def loadLearntFaces(self):
path = self.FACE_FOLDER
if os.path.exists(path):
fileList = os.listdir(path)
for file in fileList:
label = os.path.splitext(file)[0]
if label in self.faceList:
rospy.logwarn("DUPICATE FACE LABEL file_name:" + file +
", label:" + str(label))
if (file != '.gitkeep'):
rospy.loginfo("file_name:" + file + ", label:" +
str(label))
#print("check --------------->:"+str(path+file))
face_image = face_recognition.load_image_file(path + "/" +
file)
face_recognition_list = face_recognition.face_encodings(
face_image)
rospy.loginfo(len(face_recognition_list))
if len(face_recognition_list) > 0:
face_encoding = face_recognition_list[0]
current_face = Face.Face(0, 0, 0, 0, label, 0)
current_face.encode(face_encoding)
self.faceList[label] = current_face
else:
rospy.logerr(
"Unable to load face references, no such directory: " +
str(path))
return
def _processLearnFace(self,top, right, bottom, left,face_encoding,label_tmp,frame,new_learnt_face):
#save file to learn directory and crop according box
cv2.imwrite(self.FACE_FOLDER_AUTO+"/"+label_tmp+".png", frame[top:bottom, left:right])
new_face=Face.Face(0,0,0,0,label_tmp,0)
new_face.encode(face_encoding)
self.faceList[label_tmp]=new_face
new_learnt_face.append(new_face)
def _processLearnFace(self, top, right, bottom, left, face_encoding,
label_tmp, frame, new_learnt_face):
#self.mutex.acquire()
if self.mutex.acquire(False):
try:
#save file to learn directory and crop according box
cv2.imwrite(self.FACE_FOLDER_AUTO + "/" + label_tmp + ".png",
frame[top:bottom, left:right])
new_face = Face.Face(0, 0, 0, 0, label_tmp)
new_face.encode(face_encoding)
self.faceList[label_tmp] = new_face
new_learnt_face.append(new_face)
finally:
self.mutex.release()
else:
rospy.loginfo(
" lock is already aquired, learning is already in progress")
def process_img_face(self, data, name_w, current_status, isImgFace):
detected_faces_list = []
new_learnt_face = []
face_locations = []
label_r = 'NONE'
if (current_status == None):
current_status = self.STATUS
try:
# Conver image to numpy array
frame = self._bridge.imgmsg_to_cv2(data, 'bgr8')
frame_copy = self._bridge.imgmsg_to_cv2(data, 'bgr8')
if not isImgFace:
if self.face_detection_mode == 2:
face_locations = face_recognition.face_locations(
frame, number_of_times_to_upsample=0, model="cnn")
elif self.face_detection_mode == 1:
face_locations = face_recognition.face_locations(frame)
else:
face_locations = self.cascadeHaarFaceDetection.processImg(
frame)
#rospy.logwarn(face_locations)
else:
face_locations = []
face_locations.append((long(0), long(0 + frame.shape[0]),
long(0 + frame.shape[1]), long(0)))
i = 0
for location in face_locations:
i = i + 1
if i == 0:
rospy.logdebug("NO FACE DETECTED ON THE CURRENT IMG")
#if isImgFace:
# face_encodings = face_recognition.face_encodings(frame)
#else:
face_encodings = face_recognition.face_encodings(
frame, face_locations)
# Find all the faces and face enqcodings in the frame of video
top_r = 0
bottom_r = 0
left_r = 0
right_r = 0
# Loop through each face in this frame oisFaceDetectionf video
for (top, right, bottom,
left), face_encoding in zip(face_locations, face_encodings):
# See if the face is a match for the known face(s)
name, distance = self._processDetectFace(
top, right, bottom, left, face_encoding)
current_face = Face.Face(top, left, bottom, right, name,
distance)
detected_faces_list.append(current_face)
rospy.logdebug("STATUS: " + current_status)
if (current_status == self.LEARNING_STATUS
and name == "Unknown") or (
self.continuous_learn and name == "Unknown") or (
current_status == self.FORCE_LEARNING_STATUS):
label_tmp = str(uuid.uuid1())
rospy.loginfo("unknown face: launch learn operation")
