testing/app.py

   1 #!/usr/bin/env python3
   2 import connexion
   3 import logging
   4
   5 import argparse
   6 import json
   7 import time
   8 import os
   9
  10 from flask import Flask, request, current_app, make_response
  11
  12 import pandas as pd
  13 import requests
  14
  15 from cognita_client.wrap.load import load_model
  16 from face_privacy_filter.transform_detect import FaceDetectTransform
  17 import base64
  18
  19 def generate_image_df(path_image="", bin_stream=b""):
  20     # munge stream and mimetype into input sample
  21     if path_image and os.path.exists(path_image):
  22         bin_stream = open(path_image, 'rb').read()
  23     bin_stream = base64.b64encode(bin_stream)
  24     if type(bin_stream)==bytes:
  25         bin_stream = bin_stream.decode()
  26     return pd.DataFrame([['image/jpeg', bin_stream]], columns=[FaceDetectTransform.COL_IMAGE_MIME, FaceDetectTransform.COL_IMAGE_DATA])
  27
  28 def transform(mime_type, image_binary):
  29     app = current_app
  30     time_start = time.clock()
  31     image_read = image_binary.stream.read()
  32     X = generate_image_df(bin_stream=image_read)
  33     print(X)
  34
  35     if app.model_detect is not None:
  36         pred_out = app.model_detect.transform.from_native(X)
  37     if app.model_proc is not None:
  38         pred_prior = pred_out
  39         #pred_out = app.model_proc.transform.from_msg(pred_prior.as_msg())
  40         pred_out = app.model_proc.transform.from_native(pred_prior.as_native())
  41     time_stop = time.clock()
  42
  43     retStr = json.dumps(pred_out.as_native().to_dict(orient='records'), indent=4)
  44
  45     # formulate response
  46     resp = make_response((retStr, 200, { } ))
  47     # allow 'localhost' from 'file' or other;
  48     # NOTE: DO NOT USE IN PRODUCTION!!!
  49     resp.headers['Access-Control-Allow-Origin'] = '*'
  50     print(retStr[:min(200, len(retStr))])
  51     #print(pred)
  52     return resp
  53
  54
  55 if __name__ == '__main__':
  56     parser = argparse.ArgumentParser()
  57     parser.add_argument('-p', "--port", type=int, default=8884, help='port to launch the simple web server')
  58     parser.add_argument('-d', "--modeldir_detect", type=str, default='../model_detect', help='model directory for detection')
  59     parser.add_argument('-a', "--modeldir_analyze", type=str, default='../model_pix', help='model directory for detection')
  60     pargs = parser.parse_args()
  61
  62     print("Configuring local application... {:}".format(__name__))
  63     logging.basicConfig(level=logging.INFO)
  64     app = connexion.App(__name__)
  65     app.add_api('swagger.yaml')
  66     # example usage:
  67     #     curl -F image_binary=@test.jpg -F mime_type="image/jpeg" "http://localhost:8885/transform"
  68
  69     app.app.model_detect = None
  70     if pargs.modeldir_detect:
  71         if not os.path.exists(pargs.modeldir_detect):
  72             print("Failed loading of detect model '{:}' even though it was specified...".format(pargs.modeldir_detect))
  73         else:
  74             print("Loading detect model... {:}".format(pargs.modeldir_detect))
  75             app.app.model_detect = load_model(pargs.modeldir_detect)
  76
  77     app.app.model_proc = None
  78     if pargs.modeldir_analyze:
  79         if not os.path.exists(pargs.modeldir_analyze):
  80             print("Failed loading of processing model '{:}' even though it was specified...".format(
  81                 pargs.modeldir_analyze))
  82         else:
  83             print("Loading processing model... {:}".format(pargs.modeldir_analyze))
  84             app.app.model_proc = load_model(pargs.modeldir_analyze)
  85
  86     # run our standalone gevent server
  87     app.run(port=pargs.port) #, server='gevent')