fine tune; get it to work

src/deepfacex.py

@@ -20,15 +20,13 @@ from faceclass import FaceClass
 
 
 class Deepfacex(FaceClass):
-
     img1_path = ""
     img2_path = ""
     backend = "ssd"
-    imgs = {}
-    faces = {}
     fn512_model = None
     resnet_model = None
     visual = 0
+    tree = { "img1_faces":0, "img1_qual":0, "img2_faces":0, "img2_qual":0, "threshold":0, "score":0 }
      
     def init(self, backend, model):
         print("Loading models...")
@@ -53,6 +51,7 @@ class Deepfacex(FaceClass):
         detector = dlib.get_frontal_face_detector()
         #img = dlib.load_rgb_image(fname)
         dets = detector(self.imgs[name], UPSAMPLE)
+        self.tree["img1_faces"] = len(dets)
         print("Number of faces detected in %s: %d" % (name, len(dets)))
         for i, d in enumerate(dets):
             print("Detection %d: Left: %d Top: %d Right: %d Bottom: %d" % (i, d.left(), d.top(), d.right(), d.bottom() ) )
@@ -64,30 +63,47 @@ class Deepfacex(FaceClass):
         #img = dlib.load_rgb_image(fname)
         dets, scores, idx = detector.run(self.imgs[name], UPSAMPLE, -1)
         print("Number of faces detected: %d" % len(dets))
-        for i, d in enumerate(dets):
+        if len(dets)>0:
-            print("Detection %d: Score: %d Type: %d Left: %d Top: %d Right: %d Bottom: %d" % (i, scores[i], idx[i], d.left(), d.top(), d.right(), d.bottom() ) )
+            print("Face scores = ",scores[0])
-
+        return dets, scores
-
-    # Load two pics using their device labels
-    def loads(self, dev1, dev2, id_image_filepath, photo_image_filepath):
-        self.load(dev1,id_image_filepath)
-        self.load(dev2,photo_image_filepath)
 
 
     # Detects all the faces 
-    def detect_all(self, name):
+    def detect_all(self, name, fname):
+        print("Finding faces in %s: %s" % (name,fname))
         detector = FaceDetector.build_model(self.backend) #set opencv, ssd, dlib, mtcnn or retinaface
-        self.faces[name] = FaceDetector.detect_faces(detector, self.backend, self.imgs[name])
+        self.faces[name] = FaceDetector.detect_faces(detector, self.backend, fname)
-        print(" Found %d faces for %s" %  (len(self.faces[name]), name))
+        print(" Found %d faces for %s" %  (len(self.faces), name))
         return len(self.faces[name])
 
 
     # Compare the two pics
-    def process(self,impath1,impath2):
+    def process(self):
-        print("Matching %s vs %s" % (impath1,impath2))
+        print("Matching %s vs %s" % (self.imfiles[0], self.imfiles[1]))
-        verification = DeepFace.verify(img1_path = impath1, img2_path = impath2, model_name=self.model,\
+        verification = DeepFace.verify(img1_path = self.imfiles[0], img2_path = self.imfiles[1], model_name=self.model,\
          detector_backend=self.backend, distance_metric="euclidean", enforce_detection=False, align=True, normalization="base")
-        return json.dumps(verification)
+        return verification
+
+    def facematch(self):
+        dets1, scores1 =  self.dlib_detector2("localcam")
+        self.tree["img1_faces"] = len(dets1)
+        if len(dets1) > 0:
+            self.tree["img1_qual"] = scores1[0]
+
+        dets2, scores2 =  self.dlib_detector2("regula") 
+        self.tree["img2_faces"] = len(dets2)
+        if len(dets2) > 0:
+            self.tree["img2_qual"] = scores2[0]
+
+        if len(dets1) < 1:
+            return '{ "status":787101, "remark":"no faces in cam image", "data":%s }' % (json.dumps(self.tree))
+        if len(dets2) < 1:
+            return '{ "status":787102, "remark":"no faces in ID image", "data":%s }' % (json.dumps(self.tree))
+
+        verif = d.process()
+        self.tree["score"] = verif["distance"]
+        self.tree["threshold"] = verif["threshold"]
+        return '{ "status":0, "remark":"OK", "data":%s }' % (json.dumps(self.tree))
 
 
     def analyse(self):
@@ -103,7 +119,12 @@ if __name__ == '__main__':
 
     # Test the dlib image detector
     d = Deepfacex()
-    d.init("dlib","Facenet512")
+    d.init("dlib","SFace")
+
+    # kiosk test
+    if sys.argv[1]=="kiosk":
+        print(d.load("localcam","regula","/tmp/localcam.png","/tmp/regula/Portrait_0.jpg"))
+        print (d.facematch())
 
     # quick test
     if sys.argv[1]=="quick":

src/faceclass.py

@@ -8,7 +8,8 @@ class FaceClass(object):
     imgs = {}
     faces = {}
     visual = 0
-    
+    imfiles = []    
+    imnames = {}
  
     # Prep tasks
 
@@ -22,18 +23,25 @@ class FaceClass(object):
 
 
     def load(self, name1,name2,fname1,fname2):
+        print("FaceClass loading files ....................... ")
         if not os.path.isfile(fname1):
+            print("Cant access file ",fname1)
             return False
         if not os.path.isfile(fname2):
+            print("Cant access file ",fname2)
             return False
+        self.imfiles.append(fname1)
+        self.imfiles.append(fname2)
+        self.imnames[name1] = fname1
+        self.imnames[name2] = fname2
         self.imgs[name1] = cv2.imread(fname1)
         self.imgs[name2] = cv2.imread(fname2)
         if self.visual:
             p1 = plt.imshow(name1, self.imgs[name1])
             p2.imshow(name2, self.imgs[name2])
             p1.show()
-        #print(" Loaded %s from file %s" %  (name, fname))
+        print("FaceClass: Loaded %s from file %s" %  (name1, fname1))
-        return True
+        return 1
 
 
     def box(self, name, x, y, w, h):

src/yoloserv.py

@@ -63,7 +63,7 @@ class yoloserv(object):
             print("Loading deepface facematch...")
             from deepfacex import Deepfacex
             self.facematcher = Deepfacex()
-            self.facematcher.init()
+            self.facematcher.init("dlib","SFace")
         if "face_recognition" in self.devices:
             print("Loading deepface facematch...")
             from face_recognition import FaceRecognition
@@ -193,12 +193,12 @@ class yoloserv(object):
             return '{ "status":0, "remark":"OK", "data":{} }' 
 
         status = self.facematcher.load(dev1, dev2, img1, img2)
-        if status is not None:
+        if not status:
             return '{ "status":777242, "remark":"face loading failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
 
-        status = self.facematcher.analyse()
+        #status = self.facematcher.analyse()
-        if status is not None:
+        #if status is not None:
-            return '{ "status":777242, "remark":"face loading failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
+        #    return '{ "status":777242, "remark":"face loading failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
 
         #status = self.facematcher.get_faces()
         #if status is not None: