lock in open source yoloserv
This commit is contained in:
carl
parent
10798e3162
commit
6dc3331120
@ -1,6 +1,7 @@
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import sys
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import json
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import os
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import cv2
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# Generic deepface
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from deepface import DeepFace
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from deepface.detectors import FaceDetector
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@ -26,7 +27,7 @@ class Deepfacex(FaceClass):
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fn512_model = None
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resnet_model = None
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visual = 0
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tree = { "img1_faces":0, "img1_qual":0, "img2_faces":0, "img2_qual":0, "threshold":0, "score":0 }
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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 }
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def init(self, backend, model):
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print("Loading models...")
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@ -43,7 +44,7 @@ class Deepfacex(FaceClass):
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self.faces = {}
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def l2_normalize(x):
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return x / np.sqrt(np.sum(np.multiply(x, x)))
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return x / np.sqrt(np.sum(np.multiply(x, x)))
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def dlib_detector(self, name):
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@ -54,19 +55,34 @@ class Deepfacex(FaceClass):
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self.tree["img1_faces"] = len(dets)
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print("Number of faces detected in %s: %d" % (name, len(dets)))
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for i, d in enumerate(dets):
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print("Detection %d: Left: %d Top: %d Right: %d Bottom: %d" % (i, d.left(), d.top(), d.right(), d.bottom() ) )
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print("Detection %d: Left: %d Top: %d Right: %d Bottom: %d Confidence: %f" % (i, d.left(), d.top(), d.right(), d.bottom(), d.confidence() ) )
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print("Biggest face was %d" % maxi )
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return dets, scores, 0
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#def crop(self,img,topleft,botright):
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def dlib_detector2(self, name):
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UPSAMPLE = 1
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detector = dlib.get_frontal_face_detector()
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#img = dlib.load_rgb_image(fname)
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dets, scores, idx = detector.run(self.imgs[name], UPSAMPLE, -1)
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print("Number of faces detected: %d" % len(dets))
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if len(dets)>0:
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print("Face scores = ",scores[0])
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return dets, scores
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for i, d in enumerate(dets):
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#print(d.__dir__())
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print("Detection %d: Left: %d Top: %d Right: %d Bottom: %d Confidence: %f" % (i, d.left(), d.top(), d.right(), d.bottom(), scores[i] ) )
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cropped = self.crop(self.imgs[name], d.left(), d.top(), d.right(), d.bottom())
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cv2.imwrite("/tmp/%s_%d.png" % (name,i), cropped)
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return dets, scores, 0
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def crop(self, img, x1,y1,x2,y2):
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#shape(img)
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if x1<0 or x2<0 or y1<0 or y2<0:
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return img
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print("cropping %d/%d to %d/%d" % (x1,y1, x2,y2) )
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cropped = img[ x1:x2, y1:y2 ]
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return cropped
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# Detects all the faces
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def detect_all(self, name, fname):
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@ -74,6 +90,7 @@ class Deepfacex(FaceClass):
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detector = FaceDetector.build_model(self.backend) #set opencv, ssd, dlib, mtcnn or retinaface
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self.faces[name] = FaceDetector.detect_faces(detector, self.backend, fname)
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print(" Found %d faces for %s" % (len(self.faces), name))
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cv2.imwrite("/tmp/%s_det.png" % (name,i), self.faces[name])
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return len(self.faces[name])
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@ -84,16 +101,20 @@ class Deepfacex(FaceClass):
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detector_backend=self.backend, distance_metric="euclidean", enforce_detection=False, align=True, normalization="base")
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return verification
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def facematch(self):
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dets1, scores1 = self.dlib_detector2("localcam")
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def facematch(self, name1, name2):
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dets1, scores1, best1 = self.dlib_detector2(name1)
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self.tree["img1_name"] = name1
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self.tree["img1_faces"] = len(dets1)
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if len(dets1) > 0:
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self.tree["img1_qual"] = scores1[0]
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self.tree["img1_best"] = best1
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self.tree["img1_qual"] = scores1[best1]
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dets2, scores2 = self.dlib_detector2("regula")
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dets2, scores2, best2 = self.dlib_detector2(name2)
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self.tree["imgs_name"] = name2
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self.tree["img2_faces"] = len(dets2)
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if len(dets2) > 0:
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self.tree["img2_qual"] = scores2[0]