self._processLearnFace(top, right, bottom, left,
face_encoding, label_tmp,
frame_copy, new_learnt_face)
label_r = name
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255),
2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom),
(0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame,
str(round(distance * 100, 1)) + "% " + name,
(left + 6, bottom - 6), font, 0.8, (255, 255, 255),
1)
x0, y0 = self._processBoxCenter(top, right, bottom, left)
cv2.circle(frame, (x0, y0), 5, (0, 255, 0), cv2.FILLED)
top_r = top
bottom_r = bottom
left_r = left
right_r = right
#return frame,name,top,left,bottom,right
#update label
if name_w == None:
_labelToLearn = self.labelToLearn
else:
_labelToLearn = name_w
#check the biggest face learnt
if not self.continuous_learn:
max_box = 0
biggest_face = None
for f in new_learnt_face:
if max_box < f.size:
max_box = f.size
biggest_face = f
if len(new_learnt_face) > 0:
self.STATUS != 'LEARNT'
oldId = biggest_face.label
del self.faceList[oldId]
biggest_face.label = _labelToLearn
self.faceList[_labelToLearn] = biggest_face
rospy.loginfo("")
os.rename(
self.FACE_FOLDER_AUTO + "/" + oldId + '.png',
self.FACE_FOLDER_AUTO + "/" + _labelToLearn + '.png')
rospy.loginfo("BIGGEST FACE of " +
str(len(new_learnt_face)) + ":" +
biggest_face.label)
return frame, label_r, top_r, left_r, bottom_r, right_r, detected_faces_list
except CvBridgeError as e:
rospy.logwarn(e)
return "no Value"
def process_img_face(self,data, name_w, current_status, isImgFace):
#self.frames = self.frames + 1
detected_faces_list=[]
new_learnt_face=[]
face_locations=[]
label_r='NONE'
if(current_status== None):
current_status=self.STATUS
try:
# Conver image to numpy array
frame = self._bridge.imgmsg_to_cv2(data, 'bgr8')
frame_copy = self._bridge.imgmsg_to_cv2(data, 'bgr8')
if not isImgFace:
if self.face_detection_mode==2:
face_locations = face_recognition.face_locations(frame, number_of_times_to_upsample=0, model="cnn")
#print(face_locations)
elif self.face_detection_mode==1:
face_locations = face_recognition.face_locations(frame)
else:
face_locations= self.cascadeHaarFaceDetection.processImg(frame)
#rospy.logwarn(face_locations)
else:
face_locations=[]
face_locations.append((long(0), long(0 + frame.shape[0]), long(0 + frame.shape[1]), long(0)))
i=0
for location in face_locations:
i=i+1
if i==0:
rospy.logdebug("NO FACE DETECTED ON THE CURRENT IMG")
#if isImgFace:
# face_encodings = face_recognition.face_encodings(frame)
#else:
face_encodings = face_recognition.face_encodings(frame, face_locations)
# Find all the faces and face enqcodings in the frame of video
top_r=0
bottom_r=0
left_r=0
right_r=0
r_count = 0
person_detections = []##to store all detections and choose the det with highest confidence
# Loop through each face in this frame oisFaceDetectionf video
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
# See if the face is a match for the known face(s)
name,distance = self._processDetectFace(top, right, bottom, left,face_encoding)
current_face=Face.Face(top,left,bottom,right,name,distance)
detected_faces_list.append(current_face)
#self.frames = self.frames + 1
rospy.logdebug("STATUS: "+current_status)
if (current_status==self.LEARNING_STATUS and name == "Unknown") or (self.continuous_learn and name == "Unknown") or (current_status==self.FORCE_LEARNING_STATUS):
label_tmp=str(uuid.uuid1())
rospy.loginfo("unknown face: launch learn operation")
self._processLearnFace(top, right, bottom, left,face_encoding,label_tmp,frame_copy,new_learnt_face)
label_r=name
# Draw a box around the face
#cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
#cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
x0, y0 =self._processBoxCenter(top, right, bottom, left)
