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CUDA Segmentation Stats

Collect various statistics from a semantic segmentation datasets. The program will recursively search for label images and accumulate statistics. This program uses cnmem for allocation, to grab 95% of the available GPU memory on startup.

Notice: Always use the number of classes plus one. All values higher than number of classes (e.g. 255 for ignore) are grouped into the top class.

Performance: Using a TitanXp and 8 computing threads/streams I get around 36.8403 im/sec with resizing and 63.7906 im/sec without.

There are 3 available stats currently implemented:

Frequency

This is the total count for each label class.

example output:

class count
0 5.46547e+07
1 1.74758e+07
2 4.44468e+08
3 461764
4 1.70721e+07
5 1.72539e+06
6 1.69774e+06
7 2.07309e+06
8 2.16049e+08
9 2.01027e+08
10 3.62953e+08
11 8.37151e+07
12 342664
13 2.44656e+07
14 3.25476e+06
15 1.51454e+06
16 366913
17 1.24194e+06
18 1.54103e+06
19 2.31194e+09
20 1.9688e+09

Image Frequency

This is the total count for each label for each image.

example output:

path 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
img1.png 709962 351 806772 574 325 310 439 412 174552 510 214918 120 576 97949 51 44 81 72 62 9689 79383
img2.png 0 0 154973 126 135 192 331 141 14764 119 85 90 96 327 161 68 114 133 120 1.27981e+06 645368

Pixel Frequency

This is the frequency of a class for each pixel location. Note the output is in exr format. It can be easily read with opencv using the flag IMREAD_ANYDEPTH | IMREAD_ANYCOLOR.

Example output (Cityscapes sidewalk calss): Cityscapes sidewalk class

Build

cd /path/to/project
mkdir build
cmake ..
make

Usage

  • -i: Input folder to recursively search under
    • E.g. -i /path/to/data
  • -o: Output folder to store stats
    • E.g. -o stat_out/
  • -e: Ending to filter by. No regex or wild cards supported. [Defalult: png]
    • E.g. -e labelIds.png
  • -n: Number of threads to use. Each thread takes up space on GPU. [Default: 8]
    • E.g. -n 3
  • -g: List of device numbers to use, comma separated. Currently only one gpu at a time is supported [Default: 0]
    • E.g. -g 0
  • -c: Number of classes (Remember to add one class for ignore/255) [Default: 20]
    • E.g. -c 5
  • -h: Integer height to resize to. Negative number means no resize. [Default: -1]
    • E.g. -h 1024
  • -w: Integer height to resize to. Negative number means no resize. [Default: -1]
    • E.g. -w 1024
  • -f: Compute Frequency statistic [Default: disabled]
    • E.g. -f
  • -p: Compute Pixel Frequency statistic [Default: disabled]
    • E.g. -p
  • -m: Compute Image Frequency statistic [Default: disabled]
    • E.g. -m
  • -a: Compute all available statistics [Default: disabled]
    • E.g. -a

Adding all examples to a single command:

./SegStats -i /path/to/data -o stat_out/ -e labelIds.png -n 3 -g 0 -c 5 -h 1024 -w 1024 -f -p -m -a

Limitations

  • All images must be the same size (or resized to be the size).
  • Only one GPU is implemented.