = image_similarities
else:
    print("Failed to download the image, status code:", response.status_code)
  

We'll now get the top 5 best image matches:

    Python
   
   top_image_matches = df.nlargest(5, "image_similarity")

print("Top 5 best matches:")
print(top_image_matches[["frame_number", "image_similarity"]].to_string(index = False))

Example output:

    Plain Text
   
 

   Top 5 best matches:
 frame_number  image_similarity
            7         57.674603
            9         43.669739
            6         42.573799
           15         40.296551
           93         40.201733
  

We can also plot the frames:

    Python
   
   # Collect frame data and numbers for the top image matches
top_image_matches_indices = top_image_matches.index.tolist()
frames = [df.at[index, "frame_data"] for index in top_image_matches_indices]
frame_numbers = [df.at[index, "frame_number"] for index in top_image_matches_indices]

# Plot the frames
plot_frames(frames, frame_numbers)

Now let's combine both text and image by using element-wise averaging:

    Python
   
 

   # Normalise
text_query_embedding /= np.linalg.norm(
    text_query_embedding,
    axis = -1,
    keepdims = True
)
image_query_embedding /= np.linalg.norm(
    image_query_embedding,
    axis = -1,
    keepdims = True
)

combined_query_embedding = (text_query_embedding + image_query_embedding) / 2
combined_similarities = calculate_similarity(combined_query_embedding, df)
df["combined_similarity"] = combined_similarities
  

We'll now get the top 5 best-combined matches:

    Python
   
   top_combined_matches = df.nlargest(5, "combined_similarity")

print("Top 5 best matches:")
print(top_combined_matches[["frame_number", "combined_similarity"]].to_string(index = False))

Example output:

    Plain Text
   
 

   Top 5 best matches:
 frame_number  combined_similarity
            7             4.304160
            6             3.673842
            5             3.613622
           93             3.595592
           94             3.559316
  

We can also plot the frames:

    Python
   
   # Collect frame data and numbers for the top combined matches
top_combined_matches_indices = top_combined_matches.index.tolist()
frames = [df.at[index, "frame_data"] for index in top_combined_matches_indices]
frame_numbers = [df.at[index, "frame_number"] for index in top_combined_matches_indices]

# Plot the frames
plot_frames(frames, frame_numbers)

Next, we'll store the data in SingleStore. First, we'll prepare the data:

    Python
   
   frames_df = df.copy()
frames_df.drop(
    columns = ["text_similarity", "image_similarity", "combined_similarity"],
    inplace = True
)

query_string = combined_query_embedding.copy()

We'll also need to perform a little data cleanup:

    Python
   
   def process_data(arr):
    return np.array2string(arr, separator = ",").replace("\n", "")

frames_df["embedding_data"] = frames_df["embedding_data"].apply(process_data)
frames_df["frame_data"] = frames_df["frame_data"].apply(process_data)
query_string = process_data(query_string)

We'll check if we are running on the Free Shared Tier:

    Python
   
   shared_tier_check = %sql SHOW VARIABLES LIKE "is_shared_tier"
if not shared_tier_check or shared_tier_check[0][1] == "OFF":
    %sql DROP DATABASE IF EXISTS video_db;
    %sql CREATE DATABASE IF NOT EXISTS video_db;

And then get a connection to the database:

    Python
   
   from sqlalchemy import *

db_connection = create_engine(connection_url)

We'll ensure a table is available to store the data:

    SQL
   
 

   DROP TABLE IF EXISTS frames;

CREATE TABLE IF NOT EXISTS frames (
    frame_number INT(10) UNSIGNED NOT NULL,
    embedding_data VECTOR(512) NOT NULL,
    frame_data TEXT,
    KEY(frame_number)
);
  

Then write the DataFrame to SingleStore:

    Python
   
 

   frames_df.to_sql(
    "frames",
    con = db_connection,
    if_exists = "append",
    index = False,
    chunksize = 1000
)
  

We can read some data back from SingleStore:

    SQL
   
 

   SELECT frame_number,
    SUBSTRING(embedding_data, 1, 50) AS embedding_data,
    SUBSTRING(frame_data, 1, 50) AS frame_data
FROM frames
LIMIT 1;
  

We can also create an ANN index:

    SQL
   
 

   ALTER TABLE frames ADD VECTOR INDEX (embedding_data)
     INDEX_OPTIONS '{
          "index_type":"AUTO",
          "metric_type":"DOT_PRODUCT"
     }';
  

First, let's run a query without using the ANN index:

    SQL
   
 

   SELECT frame_number,
    embedding_data <*> :query_string AS similarity
FROM frames
ORDER BY similarity USE INDEX () DESC
LIMIT 5;
  

Example output:

    Plain Text
   
 

   frame_number         similarity
           7  4.304159641265869
           6  3.673842668533325
           5 3.6136221885681152
          93 3.5955920219421387
          94 3.5593154430389404
  

Now, we'll run a query using the ANN index:

    SQL
   
 

   SELECT frame_number,
    embedding_data <*> :query_string AS similarity
FROM frames
ORDER BY similarity DESC
LIMIT 5;
  

Example output:

    Plain Text
   
 

   frame_number         similarity
           7  4.304159641265869
           6  3.673842668533325
           5 3.6136221885681152
          93 3.5955920219421387
          94 3.5593154430389404
  

We can also use Python as an alternative:

    Python
   
 

   sql_query = """
SELECT frame_number, embedding_data, frame_data
FROM frames
ORDER BY embedding_data <*> %s DESC
LIMIT 5;
"""

new_frames_df = pd.read_sql(
    sql_query,
    con = db_connection,
    params = (query_string,)
)

new_frames_df.head()
  

Since we are only storing a small quantity of data (142 rows), the results are identical whether we use the ANN index or not. Our results from querying the database agree with our earlier results for the combined query.

Summary

In this article, we applied vector embeddings for video analysis using Python and OpenAI's CLIP model. We saw how to extract frames from a video, generate embeddings for each frame, and use these embeddings to perform similarity searches based on text and image queries. This allowed us to retrieve relevant video segments, making it a useful tool for video content analysis.

Today, many modern LLMs are offering multimodal capabilities and quite extensive support for audio, images, and video. However, the example in this article showed that it is possible to use freely available software to achieve some of the same capabilities.

Data (computing) Frame (networking) Python (language) neural network

Published at DZone with permission of Akmal Chaudhri. See the original article here.

Opinions expressed by DZone contributors are their own.

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Build Vector Embeddings for Video via Python Notebook and OpenAI CLIP

Delve into AI's capabilities to analyze video data and how vector embeddings, created with Python and OpenAI CLIP, can help interpret and analyze video content.

Tutorial

1. Create a SingleStore Cloud Account

2. Import the Notebook

3. Run the Notebook

Summary

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