def analyse_people(self, filePath):
client = AipBodyAnalysis(self.APP_ID, self.API_KEY, self.SECRET_KEY)
""" 读取图片 """
with open(filePath, 'rb') as fp:
data = client.bodyAttr(fp.read())
data = str(data)
res = re.findall(r"'(.*?)': {'score': (0.\d+), 'name': '(.*?)'}", data, re.S)
del res[0]
for r in res:
print("{:—<20}特征:{:—<15}精准度:{}".format(r[0],r[2], r[1]))
""" 如果有可选参数 """
def detection(self):
clients = AipBodyAnalysis(self.APP_ID, self.API_KEY, self.SECRET_KEY)
image = self.get_file_content('test.jpg')
options = {}
# """ 带参数调用人体检测与属性识别 """
res = clients.bodyAttr(image, options)
data = res['person_info'][0]['attributes']
news = {}
for k, v in data.items():
news[k] = v['name']
pprint(news)
class tylt_AipBodyAnalysis:
def __init__(self):
self.APP_ID = '14372361'
self.API_KEY = 'rwIh36pOcyqV8S9rQ6BBQlxh'
self.SERECT_KEY = 'NiHoVNuAwK1RoBRypCKo6OPQrdopWU2Y'
self.client = AipBodyAnalysis(self.APP_ID, self.API_KEY,
self.SERECT_KEY)
def body_attr(self, image):
return self.client.bodyAttr(image)
@staticmethod
def dealwith_body_attr_result(person_info_list):
"""
处理人体属性信息并返回
:param person_info_list: 人体属性信息列表
:return:
"""
response_list = []
for person in person_info_list:
attributes = person['attributes']
location = {
'left': person['location']['left'],
'top': person['location']['top'],
'width': person['location']['width'],
'height': person['location']['height'],
}
person_info_dict = {
'upper_wear_fg': attributes['upper_wear_fg']['name'],
'cellphone': attributes['cellphone']['name'],
'lower_cut': attributes['lower_cut']['name'],
'umbrella': attributes['umbrella']['name'],
'orientation': attributes['orientation']['name'],
'headwear': attributes['headwear']['name'],
'gender': attributes['gender']['name'],
'age': attributes['age']['name'],
'upper_cut': attributes['upper_cut']['name'],
'glasses': attributes['glasses']['name'],
'lower_color': attributes['lower_color']['name'],
'bag': attributes['bag']['name'],
'upper_wear_texture': attributes['upper_wear_texture']['name'],
'smoke': attributes['smoke']['name'],
'vehicle': attributes['vehicle']['name'],
'lower_wear': attributes['lower_wear']['name'],
'carrying_item': attributes['carrying_item']['name'],
'upper_wear': attributes['upper_wear']['name'],
'upper_color': attributes['upper_color']['name'],
'occlusion': attributes['occlusion']['name'],
'location': location,
}
response_list.append(person_info_dict)
return response_list
if(i==1):
""" 调用人体关键点识别 """
j=client.bodyAnalysis(image)
"""decode（’utf-8’）表示将utf-8编码的字符串转换成unicode编码。
encode(‘gb2312’)，表示将unicode编码的字符串转换成gb2312编码。"""
print(json.dumps(j,sort_keys=True,indent=4,separators=(',',':')))
print_div(a,j)
if(i==2):
""" 调用人体属性识别
client.bodyAttr(image);"""
""" 如果有可选参数 """
options = {}
options["type"] = "gender,age"
""" 带参数调用人体属性识别 """
j=client.bodyAttr(image, options)
d=json.dumps(j,sort_keys=True,indent=4,separators=(',',':'),ensure_ascii=False)
print(d)
if(i==3):
""" 调用人流量统计
client.bodyNum(image);"""
""" 如果有可选参数 """
options = {}
options["area"] = "" #x1,y1,x2,y2,x3,y3...xn,yn
options["show"] = "false"
""" 带参数调用人流量统计 """
j=client.bodyNum(image)
print(json.dumps(j,sort_keys=True,indent=4,separators=(',',':'),ensure_ascii=False))
def check_image():
image = get_file_content('test1.jpg')
