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.
[ -z "$(lsof -ti:5432)" ] || kill -9 $(lsof -ti:5432)
nohup eva_server > eva.log 2>&1 &
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
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
--2022-12-18 17:37:23-- https://www.dropbox.com/s/yxljxz6zxoqu54v/mnist.mp4
Resolving www.dropbox.com (www.dropbox.com)... 162.125.81.18, 2620:100:6031:18::a27d:5112
Connecting to www.dropbox.com (www.dropbox.com)|162.125.81.18|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: /s/raw/yxljxz6zxoqu54v/mnist.mp4 [following]
--2022-12-18 17:37:25-- https://www.dropbox.com/s/raw/yxljxz6zxoqu54v/mnist.mp4
Reusing existing connection to www.dropbox.com:443.
HTTP request sent, awaiting response... 302 Found
Location: https://uc0434f86f4e20eb47bfce2d0904.dl.dropboxusercontent.com/cd/0/inline/By1gM8gLIuELxuYPr39tgADfJbLZU-kr0e84LkeUeMUOrSbmYjLMReXusSb2odj64Ve6Pxr7tvBNT_cj_Stv1Gqpj3sjyqMP9nupUu6EWZpOkBN97XcH3djlLow_EsJlPT9fDl9elRf5UxQGD_WK97xXHHNzhE0qfsLl57m-2n5cxw/file# [following]
--2022-12-18 17:37:26-- https://uc0434f86f4e20eb47bfce2d0904.dl.dropboxusercontent.com/cd/0/inline/By1gM8gLIuELxuYPr39tgADfJbLZU-kr0e84LkeUeMUOrSbmYjLMReXusSb2odj64Ve6Pxr7tvBNT_cj_Stv1Gqpj3sjyqMP9nupUu6EWZpOkBN97XcH3djlLow_EsJlPT9fDl9elRf5UxQGD_WK97xXHHNzhE0qfsLl57m-2n5cxw/file
Resolving uc0434f86f4e20eb47bfce2d0904.dl.dropboxusercontent.com (uc0434f86f4e20eb47bfce2d0904.dl.dropboxusercontent.com)... 162.125.81.15, 2620:100:6031:15::a27d:510f
Connecting to uc0434f86f4e20eb47bfce2d0904.dl.dropboxusercontent.com (uc0434f86f4e20eb47bfce2d0904.dl.dropboxusercontent.com)|162.125.81.15|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 62156 (61K) [video/mp4]
Saving to: 'mnist.mp4'
mnist.mp4 100%[===================>] 60.70K 156KB/s in 0.4s
2022-12-18 17:37:27 (156 KB/s) - 'mnist.mp4' saved [62156/62156]
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: 0.024652965999848675
@status: ResponseStatus.SUCCESS
@batch:
0
0 Number of loaded VIDEO: 1
@query_time: 0.06907113200031745
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 already exists, nothing added.
@query_time: 0.014347114999509358
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.batch)
mnistvid.data \
0 [[[ 0 0 0]\n [ 0 0 0]\n [ 0 0 0]\n [ 0 0 0]\n [ 0 0 0]\n [ 0 0 0]\n [ 0 0 0]\n [ 0...
1 [[[2 2 2]\n [2 2 2]\n [2 2 2]\n [2 2 2]\n [2 2 2]\n [2 2 2]\n [2 2 2]\n [2 2 2]\n [2 2 2]\n [2 2...
2 [[[13 13 13]\n [ 2 2 2]\n [ 2 2 2]\n [13 13 13]\n [ 6 6 6]\n [ 0 0 0]\n [ 5 5 5]\n [22...
3 [[[ 0 0 0]\n [ 0 0 0]\n [ 0 0 0]\n [ 0 0 0]\n [ 0 0 0]\n [ 0 0 0]\n [ 1 1 1]\n [ 3...
4 [[[ 0 0 0]\n [ 0 0 0]\n [ 0 0 0]\n [ 0 0 0]\n [ 0 0 0]\n [ 0 0 0]\n [ 0 0 0]\n [ 0...
mnistcnn.label
0 6
1 2
2 3
3 7
4 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()