Image Classification Pipeline using EvaDB#

Assume the database has loaded a video mnist_video.

1. Connect to EvaDB#

import evadb
cursor = evadb.connect().cursor()

2. Register Image Classification Model as a Function in SQL#

Create an image classification function from python source code.

query = cursor.query("""
    CREATE UDF IF NOT EXISTS MnistImageClassifier
    IMPL 'evadb/udfs/mnist_image_classifier.py';
""").execute()

3. Execute Image Classification through SQL#

After the function is registered to EvaDB system, it can be directly called and used in SQL query.

Python

query = cursor.table("mnist_video").select("MnistImageClassifier(data).label")

# Get results in a DataFrame.
query.df()

SQL

SELECT MnistImageClassifier(data).label FROM mnist_video;

The result contains a projected label column, which indicates the digit of a particular frame.

+------------------------------+
|   mnistimageclassifier.label |
|------------------------------|
|                            6 |
|                            6 |
|                            6 |
|                            6 |
|                            6 |
|                            6 |
|                            4 |
|                            4 |

... ...

4. Optional: Process Only Segments of Videos based on Conditions#

Like normal SQL, you can also specify conditions to filter out some frames of the video.

Python

query = cursor.table("mnist_video") \
            .filter("id < 2") \
            .select("MnistImageClassifier(data).label")

# Return results in a DataFrame.
query.df()

SQL

SELECT MnistImageClassifier(data).label FROM mnist_video
    WHERE id < 2

Now, the DataFrame only contains 2 rows after filtering.

+------------------------------+
|   mnistimageclassifier.label |
|------------------------------|
|                            6 |
|                            6 |
+------------------------------+

Check out our Jupyter Notebook for working example.