example-apps/ROBO/aPaaS/Obj_Detection_service/detection/obj_detection_service.py

   1 #
   2 # Copyright 2020 Huawei Technologies Co., Ltd.
   3 #
   4 # Licensed under the Apache License, Version 2.0 (the "License");
   5 # you may not use this file except in compliance with the License.
   6 # You may obtain a copy of the License at
   7 #
   8 #     http://www.apache.org/licenses/LICENSE-2.0
   9 #
  10 # Unless required by applicable law or agreed to in writing, software
  11 # distributed under the License is distributed on an "AS IS" BASIS,
  12 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13 # See the License for the specific language governing permissions and
  14 # limitations under the License.
  15 #
  16
  17
  18 import os
  19 import cv2
  20 import config
  21 from flask_sslify import SSLify
  22 from flask import Flask, request, jsonify, Response
  23 from flask_cors import CORS
  24 from werkzeug import secure_filename
  25
  26
  27 class model_info():
  28     def __init__(self, model_name):
  29         self.model = 'model_info/MobileNetSSD_deploy.caffemodel'
  30         self.model_name = model_name
  31         self.prototxt = 'model_info/MobileNetSSD_deploy.prototxt'
  32         self.confidenceLevel = 80
  33
  34     def get_model(self):
  35         return self.model
  36
  37     def get_prototxt(self):
  38         return self.prototxt
  39
  40     def get_model_name(self):
  41         return self.model_name
  42
  43     def set_confidence_level(self, confidenceLevel):
  44         self.confidenceLevel = confidenceLevel
  45
  46     def get_confidence_level(self):
  47         return self.confidenceLevel
  48
  49     def update_model(self, model, prototxt, model_name):
  50         self.prototxt = prototxt
  51         self.model = model
  52         self.model_name = model_name
  53
  54
  55 # Labels of Network.
  56 classNames = {0: 'background',
  57               1: 'aeroplane', 2: 'bicycle', 3: 'bird', 4: 'boat',
  58               5: 'bottle', 6: 'bus', 7: 'car', 8: 'cat', 9: 'chair',
  59               10: 'cow', 11: 'diningtable', 12: 'dog', 13: 'horse',
  60               14: 'motorbike', 15: 'person', 16: 'pottedplant',
  61               17: 'sheep', 18: 'sofa', 19: 'train', 20: 'tvmonitor'}
  62
  63
  64 app = Flask(__name__)
  65 CORS(app)
  66 sslify = SSLify(app)
  67 app.config['JSON_AS_ASCII'] = False
  68 app.config['UPLOAD_PATH'] = '/usr/app/images/'
  69 app.config['supports_credentials'] = True
  70 app.config['CORS_SUPPORTS_CREDENTIALS'] = True
  71 app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024
  72 ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
  73 count = 0
  74 listOfMsgs = []
  75
  76
  77 def allowed_file(filename):
  78     return '.' in filename and filename.rsplit('.', 1)[1].lower() \
  79        in ALLOWED_EXTENSIONS
  80
  81
  82 # Obj-detection from input frame
  83 def Detection(img):
  84
  85     modelInfo = model_info("caffe")
  86     ConfPercent = modelInfo.get_confidence_level()
  87     model = modelInfo.get_model()
  88     prototxt = modelInfo.get_prototxt()
  89
  90     model = '/home/root1/My_Work/Akraino/MEC_BP/Rel4/Retail-apps/aPaaS/' \
  91             'src/Obj_Detection_service/' + 'MobileNetSSD_deploy.caffemodel'
  92
  93     prototxt = '/home/root1/My_Work/Akraino/MEC_BP/Rel4/Retail-apps/aPaaS/' \
  94                'src/Obj_Detection_service/' + 'MobileNetSSD_deploy.prototxt'
  95     print(ConfPercent)
  96     print(model)
  97     print(prototxt)
  98
  99     # Load the Caffe model
 100     net = cv2.dnn.readNetFromCaffe(prototxt, model)
 101     # Load image fro
 102     frame = cv2.imread('/home/root1/My_Work/Akraino/MEC_BP/Rel4/Retail-apps/'
 103                        'aPaaS/src/Obj_Detection_service/images/' + img)
 104     print('/home/root1/My_Work/Akraino/MEC_BP/Rel4/Retail-apps/aPaaS/'
 105           'src/Obj_Detection_service/images/' + img)
 106     frame_resized = cv2.resize(frame, (300, 300))  # resize frame for
 107     # prediction
 108     heightFactor = frame.shape[0]/300.0
 109     widthFactor = frame.shape[1]/300.0
 110
 111     # MobileNet requires fixed dimensions for input image(s)
 112     # so we have to ensure that it is resized to 300x300 pixels.
