Automate data collection

Algorithms/GUI/CollectData.py

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+import itertools
+import re
+import math
+from pathlib import Path
+import glob
+import os
+from datetime import datetime
+
+import cv2
+import numpy as np
+import xlsxwriter
+from PyQt5.QtGui import QImageReader
+
+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_Subject2.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 = []
+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 = 0
+counter_end = 3
+# Initial SIFT
+sift_query = SIFT()
+sift_train = SIFT()
+row= 1
+altered_type ="Easy"
+verdict = ""
+verdict_minutiae = ""
+# Loop to pairs
+for i in range(5,6): # where to end multiply by 6 control where to start and end,
+    for j in range(counter, counter_end):
+        MIN_MATCH_COUNT = 18
+        query = cv2.imread(path_to_real+real_images[i],0)
+        train = cv2.imread(path_to_altered+altered_easy[j],0)
+        kp1, des1 = sift_query.computeKeypointsAndDescriptors(query)
+        kp2, des2 = sift_train.computeKeypointsAndDescriptors(train)
+
+        start = datetime.now()
+
+        # # 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])
+        # Image Profiles
+        img_profile1_term = generate_tuple_profile(coor_termination1)  # Image 1 Termination
+        img_profile1_bif = generate_tuple_profile(coor_bifurcation1)  # Image 1 Bifurcation
+        # This was created only for display purposes
+        termin_disp = img_profile1_term
+        bif_disp = img_profile1_bif
+        # Image 2 Profiles
+        img_profile2_term = generate_tuple_profile(coor_termination2)
+        img_profile2_bif = generate_tuple_profile(coor_bifurcation2)
+        # For caluclation process
+        calc_bif_term1 = generate_tuple_profile(total_bif_term1)
+        calc_bif_term2 = generate_tuple_profile(total_bif_term2)
+        # Load Images here (should already be loaded when tranformed into class)
+        # # Common points Termination
+        common_points_query_termination, common_points_train_termination = match_tuples(img_profile1_term,
+                                                                                        img_profile2_term)
+        common_points_query_bifurcation, common_points_train_bifurcation = match_tuples(img_profile1_bif,
+                                                                                        img_profile2_bif)
+
+        common_points_both_train, common_points_both_query = match_tuples(calc_bif_term1, calc_bif_term2)
+
+        minutiae_value = len(common_points_both_query)
+        # 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
+    counter += 3
+    counter_end += 3
+
+print("Saved Workbook successfully")
+workbook.close()