Object detection with WaldBoost detector for Python/Numpy. The detection algortithm is similar to Aggregated Channel Features detector by Piotr Dollar. The training algortithm is different - WaldBoost instead of Constant Soft Cascade.

The package supports

• Custom channel features - any channel type, shrinking, smoothing
• Decision tree weak classifiers
• FPGA-friendly training and inference pipeline

The purpose of this package is to provide reference implementation of detector training and inference of images for Python. It is not meant to be fast. We however did our best to speed up things using Numba while keeping the code as simple as possible.

Acknowledgment Development of this software was funded by TACR project and V3C Center of Competence (TE01020415) and ECSEL FitOptiVis (No 783162).

Necessary requirements include:

• numpy
• numba
• scipy
• scikit-image
• scikit-learn
• opencv-python
• protobuf
• bbx >= 0.0.5

The package can be installed through pip

pip install waldboost-*.tgz

Following example show basic pipeline for training the detector.

1. Include the package and other required packages (e.g. dataset generators etc.)

import waldboost as wb
# ...

2. Setup training parameters. Define how image channels are calculated and detector window size. For detailed info see wb.channels.channel_pyramid.

channel_opts = dict(shrink=2, n_per_oct=8, smooth=1, channels=wb.channels.grad_hist)
shape = (12,12,4)

3. Initialize new model, sample pool (source of training data), and learner (training algorithm and state).

model = wb.Model(shape, channel_opts)
pool = wb.Pool(shape, min_tp=1000, min_fp=1000)
learner = wb.Learner(alpha=0.2, max_depth=2))

4. Run the training. Each iteration updates training set from images produced by user-specified generator, and adds new stage to the model.

for stage in range(len(model),T):
    pool.update(M, training_images)
    X0,H0 = P.gather_samples(0)
    X1,H1 = P.gather_samples(1)
    learner.fit_stage(model, X0, H0, X1, H1)

4. Finally model can be used for detection on new images, and saved to file.

model.save("detector.pb")
image,*_ = next(training_images)
boxes = model.detect(image)

5. Function wb.load_model can load the model form file.

model = wb.Model.load("detector.pb")