 113     # set a scale factor to image because network the objects has
 114     # differents size.
 115     # We perform a mean subtraction (127.5, 127.5, 127.5)
 116     # to normalize the input;
 117     # after executing this command our "blob" now has the shape:
 118     # (1, 3, 300, 300)
 119     blob = cv2.dnn.blobFromImage(frame_resized, 0.007843, (300, 300),
 120                                  (127.5, 127.5, 127.5), False)
 121     # Set to network the input blob
 122     net.setInput(blob)
 123     # Prediction of network
 124     detections = net.forward()
 125
 126     frame_copy = frame.copy()
 127     # Size of frame resize (300x300)
 128     cols = frame_resized.shape[1]
 129     rows = frame_resized.shape[0]
 130
 131     # For get the class and location of object detected,
 132     # There is a fix index for class, location and confidence
 133     # value in @detections array .
 134     for i in range(detections.shape[2]):
 135         confidence = detections[0, 0, i, 2]  # Confidence of prediction
 136         if confidence > ConfPercent:  # Filter prediction
 137             class_id = int(detections[0, 0, i, 1])  # Class label
 138
 139             # Object location
 140             xLeftBottom = int(detections[0, 0, i, 3] * cols)
 141             yLeftBottom = int(detections[0, 0, i, 4] * rows)
 142             xRightTop = int(detections[0, 0, i, 5] * cols)
 143             yRightTop = int(detections[0, 0, i, 6] * rows)
 144
 145             xLeftBottom_ = int(widthFactor * xLeftBottom)
 146             yLeftBottom_ = int(heightFactor * yLeftBottom)
 147             xRightTop_ = int(widthFactor * xRightTop)
 148             yRightTop_ = int(heightFactor * yRightTop)
 149             # Draw location of object
 150             cv2.rectangle(frame_resized, (xLeftBottom, yLeftBottom),
 151                           (xRightTop, yRightTop),
 152                           (0, 255, 0))
 153
 154             cv2.rectangle(frame_copy, (xLeftBottom_, yLeftBottom_),
 155                           (xRightTop_, yRightTop_),
 156                           (0, 255, 0), -1)
 157     opacity = 0.3
 158     cv2.addWeighted(frame_copy, opacity, frame, 1 - opacity, 0, frame)
 159
 160     count = 0
 161     for i in range(detections.shape[2]):
 162         confidence = detections[0, 0, i, 2]  # Confidence of prediction
 163         if confidence > ConfPercent:  # Filter prediction
 164             class_id = int(detections[0, 0, i, 1])  # Class label
 165
 166             # Object location
 167             xLeftBottom = int(detections[0, 0, i, 3] * cols)
 168             yLeftBottom = int(detections[0, 0, i, 4] * rows)
 169             xRightTop = int(detections[0, 0, i, 5] * cols)
 170             yRightTop = int(detections[0, 0, i, 6] * rows)
 171
 172             xLeftBottom_ = int(widthFactor * xLeftBottom)
 173             yLeftBottom_ = int(heightFactor * yLeftBottom)
 174             xRightTop_ = int(widthFactor * xRightTop)
 175             yRightTop_ = int(heightFactor * yRightTop)
 176             cv2.rectangle(frame, (xLeftBottom_, yLeftBottom_),
 177                           (xRightTop_, yRightTop_),
 178                           (0, 0, 0), 2)
 179
 180             # Draw label and confidence of prediction in frame resized
 181             if class_id in classNames:
 182                 label = classNames[class_id] + ": " + str(confidence)
 183                 labelSize, baseLine = cv2.getTextSize(label,
 184                                                       cv2.FONT_HERSHEY_TRIPLEX,
 185                                                       0.8, 1)
 186
 187                 yLeftBottom_ = max(yLeftBottom_, labelSize[1])
 188                 cv2.rectangle(
 189                     frame,
