lock in open source yoloserv

src/deepfacex.py

@@ -1,6 +1,7 @@
 import sys 
 import json
 import os
+import cv2
 # Generic deepface
 from deepface import DeepFace
 from deepface.detectors import FaceDetector
@@ -26,7 +27,7 @@ class Deepfacex(FaceClass):
     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 }
+    tree = { "img1_faces":0, "img1_best":0, "img1_qual":0, "img2_faces":0, "img2_best": 0, "img2_qual":0, "threshold":0, "score":0, "matched":0 }
      
     def init(self, backend, model):
         print("Loading models...")
@@ -43,7 +44,7 @@ class Deepfacex(FaceClass):
         self.faces = {}
 
     def l2_normalize(x):
-        return x / np.sqrt(np.sum(np.multiply(x, x)))
+       return x / np.sqrt(np.sum(np.multiply(x, x)))
 
 
     def dlib_detector(self, name):
@@ -54,7 +55,12 @@ class Deepfacex(FaceClass):
         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() ) )
+            print("Detection %d: Left: %d Top: %d Right: %d Bottom: %d  Confidence: %f" % (i, d.left(), d.top(), d.right(), d.bottom(), d.confidence() ) )
+        print("Biggest face was %d" % maxi )
+        return dets, scores, 0
+
+
+    #def crop(self,img,topleft,botright):
         
 
     def dlib_detector2(self, name):
@@ -63,10 +69,20 @@ 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))
-        if len(dets)>0:
+        for i, d in enumerate(dets):
-            print("Face scores = ",scores[0])
+            #print(d.__dir__())
-        return dets, scores
+            print("Detection %d: Left: %d Top: %d Right: %d Bottom: %d Confidence: %f" % (i, d.left(), d.top(), d.right(), d.bottom(), scores[i] ) )
+            cropped = self.crop(self.imgs[name], d.left(), d.top(), d.right(), d.bottom())
+            cv2.imwrite("/tmp/%s_%d.png" % (name,i), cropped)
+        return dets, scores, 0
 
+    def crop(self, img, x1,y1,x2,y2):
+        #shape(img)
+        if x1<0 or x2<0 or y1<0 or y2<0:
+            return img
+        print("cropping %d/%d to %d/%d" % (x1,y1, x2,y2) )
+        cropped = img[ x1:x2, y1:y2 ]
+        return cropped
 
     # Detects all the faces 
     def detect_all(self, name, fname):
@@ -74,6 +90,7 @@ class Deepfacex(FaceClass):
         detector = FaceDetector.build_model(self.backend) #set opencv, ssd, dlib, mtcnn or retinaface
         self.faces[name] = FaceDetector.detect_faces(detector, self.backend, fname)
         print(" Found %d faces for %s" %  (len(self.faces), name))
+        cv2.imwrite("/tmp/%s_det.png" % (name,i), self.faces[name])
         return len(self.faces[name])
 
 
@@ -84,16 +101,20 @@ class Deepfacex(FaceClass):
          detector_backend=self.backend, distance_metric="euclidean", enforce_detection=False, align=True, normalization="base")
         return verification
 
-    def facematch(self):
+    def facematch(self, name1, name2):
-        dets1, scores1 =  self.dlib_detector2("localcam")
+        dets1, scores1, best1 =  self.dlib_detector2(name1)
+        self.tree["img1_name"] = name1
         self.tree["img1_faces"] = len(dets1)
         if len(dets1) > 0:
-            self.tree["img1_qual"] = scores1[0]
+            self.tree["img1_best"] = best1
+            self.tree["img1_qual"] = scores1[best1]
 
-        dets2, scores2 =  self.dlib_detector2("regula") 
+        dets2, scores2, best2 =  self.dlib_detector2(name2) 
+        self.tree["imgs_name"] = name2
         self.tree["img2_faces"] = len(dets2)
         if len(dets2) > 0:
-            self.tree["img2_qual"] = scores2[0]
+            self.tree["img2_best"] = best2
+            self.tree["img2_qual"] = scores2[best2]
 
         if len(dets1) < 1:
             return '{ "status":787101, "remark":"no faces in cam image", "data":%s }' % (json.dumps(self.tree))
@@ -103,6 +124,9 @@ class Deepfacex(FaceClass):
         verif = d.process()
         self.tree["score"] = verif["distance"]
         self.tree["threshold"] = verif["threshold"]
+        if verif["distance"] > verif["threshold"]:
+            self.tree["matched"] = 1
+
         return '{ "status":0, "remark":"OK", "data":%s }' % (json.dumps(self.tree))
 
