Preparing 0.1.0: Facial Authentication Server updated

Author: AdamMiltonBarker

Facial-Auth/Classes/Facenet.py

@@ -0,0 +1,126 @@
+############################################################################################
+#
+# Project:       Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project
+# Repository:    ALL Detection System 2019
+# Project:       Facial Authentication Server
+#
+# Author:        Adam Milton-Barker (AdamMiltonBarker.com)
+# Contributors:
+# Title:         Facenet Class
+# Description:   Facenet class for the ALL Detection System 2019 Facial Authentication Server.
+# License:       MIT License
+# Last Modified: 2020-07-16
+#
+############################################################################################
+
+import os, json, cv2, dlib, imutils
+
+import numpy as np
+
+from imutils import face_utils
+from datetime import datetime
+
+from Classes.Helpers import Helpers
+from Classes.OpenCV import OpenCV
+
+
+class Facenet():
+    """ ALL Detection System 2019 Facenet Class
+
+    Facenet helper functions for the ALL Detection System 2019 Facial Authentication Server project. 
+    """
+    
+    def __init__(self, LogPath):
+        """ Initializes the Facenet class. """
+
+        # Class settings
+        self.Known = []
+        
+        self.Helpers = Helpers()
+        self.LogFile = self.Helpers.setLogFile(self.Helpers.confs["Server"]["Logs"]+"/Facenet")
+        
+        # OpenCV settings 
+        self.OpenCV = OpenCV(self.Helpers)
+        
+        # Dlib settings 
+        self.Detector   = dlib.get_frontal_face_detector()
+        self.Predictor  = dlib.shape_predictor(self.Helpers.confs["Classifier"]["Dlib"])
+
+    def PreprocessKnown(self, ValidDir, Graph):
+        """ Preprocesses known images. """
+        
+        for validFile in os.listdir(ValidDir):
+            if os.path.splitext(validFile)[1] in self.Helpers.confs["Classifier"]["ValidIType"]:
+                self.Known.append({"File": validFile, "Score": self.Infer(cv2.resize(cv2.imread(ValidDir+validFile), (640, 480)), Graph)})
+        
+        self.Helpers.logMessage(self.LogFile,
+                                "Facenet",
+                                "STATUS",
+                                str(len(self.Known)) + " known images found.") 
+
+    def ProcessFrame(self, Frame):
+        """ Preprocesses frame. """
+
+        Known = []
+
+        Frame = cv2.resize(cv2.imdecode(Frame, cv2.IMREAD_UNCHANGED), (640, 480)) 
+        RawFrame = Frame.copy()
+        Gray = cv2.cvtColor(Frame, cv2.COLOR_BGR2GRAY)
+
+        Path = "Data/Captured/" + datetime.now().strftime("%Y-%m-%d") + "/" + datetime.now().strftime("%H") + "/"
+        FileName = datetime.now().strftime('%M-%S') + ".jpg"
+        FileNameGray = datetime.now().strftime('%M-%S') + "-Gray.jpg"
+
+        self.OpenCV.SaveFrame(Path + "/", FileName, Frame)
+        self.OpenCV.SaveFrame(Path + "/", FileNameGray, Gray)
+
+        return Frame
+
+    def LoadGraph(self):
+        """ Loads Facenet graph. """
+
+        with open(self.Helpers.confs["Classifier"]["Graph"], mode='rb') as f:
+            graphFile = f.read()
+            
+        self.Helpers.logMessage(self.LogFile, "Facenet", "Status", "Loaded TASS Graph")
+
+        return graphFile
+        
+    def Infer(self, face, graph):
+        """ Runs the image through Facenet. """
+        
+        graph.LoadTensor(self.PreProcess(face).astype(np.float16), None)
+        output, userobj = graph.GetResult()
+
+        return output
+
+    def PreProcess(self, src):
+        """ Preprocesses an image. """
+        
+        NETWORK_WIDTH = 160
+        NETWORK_HEIGHT = 160
+        
+        preprocessed_image = cv2.resize(src, (NETWORK_WIDTH, NETWORK_HEIGHT))
+        preprocessed_image = cv2.cvtColor(preprocessed_image, cv2.COLOR_BGR2RGB)
+        preprocessed_image = self.OpenCV.whiten(preprocessed_image)
+        
+        return preprocessed_image
+
+    def Compare(self, face1, face2):
+        """ Determines whether two images are a match. """
+
+        if (len(face1) != len(face2)):
+            self.Helpers.logMessage(self.LogFile, "Facenet", "!ERROR!", "Distance Missmatch")
+            return False
+
+        tdiff = 0
+        for index in range(0, len(face1)):
+            diff = np.square(face1[index] - face2[index])
+            tdiff += diff
+
+        if (tdiff < 1.3):
+            self.Helpers.logMessage(self.LogFile, "Facenet", "Classification", "Calculated Match: " + str(tdiff))
+            return True, tdiff
+        else:
+            self.Helpers.logMessage(self.LogFile, "Facenet", "Classification", "Calculated Mismatch: " + str(tdiff))
+            return False, tdiff

