Merge pull request #18 from Mini-Sylar/add-mini-sylar

Add mini sylar

Algorithms/GUI/AlgorithmExamination.py

@@ -79,7 +79,7 @@ def setupUi(self, MainWindow):
         self.verticalLayout_9.setSizeConstraint(QtWidgets.QLayout.SetNoConstraint)
         self.verticalLayout_9.setObjectName("verticalLayout_9")
         self.Show_Gaussian_Images = QtWidgets.QVBoxLayout()
-        self.Show_Gaussian_Images.setSizeConstraint(QtWidgets.QLayout.SetFixedSize)
+        self.Show_Gaussian_Images.setSizeConstraint(QtWidgets.QLayout.SetMaximumSize)
         self.Show_Gaussian_Images.setObjectName("Show_Gaussian_Images")
         self.verticalLayout_9.addLayout(self.Show_Gaussian_Images)
         self.formLayout_4 = QtWidgets.QFormLayout()
@@ -149,7 +149,6 @@ def setupUi(self, MainWindow):
         self.label_8.setObjectName("label_8")
         self.min_match_info.setWidget(1, QtWidgets.QFormLayout.LabelRole, self.label_8)
         self.minutiae_min_match = QtWidgets.QLabel(self.Minutiae_Matches)
-        self.minutiae_min_match.setText("")
         self.minutiae_min_match.setObjectName("minutiae_min_match")
         self.min_match_info.setWidget(1, QtWidgets.QFormLayout.FieldRole, self.minutiae_min_match)
         self.label_11 = QtWidgets.QLabel(self.Minutiae_Matches)
@@ -293,10 +292,10 @@ def setupUi(self, MainWindow):
         self.generate_gaussian_images.setMinimumSize(QtCore.QSize(200, 0))
         self.generate_gaussian_images.setObjectName("generate_gaussian_images")
         self.gridLayout_2.addWidget(self.generate_gaussian_images, 1, 0, 1, 1, QtCore.Qt.AlignLeft)
-        self.run_sift_performance = QtWidgets.QPushButton(self.ExtraActionsResearch)
-        self.run_sift_performance.setEnabled(False)
-        self.run_sift_performance.setObjectName("run_sift_performance")
-        self.gridLayout_2.addWidget(self.run_sift_performance, 0, 1, 1, 1, QtCore.Qt.AlignHCenter)
+        self.record_data = QtWidgets.QPushButton(self.ExtraActionsResearch)
+        self.record_data.setEnabled(False)
+        self.record_data.setObjectName("record_data")
+        self.gridLayout_2.addWidget(self.record_data, 0, 1, 1, 1, QtCore.Qt.AlignHCenter)
         self.generate_DOG_images = QtWidgets.QPushButton(self.ExtraActionsResearch)
         self.generate_DOG_images.setEnabled(False)
         self.generate_DOG_images.setMinimumSize(QtCore.QSize(200, 0))
@@ -364,6 +363,7 @@ def retranslateUi(self, MainWindow):
         self.SIFT.setTabText(self.SIFT.indexOf(self.DoG), _translate("MainWindow", "Difference of Gaussian"))
         self.min_match_score.setText(_translate("MainWindow", "Match Score"))
         self.label_8.setText(_translate("MainWindow", "Min Match"))
+        self.minutiae_min_match.setText(_translate("MainWindow", "5"))
         self.label_11.setText(_translate("MainWindow", "Terminations"))
         self.label_13.setText(_translate("MainWindow", "Bifurcations"))
         self.label_15.setText(_translate("MainWindow", "Verdict"))
@@ -383,7 +383,7 @@ def retranslateUi(self, MainWindow):
         self.Minutiae.setTabText(self.Minutiae.indexOf(self.Thinned), _translate("MainWindow", "Thinning"))
         self.run_sift_research.setText(_translate("MainWindow", "Run SIFT Algorithm (Research)"))
         self.generate_gaussian_images.setText(_translate("MainWindow", "Generate Gaussian Images"))
-        self.run_sift_performance.setText(_translate("MainWindow", "Run SIFT Algorithm (Performance)"))
+        self.record_data.setText(_translate("MainWindow", "Record Data"))
         self.generate_DOG_images.setText(_translate("MainWindow", "Generate DoG Images"))
         self.run_minutiae.setText(_translate("MainWindow", "Run Minutiae Algorithm"))
         self.QueryImage.setText(_translate("MainWindow", "Select Query Image"))

