def main():
# random.seed(123454321) # Use deterministic samples.
# Parse arguments:
parser = argparse.ArgumentParser(
description='Train a HOG + Linear SVM classifier.')
parser.add_argument(
'classifier_yaml',
type=str,
nargs='?',
default='template.yaml',
help='Filename of the YAML file describing the classifier to train.')
args = parser.parse_args()
# Read classifier training file:
classifier_yaml = fileutils.load_yaml_file(args.classifier_yaml)
output_dir = args.classifier_yaml.split('.yaml')[0]
window_dims = tuple(
map(int, classifier_yaml['training']['svm']['window_dims']))
print 'window_dims:', window_dims
print 'Preview negative generation:'
print ' [ESC] Stop viewing negatives'
print ' [ S ] Save negative regions to disk'
neg_num = int(classifier_yaml['training']['svm']['neg_num'])
neg_output_dir = classifier_yaml['dataset']['directory']['generation'][
'output']['negative']
def get_neg_reg_gen():
# return generate_samples.load_negative_region_generator(classifier_yaml)
# return generate_samples.load_exclusion_region_generator(classifier_yaml)
return generate_samples.load_hard_negative_region_generator(
classifier_yaml)
# # TODO: REMOVE THIS CODE:
# save_regions(get_neg_reg_gen(), neg_num, window_dims, neg_output_dir)
# sys.exit(1)
# neg_reg_generator = generate_samples.generate_negative_regions_in_image_with_exclusions(all_images[0], exl_info_map, window_dims)
# print len(list(neg_reg_generator))
mosaic_gen = utils.mosaic_generator(get_neg_reg_gen(), (5, 5), (100, 100))
# mosaic_gen = utils.mosaic_generator(get_neg_reg_gen(), (10, 15), (40, 60))
stop = False
for mosaic in mosaic_gen:
print 'mosaic'
cv2.imshow('mosaic', mosaic)
while True:
key = cv2.waitKey(1) & 0xFF
if key == 27:
stop = True
break
if key == ord('s'):
save_regions(get_neg_reg_gen(), neg_num, window_dims,
neg_output_dir)
stop = True
break
if key != 255:
break
if stop:
break
print 'Preview positive generation:'
print ' [ESC] Stop viewing positives'
print ' [ S ] Save positive regions to disk'
pos_num = int(classifier_yaml['training']['svm']['pos_num'])
bbinfo_dir = classifier_yaml['dataset']['directory']['bbinfo']
pos_output_dir = classifier_yaml['dataset']['directory']['generation'][
'output']['positive']
def get_pos_reg_gen():
return generate_samples.load_positive_region_generator(classifier_yaml)
mosaic_gen = utils.mosaic_generator(get_pos_reg_gen(), (4, 6),
(window_dims[1], window_dims[0]))
# mosaic_gen = utils.mosaic_generator(pos_reg_generator, (20, 30), (40, 60))
stop = False
for mosaic in mosaic_gen:
print 'mosaic'
cv2.imshow('mosaic', mosaic)
while True:
key = cv2.waitKey(1) & 0xFF
if key == 27:
stop = True
break
if key == ord('s'):
save_regions(get_pos_reg_gen(), pos_num, window_dims,
pos_output_dir)
save_generated_bbinfo(pos_num, window_dims, pos_output_dir,
bbinfo_dir)
stop = True
break
if key != 255:
break
if stop:
break
def main():
# random.seed(123454321) # Use deterministic samples.
# Parse arguments:
parser = argparse.ArgumentParser(description='Train a HOG + Linear SVM classifier.')
parser.add_argument('classifier_yaml', type=str, nargs='?', default='template.yaml', help='Filename of the YAML file describing the classifier to train.')
