import numpy as np
import pandas as pd
-from face_privacy_filter.transform_detect import FaceDetectTransform
-from face_privacy_filter.transform_region import RegionTransform
-from face_privacy_filter._version import MODEL_NAME
-
def model_create_pipeline(transformer):
from acumos.session import Requirements
def main(config={}):
+ from face_privacy_filter.transform_detect import FaceDetectTransform
+ from face_privacy_filter.transform_region import RegionTransform
+ from face_privacy_filter._version import MODEL_NAME
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--predict_path', type=str, default='', help="save detections from model (model must be provided via 'dump_model')")
# formulate the pipeline to be used
model_name = MODEL_NAME + "_" + config['function']
- if 'push_address' in config and config['push_address']:
+ if config['push_address']:
from acumos.session import AcumosSession
print("Pushing new model to '{:}'...".format(config['push_address']))
session = AcumosSession(push_api=config['push_address'], auth_api=config['auth_address'])
session.push(pipeline, model_name, reqs) # creates ./my-iris.zip
- if 'dump_model' in config and config['dump_model']:
+ if config['dump_model']:
from acumos.session import AcumosSession
from os import makedirs
if not os.path.exists(config['dump_model']):
if __name__ == '__main__':
+ # patch the path to include this object
+ pathRoot = os.path.dirname(os.path.basename(os.path.abspath(__file__)))
+ if pathRoot not in sys.path:
+ sys.path.append(pathRoot)
main()