MNIST TUTORIAL#
Run on Google Colab | View source on GitHub | Download notebook |
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.
nohup eva_server > eva.log 2>&1 &
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 MNISTVid')
response = cursor.fetch_all()
print(response)
cursor.execute('LOAD VIDEO "mnist.mp4" INTO MNISTVid')
response = cursor.fetch_all()
print(response)
@status: ResponseStatus.SUCCESS
@batch:
0
0 Table Successfully dropped: MNISTVid
@query_time: 0.26187248099995486
@status: ResponseStatus.SUCCESS
@batch:
0
0 Number of loaded VIDEO: 1
@query_time: 1.0106322069996168
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()
print(response)
@status: ResponseStatus.SUCCESS
@batch:
0
0 UDF MnistCNN successfully added to the database.
@query_time: 4.144361197000762
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()
print(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()