# ID setup
APP_ID = '19378067'
API_KEY = 'DAYZeonKGtfLoPXA3UFivyl7'
SECRET_KEY = 'mGt3Lomozy1G5tueq4Pu1kkQ4xwFhdC'
client = AipBodyAnalysis(APP_ID, API_KEY, SECRET_KEY)
""" 调用人体关键点识别 """
keyword = client.bodyAttr(image)
print(keyword)
""" 打印json格式 """
print
json.dumps(keyword, sort_keys=True, indent=2)
print("person_num:%d" % (keyword['person_num']))
draw_image = cv2.imread('test1.jpg')
for i in range(0, keyword['person_num']):
# 识别人体的位置（x，y）
x1 = keyword['person_info'][i]['location']['left']
y1 = keyword['person_info'][i]['location']['top']
x2 = keyword['person_info'][i]['location']['width'] + x1
y2 = keyword['person_info'][i]['location']['height'] + y1
# 点位取整
round(x1)
round(x2)
round(y1)
round(y2)
# 打开图片并绘画出来人体框图
print("draw_image")
print("( %d , %d )" % (x1, y1))
print("( %d , %d )" % (x2, y2))
# 画出矩形
cv2.rectangle(draw_image, (int(x1), int(y1)), (int(x2), int(y2)),
(0, 255, 0), 4)
face_mask = keyword['person_info'][i]['attributes']['face_mask'][
'name']
mask1 = '无口罩'
mask2 = '戴口罩'
mask3 = '不确定'
if face_mask == mask1:
mask = "No mask"
if face_mask == mask2:
mask = "wear mask"
if face_mask == mask3:
mask = "uncertain"
print(face_mask)
person_name = 'person' + str(i) + mask
print(person_name)
# 标注文本
cv2.putText(draw_image, person_name, (int(x1), int(y1)),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)
while (1):
cv2.imshow('person_detect', draw_image)
if cv2.waitKey(1) & 0xFF == ord('w'):
cv2.imwrite("test1.jpg", draw_image)
break
if cv2.getWindowProperty('person_detect', cv2.WND_PROP_AUTOSIZE) < 1:
break
cv2.destroyAllWindows()
API_KEY = 'TxGkNH961xrF41oxeP86pdKr'
SECRET_KEY = 'IKAacd4z9CoPp06RaWlwgYqe3g5TfelN'
client = AipBodyAnalysis(APP_ID, API_KEY, SECRET_KEY)
# 读取图片
def get_file_content(filePath):
with open(filePath, 'rb') as fp:
return fp.read()
image = get_file_content('example.jpg')
# 可选参数
options = {}
options['type'] = 'gender,age,headwear,glasses,orientation'
# 调用人体属性识别
t1 = time.time()
details = client.bodyAttr(image, options)
# print(details['person_info'][0]['attributes']['gender']['name'])
# print(details['person_info'][0]['attributes']['glasses']['name'])
# print(details['person_info'][0]['attributes']['age']['name'])
# print(details['person_info'][0]['attributes']['headwear']['name'])
# print(details['person_info'][0]['attributes']['orientation']['name'])
for i in ['gender', 'glasses', 'age', 'headwear', 'orientation']:
print(details['person_info'][0]['attributes'][i]['name'])
t2 = time.time()
print(t2 - t1)
def chack_image():
image = get_file_content('test1.jpg')
#ID setup
APP_ID = '19103828'
API_KEY = 'vxYkTAqGYoWe4XRonMUeCvtH'
SECRET_KEY = 'etnQGZksMjPFU7GcGUIwtVOL7MBUK0Pa'
client = AipBodyAnalysis(APP_ID, API_KEY, SECRET_KEY)
""" 调用人体关键点识别 """
keyword = client.bodyAttr(image)
""" 打印json格式 """
print(json.dumps(keyword, sort_keys=True, indent=2))
print("person_num:%d" % (keyword['person_num']))
draw_image = cv2.imread('test1.jpg')
for i in range(0, keyword['person_num']):
#识别人体的位置（x，y）
x1 = keyword['person_info'][i]['location']['left']
y1 = keyword['person_info'][i]['location']['top']
x2 = keyword['person_info'][i]['location']['width'] + x1
y2 = keyword['person_info'][i]['location']['height'] + y1
#点位取整
round(x1)