Algorithms/GUI/AlgorithmExamination.ui

@@ -132,7 +132,7 @@
           <item>
            <layout class="QVBoxLayout" name="Show_Gaussian_Images">
             <property name="sizeConstraint">
-             <enum>QLayout::SetFixedSize</enum>
+             <enum>QLayout::SetMaximumSize</enum>
             </property>
            </layout>
           </item>
@@ -256,7 +256,7 @@
             <item row="1" column="1">
              <widget class="QLabel" name="minutiae_min_match">
               <property name="text">
-               <string/>
+               <string>5</string>
               </property>
              </widget>
             </item>
@@ -526,12 +526,12 @@
         </widget>
        </item>
        <item row="0" column="1" alignment="Qt::AlignHCenter">
-        <widget class="QPushButton" name="run_sift_performance">
+        <widget class="QPushButton" name="record_data">
          <property name="enabled">
           <bool>false</bool>
          </property>
          <property name="text">
-          <string>Run SIFT Algorithm (Performance)</string>
+          <string>Record Data</string>
          </property>
         </widget>
        </item>

Algorithms/GUI/CollectData.py

@@ -0,0 +1,159 @@
+import glob
+import math
+import re
+from datetime import datetime
+from pathlib import Path
+
+import cv2
+import numpy as np
+import xlsxwriter
+
+from Algorithms.Minutiae.Libs.matching import match_tuples
+from Algorithms.Minutiae.Libs.minutiae import generate_tuple_profile
+# Import Minutiae
+from Algorithms.Minutiae.Minutiae_OBJ import *
+# Import SIFT
+from Algorithms.SIFT.SIFT_OBJ import SIFT
+
+#
+# # collect Data Here
+workbook = xlsxwriter.Workbook(f"Data_Subject_First_600.xlsx")
+worksheet = workbook.add_worksheet()
+worksheet.set_column(0, 13, 50)
+# Set titles here
+sheet_titles = {0: "Fingerprint image",
+
+                1: "Alteration Type",
+
+                2: "Match Score (SIFT)",
+                3: "Time (SIFT)",
+                4: "Verdict (SIFT)",
+
+                5: "Match Score (Minutiae)",
+                6: "Time (Minutiae)",
+                7: "Verdict (Minutiae)",
+                }
+for value, title in enumerate(sheet_titles.values()):
+    worksheet.write(0, value, title)
+
+# Glob Here
+file_pattern = re.compile(r'.*?(\d+).*?')
+def get_order(file):
+    match = file_pattern.match(Path(file).name)
+    if not match:
+        return math.inf
+    return int(match.groups()[0])
+
+# Path to Real
+path_to_real = "C:\\Users\\Ugo\\Desktop\\Fingerprint-Matching-System\\SOCOFing\\Real\\"
+# Get Real Images
+real_images = []
+for img in sorted(glob.glob(f"{path_to_real}*.BMP"),
+                  key=get_order):
+    real_images.append(img.strip(path_to_real))
+
+# # Get Altered Images
+path_to_altered = "C:\\Users\\Ugo\\Desktop\\Fingerprint-Matching-System\\SOCOFing\\Altered\\Altered-Easy\\"
+altered_easy = []
+
+try:
+    for img in sorted(glob.glob(f"{path_to_altered}*.BMP"), key=get_order):
+        # n= cv2.imread(img)
+        altered_easy.append(img.strip(path_to_altered))
+
+
+    counter = 405
+    counter_end = counter + 3
+    # Initial SIFT
+    sift_query = SIFT()
+    sift_train = SIFT()
+    row= 1
+    altered_type ="Easy"
+    verdict = ""
+    verdict_minutiae = ""
+    # Loop to pairs
+    for i in range(135,151): # where to end multiply by 6 control where to start and end,
+        for j in range(counter, counter_end):
+            print(f"Now On {real_images[i]} and {altered_easy[j]}")
+            MIN_MATCH_COUNT = 18
+            query = cv2.imread(path_to_real+real_images[i],0)
