Object Detection Tutorial#
 Run on Google Colab
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 View source on GitHub
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 Download notebook
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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.
Download the Videos#
# Getting the video files
!wget -nc https://www.dropbox.com/s/k00wge9exwkfxz6/ua_detrac.mp4
# Getting the Yolo object detector
!wget -nc https://raw.githubusercontent.com/georgia-tech-db/eva/master/eva/udfs/yolo_object_detector.py
File ‘ua_detrac.mp4’ already there; not retrieving.
File ‘yolo_object_detector.py’ already there; not retrieving.
Load the surveillance videos for analysis#
We use regular expression to load all the videos into the table#
cursor.execute('DROP TABLE ObjectDetectionVideos')
response = cursor.fetch_all()
print(response)
cursor.execute('LOAD VIDEO "*.mp4" INTO ObjectDetectionVideos;')
response = cursor.fetch_all()
print(response)
@status: ResponseStatus.SUCCESS
@batch:
0
0 Table Successfully dropped: ObjectDetectionVideos
@query_time: 0.05684273294173181
@status: ResponseStatus.SUCCESS
@batch:
0
0 Number of loaded VIDEO: 10
@query_time: 0.23361483099870384
Visualize Video#
from IPython.display import Video
Video("ua_detrac.mp4", embed=True)
Register YOLO Object Detector an an User-Defined Function (UDF) in EVA#
cursor.execute("""CREATE UDF IF NOT EXISTS YoloV5
INPUT (frame NDARRAY UINT8(3, ANYDIM, ANYDIM))
OUTPUT (labels NDARRAY STR(ANYDIM), bboxes NDARRAY FLOAT32(ANYDIM, 4),
scores NDARRAY FLOAT32(ANYDIM))
TYPE Classification
IMPL 'yolo_object_detector.py';
""")
response = cursor.fetch_all()
print(response)
@status: ResponseStatus.SUCCESS
@batch:
0
0 UDF YoloV5 already exists, nothing added.
@query_time: 0.01149210100993514
Run Object Detector on the video#
cursor.execute("""SELECT id, YoloV5(data)
FROM ObjectDetectionVideos
WHERE id < 20""")
response = cursor.fetch_all()
print(response)
@status: ResponseStatus.SUCCESS
@batch:
objectdetectionvideos.id \
0 0
1 1
2 2
3 3
4 4
.. ...
185 15
186 16
187 17
188 18
189 19
yolov5.labels \
0 [car, car, car, car, truck, car, car, cell phone, car, car, truck, car, cell phone]
1 [car, car, car, car, truck, cell phone, car, cell phone, car, car, truck, bus, car, car, bus]
2 [cell phone, car, cell phone, truck, car, car, car, truck, truck]
3 [cell phone, car, traffic light, car, truck, cell phone, car, truck, truck, truck, car]
4 [car, truck, cell phone, car, traffic light, car, truck, car, car, truck, car, car, bus, truck]
.. ...
185 [car, car, car, car, car, car, car, person, car, car, car, motorcycle, car, car, car, motorcycle...
186 [car, car, car, car, car, car, person, car, car, car, car, motorcycle, motorcycle, truck, car, c...
187 [car, car, car, car, car, car, car, person, car, car, car, car, car, truck, motorcycle, motorcyc...
188 [car, car, car, car, car, car, car, car, car, person, car, car, car, bus, truck, motorcycle, car...
189 [car, car, car, car, car, car, car, car, car, person, car, car, car, car, truck, bus, car, car, ...
yolov5.bboxes \
0 0 [613.0485229492188, 216.29083251953125, 718.1734619140625, 275.6402282714844]
1 ...
1 0 [614.474365234375, 217.62545776367188, 721.433837890625, 278.5963134765625]
1 [...
2 0 [150.92086791992188, 468.4427490234375, 279.14739990234375, 535.2306518554688]
1 ...
3 0 [153.17645263671875, 463.0412292480469, 280.0609436035156, 534.4542846679688]
1 ...
4 0 [847.2015380859375, 289.1953430175781, 959.904052734375, 356.5442810058594]
1 [...