Facial-Auth/Classes/Helpers.py

@@ -0,0 +1,69 @@
+############################################################################################
+#
+# Project:       Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project
+# Repository:    ALL Detection System 2019
+# Project:       Facial Authentication Server
+#
+# Author:        Adam Milton-Barker (AdamMiltonBarker.com)
+# Contributors:
+# Title:         Helper Class
+# Description:   Helper class for the ALL Detection System 2019.
+# License:       MIT License
+# Last Modified: 2020-07-14
+#
+############################################################################################
+
+import json
+import time
+
+from datetime import datetime
+
+
+class Helpers():
+    """ ALL Detection System 2019 Helper Class
+
+    Common helper functions for the ALL Detection System 2019 Facial Authentication Server project. 
+    """
+
+    def __init__(self):
+        """ Initializes the Helper class. """
+
+        self.confs = self.loadConfs()
+
+    def loadConfs(self):
+        """ Loads the configuration. """
+
+        confs = {}
+        with open('Required/confs.json') as confs:
+            confs = json.loads(confs.read())
+        return confs
+
+    def currentDateTime(self):
+        """ Gets the current date and time in words. """
+
+        return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    def timerStart(self):
+        """ Starts the timer. """
+
+        return str(datetime.now()), time.time()
+
+    def timerEnd(self, start):
+        """ Ends the timer. """
+
+        return time.time(), (time.time() - start), str(datetime.now())
+
+    def setLogFile(self, path):
+        """ Ends a log file path. """
+
+        return path + datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d-%H-%M-%S') + ".txt"
+
+    def logMessage(self, logfile, process, messageType, message, hide=False):
+        """ Logs a message to a log file. """
+
+        logString = datetime.fromtimestamp(time.time()).strftime(
+            '%Y-%m-%d %H:%M:%S') + "|" + process + "|" + messageType + ": " + message
+        with open(logfile, "a") as logLine:
+            logLine.write(logString+'\r\n')
+        if hide == False:
+            print(logString)