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self.tree["img2_best"] = best2
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self.tree["img2_qual"] = scores2[best2]
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if len(dets1) < 1:
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return '{ "status":787101, "remark":"no faces in cam image", "data":%s }' % (json.dumps(self.tree))
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@ -103,6 +124,9 @@ class Deepfacex(FaceClass):
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verif = d.process()
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self.tree["score"] = verif["distance"]
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self.tree["threshold"] = verif["threshold"]
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if verif["distance"] > verif["threshold"]:
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self.tree["matched"] = 1
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return '{ "status":0, "remark":"OK", "data":%s }' % (json.dumps(self.tree))
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@ -119,12 +143,14 @@ if __name__ == '__main__':
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# Test the dlib image detector
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d = Deepfacex()
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d.init("dlib","SFace")
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d.init("yolov8","SFace")
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# kiosk test
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if sys.argv[1]=="kiosk":
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print(d.load("localcam","regula","/tmp/localcam.png","/tmp/regula/Portrait_0.jpg"))
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print (d.facematch())
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d.detect_all("localcam", "/tmp/localcam.png")
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d.detect_all("regula", "/tmp/regula/Portrait_0.jpg")
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#print (d.facematch("localcam","regula"))
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# quick test
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if sys.argv[1]=="quick":
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@ -39,131 +39,98 @@ import os
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import face_recognition
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import json
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import urllib.request
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from faceclass import FaceClass
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import time
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class FaceRecognition(object):
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class FaceRecognition(FaceClass):
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name = "face_recognition_face_match"
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IN_ENGLISH = "A class for checking the similarity between two faces."
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dev1 = "some_device"
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dev2 = "some_other_device"
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id_qual = 0.7
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photo_qual = 0.7
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face_match_json = None
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conf = []
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imgs = {}
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# Load the config
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def init(self):
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with open("/etc/ukdi.json","r") as f:
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self.conf = json.loads(f.read())
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#id_image_filepath = '/home/lucas-acm/Dispension/UKDI_testdata/LW_cardscan/Portrait_0.jpg'
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#photo_image_filepath = '/home/lucas-acm/Dispension/UKDI_testdata/LW.jpg'
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#id_image_filepath = '/tmp/regula/Portrait_0.jpg'
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#photo_image_filepath = '/home/disp/Pictures/realsense_test.jpg'
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def load(self, dev, image_filepath):
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self.dev1 = dev1
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self.dev2 = dev2
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print("id_image_filepath: " + id_image_filepath)
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try:
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# load/encode pic1
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img1 = cv2.imread(id_image_filepath)
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self.id_image = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
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# load/encode pic2
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img2 = cv2.imread(photo_image_filepath)
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self.photo_image = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)
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#cv2.imshow("id..",self.id_image)
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#cv2.imshow("id..",self.photo_image)
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#cv2.waitKey(0)
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return True
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except:
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return None
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def load2(self, dev1, dev2, id_image_filepath, photo_image_filepath):
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self.dev1 = dev1
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self.dev2 = dev2
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print("id_image_filepath: " + id_image_filepath)
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try:
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# load/encode pic1
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img1 = cv2.imread(id_image_filepath)
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self.id_image = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
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# load/encode pic2
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img2 = cv2.imread(photo_image_filepath)
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self.photo_image = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)
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#cv2.imshow("id..",self.id_image)
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#cv2.imshow("id..",self.photo_image)
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#cv2.waitKey(0)
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return True
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except:
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return None
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def get_faces(self):
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print("** get_faces ...")
