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?raw=1 -O 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 "ua_detrac.mp4" INTO ObjectDetectionVideos;')
response = cursor.fetch_all()
print(response)
@status: ResponseStatus.SUCCESS
@batch:
0
0 Table Successfully dropped: ObjectDetectionVideos
@query_time: 0.03936416003853083
@status: ResponseStatus.SUCCESS
@batch:
0
0 Number of loaded VIDEO: 1
@query_time: 0.07350237295031548
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: 0.014365370385348797
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
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
yolov5.labels \
0 [car, car, car, car, car, car, car, car, car, person, car, motorcycle, car, truck, car, car, car...
1 [car, car, car, car, car, car, car, car, car, person, car, car, truck, car, car, car, car, motor...
2 [car, car, car, car, car, car, car, car, car, car, person, car, truck, car, motorcycle, car, car...
3 [car, car, car, car, car, car, car, car, car, car, car, car, truck, person, motorcycle, truck, c...
4 [car, car, car, car, car, car, car, car, car, car, car, person, car, car, motorcycle, truck, car...
5 [car, car, car, car, car, car, car, car, car, car, car, person, car, car, car, car, motorcycle, ...
6 [car, car, car, car, car, car, car, car, person, car, car, car, car, car, motorcycle, car, car, ...
7 [car, car, car, car, car, car, car, car, car, car, person, car, car, car, motorcycle, car, car, ...
8 [car, car, car, car, car, car, car, car, car, person, car, motorcycle, car, car, car, car, truck...
9 [car, car, car, car, car, car, car, car, car, car, person, car, car, car, car, truck, car, perso...
10 [car, car, car, car, car, car, car, car, car, person, car, car, car, car, truck, car, car, car, ...
11 [car, car, car, car, car, car, car, car, person, car, truck, car, truck, car, car, car, car, car...
12 [car, car, car, car, car, car, car, car, car, person, car, car, motorcycle, car, car, car, truck...
13 [car, car, car, car, car, car, car, car, car, truck, car, person, motorcycle, truck, car, car, m...
14 [car, car, car, car, car, car, car, person, car, car, car, truck, truck, motorcycle, motorcycle,...
15 [car, car, car, car, car, car, car, person, car, car, car, motorcycle, car, car, car, motorcycle...
16 [car, car, car, car, car, car, person, car, car, car, car, motorcycle, motorcycle, truck, car, c...
17 [car, car, car, car, car, car, car, person, car, car, car, car, car, truck, motorcycle, motorcyc...
18 [car, car, car, car, car, car, car, car, car, person, car, car, car, bus, truck, motorcycle, car...
19 [car, car, car, car, car, car, car, car, car, person, car, car, car, car, truck, bus, car, car, ...
yolov5.bboxes \
0 0 [830.513916015625, 276.9407958984375, 960.0, 360.9553527832031]
1 ...
1 0 [833.4142456054688, 277.43121337890625, 960.0, 363.24542236328125]
1 [61...
2 0 [837.3671875, 278.4643859863281, 960.0, 363.9206848144531]
1 ...
3 0 [840.7059326171875, 279.1480712890625, 960.0, 360.564208984375]
1 ...
4 0 [623.8599853515625, 218.58824157714844, 729.417724609375, 280.35791015625]
1 ...
5 0 [624.716796875, 217.99139404296875, 731.593994140625, 280.8290100097656]
1 ...
6 0 [625.336669921875, 217.91189575195312, 734.4633178710938, 281.4993896484375]
1 ...
7 0 [626.2813110351562, 218.29205322265625, 736.9923095703125, 282.62371826171875]
1 ...
8 0 [628.342041015625, 219.6263427734375, 738.7610473632812, 284.00531005859375]
1 ...
9 0 [630.125732421875, 221.2857666015625, 740.33349609375, 284.7383728027344]
1 ...
10 0 [632.170654296875, 222.32720947265625, 743.0787353515625, 284.72711181640625]
1 [...
11 0 [634.2802734375, 222.06613159179688, 747.0035400390625, 287.9921569824219]
1 ...
12 0 [636.1023559570312, 222.5532989501953, 748.899658203125, 288.3439636230469]
1 ...
13 0 [636.18408203125, 222.69622802734375, 751.6583862304688, 287.90509033203125]
1 ...
14 0 [638.757080078125, 223.4717559814453, 753.6521606445312, 288.96051025390625]
1 [1...
15 0 [641.4051513671875, 224.3956298828125, 757.6414794921875, 290.0587158203125]
1 ...
16 0 [644.2322387695312, 225.87051391601562, 761.1083984375, 290.7424011230469]
1 ...
17 0 [646.2791137695312, 225.9928436279297, 763.2777709960938, 291.8134765625]
1 ...
18 0 [647.1568603515625, 226.51368713378906, 765.8187866210938, 292.62884521484375]
1 ...
19 0 [648.61767578125, 227.11984252929688, 768.1045532226562, 293.3794250488281]
1 ...
yolov5.scores
0 [0.9019389748573303, 0.8878238201141357, 0.8540899157524109, 0.8021702766418457, 0.7838875651359...
1 [0.8980059623718262, 0.8685558438301086, 0.8364439606666565, 0.8237311244010925, 0.7718729972839...
2 [0.8956702351570129, 0.8518660068511963, 0.8487427234649658, 0.8453817963600159, 0.7860085368156...
3 [0.8803829550743103, 0.866111695766449, 0.8498753905296326, 0.8373444676399231, 0.82564580440521...
4 [0.8975156545639038, 0.8809236884117126, 0.8435657024383545, 0.8381263613700867, 0.7975137829780...
5 [0.9156807661056519, 0.8994482159614563, 0.8464253544807434, 0.8432754278182983, 0.7775278091430...
6 [0.9120901226997375, 0.9068282246589661, 0.8413792848587036, 0.8137449026107788, 0.7970511317253...
7 [0.9046720266342163, 0.8707322478294373, 0.8454322218894958, 0.8174154162406921, 0.7925598621368...
8 [0.8893280625343323, 0.8552685379981995, 0.8536511063575745, 0.8232781887054443, 0.7951847910881...
9 [0.8876322507858276, 0.8557678461074829, 0.8532631993293762, 0.7946798205375671, 0.7720419764518...
10 [0.8991596102714539, 0.835357129573822, 0.8304201364517212, 0.8152026534080505, 0.78578490018844...
11 [0.9085388779640198, 0.8469469547271729, 0.8388872742652893, 0.7947012186050415, 0.7883725166320...
12 [0.9089047908782959, 0.861946165561676, 0.8548004031181335, 0.8538579344749451, 0.80343031883239...
13 [0.8987026214599609, 0.8819432854652405, 0.8584625720977783, 0.8529505729675293, 0.8020280003547...
14 [0.8915325999259949, 0.8549790978431702, 0.8541241884231567, 0.8505778312683105, 0.8061172962188...
15 [0.8875617980957031, 0.863188624382019, 0.8474203944206238, 0.8387583494186401, 0.82478755712509...
16 [0.8969656229019165, 0.8630248308181763, 0.8239783644676208, 0.8124073147773743, 0.8089075684547...
17 [0.8984967470169067, 0.8612021207809448, 0.8340322971343994, 0.8170910477638245, 0.8132005929946...
18 [0.8983427882194519, 0.8600170016288757, 0.8409520983695984, 0.8305366635322571, 0.8134506940841...
19 [0.8998425006866455, 0.8530007004737854, 0.8488078713417053, 0.8350291848182678, 0.8190277814865...
@query_time: 7.09188617952168
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: 0.019077396020293236