.. ...
185 0 [641.4051513671875, 224.3956298828125, 757.6414794921875, 290.0587158203125]
1 ...
186 0 [644.232177734375, 225.87051391601562, 761.1083984375, 290.7424011230469]
1 ...
187 0 [646.2791137695312, 225.9928436279297, 763.2777709960938, 291.8134765625]
1 ...
188 0 [647.1568603515625, 226.51370239257812, 765.8187866210938, 292.62884521484375]
1 ...
189 0 [648.61767578125, 227.11984252929688, 768.1045532226562, 293.3794250488281]
1 ...
yolov5.scores
0 [0.7243762016296387, 0.6949567794799805, 0.507372260093689, 0.48990708589553833, 0.4015621244907...
1 [0.700208842754364, 0.6787835955619812, 0.5990053415298462, 0.5681858062744141, 0.51617020368576...
2 [0.7441924810409546, 0.6931878924369812, 0.6251559853553772, 0.5646522641181946, 0.5072109699249...
3 [0.7614237666130066, 0.7540610432624817, 0.651155412197113, 0.5523987412452698, 0.48905572295188...
4 [0.7324366569519043, 0.5914739966392517, 0.5453053116798401, 0.5058165192604065, 0.5009402036666...
.. ...
185 [0.8875618577003479, 0.8631888031959534, 0.8474202752113342, 0.8387584686279297, 0.8247876167297...
186 [0.8969655632972717, 0.8630247712135315, 0.8239787817001343, 0.8124074339866638, 0.8089078664779...
187 [0.8984967470169067, 0.8612020015716553, 0.8340322375297546, 0.8170915842056274, 0.8132005929946...
188 [0.8983428478240967, 0.860016942024231, 0.8409522175788879, 0.8305366635322571, 0.81345069408416...
189 [0.8998422622680664, 0.8530005216598511, 0.8488077521324158, 0.835029125213623, 0.81902801990509...
[190 rows x 4 columns]
@query_time: 12.921193392947316
Visualizing output of the Object Detector on the video#
import cv2
from pprint import pprint
from matplotlib import pyplot as plt
def annotate_video(detections, input_video_path, output_video_path):
color1=(207, 248, 64)
color2=(255, 49, 49)
thickness=4
vcap = cv2.VideoCapture(input_video_path)
width = int(vcap.get(3))
height = int(vcap.get(4))
fps = vcap.get(5)
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v') #codec
video=cv2.VideoWriter(output_video_path, fourcc, fps, (width,height))
frame_id = 0
# Capture frame-by-frame
# ret = 1 if the video is captured; frame is the image
ret, frame = vcap.read()
while ret:
df = detections
df = df[['yolov5.bboxes', 'yolov5.labels']][df.index == frame_id]
if df.size:
dfLst = df.values.tolist()
for bbox, label in zip(dfLst[0][0], dfLst[0][1]):
x1, y1, x2, y2 = bbox
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
# object bbox
frame=cv2.rectangle(frame, (x1, y1), (x2, y2), color1, thickness)
# object label
cv2.putText(frame, label, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color1, thickness)
# frame label
cv2.putText(frame, 'Frame ID: ' + str(frame_id), (700, 500), cv2.FONT_HERSHEY_SIMPLEX, 1.2, color2, thickness)
video.write(frame)
# Stop after twenty frames (id < 20 in previous query)
if frame_id == 20:
break
# Show every fifth frame
if frame_id % 5 == 0:
plt.imshow(frame)
plt.show()
frame_id+=1
ret, frame = vcap.read()
video.release()
vcap.release()
from ipywidgets import Video, Image
input_path = 'ua_detrac.mp4'
output_path = 'video.mp4'
dataframe = response.batch.frames
annotate_video(dataframe, input_path, output_path)
Video.from_file(output_path)
Dropping an User-Defined Function (UDF)#
cursor.execute("DROP UDF YoloV5;")
response = cursor.fetch_all()
print(response)
@status: ResponseStatus.SUCCESS
@batch:
0
0 UDF YoloV5 successfully dropped
@query_time: 0.017834065947681665