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def get_faces(self, name):
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print("** get_faces ... %s" % name)
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try:
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# Get all faces from images with qualities, landmarks, and embeddings
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boxes1 = face_recognition.face_locations(self.photo_image, number_of_times_to_upsample=2, model='hog')
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boxes2 = face_recognition.face_locations(self.id_image, number_of_times_to_upsample=2, model='hog')
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#print("n boxes = ", boxes)
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print("found %d + %d boxes" % (len(boxes1), len(boxes2)))
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boxes = face_recognition.face_locations(self.imgs[name], number_of_times_to_upsample=2, model='hog')
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print("found %d boxes for %s" % (len(boxes), name) )
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# Get numerical representation of faces (required for face match)
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self.id_enc = face_recognition.face_encodings(self.id_image)[0]
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self.photo_enc = face_recognition.face_encodings(self.photo_image)[0]
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#print(self.id_enc)
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#print(self.photo_enc)
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self.encs[name] = face_recognition.face_encodings(self.imgs[name])[0]
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#print("encoding for %s : " % name, self.encs[name])
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except Exception as ex:
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return "image processing exception "+str(ex)
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return len(boxes1)+len(boxes2)
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self.errstr = "image processing exception at get_faces: "+str(ex)
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return -1
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return len(boxes)
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# return " id=%d photo=%d result=%d " % (self.id_face, self.photo_face, len(self.image_inference_result))
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def get_landmarks(self):
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self.landmarks = face_recognition.face_landmarks(self.photo_image)
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def get_landmarks(self, name):
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self.landmarks = face_recognition.face_landmarks(self.imgs[name])
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print(self.landmarks)
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return self.landmarks
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def compute_scores(self):
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print("** computing...")
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def compute_scores(self, name1, name2):
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print("** computing ... %s vs %s" % (name1,name2))
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try:
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res = face_recognition.compare_faces([self.id_enc], self.photo_enc)
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res = face_recognition.compare_faces([self.encs[name1]], self.encs[name2])
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print("Match is ",res)
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self.match_score = 1000 * (1 - face_recognition.face_distance([self.id_enc], self.photo_enc))
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self.match_score = 1000 * (1 - face_recognition.face_distance([self.encs[name1]], self.encs[name2]))
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print("Score is ",self.match_score)
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# Create .json
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self.face_match_json = {"device1":self.dev1,
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"device2":self.dev2,
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"passmark":380,
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"device1_qual":0.5,
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"device2_qual":0.5,
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"match_score":self.match_score[0]}
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# Create .json
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self.tree["score"] = self.match_score[0]
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except Exception as ex:
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return "image comparison exception "+str(ex)
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self.errstr = "image comparison exception at compute_scores: "+str(ex)
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return -1
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return self.match_score
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def get_scores(self):
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return json.dumps(self.face_match_json)
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return json.dumps(self.tree)
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def detect_all(self):
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n=0
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self.load("localcam","regula", "/tmp/localcam.png", "/tmp/regula/Portrait_0.jpg")
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self.get_faces("localcam")
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self.get_faces("regula")
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print("computed ... ", d.compute_scores("regula","localcam"))
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print("scores ... " , d.get_scores())
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if __name__ == '__main__':
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d = FaceRecognition()
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if sys.argv[1]=="kiosk":
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# lfw
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n=0
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d.load("localcam","regula", "/tmp/localcam.png", "/tmp/regula/Portrait_0.jpg")
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d.get_faces("localcam")
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d.get_faces("regula")
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print("computed ... ", d.compute_scores("regula","localcam"))
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print("scores ... " , d.get_scores())
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#print(d.get_landmarks("localcam"))
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#print(d.get_landmarks("regula"))
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sys.exit(0)
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if sys.argv[1]=="match":
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# lfw
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n=0
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print("LFW Matching")
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for lfw in sorted(os.listdir("/home/carl/Downloads/lfw/0002")):
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d.load2("0001","0002", "/home/carl/Downloads/lfw/0001/" + lfw, "/home/carl/Downloads/lfw/0002/" + lfw)
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d.load2("localcam","regula", "/home/carl/" + lfw, "/home/carl/Downloads/lfw/0002/" + lfw)
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d.get_faces()
