First image

.gitignore

+img/
\ No newline at end of file

compute-baseline-scores.py

+import cv2
+import numpy as np
+import insightface
+from insightface.app import FaceAnalysis
+from insightface.data import get_image as ins_get_image
+from PIL import Image
+import os
+from utils.baseScore import BaseHandler
+
+path = '/Users/krunal/Desktop/code/database'
+
+
+
+pardir = 'C:/Users/maria/Desktop/Biometrics/TermPaper/imt4126-biometricqualitymetrics/img/'
+imgsnr = 10
+change_me = 5
+
+img_obs = []
+
+
+
+# TODO make better
+def convert(img: str, new_name: str, add_path: str): 
+    img = Image.open(pardir + 'Casia/' + img)
+    fullpath = pardir + 'Casia_Converted/' + add_path
+    isExist = os.path.exists(fullpath)
+
+    if not isExist:
+        # Create a new directory because it does not exist 
+        os.makedirs(fullpath)
+        print("The new directory is created!")
+
+    img.save( fullpath + new_name, 'jpeg')
+    # Check whether the specified path exists or not
+    
+
+
+    
+
+# Set up detector and recognizer
+
+
+
+app = FaceAnalysis(providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+app.prepare(ctx_id=0, det_size=(640, 640))
+handler = insightface.model_zoo.get_model('buffalo_l')
+handler.prepare(ctx_id=0)
+
+print("########################################################")
+
+
+class img_o:
+    def __init__(self, img, face, name ):
+        self.img = img
+        self.face = face
+        self.feat = None
+        self.name = name
+        
+    
+
+# Read in images, faces and features 
+
+for i in range (0, imgsnr):
+    
+    nr =  '00' + str(i) if i<10 else '0' + str(i)
+    path =   nr + '/'
+    fullpath = pardir + 'Casia_Converted/' + path
+    
+    img_obs.append([])
+    
+
+    for j in range(0, change_me):
+        
+        name =  nr + '_' + str(j)
+        #print(path + name)
+        #convert(path + name + ".bmp", name + ".jpg", path )
+        image = ins_get_image(fullpath + name)
+        face = app.get(image)
+        imgob = img_o(image, face, name)
+        imgob.feat = handler.get(imgob.img, imgob.face[0])
+
+        img_obs[i].append(imgob)
+
+sim_scores = []
+for i in range(0, len(img_obs)):
+    for j in range(0, len(img_obs[i])):
+        pin = j
+        for k in range(i, len(img_obs)): 
+            for l in range (pin, len(img_obs[k])): 
+                sim_scores.append(img_obs[i][j].name + " x " + img_obs[k][l].name)
+            pin = 0 
+
+bh = BaseHandler(imgsnr, change_me)
+# total number of instances 
+
+indx =  bh.get(5,4,7,2,)
+
+
+print("Res: " + str(sim_scores[indx]))
+
+sim_imposter = handler.compute_sim(img_obs[0][0].feat, img_obs[3][0].feat )
+sim_genuine = handler.compute_sim(img_obs[0][1].feat, img_obs[0][0].feat )
+sim_exac = handler.compute_sim(img_obs[0][0].feat, img_obs[0][0].feat )
+
+print("Final test, genuine: " + str(sim_genuine))
+print("Final test, imposter: " + str(sim_imposter))
+print("Final test, exac: " + str(sim_exac))
+
+'''
+
+img1 = ins_get_image(pardir + "img1")
+img2 = ins_get_image(pardir + "img2")
+img3 = ins_get_image(pardir + "img3")
+img4 = ins_get_image(pardir + "img4")
+
+
+
+face1 = app.get(img1)
+face2 = app.get(img2)
+face3 = app.get(img3)
+face4 = app.get(img4)
+
+
+feat1 = handler.get(img1, face1[0])
+feat2 = handler.get(img2, face2[0])
+feat3 = handler.get(img3, face3[0])
+feat4 = handler.get(img4, face4[0])
+
+# Compute matrix 
+
+sim_imposter = handler.compute_sim(feat1, feat2)
+sim_exact = handler.compute_sim(feat1, feat3)
+sim_genuine = handler.compute_sim(feat1, feat4)
+
+print("Imposter: " + str(sim_imposter) + " || " + str(type(sim_imposter)))
+print("Genuine: " + str(sim_genuine) + " || " + str(type(sim_genuine)))
+print("Exac: " + str(sim_exact) + " || " + str(type(sim_genuine)))
+
+# Save matrix to file 
+'''
+'''
+# -- Locate and load the database
+datasetPath = ".\\"
+compariosnMatrix = []
+
+print("Hello darknes my old friend")
+    # Check  for format
+
+
+
+#  -- Compute scores using the insightface api 
+
+app = FaceAnalysis(providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+print("Maria: " + str(type(app)))
+app.prepare(ctx_id=0, det_size=(640, 640))
+
+faces = app.get(img)
+rimg = app.draw_on(img, faces)
+cv2.imwrite( "t1_output.jpg", rimg)
+
+# -- Write matrix to file
+
+# TODO: What file format to use? 
+
+'''
\ No newline at end of file

convert-image.py

+from PIL import Image
+pardir = 'C:/Users/maria/Desktop/Biometrics/TermPaper/imt4126-biometricqualitymetrics/img/'
+img = Image.open(pardir + '001_2.bmp')
+#new_img = img.resize( (256, 256) )
+img.save( pardir + 'img4.jpg', 'jpeg')
+
+

utils/baseScore.py

+
+
+class BaseHandler:
+    def __init__(self, subject_tot: int, sample_tot: int) -> None:
+        self.subject_tot = subject_tot
+        self.sample_tot = sample_tot
+
+        pass
+    def get(self, subject_nr_1: int, saple_nr_1: int, subject_nr_2: int, saple_nr_2: int): 
+       
+        n = self.subject_tot * self.sample_tot
+        a = self.sample_tot
+
+        
+        if(subject_nr_1<subject_nr_2 or (subject_nr_1 == subject_nr_2 and saple_nr_1< saple_nr_2)):
+            i = subject_nr_1
+            j = saple_nr_1
+            k = subject_nr_2
+            l = saple_nr_2
+        else:
+            k = subject_nr_1
+            l = saple_nr_1
+            i = subject_nr_2
+            j = saple_nr_2
+        
+        i = subject_nr_1
+        j = saple_nr_1
+        k = subject_nr_2
+        l = saple_nr_2
+        
+        v = self.subject_tot - i -1 
+        u = self.sample_tot - j
+
+        scnd_element = k*a + l - (i * a + j)
+
+        indx = int((n*(n+1))/2) - int(((v*a+u)*(v*a+u+1))/2) + scnd_element
+        return indx
+    
+    def get_by_string(self, imgstr1: str, imgstr2:str):
+        # retrive numbers 
+
+        
+        return self.get(0,0,0,0)
+    
+    def write(sim_scores: list) -> None:
+
+        return
+    
+    def read() -> list:
+        return []