Facial-Auth/Classes/Movidius.py

@@ -0,0 +1,57 @@
+############################################################################################
+#
+# Project:       Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project
+# Repository:    ALL Detection System 2019
+# Project:       Facial Authentication Server
+#
+# Author:        Adam Milton-Barker (AdamMiltonBarker.com)
+# Contributors:
+# Title:         Movidius Class
+# Description:   Movidius class for the ALL Detection System 2019 Facial Authentication Server.
+# License:       MIT License
+# Last Modified: 2020-07-16
+#
+############################################################################################
+
+import os, json, cv2
+
+from mvnc import mvncapi as mvnc
+
+from Classes.Helpers import Helpers
+
+
+class Movidius():
+    """ ALL Detection System 2019 Movidius Class
+
+    Movidius helper functions for the ALL Detection System 2019 Facial Authentication Server project. 
+    """
+    
+    def __init__(self, LogPath):
+        """ Initializes the Movidius class. """
+         
+        self.Helpers = Helpers()
+        self.LogFile = self.Helpers.setLogFile(LogPath+"/Movidius")
+
+        self.ncsDevices = None
+        self.ncsDevice = None
+        
+    def checkNCS(self):
+        """ Checks for NCS devices and returns True if devices are found. """
+
+        self.ncsDevices = mvnc.EnumerateDevices()
+        
+        if len(self.ncsDevices) == 0:
+            self.Helpers.logMessage(self.LogFile, "Movdius", "Status", "No NCS devices found, TASS exiting!")
+            quit()
+            
+        self.ncsDevice = mvnc.Device(self.ncsDevices[0])
+        self.ncsDevice.OpenDevice()
+        
+        self.Helpers.logMessage(self.LogFile, "Movdius", "Status", "Connected To NCS")
+
+    def allocateGraph(self, graphfile):
+        """ Allocates the NCS graph. """
+
+        self.Graph = self.ncsDevice.AllocateGraph(graphfile)
+        
+        self.Helpers.logMessage(self.LogFile, "Movdius", "Status", "Graph Allocated")

Facial-Auth/Classes/OpenCV.py

@@ -0,0 +1,73 @@
+############################################################################################
+#
+# Project:       Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project
+# Repository:    ALL Detection System 2019
+# Project:       Facial Authentication Server
+#
+# Author:        Adam Milton-Barker (AdamMiltonBarker.com)
+# Contributors:
+# Title:         OpenCV Class
+# Description:   OpenCV class for the ALL Detection System 2019 Facial Authentication Server.
+# License:       MIT License
+# Last Modified: 2020-07-16
+#
+############################################################################################
+
+import os, json, cv2, time
+import numpy as np
+from datetime import datetime
+
+
+class OpenCV():
+    """ ALL Detection System 2019 OpenCV Class
+
+    OpenCV helper functions for the ALL Detection System 2019 Facial Authentication Server project. 
+    """
+
+    def __init__(self, helpers):
+        """ Initializes the OpenCV class. """
+        
+        self.Helpers = helpers
+
+    def SaveFrame(self, path, fileName, frame):
+        """ Saves an image. """
+
+        if not os.path.exists(path):
+            os.makedirs(path)
+
+        currentImage=path+'/'+datetime.now().strftime('%M-%S')+'.jpg'
+        cv2.imwrite(currentImage, frame)
+
+        return currentImage
+
+    def loadImage(self, imgID):
+        """ Loads an image. """
+    
+        imgLoadStart  = time.time()
+
+        img = cv2.imread("data/captured/"+str(imgID)+'.png')
+
+        imgLoadEnd    = (imgLoadStart - time.time())
+
+        return img
+
+    def whiten(self, source_image):
+        """ Creates a whitened image.  """
+
+        source_mean = np.mean(source_image)
+        source_standard_deviation = np.std(source_image)
+        std_adjusted = np.maximum(source_standard_deviation, 1.0 / np.sqrt(source_image.size))
+        whitened_image = np.multiply(np.subtract(source_image, source_mean), 1 / std_adjusted)
+        return whitened_image
+
+    def preprocess(self, src):
+        """ Preprocesses an image.  """
+        
+        NETWORK_WIDTH = 160
+        NETWORK_HEIGHT = 160
+        
+        preprocessed_image = cv2.resize(src, (NETWORK_WIDTH, NETWORK_HEIGHT))
+        preprocessed_image = cv2.cvtColor(preprocessed_image, cv2.COLOR_BGR2RGB)
+        preprocessed_image = self.whiten(preprocessed_image)
+
+        return preprocessed_image