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
cv2.putText(frame, str(round(distance*100,1))+"% "+name, (left + 6, bottom - 6), font, 0.8, (255, 255, 255), 1)
cv2.circle(frame, (x0, y0), 5, (0, 255, 0), cv2.FILLED)
#print(name[0:8],left,top,right,bottom,str(round(distance*100,1)))
if(name[0:8]=="ruthrash"):
person_detections.append((left,top,right,bottom,str(round(distance*100,1))))
r_count = r_count+1
if(r_count>1):
self.fp_count = self.fp_count+1
self.count = self.count+1
#cv2.putText(frame, str(round(distance*100,1))+"% "+name, (left + 6, bottom - 6), font, 0.8, (255, 255, 255), 1)
#x0, y0 =self._processBoxCenter(top, right, bottom, left)
#cv2.circle(frame, (x0, y0), 5, (0, 255, 0), cv2.FILLED)
top_r=top
bottom_r=bottom
left_r=left
right_r=right
#return frame,name,top,left,bottom,right
max_conf = 0
#print(str(text))
#choose person detection with highest confidence
#'''
for i in range(len(person_detections)):
if(person_detections[i][4]>=max_conf):
max_conf = person_detections[i][4]
self.face_location = person_detections[i][0:4]
##self.face_location
poses = []
#print(self.face_location)
if len(person_detections)>=1:
self.detected_person_flag = True
x0, y0 =self._processBoxCenter(self.face_location[1], self.face_location[2], self.face_location[3], self.face_location[0])
cv2.rectangle(frame, (self.face_location[0], self.face_location[1]), (self.face_location[2], self.face_location[3]), (0, 0, 255), 2)
cv2.rectangle(frame, (self.face_location[0], self.face_location[3] - 35), (self.face_location[2], self.face_location[3]), (0, 0, 255), cv2.FILLED)
cv2.putText(frame, str(round(distance*100,1))+"% "+'ruthrash', (self.face_location[0] + 6, self.face_location[3] - 6), font, 0.8, (255, 255, 255), 1)
cv2.circle(frame, (x0, y0), 10, (0, 255, 0), cv2.FILLED)
#poses =
min_dist = 100
min_x0 = 0
min_y0 = 0
if self.poses_flag==True:
for person in self.pose_detections.persons:
pose = []
for bodyPart in person.bodyParts:
pose.append(bodyPart.pixel)
#print(pose[0] ,(x0,y0))
#print(np.linalg.norm(np.array((pose[0].x,pose[0].y)) - np.array((x0,y0))))
dist = np.linalg.norm(np.array((pose[0].x,pose[0].y)) - np.array((x0,y0)))
if(min_dist > dist):
min_dist = dist
index = len(poses)
min_x0 = x0
min_y0 = y0
poses.append((pose))
#cv2.circle(frame, (min_x0, min_y0), 10, (0, 255, 0), cv2.FILLED)
self.neck = [int(poses[index][1].x), int(poses[index][1].y)]
cv2.circle(frame, (int(poses[index][0].x), int(poses[index][0].y)), 10, (0, 255, 255), cv2.FILLED)
#print(len(poses))
# '''
if(self.neck[0]!=0 and self.neck[1]!=0):
cv2.circle(frame, (int(self.neck[0]), int(self.neck[1])), 10, (255, 0, 0), cv2.FILLED)
cv2.putText(frame, 'ruthrash', (int(self.neck[0] + 6), int(self.neck[1]-6)), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255, 255, 255), 1)
self.track = 0
#print((poses[index][0].x,poses[index][0].y),(min_x0,min_y0))
#update label
if name_w== None:
_labelToLearn=self.labelToLearn
else:
_labelToLearn=name_w
#check the biggest face learnt
if not self.continuous_learn:
max_box=0
biggest_face=None
for f in new_learnt_face:
if max_box<f.size:
max_box=f.size
biggest_face=f
if len(new_learnt_face)>0:
self.STATUS!='LEARNT'
oldId=biggest_face.label
del self.faceList[oldId]
biggest_face.label=_labelToLearn
self.faceList[_labelToLearn]=biggest_face
rospy.loginfo("")
os.rename(self.FACE_FOLDER_AUTO+"/"+oldId+'.png', self.FACE_FOLDER_AUTO+"/"+_labelToLearn+'.png')
rospy.loginfo("BIGGEST FACE of "+str(len(new_learnt_face))+":"+biggest_face.label)
print("no.of frames, detections_count ("+str(self.frames)+","+str(self.count)+","+str(self.fp_count)+")")
return frame,label_r,top_r,left_r,bottom_r,right_r,detected_faces_list
except CvBridgeError as e:
rospy.logwarn(e)
return "no Value"