def main():
out_file = 'out.json'
parser = argparse.ArgumentParser()
parser.add_argument('--demo', action='store_true')
parser.add_argument('--eye', default=True, action='store_true')
parser.add_argument('--emo', default=True, action='store_true')
parser.add_argument('--gcloud', default=True, action='store_true')
parser.add_argument('--out_dir', default='logs/')
args = parser.parse_args()
setup()
stats = StatsCollector()
interviewer = InterviewAI(stats,
is_demo=args.demo,
run_eye_tracker=args.eye,
run_emo_threads=args.emo,
run_gcloud_threads=args.gcloud)
interviewer.begin()
stats.print_stats()
stats.to_json(args.out_dir + out_file)
class Pipeline:
def __init__(self, pipeline_setup):
self.pipeline_setup = pipeline_setup
def run(self):
image_processor = ImageAttributeProcessor(self.pipeline_setup.base_dir)
self.volume_estimator = self.createVolumeEstimatorByAggregation()
self.stats = StatsCollector()
if self.pipeline_setup.category is not None:
image_processor.process_single_target(self.pipeline_setup.category)
self.run_instance(
os.path.join(self.pipeline_setup.base_dir,
self.pipeline_setup.category),
self.pipeline_setup.category)
else:
for category_name in utils.dirs_at_dir(
self.pipeline_setup.base_dir):
image_processor.process_all(category_name)
for instance_path in utils.dirpath_at_dir(
os.path.join(self.pipeline_setup.base_dir,
category_name)):
self.run_instance(instance_path, category_name)
self.stats.print_stats()
def run_instance(self, category_path, category):
print 'Estimating volume for path', category_path
try:
image_data = ImageData(category_path)
instance = category_path.split('/')[-2] if category_path.endswith(
'/') else category_path.split('/')[-1]
self.stats.add_true_for_instance(category, instance,
image_data.volume['value'])
volume = self.volume_estimator.with_kmeans().estimate(image_data)
self.stats.add_predicted_for_instance(
category, instance, self.volume_estimator.method.desc, volume)
if debug():
print u'Predicted volume for target %s = %3.9f %s\u00B3 with %s.' % (
instance, volume, image_data.volume['units'],
self.volume_estimator.method.desc)
volume = self.volume_estimator.with_meanshift().estimate(
image_data)
self.stats.add_predicted_for_instance(
category, instance, self.volume_estimator.method.desc, volume)
if debug():
print u'Predicted volume for target %s = %3.9f %s\u00B3 with %s.' % (
instance, volume, image_data.volume['units'],
self.volume_estimator.method.desc)
except Exception as e:
print "Exception occurred dring volume estimation, skipping image at", category_path, e
def createVolumeEstimatorByAggregation(self):
return VolumeEstimatorByAggregation(self.pipeline_setup.pixel_width,
self.pipeline_setup.scale)
def createVolumeEstimator(self):
return VolumeEstimator()