args = parser.parse_args()
# Read classifier training file:
classifier_yaml = fileutils.load_yaml_file(args.classifier_yaml)
output_dir = args.classifier_yaml.split('.yaml')[0]
window_dims = tuple(map(int, classifier_yaml['training']['svm']['window_dims']))
print 'window_dims:', window_dims
print 'Preview negative generation:'
print ' [ESC] Stop viewing negatives'
print ' [ S ] Save negative regions to disk'
neg_num = int(classifier_yaml['training']['svm']['neg_num'])
neg_output_dir = classifier_yaml['dataset']['directory']['generation']['output']['negative']
def get_neg_reg_gen():
# return generate_samples.load_negative_region_generator(classifier_yaml)
# return generate_samples.load_exclusion_region_generator(classifier_yaml)
return generate_samples.load_hard_negative_region_generator(classifier_yaml)
# # TODO: REMOVE THIS CODE:
# save_regions(get_neg_reg_gen(), neg_num, window_dims, neg_output_dir)
# sys.exit(1)
# neg_reg_generator = generate_samples.generate_negative_regions_in_image_with_exclusions(all_images[0], exl_info_map, window_dims)
# print len(list(neg_reg_generator))
mosaic_gen = utils.mosaic_generator(get_neg_reg_gen(), (5, 5), (100, 100))
# mosaic_gen = utils.mosaic_generator(get_neg_reg_gen(), (10, 15), (40, 60))
stop = False
for mosaic in mosaic_gen:
print 'mosaic'
cv2.imshow('mosaic', mosaic)
while True:
key = cv2.waitKey(1) & 0xFF
if key == 27:
stop = True
break
if key == ord('s'):
save_regions(get_neg_reg_gen(), neg_num, window_dims, neg_output_dir)
stop = True
break
if key != 255:
break
if stop:
break
print 'Preview positive generation:'
print ' [ESC] Stop viewing positives'
print ' [ S ] Save positive regions to disk'
pos_num = int(classifier_yaml['training']['svm']['pos_num'])
bbinfo_dir = classifier_yaml['dataset']['directory']['bbinfo']
pos_output_dir = classifier_yaml['dataset']['directory']['generation']['output']['positive']
def get_pos_reg_gen():
return generate_samples.load_positive_region_generator(classifier_yaml)
mosaic_gen = utils.mosaic_generator(get_pos_reg_gen(), (4, 6), (window_dims[1], window_dims[0]))
# mosaic_gen = utils.mosaic_generator(pos_reg_generator, (20, 30), (40, 60))
stop = False
for mosaic in mosaic_gen:
print 'mosaic'
cv2.imshow('mosaic', mosaic)
while True:
key = cv2.waitKey(1) & 0xFF
if key == 27:
stop = True
break
if key == ord('s'):
save_regions(get_pos_reg_gen(), pos_num, window_dims, pos_output_dir)
save_generated_bbinfo(pos_num, window_dims, pos_output_dir, bbinfo_dir)
stop = True
break
if key != 255:
break
if stop:
break
pos_reg_generator = get_pos_generator()
# mosaic_gen = utils.mosaic_generator(pos_reg_generator, (10, 10), (window_dims[1], window_dims[0]))
output_dir = 'output'
if not os.path.isdir(output_dir):
print 'ERROR: Output directory "{}" does not exist.'.format(output_dir)
sys.exit(1)
else:
print 'Using output directory "{}".'.format(output_dir)
base_fname = os.path.join(output_dir, 'cluster')
# Get the HOG object to use for clustering:
hog = trainhog.get_hog_object(window_dims)
# Find the clusters:
num_clusters = 5
clusters = alignment.find_sample_clusters(pos_reg_generator, window_dims, hog, num_clusters)
# Save the clusters:
for i, cluster in enumerate(clusters):
mosaic_fname = '{}_{}_mosaic.png'.format(base_fname, i)
mosaic_gen = utils.mosaic_generator(cluster, (10, 10), window_shape)
cv2.imwrite(mosaic_fname, mosaic_gen.next())
avg_fname = '{}_{}_avg.png'.format(base_fname, i)
avg_img = utils.average_image(cluster, window_shape, avg_num=None)
cv2.imwrite(avg_fname, avg_img)
# sys.exit(0)
