MNIST TUTORIAL#

Start EVA server#

We are reusing the start server notebook for launching the EVA server.

!wget -nc "https://raw.githubusercontent.com/georgia-tech-db/eva/master/tutorials/00-start-eva-server.ipynb"
%run 00-start-eva-server.ipynb
cursor = connect_to_server()

File ‘00-start-eva-server.ipynb’ already there; not retrieving.

[notice] A new release of pip is available: 23.0.1 -> 23.1.2
[notice] To update, run: pip install --upgrade pip

Note: you may need to restart the kernel to use updated packages.

nohup eva_server > eva.log 2>&1 &

[notice] A new release of pip is available: 23.0.1 -> 23.1.2
[notice] To update, run: pip install --upgrade pip

Note: you may need to restart the kernel to use updated packages.

Downloading the videos#

# Getting MNIST as a video
!wget -nc https://www.dropbox.com/s/yxljxz6zxoqu54v/mnist.mp4
# Getting a udf
!wget -nc https://raw.githubusercontent.com/georgia-tech-db/eva/master/tutorials/apps/mnist/eva_mnist_udf.py

File ‘mnist.mp4’ already there; not retrieving.

File ‘eva_mnist_udf.py’ already there; not retrieving.

Upload the video for analysis#

cursor.execute('DROP TABLE IF EXISTS MNISTVid')
response = cursor.fetch_all()
response.as_df()
cursor.execute("LOAD VIDEO 'mnist.mp4' INTO MNISTVid")
response = cursor.fetch_all()
response.as_df()

	0
0	Number of loaded VIDEO: 1

Visualize Video#

from IPython.display import Video
Video("mnist.mp4", embed=True)

Create an user-defined function (UDF) for analyzing the frames#

cursor.execute("""CREATE UDF IF NOT EXISTS
                  MnistCNN 
                  INPUT  (data NDARRAY (3, 28, 28))
                  OUTPUT (label TEXT(2))
                  TYPE  Classification
                  IMPL  'eva_mnist_udf.py'
        """)
response = cursor.fetch_all()
response.as_df()

	0
0	UDF MnistCNN successfully added to the database.

Run the Image Classification UDF on video#

cursor.execute("""SELECT data, MnistCNN(data).label 
                  FROM MNISTVid
                  WHERE id = 30 OR id = 50 OR id = 70 OR id = 0 OR id = 140""")
response = cursor.fetch_all()
response.as_df()

	mnistvid.data	mnistcnn.label
0	[[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...	6
1	[[[2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], ...	2
2	[[[13, 13, 13], [2, 2, 2], [2, 2, 2], [13, 13,...	3
3	[[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...	7
4	[[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...	5

Visualize output of query on the video#

# !pip install matplotlib
import matplotlib.pyplot as plt
import numpy as np

# create figure (fig), and array of axes (ax)
fig, ax = plt.subplots(nrows=1, ncols=5, figsize=[6,8])

df = response.batch.frames
for axi in ax.flat:
    idx = np.random.randint(len(df))
    img = df['mnistvid.data'].iloc[idx]
    label = df['mnistcnn.label'].iloc[idx]
    axi.imshow(img)
    
    axi.set_title(f'label: {label}')

plt.show()