round(x2)
round(y1)
round(y2)
#打开图片并绘画出来人体框图
print("draw_image")
print("( %d , %d )" % (x1, y1))
print("( %d , %d )" % (x2, y2))
#画出矩形
cv2.rectangle(draw_image, (int(x1), int(y1)), (int(x2), int(y2)),
(0, 255, 0), 4)
face_mask = keyword['person_info'][i]['attributes']['face_mask'][
'name']
face_mask = face_mask.encode('utf-8').decode('utf-8')
mask1 = '无口罩'
mask1.encode('utf-8').decode('utf-8')
mask2 = '戴口罩'
mask2.encode('utf-8').decode('utf-8')
mask3 = '不确定'
mask3.encode('utf-8').decode('utf-8')
if face_mask == mask1:
mask = "No mask"
if face_mask == mask2:
mask = "wear mask"
if face_mask == mask3:
mask = "uncertain"
print(face_mask)
person_name = str(i) + mask
#标注文本
cv2.putText(draw_image, person_name, (int(x1), int(y1)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
while (1):
# cv2.imshow('porson_detect',draw_image) #打开窗口显示图片
# if cv2.waitKey(1) & 0xFF == ord('w'): # 当按下w时保存图片并退出显示窗口
cv2.imwrite("test1.jpg", draw_image)
break
cv2.destroyAllWindows()
def detection(self):
self.start_detect = False
encode_image = self.image_encode()
print (type(encode_image))
image = cv2.imread(self.path_to_save_image)
#初始化aipfacce对象
client = AipBodyAnalysis(self.APP_ID, self.API_KEY, self.SECRET_KEY)
#接收返回的检测结果
result = client.bodyAttr(encode_image)
print (result)
self.rec_count += 1
num = result["person_num"]
if result["person_num"] == 0:
rospy.loginfo("Start Turning robot")
self.turn_robot(1.5)
self.angle_count += 1.5
self.take_photo_signal = True
if result["person_num"] > 1:
for i in range(num):
person_info = result["person_info"][i]
point1 = (person_info["location"]["left"], person_info["location"]["top"])
point2 = (person_info["location"]["left"]+person_info["location"]["width"], person_info["location"]["top"]+person_info["location"]["height"])
cv2.rectangle(image, point1, point2, (0,0,255), 2)
cv2.imwrite(self.path_to_save_result, image)
rospy.loginfo("Start Turning robot")
self.turn_robot(1.5)
self.angle_count += 1.5
self.take_photo_signal = True
if result["person_num"] == 1:
person_info = result["person_info"][0]
person_x = person_info["location"]["left"] + person_info["location"]["width"]/2
person_y = person_info["location"]["top"] + person_info["location"]["height"]/2
point1 = (person_info["location"]["left"], person_info["location"]["top"])
point2 = (person_info["location"]["left"]+person_info["location"]["width"], person_info["location"]["top"]+person_info["location"]["height"])
cv2.rectangle(image, point1, point2, (0,0,255), 2)
cv2.imwrite(self.path_to_save_result, image)
if person_x-320 > 80:
self.turn_robot(-1.1)
if person_x -320 < -80:
self.turn_robot(1.1)
person_area = person_info["location"]["width"] * person_info["location"]["height"]
print ("person area {}".format(person_area))
print ("person width {}".format(person_info["location"]["width"]))
print ("person height {}".format(person_info["location"]["height"]))
if (person_area >= 13000):
self.forward_robot(0.5)
rospy.sleep(2)
self.find_person = True
rospy.loginfo("Person Target Reached!")
msg = String()
msg.data = "Person Target Reached!"
self.pub_talkback.publish(msg)
if self.rec_count ==8:
self.find_person = True
rospy.loginfo("Person Not Found")
msg = String()
msg.data = "Person Not Found!"
