Finishing and Cleanup

.gitignore

 img/
+sim_scores.npy
+utils/__pycache__/

compute-baseline-scores.py

@@ -3,52 +3,19 @@ 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
+from utils.imageHandling import convert
 
-path = '/Users/krunal/Desktop/code/database'
+# Vars
+#path = '/Users/krunal/Desktop/code/database'
+projectpath = 'C:/Users/maria/Desktop/Biometrics/TermPaper/imt4126-biometricqualitymetrics/'
+imgsdir = 'img/Casia_Converted/'
 
-
-
-pardir = 'C:/Users/maria/Desktop/Biometrics/TermPaper/imt4126-biometricqualitymetrics/img/'
-imgsnr = 10
+# TODO change names
+imgsnr = 2
 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
@@ -56,110 +23,53 @@ class img_o:
         self.feat = None
         self.name = name
 
-    
+# 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)
 
 # 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
-    
+    subjectnr =  '00' + str(i) if i<10 else '0' + str(i)
+    subjectdir =   subjectnr + '/'
+    fullpath = projectpath + imgsdir + subjectdir
     img_obs.append([])
-    
+    print("Read in for: " + str(i) + "-th subject")
     
     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)
+        imgname =  subjectnr + '_' + str(j)
+        #print(subjectdir + name)
+        #convert(projectpath + 'Casia/' + subjectdir + imgname + ".bmp", imgname + ".jpg", fullpath )
+        image = ins_get_image(fullpath + imgname)
         face = app.get(image)
-        imgob = img_o(image, face, name)
+        imgob = img_o(image, face, imgname)
         imgob.feat = handler.get(imgob.img, imgob.face[0])
 
         img_obs[i].append(imgob)
 
+# Calculate sim scores
 sim_scores = []
+
 for i in range(0, len(img_obs)):
+    print("Compute sim scores for: " + str(i) + "-th subject")
     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)
+                sim_scores.append([])
+                pin = len(sim_scores)
+                sim_scores[pin-1].append(handler.compute_sim(img_obs[i][j].feat, img_obs[k][l].feat ))
+                sim_scores[pin-1].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")
 
+#bh = BaseHandler(imgsnr, change_me)
 
+# Save to file
+new_array = np.array(sim_scores)
+np.save("sim_scores.npy", new_array)
 
-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

@@ -23,10 +23,7 @@ class BaseHandler:
             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

utils/imageHandling.py

+from PIL import Image
+import os
+
+
+# TODO make better
+def convert(img: str, new_name: str, new_path: str): 
+    # Convert
+    img = Image.open(img)
+    
+    # Save
+    isExist = os.path.exists(new_path)
+    if not isExist:
+        os.makedirs(new_path)
+    img.save( new_path + new_name, 'jpeg')
+    
+