// Author: Juergen Gall, BIWI, ETH Zurich // Email: gall@vision.ee.ethz.ch
make all
make clear
./run.sh mode [config.txt] [tree_offset] mode: 0 - train; 1 - show leafs; 2 - detect config.txt: config file tree_offset: output number for trees (treetable[index+offset].txt)
A config.txt example is given in the subdirectory 'example'
./run_train.sh
./run_detect.sh
Config.txt:
Information for storing and loading trees:
# Path to trees + prefix 'treetable'
/scratch/tmp/forest/example/trees/treetable
# Number of trees
10
Image patch size (needs to be the same for training and learning):
# Patch width
16
# Patch height
16
Information about the test set:
# Path to images
/scratch/tmp/forest/example/testimages
# File with names of images
/scratch/tmp/forest/example/test.txt
Not used:
# Extract features
1
Specify scale and ratio if necessary:
# Scales (Number of scales - Scales)
3 0.5 1 2 // 3 scales with factors 0.5, 1, and 2
# Ratios (Number of ratios - ratio)
1 1 // 1 aspect ratio with factor 1 = fixed aspect ratio
Information for storing the Hough images (scaled by a fixed factor such that max<=255):
# Output path
/scratch/tmp/forest/example/detect
# Scale factor for output image (default: 128)
50
Information about positive and negative examples:
# Path to positive examples
/scratch/tmp/forest/example/trainimages
# File with positive examples
/scratch/tmp/forest/example/train_pos.txt
# Subset of positive images -1: all images
-1
# Sample patches from pos. examples
50
# Path to negative examples
/scratch/tmp/forest/example/trainimages
# File with negative examples
/scratch/tmp/forest/example/train_neg.txt
# Subset of negative images -1: all images
-1
# Sample patches from neg. examples
50
train_neg.txt:
50 1 // number of images + dummy value (1)
neg0.png 0 0 100 40 // filename + boundingbox (top left - bottom right)
train_pos.txt:
50 1 // number of images + dummy value (1)
pos0.png 0 0 74 36 37 18 // filename + boundingbox (top left - bottom right) + center of bounding box
Output:
detect-[I]_sc[S]_c[R].png
I: image id
S: scale id
C: ratio id
The images are slides of the 4D (x,y,scale,ratio) voting space for an image I.
For detection, the space needs to be smoothed and the maxima detected (or using mean shift).
This is not part of the source code.
Juergen Gall
BIWI, ETH Zurich 17 July 2009