Object Detection Tutorial#
 Run on Google Colab
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Connect to EvaDB#
%pip install evadb
import evadb
cursor = evadb.connect().cursor()
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Note: you may need to restart the kernel to use updated packages.
Download the Surveillance Videos#
In this tutorial, we focus on the UA-DETRAC benchmark. UA-DETRAC is a challenging real-world multi-object detection and multi-object tracking benchmark. The dataset consists of 10 hours of videos captured with a Cannon EOS 550D camera at 24 different locations at Beijing and Tianjin in China.
# Getting the video files
!wget -nc "https://www.dropbox.com/s/k00wge9exwkfxz6/ua_detrac.mp4?raw=1" -O ua_detrac.mp4
File 'ua_detrac.mp4' already there; not retrieving.
Load the videos for analysis#
We use a regular expression to load all the videos into the table in a single command
cursor.query("DROP TABLE IF EXISTS ObjectDetectionVideos;").df()
cursor.load(file_regex='ua_detrac.mp4', format="VIDEO", table_name='ObjectDetectionVideos').df()
| 0 | |
|---|---|
| 0 | Number of loaded VIDEO: 1 |
Register YOLO Object Detector as an User-Defined Function (UDF) in EVA#
cursor.query("""
CREATE UDF IF NOT EXISTS Yolo
TYPE ultralytics
'model' 'yolov8m.pt';
""").df()
| 0 | |
|---|---|
| 0 | UDF Yolo successfully added to the database. |
Run the YOLO Object Detector on the video#
yolo_query = cursor.table("ObjectDetectionVideos")
yolo_query = yolo_query.filter("id < 20")
yolo_query = yolo_query.select("id, Yolo(data)")
response = yolo_query.df()
print(response)
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
yolo.labels
0 [car, car, car, car, car, car, person, car, ca... \
1 [car, car, car, car, car, car, car, car, car, ...
2 [car, car, car, car, car, car, car, person, ca...
3 [car, car, car, car, car, car, car, car, car, ...
4 [car, car, car, car, car, car, car, car, car, ...
5 [car, car, car, car, car, car, person, car, ca...
6 [car, car, car, car, car, car, car, person, ca...
7 [car, car, car, car, car, car, car, car, car, ...
8 [car, car, car, car, car, car, person, car, ca...
9 [car, car, car, car, car, car, car, person, ca...
10 [car, car, car, car, car, car, car, person, ca...
11 [car, car, car, car, car, car, person, car, ca...
12 [car, car, car, car, car, car, car, person, ca...
13 [car, car, car, car, car, car, person, car, ca...
14 [car, car, car, car, car, car, person, car, ca...
15 [car, car, car, car, car, car, person, car, ca...
16 [car, car, car, car, car, car, car, person, ca...
17 [car, car, car, car, car, car, car, person, ca...
18 [car, car, car, car, car, car, car, person, mo...
19 [car, car, car, car, car, person, car, car, ca...
yolo.bboxes
0 [[828.603515625, 277.01861572265625, 959.62792... \
1 [[832.1552734375, 278.1466064453125, 959.63830...
2 [[835.5642700195312, 279.1689147949219, 959.64...
3 [[839.2291259765625, 279.876953125, 959.635620...
4 [[843.063232421875, 280.84881591796875, 959.62...
5 [[847.2269897460938, 282.40478515625, 959.6654...
6 [[850.6966552734375, 283.1654052734375, 959.43...
7 [[854.817626953125, 283.79345703125, 959.55505...
8 [[859.06787109375, 285.23321533203125, 959.824...
9 [[862.5375366210938, 286.3861083984375, 959.53...
10 [[631.713623046875, 222.67929077148438, 744.18...
11 [[871.2513427734375, 288.50506591796875, 959.5...
12 [[636.3242797851562, 223.41517639160156, 750.0...
13 [[170.86529541015625, 409.34344482421875, 291....
14 [[174.27420043945312, 404.84698486328125, 293....
15 [[887.9122924804688, 292.99810791015625, 959.5...
16 [[892.8345336914062, 293.4510803222656, 959.53...
17 [[182.45166015625, 392.1574401855469, 296.3449...
18 [[901.4002075195312, 295.100341796875, 959.572...
19 [[647.9462890625, 226.44598388671875, 770.2973...
yolo.scores
0 [0.91, 0.86, 0.85, 0.83, 0.76, 0.73, 0.72, 0.7...
1 [0.92, 0.85, 0.84, 0.83, 0.78, 0.76, 0.76, 0.7...
2 [0.92, 0.84, 0.84, 0.82, 0.81, 0.75, 0.73, 0.7...
3 [0.91, 0.84, 0.82, 0.8, 0.8, 0.75, 0.74, 0.72,...
4 [0.9, 0.85, 0.83, 0.8, 0.76, 0.73, 0.72, 0.72,...
5 [0.89, 0.86, 0.84, 0.8, 0.78, 0.74, 0.72, 0.72...
6 [0.89, 0.87, 0.85, 0.81, 0.79, 0.73, 0.72, 0.7...
7 [0.9, 0.87, 0.84, 0.83, 0.83, 0.79, 0.73, 0.67...
8 [0.89, 0.88, 0.83, 0.82, 0.79, 0.71, 0.68, 0.6...
9 [0.88, 0.87, 0.84, 0.82, 0.8, 0.75, 0.74, 0.74...
10 [0.88, 0.88, 0.85, 0.82, 0.8, 0.79, 0.76, 0.71...
11 [0.9, 0.9, 0.85, 0.8, 0.79, 0.77, 0.69, 0.68, ...
12 [0.9, 0.88, 0.83, 0.81, 0.78, 0.78, 0.78, 0.67...
13 [0.9, 0.89, 0.89, 0.83, 0.81, 0.81, 0.72, 0.71...
14 [0.9, 0.89, 0.88, 0.84, 0.82, 0.81, 0.75, 0.72...
15 [0.89, 0.88, 0.87, 0.84, 0.82, 0.78, 0.76, 0.7...
16 [0.88, 0.88, 0.87, 0.82, 0.81, 0.76, 0.75, 0.7...
17 [0.9, 0.89, 0.87, 0.83, 0.82, 0.78, 0.72, 0.69...
18 [0.88, 0.88, 0.83, 0.82, 0.8, 0.78, 0.75, 0.7,...
19 [0.89, 0.87, 0.81, 0.8, 0.78, 0.77, 0.73, 0.72...
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[['yolo.bboxes', 'yolo.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'
annotate_video(response, input_path, output_path)
Video.from_file(output_path)
Drop the function if needed#
cursor.query("DROP UDF IF EXISTS Yolo").df()
| 0 | |
|---|---|
| 0 | UDF Yolo successfully dropped |