self.pub_talkback.publish(msg)
def detection(self):
self.start_detect = False
encode_image = self.image_encode()
print(type(encode_image))
image = cv2.imread(self.path_to_save_image)
#初始化aipfacce对象
client = AipBodyAnalysis(self.APP_ID, self.API_KEY, self.SECRET_KEY)
#接收返回的检测结果
result = client.bodyAttr(encode_image)
print(result)
num = result["person_num"]
if result["person_num"] == 0:
rospy.loginfo("Start Turning robot")
self.turn_robot(1.5)
self.angle_count += 1.5
self.take_photo_signal = True
if result["person_num"] > 1:
self.pub_speech.publish(
"I can see more than one person, I have found the target person"
)
self.pub_speech.publish("Please stand in front of me and lead me")
for i in range(num):
person_info = result["person_info"][i]
point1 = (person_info["location"]["left"],
person_info["location"]["top"])
point2 = (person_info["location"]["left"] +
person_info["location"]["width"],
person_info["location"]["top"] +
person_info["location"]["height"])
cv2.rectangle(image, point1, point2, (0, 0, 255), 2)
cv2.imwrite(self.path_to_save_result, image)
self.find_person = True
msg = Result()
msg.mission_type = "person"
msg.result = "success"
self.pub_control.publish(msg)
if result["person_num"] == 1:
person_info = result["person_info"][0]
person_x = person_info["location"][
"left"] + person_info["location"]["width"] / 2
person_y = person_info["location"][
"top"] + person_info["location"]["height"] / 2
point1 = (person_info["location"]["left"],
person_info["location"]["top"])
point2 = (person_info["location"]["left"] +
person_info["location"]["width"],
person_info["location"]["top"] +
person_info["location"]["height"])
cv2.rectangle(image, point1, point2, (0, 0, 255), 2)
cv2.imwrite(self.path_to_save_result, image)
if person_x - 320 > 80:
self.turn_robot(-1.1)
if person_x - 320 < -80:
self.turn_robot(1.1)
person_area = person_info["location"]["width"] * person_info[
"location"]["height"]
print("person area {}".format(person_area))
print("person width {}".format(person_info["location"]["width"]))
print("person height {}".format(person_info["location"]["height"]))
if (person_area >= 13000):
self.forward_robot(0.5)
rospy.sleep(2)
self.find_person = True
rospy.loginfo("Person Target Reached!")
control_msg = Result()
speech_msg = String()
control_msg.mission_type = "person"
control_msg.result = "success"
speech_msg.data = "I have reached the target person"
self.pub_speech.publish(speech_msg)
self.pub_control.publish(control_msg)
class Guest_Recognition():
def __init__(self):
rospy.init_node('receptionist_image')
# rospy.on_shutdown(self.cleanup)
self.rate = rospy.Rate(1)
APP_ID = '18721308'
API_KEY = 'lNQGdBNazTPv8LpSP4x0GQlI'
SECRET_KEY = 'nW8grONY777n4I2KvpOVuKGDNiY03omI'
self.client_face = AipFace(APP_ID, API_KEY, SECRET_KEY)
self.client_body = AipBodyAnalysis(APP_ID, API_KEY, SECRET_KEY)
self.bridge = CvBridge()
self.image_type = "BASE64"
self.filepath = "/home/lurejewel/catkin_ws/src/receptionist_image/receptionist_image_core/pictures/image.jpg"
# self.filepath = "/home/tianyili/robocup/src/image.jpg"
self.FindGuest = 1
self.FindJohn = 2
self.FindSofa = 3
self.guest = Guest()
self.john = user()
self.john.user_id = 'john'
self.john.facenum = 0
self.guest1 = user()
self.guest1.user_id = 'guest1'
self.guest1.facenum = 0
self.groupIdList = 'reception_group'
self.options_body = {}
self.options_body["type"] = "gender,age"
self.options_face = {}
self.options_face["face_field"] = "age,gender"
self.options_face["max_face_num"] = 3
self.options_face["face_type"] = "LIVE"
#
self.boxes = BoundingBoxes()
self.human_boxes = BoundingBoxes()
self.sofa = BoundingBox()