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d.compute_scores()
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print(d.get_scores())
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@ -1,17 +1,25 @@
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import sys
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import os
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import cv2
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import json
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from matplotlib import pyplot as plt
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class FaceClass(object):
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imgs = {}
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encs = {}
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faces = {}
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visual = 0
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imfiles = []
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imnames = {}
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errstr = ""
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tree = { "device1":"NA", "device2":"NA", "threshold":380, "device1_qual":0.5, "device2_qual":0.5, "score":0 }
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# Prep tasks
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def init(self):
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return None
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# Load a pics using the device label
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def load1(self, name,fname):
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@ -26,10 +34,10 @@ class FaceClass(object):
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print("FaceClass loading files ....................... ")
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if not os.path.isfile(fname1):
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print("Cant access file ",fname1)
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return False
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return -1
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if not os.path.isfile(fname2):
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print("Cant access file ",fname2)
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return False
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return -1
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self.imfiles.append(fname1)
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self.imfiles.append(fname2)
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self.imnames[name1] = fname1
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@ -55,14 +63,24 @@ class FaceClass(object):
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# Find all faces
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def get_faces(self,name):
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return -1
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# Find best face
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def get_best_face(self):
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return None
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# Find landmarks
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def get_landmarks(self):
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return '{ "status": 9999 }'
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# Find metadata (age etc)
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def get_metadata(self,name):
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return None
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# Match two faces
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def process(self,name1,name2):
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return None
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@ -66,7 +66,7 @@ class yoloserv(object):
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self.facematcher.init("dlib","SFace")
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if "face_recognition" in self.devices:
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print("Loading deepface facematch...")
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from face_recognition import FaceRecognition
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from face_recognitionx import FaceRecognition
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self.facematcher = FaceRecognition()
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self.facematcher.init()
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@ -179,7 +179,7 @@ class yoloserv(object):
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return '{ "status":777244, "remark":"suitable yolo_device" }'
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if self.conf["emulate_facematch"]:
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return '{ "status":0, "remark":"OK(Emulated)", "data":{"device1":"%s","device2":"%s","device1_qual":123,"device2_qual":234,"match_score":600} }' % (dev1,dev2)
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return '{ "status":0, "remark":"OK(Emulated)", "data":{"device1":"%s","device2":"%s","device1_qual":123,"device2_qual":234,"score":600} }' % (dev1,dev2)
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if dev1 == "regula":
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img1 = "/tmp/regula/Portrait_0.jpg"
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@ -195,21 +195,14 @@ class yoloserv(object):
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status = self.facematcher.load(dev1, dev2, img1, img2)
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if not status:
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return '{ "status":777242, "remark":"face loading failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
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#status = self.facematcher.analyse()
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#if status is not None:
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# return '{ "status":777242, "remark":"face loading failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
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#status = self.facematcher.get_faces()
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#if status is not None:
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# return '{ "status":777245, "remark":"face finding failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
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#status = self.facematcher.compute_scores()
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#if status is not None:
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# return '{ "status":777243, "remark":"face scoring failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
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jsonstr = self.facematcher.process()
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return '{ "status":0, "remark":"OK", "data":%s }' % (jsonstr)
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if self.facematcher.get_faces(dev1) < 1:
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return '{ "status":777243, "remark":"face loading failed", "guilty_param":"image", "guilty_value":"%s" }' % (dev1)
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if self.facematcher.get_faces(dev2) < 1:
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return '{ "status":777244, "remark":"face loading failed", "guilty_param":"image", "guilty_value":"%s" }' % (dev2)
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if self.facematcher.compute_scores(dev1,dev2) < 1:
|
||||
return '{ "status":777245, "remark":"face matching failed", "guilty_param":"image", "guilty_value":"%s" }' % (self.facematcher.errstr)
|
||||
jsonstr = self.facematcher.get_scores()
|
||||
return '{ "status":0, "remark":"OK", "data": %s }' % (jsonstr)
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user