This project based on ncappzoo project - by security-cam Now.. it's universavity of classifire - use any classes from 0 to 10 and you will get detect this object real-time.
0: background
1: aeroplane
2: bicycle
3: bird
4: boat
5: bottle
6: bus
7: car
8: cat
9: chair
10: cow
11: diningtable
12: dog
13: horse
14: motorbike
15: person
16: pottedplant
17: sheep
18: sofa
19: train
20: tvmonitor
Sample code for a camera classification - built using Intel® Movidius™ Neural Compute Stick (NCS).
git clone https://github.com/rvjenya/Person-detect-NCS.git
cd Person-detect-NCS
This example grabs camera frames from /dev/video0 by default; If your system has multiple cameras you can choose the required camera using the --video option. Below is an example:
python3 security-cam.py --video 1
'-g', '--graph', type=str,
default='graph',
help="Absolute path to the neural network graph file."
'-v', '--video', type=int,
default=0,
help="Index of your computer's V4L2 video device. \
ex. 0 for /dev/video0"
'-l', '--labels', type=str,
default='labels.txt',
help="Absolute path to labels file."
'-M', '--mean', type=float,
nargs='+',
default=[127.5, 127.5, 127.5],
help="',' delimited floating point values for image mean."
'-S', '--scale', type=float,
default=0.00789,
help="Absolute path to labels file."
'-D', '--dim', type=int,
nargs='+',
default=[300, 300],
help="Image dimensions. ex. -D 224 224"
'-c', '--colormode', type=str,
default="bgr",
help="RGB vs BGR color sequence. This is network dependent."