 
@@ -119,12 +143,14 @@ if __name__ == '__main__':
 
     # Test the dlib image detector
     d = Deepfacex()
-    d.init("dlib","SFace")
+    d.init("yolov8","SFace")
 
     # kiosk test
     if sys.argv[1]=="kiosk":
         print(d.load("localcam","regula","/tmp/localcam.png","/tmp/regula/Portrait_0.jpg"))
-        print (d.facematch())
+        d.detect_all("localcam", "/tmp/localcam.png")
+        d.detect_all("regula", "/tmp/regula/Portrait_0.jpg")
+        #print (d.facematch("localcam","regula"))
 
     # quick test
     if sys.argv[1]=="quick":

src/face_recognitionx.py

@@ -39,131 +39,98 @@ import os
 import face_recognition
 import json
 import urllib.request
+from faceclass import FaceClass
 
 import time
 
  
-class FaceRecognition(object):
+class FaceRecognition(FaceClass):
     
     name = "face_recognition_face_match"
     IN_ENGLISH = "A class for checking the similarity between two faces."
-    dev1 = "some_device"
-    dev2 = "some_other_device"
     id_qual = 0.7
     photo_qual = 0.7
     face_match_json = None
     conf = []
-    imgs = {}
- 
-    # Load the config
-    def init(self):
-        with open("/etc/ukdi.json","r") as f:
-            self.conf = json.loads(f.read())
  
 
-    #id_image_filepath = '/home/lucas-acm/Dispension/UKDI_testdata/LW_cardscan/Portrait_0.jpg'
+    def get_faces(self, name):
-    #photo_image_filepath = '/home/lucas-acm/Dispension/UKDI_testdata/LW.jpg'
+        print("** get_faces ... %s" % name)     
-    #id_image_filepath = '/tmp/regula/Portrait_0.jpg'
-    #photo_image_filepath = '/home/disp/Pictures/realsense_test.jpg'
-    
-    def load(self, dev, image_filepath):
-        self.dev1 = dev1
-        self.dev2 = dev2
-        print("id_image_filepath: " + id_image_filepath)
-        try:
-            # load/encode pic1
-            img1 = cv2.imread(id_image_filepath)
-            self.id_image = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
-            # load/encode pic2
-            img2 = cv2.imread(photo_image_filepath)
-            self.photo_image = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)
-            #cv2.imshow("id..",self.id_image)
-            #cv2.imshow("id..",self.photo_image)
-            #cv2.waitKey(0)
-            return True
-        except:
-            return None
-        
-
-    def load2(self, dev1, dev2, id_image_filepath, photo_image_filepath):
-        self.dev1 = dev1
-        self.dev2 = dev2
-        print("id_image_filepath: " + id_image_filepath)
-        try:
-            # load/encode pic1
-            img1 = cv2.imread(id_image_filepath)
-            self.id_image = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
-            # load/encode pic2
-            img2 = cv2.imread(photo_image_filepath)
-            self.photo_image = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)
-            #cv2.imshow("id..",self.id_image)
-            #cv2.imshow("id..",self.photo_image)
-            #cv2.waitKey(0)
-            return True
-        except:
-            return None
-
-
-    def get_faces(self):
-        print("** get_faces ...")     
         try:
             # Get all faces from images with qualities, landmarks, and embeddings
-            boxes1 = face_recognition.face_locations(self.photo_image, number_of_times_to_upsample=2, model='hog')
+            boxes = face_recognition.face_locations(self.imgs[name], number_of_times_to_upsample=2, model='hog')
-            boxes2 = face_recognition.face_locations(self.id_image, number_of_times_to_upsample=2, model='hog')
+            print("found %d boxes for %s" % (len(boxes), name) )
-            #print("n boxes = ", boxes)
-            print("found %d + %d boxes" % (len(boxes1), len(boxes2)))
             # Get numerical representation of faces (required for face match)
-            self.id_enc = face_recognition.face_encodings(self.id_image)[0]
+            self.encs[name] = face_recognition.face_encodings(self.imgs[name])[0]
-            self.photo_enc = face_recognition.face_encodings(self.photo_image)[0]
+            #print("encoding for %s : " % name, self.encs[name])
-            #print(self.id_enc)
-            #print(self.photo_enc)
         except Exception as ex:
-            return "image processing exception "+str(ex)
+            self.errstr = "image processing exception at get_faces: "+str(ex)
-        return len(boxes1)+len(boxes2)
+            return -1
+        return len(boxes)
 
