+++ /dev/null
-# face-privacy-filter
-A model for face detection and suppression.
-
-## Image Analysis for Face-based Privacy Filtering
-This source code creates and pushes a model into Acumos that processes
-incoming images and outputs a detected faces as well as the original image
-input (if configured that way). The model uses a [python interface](https://pypi.python.org/pypi/opencv-python)
-to the [OpenCV library](https://opencv.org/) to detect faces and perform
-subsequent image processing. This module does not support training
-at this time and instead uses a pre-trained face cascade, which is
-included (from OpenCV) in this module.
-
-### Usage
-This package contains runable scripts for command-line evaluation,
-packaging of a model (both dump and posting), and simple web-test
-uses. All functionality is encapsulsted in the `filter_image.py`
-script and has the following arguments.
-
-```
-usage: run_face-privacy-filter_reference.py [-h] [-p PREDICT_PATH] [-i INPUT]
- [-c] [-s] [-f {detect,pixelate}]
- [-a PUSH_ADDRESS] [-d DUMP_MODEL]
-
-optional arguments:
- -h, --help show this help message and exit
- -p PREDICT_PATH, --predict_path PREDICT_PATH
- save detections from model (model must be provided via
- 'dump_model')
- -i INPUT, --input INPUT
- absolute path to input data (image or csv, only during
- prediction / dump)
- -c, --csv_input input as CSV format not an image
- -s, --suppress_image do not create an extra row for a returned image
- -f {detect,pixelate}, --function {detect,pixelate}
- which type of model to generate
- -a PUSH_ADDRESS, --push_address PUSH_ADDRESS
- server address to push the model (e.g.
- http://localhost:8887/v2/models)
- -d DUMP_MODEL, --dump_model DUMP_MODEL
- dump model to a pickle directory for local running
-```
-
-
-### Examples
-This single repo has a number of different models that can be
-composed together for operation.
-
-* Dump the `detect` model to disk.
-```
-./bin/run_local.sh -d model_detect -f detect
-```
-* Dump the `pixelate` model to disk.
-```
-./bin/run_local.sh -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
-```
-* 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
-```
-
-
-
-### Installation Troubleshoting
-Using some environment-based versions of python (e.g. conda),
-one problem seemed to come up with the installation of the dependent
-package `opencv-python`. If you launch your python instance and see
-an error like the one below, keep reading.
-
-```
->>> import cv2
-Traceback (most recent call last):
- File "<stdin>", line 1, in <module>
-ImportError: dynamic module does not define module export function (PyInit_cv2)
->>>
-```
-
-This is likely because your `PYTHONPATH` is not correctly configured to
-point to the additional installed libraries.
-
-* From the [simple example here](https://stackoverflow.com/a/42160595)
-you can check your environment with `echo $PYTHONPATH`. If it does not
-contain the directory that you installed to, then you have a problem.
-* Please check your installation by running `python -v -v; import cv2` and checking
-that the last loaded library is in the right location.
-* In some instances, this variable needed to be blank to work properly (i.e.
-`export PYTHONPATH=`) run at some time during start up.
-
-
-## Face-based Use Cases
-This project includes a number of face-based use cases including raw
-detection, blurring, and other image-based modifications based on
-detected image regions.
-
-* **Face Detection Use-case** - This source code creates and pushes a model that processes
-incoming images and outputs detected faces.
-
-## Example Interface
-An instance should first be built and downloaded and then
-launched locally. Afterwards, the sample application found in
-[web_demo](web_demo) uses a `localhost` service to classify
-and visualize the results of image classification.
-
-* [Commercial example](web_demo/images/commercial.jpg) ([youtube source](https://www.youtube.com/watch?v=34KfCNapnUg))
-* [Reunion face sample](web_demo/images/face_reunion.jpg) ([flickr source](https://flic.kr/p/bEgYbs))
-* [family face example](web_demo/images/face_family.jpg) ([pexel source](https://www.pexels.com/photo/adult-affection-beautiful-beauty-265764/))
-* [DiCaprio celebrity face sample](web_demo/images/face_DiCaprio.jpg) ([wikimedia source](https://en.wikipedia.org/wiki/Celebrity#/media/File:Leonardo_DiCaprio_visited_Goddard_Saturday_to_discuss_Earth_science_with_Piers_Sellers_(26105091624)_cropped.jpg))
-* [Schwarzenegger celebrity face sample](web_demo/images/face_Schwarzenegger.jpg) ([wikimedia source](https://upload.wikimedia.org/wikipedia/commons/thumb/0/0f/A._Schwarzenegger.jpg/220px-A._Schwarzenegger.jpg))
-
-
-before | after
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- | 
- | 