-
Notifications
You must be signed in to change notification settings - Fork 0
/
kw-README.txt
64 lines (48 loc) · 1.51 KB
/
kw-README.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
Dir structure:
NAISC
| README.txt
|
+---env # Virtual env
|
\---human_detection # A PeekingDuck project. Might create more idk
| pipeline_config.yml
|
+---data # Inputs for our project
| hawker01.png
| hawker_video.mp4
|
+---output # Output directory
| hawker01_230119_234230.png
|
\---src # More advanced stuff will go here. Still figuring it out
\---custom_nodes
\---configs
Usage guide:
cd to NAISC folder (it will act as the home directory)
run the following line to activate virtual env:
env\Scripts\activate
(i think?) for macOS run:
source pkd/bin/activate
To run a specific peekingduck project, cd into the project folder (e.g. cd human_detection)
Then do `peekingduck run`
To set up a new project, create a new folder with the project name. cd into it and do `peekingduck init`.
To edit a project in the basic ways, edit the pipeline_config.yml file.
Examples: # So far yolo seems to be the best model
# The following config will save the processed file (pic or vid) to the default dir (which is ~/proj_name/PeekingDuck/data/output)
nodes:
- input.visual:
source: data/hawker01.png
- model.yolo:
detect: ["person"]
- draw.bbox:
show_labels: True
- output.media_writer
# The following will display the processed video
nodes:
- input.visual:
source: data/hawker_video.mp4
- model.yolo:
detect: ["person", "cup", "dining table", "chair"]
- draw.bbox:
show_labels: True
- output.screen