 
 #       return " id=%d photo=%d result=%d " % (self.id_face, self.photo_face, len(self.image_inference_result))
 
 
-    def get_landmarks(self):
+    def get_landmarks(self, name):
-        self.landmarks = face_recognition.face_landmarks(self.photo_image)
+        self.landmarks = face_recognition.face_landmarks(self.imgs[name])
         print(self.landmarks)
+        return self.landmarks
 
 
-    def compute_scores(self):   
+    def compute_scores(self, name1, name2):   
-        print("** computing...")     
+        print("** computing ... %s vs %s" % (name1,name2))
         try:
-            res = face_recognition.compare_faces([self.id_enc], self.photo_enc)            
+            res = face_recognition.compare_faces([self.encs[name1]], self.encs[name2])            
             print("Match is ",res)
-            self.match_score = 1000 * (1 - face_recognition.face_distance([self.id_enc], self.photo_enc))            
+            self.match_score = 1000 * (1 - face_recognition.face_distance([self.encs[name1]], self.encs[name2]))            
             print("Score is ",self.match_score)
             
             # Create .json
-            self.face_match_json = {"device1":self.dev1,
+            self.tree["score"] = self.match_score[0]
-                                    "device2":self.dev2,
-                                   "passmark":380,
-                               "device1_qual":0.5, 
-                               "device2_qual":0.5,
-                                "match_score":self.match_score[0]}
-            
         except Exception as ex:
-            return "image comparison exception "+str(ex)
+            self.errstr =  "image comparison exception at compute_scores: "+str(ex)
-        
+            return -1
+        return self.match_score
 
     def get_scores(self):
-        return json.dumps(self.face_match_json)
+        return json.dumps(self.tree)
+
+
+    def detect_all(self):
+        n=0
+        self.load("localcam","regula", "/tmp/localcam.png", "/tmp/regula/Portrait_0.jpg")
+        self.get_faces("localcam")
+        self.get_faces("regula")
+        print("computed ... ", d.compute_scores("regula","localcam"))
+        print("scores ... " , d.get_scores())
+
+
 
 
 if __name__ == '__main__':
     
     d = FaceRecognition()
 
+    if sys.argv[1]=="kiosk":
+        # lfw
+        n=0
+        d.load("localcam","regula", "/tmp/localcam.png", "/tmp/regula/Portrait_0.jpg")
+        d.get_faces("localcam")
+        d.get_faces("regula")
+        print("computed ... ", d.compute_scores("regula","localcam"))
+        print("scores ... " , d.get_scores())
+        #print(d.get_landmarks("localcam"))
+        #print(d.get_landmarks("regula"))
+        sys.exit(0)
+
+
     if sys.argv[1]=="match":
         # lfw
         n=0
         print("LFW Matching")
         for lfw in sorted(os.listdir("/home/carl/Downloads/lfw/0002")):
-            d.load2("0001","0002", "/home/carl/Downloads/lfw/0001/" + lfw, "/home/carl/Downloads/lfw/0002/" + lfw)
+            d.load2("localcam","regula", "/home/carl/" + lfw, "/home/carl/Downloads/lfw/0002/" + lfw)
             d.get_faces()
             d.compute_scores()
             print(d.get_scores())

src/faceclass.py

@@ -1,17 +1,25 @@
 import sys
 import os
 import cv2
+import json
 from matplotlib import pyplot as plt
 
 class FaceClass(object):
 
     imgs = {}
+    encs = {}
     faces = {}
     visual = 0
     imfiles = []    
     imnames = {}
+    errstr = ""
+    tree = { "device1":"NA", "device2":"NA", "threshold":380, "device1_qual":0.5, "device2_qual":0.5, "score":0 }
+ 
 