+            train = cv2.imread(path_to_altered+altered_easy[j],0)
+
+            start = datetime.now()
+            kp1, des1 = sift_query.computeKeypointsAndDescriptors(query)
+            kp2, des2 = sift_train.computeKeypointsAndDescriptors(train)
+            # # Initialize and use FLANN
+            FLANN_INDEX_KDTREE = 1
+            index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
+            search_params = dict(checks=37)
+            flann = cv2.FlannBasedMatcher(index_params, search_params)
+            matches = flann.knnMatch(des1, des2, k=2)
+            #
+            # # Lowe's ratio test
+            good = set()
+            for m, n in matches:
+                if m.distance < 0.6 * n.distance:
+                    good.add(m)
+            if len(good) > MIN_MATCH_COUNT:
+                src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
+                dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
+
+                M = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)[0]
+
+            time_taken = datetime.now() - start
+        #     Verdict Here
+            if len(good) > 35:
+                verdict = "Fingerprints/Images Are A Good Match!"
+            elif len(good) > 18:
+                verdict = "Fingerprints/Images Match With A Low Score!"
+            else:
+                verdict = "Not enough matches are found"
+
+        #     MINUTIAE
+            start = datetime.now()
+            coor_termination1, coor_bifurcation1, total_bif_term1 = detectAndComputeMinutiae(path_to_real+real_images[i])
+            coor_termination2, coor_bifurcation2, total_bif_term2 = detectAndComputeMinutiae(path_to_altered+altered_easy[j])
+            # For caluclation process
+            calc_bif_term1 = generate_tuple_profile(total_bif_term1)
+            calc_bif_term2 = generate_tuple_profile(total_bif_term2)
+
+            try:
+                common_points_both_train, common_points_both_query = match_tuples(calc_bif_term1, calc_bif_term2)
+                minutiae_value = len(common_points_both_query)
+            except Exception:
+                print("Unable to find common points defaulting to score 0")
+                minutiae_value = 0
+            # # Score here
+            # Time ends here
+            time_taken_minutiae = datetime.now() - start
+        #     Minutiae Verdict
+            if minutiae_value >= 7:
+                verdict_minutiae = "Fingerprints Are A Good Match"
+            elif minutiae_value >= 3:
+                verdict_minutiae= "Fingerprints Match With A Really Low Score"
+            else:
+                verdict_minutiae="Fingerprints do not match"
+        #     Record DATA
+            # Write Query Image Here
+            worksheet.write(row, 0, f"{real_images[i]}\n{altered_easy[j]}")
+            # Add Alteration Type
+            worksheet.write(row, 1, altered_type)
+            #### SIFT ####
+            worksheet.write(row, 2, str(len(good)))
+            # Time
+            worksheet.write(row, 3, time_taken)
+            # Verdict
+            worksheet.write(row, 4, verdict)
+            #--------- MINUTIAE ---------------
+            worksheet.write(row,5,str(len(common_points_both_query)))
+            worksheet.write(row,6,time_taken_minutiae)
+            worksheet.write(row,7,verdict_minutiae)
+            row = row + 2
+            print("=====================================")
+        counter += 3
+        counter_end += 3
+except Exception:
+    print("User stopped operation")
+    workbook.close()
+print("Saved Workbook successfully")
+workbook.close()