--- a
+++ b/Projects/NCS1/Classifier.py
@@ -0,0 +1,158 @@
+############################################################################################
+#
+# 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:         Classifier Class
+# Description:   Classifier for the ALL Detection System 2019.
+# License:       MIT License
+# Last Modified: 2020-07-21
+#
+############################################################################################
+
+import cv2, os, sys, time
+
+import numpy as np
+
+from mvnc import mvncapi as mvnc
+
+from Classes.Helpers import Helpers
+from Classes.Movidius import Movidius
+
+
+class Classifier():
+    """ ALL Detection System 2019 Classifier Class
+
+    Classifier for the ALL Detection System 2019.
+    """
+
+    def __init__(self):
+        """ Initializes the Classifier Class. """
+
+        self.Helpers = Helpers("Classifier")
+        self.confs = self.Helpers.confs
+
+        self.Movidius = Movidius()
+        self.Movidius.checkNCS()
+        self.Movidius.loadInception()
+
+        self.Helpers.logger.info("Classifier class initialization complete.")
+
+
+Classifier = Classifier()
+
+
+def main(argv):
+
+    humanStart, clockStart = Classifier.Helpers.timerStart()
+
+    Classifier.Helpers.logger.info(
+        "ALL Detection System 2019 Classifier started.")
+
+    files = 0
+    correct = 0
+    incorrect = 0
+    low = 0
+    lowCorrect = 0
+    lowIncorrect = 0
+
+    rootdir = Classifier.confs["Classifier"]["TestImagePath"]
+
+    for testFile in os.listdir(rootdir):
+        if os.path.splitext(testFile)[1] in Classifier.confs["Classifier"]["ValidIType"]:
+
+            files += 1
+            fileName = rootdir + "/" + testFile
+
+            img = cv2.imread(fileName).astype(np.float32)
+
+            Classifier.Helpers.logger.info(
+                "Loaded test image " + fileName)
+
+            dx, dy, dz = img.shape
+            delta = float(abs(dy-dx))
+
+            if dx > dy:
+                img = img[int(0.5*delta):dx-int(0.5*delta), 0:dy]
+            else:
+                img = img[0:dx, int(0.5*delta):dy-int(0.5*delta)]
+
+            img = cv2.resize(img, (Classifier.Movidius.reqsize,
+                                   Classifier.Movidius.reqsize))
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+            for i in range(3):
+                img[:, :, i] = (
+                    img[:, :, i] - Classifier.Movidius.mean) * Classifier.Movidius.std
+
+            detectionStart, detectionStart = Classifier.Helpers.timerStart()
+
+            Classifier.Movidius.ncsGraph.LoadTensor(
+                img.astype(np.float16), 'user object')
+            output, userobj = Classifier.Movidius.ncsGraph.GetResult()
+
+            detectionClockEnd, difference, detectionEnd = Classifier.Helpers.timerEnd(
+                detectionStart)
+
+            top_inds = output.argsort()[::-1][:5]
+
+            if output[top_inds[0]] >= Classifier.confs["Classifier"]["InceptionThreshold"] and Classifier.Movidius.classes[top_inds[0]] == "1":
+                if "_1." in fileName:
+                    correct += 1
+                    Classifier.Helpers.logger.info(
+                        "ALL correctly detected with confidence of " + str(output[top_inds[0]]) + " in " + str(difference) + " seconds.")
+                else:
+                    incorrect += 1
+                    Classifier.Helpers.logger.warning(
+                        "ALL incorrectly detected with confidence of " + str(output[top_inds[0]]) + " in " + str(difference) + " seconds.")
+
+            elif output[top_inds[0]] >= Classifier.confs["Classifier"]["InceptionThreshold"] and Classifier.Movidius.classes[top_inds[0]] == "0":
+                if "_0." in fileName:
+                    correct += 1
+                    Classifier.Helpers.logger.info(
+                        "ALL correctly not detected with confidence of " + str(output[top_inds[0]]) + " in " + str(difference) + " seconds.")
+                else:
+                    incorrect += 1
+                    Classifier.Helpers.logger.warning(
+                        "ALL incorrectly not detected with confidence of " + str(output[top_inds[0]]) + " in " + str(difference) + " seconds.")
+
+            elif output[top_inds[0]] <= Classifier.confs["Classifier"]["InceptionThreshold"] and Classifier.Movidius.classes[top_inds[0]] == "1":
+                if "_1." in fileName:
+                    correct += 1
+                    low += 1
+                    lowCorrect += 1
+                    Classifier.Helpers.logger.info(
+                        "ALL correctly detected with LOW confidence of " + str(output[top_inds[0]]) + " in " + str(difference) + " seconds.")
+                else:
+                    incorrect += 1
+                    low += 1
+                    lowIncorrect += 1
+                    Classifier.Helpers.logger.warning(
+                        "ALL incorrectly detected with LOW confidence of " + str(output[top_inds[0]]) + " in " + str(difference) + " seconds.")
+
+            elif output[top_inds[0]] <= Classifier.confs["Classifier"]["InceptionThreshold"] and Classifier.Movidius.classes[top_inds[0]] == "0":
+                if "_0." in fileName:
+                    correct += 1
+                    low += 1
+                    lowCorrect += 1
+                    Classifier.Helpers.logger.info(
+                        "ALL correctly not detected with LOW confidence of " + str(output[top_inds[0]]) + " in " + str(difference) + " seconds.")
+                else:
+                    low += 1
+                    incorrect += 1
+                    lowIncorrect += 1
+                    Classifier.Helpers.logger.warning(
+                        "ALL incorrectly not detected with LOW confidence of " + str(output[top_inds[0]]) + " in " + str(difference) + " seconds.")
+
+    clockEnd, difference, humanEnd = Classifier.Helpers.timerEnd(clockStart)
+    Classifier.Helpers.logger.info("Testing ended. " + str(correct) + " correct, " + str(incorrect) +
+                                   " incorrect, " + str(low) + " low confidence: (" + str(lowCorrect) + " correct, " + str(lowIncorrect) + " incorrect)")
+
+    Classifier.Movidius.ncsDevice.CloseDevice()
+
+
+if __name__ == "__main__":
+    main(sys.argv[1:])