 190                     (xLeftBottom_, yLeftBottom_ - labelSize[1]),
 191                     (xLeftBottom_ + labelSize[0], yLeftBottom_ + baseLine),
 192                     (255, 255, 255), cv2.FILLED)
 193                 cv2.putText(frame, label, (xLeftBottom_, yLeftBottom_),
 194                             cv2.FONT_HERSHEY_TRIPLEX, 0.8, (0, 0, 0))
 195                 print(label)
 196                 count = count + 1
 197
 198     print("total item count", count)
 199     cv2.namedWindow("frame", cv2.WINDOW_NORMAL)
 200     cv2.imwrite("/home/root1/My_Work/Akraino/MEC_BP/tmp/1.jpeg", frame)
 201     cv2.imshow("frame", frame)
 202     cv2.waitKey(0)
 203     cv2.destroyAllWindows()
 204     # Detect_result = {'ImposedImage': 'frame', 'ObjCount': count,
 205     # 'ObjType': type, 'Time': time}
 206     Detect_result = {'ImposedImage': "frame", 'ObjCount': count,
 207                      'labels': label}
 208     return Detect_result
 209
 210
 211 @app.route('/mep/v1/obj_detection/uploadModel', methods=['POST'])
 212 def uploadModel():
 213     """
 214     upload model
 215     :return: html file
 216     """
 217     app.logger.info("Received message from ClientIP [" + request.remote_addr
 218                     + "] Operation [" + request.method + "]" +
 219                     " Resource [" + request.url + "]")
 220
 221     modelInfo = model_info("caffe")
 222     modelInfo.update_model("caffe", "mobilenet_ssd", "prototext")
 223     return Response("success")
 224
 225
 226 @app.route('/mep/v1/obj_detection/confidencelevel', methods=['POST'])
 227 def setConfidenceLevel():
 228     """
 229     Trigger the video_feed() function on opening "0.0.0.0:5000/video_feed" URL
 230     :return:
 231     """
 232     app.logger.info("Received message from ClientIP [" + request.remote_addr
 233                     + "] Operation [" + request.method + "]" +
 234                     " Resource [" + request.url + "]")
 235
 236     confidenceLevel = 80
 237     modelInfo = model_info("caffe")
 238     modelInfo.set_confidence_level(confidenceLevel)
 239     return Response("success")
 240
 241
 242 @app.route('/mep/v1/obj_detection/detect', methods=['GET'])
 243 def Obj_Detection():
 244     """
 245     Trigger the Obj detection on input frame/image
 246     Input: frame/image
 247     :return: imposed frame, count, Obj type, time taken by detection
 248     """
 249     app.logger.info("Received message from ClientIP [" + request.remote_addr
 250                     + "] Operation [" + request.method + "]" +
 251                     " Resource [" + request.url + "]")
 252
 253     if 'file' not in request.files:
 254         raise IOError('No file')
 255
 256     file = request.files['file']
 257     if file.filename == '':
 258         app.logger.info('No file selected for uploading')
 259         raise IOError('No file')
 260     if file and allowed_file(file.filename):
 261         filename = secure_filename(file.filename)
 262         file.save(os.path.join(app.config['UPLOAD_PATH'], filename))
 263         app.logger.info('File successfully uploaded')
 264         Detect_result = Detection(filename)
 265     else:
 266         app.logger.info('Allowed file types are txt, pdf, png, jpg, jpeg, gif')
 267         return Response("failure")
 268     return jsonify(Detect_result)
 269
 270
 271 def start_server(handler):
 272     app.logger.addHandler(handler)
 273     if config.ssl_enabled:
 274         context = (config.ssl_certfilepath, config.ssl_keyfilepath)
 275         app.run(host=config.server_address, debug=True, ssl_context=context,
 276                 threaded=True, port=config.server_port)
 277     else:
 278         app.run(host=config.server_address, debug=True, threaded=True,
 279                 port=config.server_port)