     # Prep tasks
+    def init(self):
+        return None
+
 
     # Load a pics using the device label
     def load1(self, name,fname):
@@ -26,10 +34,10 @@ class FaceClass(object):
         print("FaceClass loading files ....................... ")
         if not os.path.isfile(fname1):
             print("Cant access file ",fname1)
-            return False
+            return -1
         if not os.path.isfile(fname2):
             print("Cant access file ",fname2)
-            return False
+            return -1
         self.imfiles.append(fname1)
         self.imfiles.append(fname2)
         self.imnames[name1] = fname1
@@ -55,14 +63,24 @@ class FaceClass(object):
  
 
     # Find all faces
+    def get_faces(self,name):
+        return -1
 
     # Find best face
+    def get_best_face(self):
+        return None
 
     # Find landmarks
+    def get_landmarks(self):
+        return '{ "status": 9999 }'
 
     # Find metadata (age etc)
+    def get_metadata(self,name):
+        return None
 
     # Match two faces
+    def process(self,name1,name2):
+        return None
 
 
 

src/yoloserv.py

@@ -66,7 +66,7 @@ class yoloserv(object):
             self.facematcher.init("dlib","SFace")
         if "face_recognition" in self.devices:
             print("Loading deepface facematch...")
-            from face_recognition import FaceRecognition
+            from face_recognitionx import FaceRecognition
             self.facematcher = FaceRecognition()
             self.facematcher.init()
 
@@ -179,7 +179,7 @@ class yoloserv(object):
             return '{ "status":777244, "remark":"suitable yolo_device" }' 
 
         if self.conf["emulate_facematch"]:
-            return '{ "status":0, "remark":"OK(Emulated)", "data":{"device1":"%s","device2":"%s","device1_qual":123,"device2_qual":234,"match_score":600} }' % (dev1,dev2)
+            return '{ "status":0, "remark":"OK(Emulated)", "data":{"device1":"%s","device2":"%s","device1_qual":123,"device2_qual":234,"score":600} }' % (dev1,dev2)
 
         if dev1 == "regula":
             img1 = "/tmp/regula/Portrait_0.jpg"
@@ -195,21 +195,14 @@ class yoloserv(object):
         status = self.facematcher.load(dev1, dev2, img1, img2)
         if not status:
             return '{ "status":777242, "remark":"face loading failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
-
+        if self.facematcher.get_faces(dev1) < 1:
-        #status = self.facematcher.analyse()
+            return '{ "status":777243, "remark":"face loading failed", "guilty_param":"image", "guilty_value":"%s" }' % (dev1)
-        #if status is not None:
+        if self.facematcher.get_faces(dev2) < 1:
-        #    return '{ "status":777242, "remark":"face loading failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
+            return '{ "status":777244, "remark":"face loading failed", "guilty_param":"image", "guilty_value":"%s" }' % (dev2)
-
+        if self.facematcher.compute_scores(dev1,dev2) < 1:
-        #status = self.facematcher.get_faces()
+            return '{ "status":777245, "remark":"face matching failed", "guilty_param":"image", "guilty_value":"%s" }' % (self.facematcher.errstr)
-        #if status is not None:
+        jsonstr = self.facematcher.get_scores()
-        #    return '{ "status":777245, "remark":"face finding failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
+        return '{ "status":0, "remark":"OK", "data": %s }' % (jsonstr)
-
-        #status = self.facematcher.compute_scores()
-        #if status is not None:
-        #    return '{ "status":777243, "remark":"face scoring failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
-
-        jsonstr = self.facematcher.process()
-        return '{ "status":0, "remark":"OK", "data":%s }' % (jsonstr)