* Dump the `detect` model to disk.
```
-./bin/run_local.sh -d model_detect -f detect
+python face_privacy_filter/filter_image.py -d model_detect -f detect
```
* Dump the `pixelate` model to disk.
```
-./bin/run_local.sh -d model_pix -f pixelate
+python face_privacy_filter/filter_image.py -d model_pix -f pixelate
```
* Evaluate the `detect` model from disk and a previously produced detect object
```
-./bin/run_local.sh -d model_detect -p output.csv -i web_demo/images/face_DiCaprio.jpg
+python face_privacy_filter/filter_image.py -d model_detect -p output.csv -i web_demo/images/face_DiCaprio.jpg
```
* Example for evaluating the `pixelate` model from disk and a previously produced detect object
```
-./bin/run_local.sh -d model_pix -i detect.csv -p output.jpg --csv_input
+python face_privacy_filter/filter_image.py -d model_pix -i detect.csv -p output.jpg --csv_input
```
### Installation Troubleshoting
+++ /dev/null
-#! python
-# -*- coding: utf-8 -*-
-"""
-Command line code for face privacy filter
-"""
-
-from face_privacy_filter.filter_image import main
-
-if __name__ == "__main__":
- main()
+++ /dev/null
-#!/bin/bash
-#------------------------------------------------------------------------
-# run_local.sh - locally starts a face privacy filter instance
-#------------------------------------------------------------------------
-
-# infer the project location
-MODEL_DIR=$(dirname $( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd ) )
-echo "Local run directory '$MODEL_DIR'..."
-
-# inject into python path and run with existing args (for unix-like environments)
-PYTHONPATH="$MODEL_DIR:$PYTHONPATH" python $MODEL_DIR/bin/run_face-privacy-filter_reference.py $*
# -*- coding: utf-8 -*-
-__version__ = "0.2.0"
+__version__ = "0.2.1"
MODEL_NAME = 'face_privacy_filter'
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()
description=("Face detection and privacy filtering models"),
long_description=("Face detection and privacy filtering models"),
license="Apache",
- package_data={globals_dict['MODEL_NAME']: ['data/*']},
- scripts=['bin/run_face-privacy-filter_reference.py'],
+ #package_data={globals_dict['MODEL_NAME']: ['data/*']},
setup_requires=['pytest-runner'],
entry_points="""
[console_scripts]