Object Detection 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.
Note: you may need to restart the kernel to use updated packages.
Stopping EVA Server ...
Starting EVA Server ...
nohup eva_server > eva.log 2>&1 &
Download the Videos#
# 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 surveillance videos for analysis#
We use regular expression to load all the videos into the table#
response = (
cursor.execute("DROP TABLE IF EXISTS ObjectDetectionVideos;").fetch_all().as_df()
)
cursor.execute(
'LOAD VIDEO "ua_detrac.mp4" INTO ObjectDetectionVideos;'
).fetch_all().as_df()
0 | |
---|---|
0 | Number of loaded VIDEO: 1 |
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 Yolo
TYPE ultralytics
'model' 'yolov8m.pt';
""").fetch_all().as_df()
0 | |
---|---|
0 | UDF Yolo already exists, nothing added. |
Run Object Detector on the video#
response = cursor.execute("""SELECT id, Yolo(data)
FROM ObjectDetectionVideos
WHERE id < 20""").fetch_all().as_df()
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)
Dropping an User-Defined Function (UDF)#
cursor.execute("DROP UDF IF EXISTS Yolo;").fetch_all().as_df()
0 | |
---|---|
0 | UDF Yolo successfully dropped |