def canonize(img_file, depth_file, opts, params):
img = preprocess_image(img_file)
segmask = segmentation(depth_file, opts["segmentation"])
img, segmask = _canonize(img, segmask, opts)
# Determine if image is upside down.
diff = np.sum(np.abs(img - params["img_avg"]))
diff_upsidedown = np.sum(np.abs(img - params["img_avg_upsidedown"]))
upsidedown = diff > diff_upsidedown
if dataset.is_upside_down(img_file) != upsidedown:
print "Canonization failed!"
if upsidedown:
img = np.fliplr(np.flipud(img))
segmask = np.fliplr(np.flipud(segmask))
return img, segmask
def canonization_training(opts):
print "# Canonization training"
params = caching.nul_repr_dict()
# Generate average intensity image from a subset of the dataset.
img_avg = np.zeros(opts["img_shape"], dtype=int)
files = dataset.training_files(opts["num_train_images"])
for img_file, depth_file in print_progress(files):
img = preprocess_image(img_file)
segmask = segmentation(depth_file, opts["segmentation"])
img, segmask = _canonize(img, segmask, opts)
# Orient correctly if image is upside down
if dataset.is_upside_down(img_file):
img = np.fliplr(np.flipud(img))
img_avg += img
img_avg /= len(files)
params["img_avg"] = img_avg
params["img_avg_upsidedown"] = np.fliplr(np.flipud(img_avg))
return params