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