self.seat = RegionOfInterest()
# 工作状态
self.workstate = 0
#发布器
self.control_pub = rospy.Publisher("/control", reception, queue_size=1)
self.roi_pub = rospy.Publisher('/image/roi',
RegionOfInterest,
queue_size=1) # turn robot
self.seat_pos = rospy.Publisher('/image/found_seat_pos',
RegionOfInterest,
queue_size=1) # turn robot
self.seat_pub = rospy.Publisher('/image/found_seat',
Bool,
queue_size=1) # to arm
self.introduce = rospy.Publisher('/image/found_john',
Bool,
queue_size=1) # turn robot
#订阅器
self.control_sub = rospy.Subscriber("/control",
reception,
self.controlCallback,
queue_size=1)
self.sofa_sub = rospy.Subscriber("/darknet_ros/bounding_boxes",
BoundingBoxes,
self.sofaCallback,
queue_size=1)
self.img_sub = rospy.Subscriber("/usb_cam/image_raw",
Image,
self.imgCallback,
queue_size=1)
self.time = time.time()
self.ROI = RegionOfInterest()
def controlCallback(self, msg):
if msg.NowTask == msg.GuestRecognition and msg.FinishState == False:
print("===Start to find guest===")
self.workstate = self.FindGuest
elif msg.NowTask == msg.Introducing and msg.FinishState == False:
print("===Start to find John===")
self.workstate = self.FindJohn
# self.can_speak(self.groupIdList)
elif msg.NowTask == msg.Serving and msg.FinishState == False:
print("===Start to find Sofa===")
self.workstate = self.FindSofa
else:
# 其他情况不进行工作
# TODO: DoorDetection
self.workstate = 0
# 沙发检测
def sofaCallback(self, msg):
if self.workstate != self.FindSofa:
return
self.boxes = msg.bounding_boxes
self.sofa = None
self.human_boxes = []
t = time.time()
# 检查视野目标
for box in self.boxes:
if box.Class == 'bed':
self.sofa = box
break
for box in self.boxes:
if box.Class == 'person':
self.human_boxes.append(box)
# 判断空位置
if self.sofa is not None:
sofa_width = box.xmax - box.xmin
hum_in_sofa = []
if len(self.human_boxes) != 0:
# 计算一个人的平均占据宽度
hum_width = 0.0
flag = 0
for human in self.human_boxes:
# 人在沙发位置
if (human.xmax < self.sofa.xmax) and (human.xmin >
self.sofa.xmin):
flag += 1
hum_width += human.xmax - human.xmin
hum_in_sofa.append(human)
# 判断情况
if flag == 0:
# 没有人——指向沙发
self.seat.x_offset = self.sofa.xmin
self.seat.width = self.sofa.xmax - self.sofa.xmin
self.seat.y_offset = self.sofa.ymin
self.seat.height = self.sofa.ymax - self.sofa.ymin
if t - self.time > 3:
self.time = t
self.seat_pos.publish(self.seat)
elif flag == 1:
# 只有一个人
dis_R = self.sofa.xmax - hum_in_sofa[0].xmax
dis_L = hum_in_sofa[0].xmin - self.sofa.xmin
if dis_R >= dis_L:
self.seat.x_offset = hum_in_sofa[0].xmax
self.seat.width = self.sofa.xmax - hum_in_sofa[0].xmax
self.seat.y_offset = self.sofa.ymin
self.seat.height = self.sofa.ymax - self.sofa.ymin
if t - self.time > 3:
self.time = t
self.seat_pos.publish(self.seat)
else:
self.seat.x_offset = self.sofa.xmin
self.seat.width = hum_in_sofa[0].xmin - self.sofa.xmin
self.seat.y_offset = self.sofa.ymin
self.seat.height = self.sofa.ymax - self.sofa.ymin
if t - self.time > 3:
self.time = t
self.seat_pos.publish(self.seat)
else:
# 为人体数据排序——只需要按照一个边
hum_width = hum_width / flag
for i in range(len(hum_in_sofa)):
for j in range(0, len(hum_in_sofa) - 1):
if hum_in_sofa[j].xmax > hum_in_sofa[j + 1].xmax:
data = copy.deepcopy(hum_in_sofa[j + 1])
hum_in_sofa[j + 1] = hum_in_sofa[j]
hum_in_sofa[j] = data
# 检查每一个人的纵向差异
STATE = False
for i in range(len(hum_in_sofa - 1)):
if hum_in_sofa[i].xmax - hum_in_sofa[
i + 1].xmin < hum_width:
continue
else:
self.seat.x_offset = hum_in_sofa[i].xmax
self.seat.width = hum_in_sofa[i + 1].xmin
self.seat.y_offset = hum_in_sofa[i].ymin
self.seat.height = hum_in_sofa[i].ymax
if t - self.time > 3:
self.time = t
self.seat_pos.publish(self.seat)
STATE = True
# 没有空余位置
if STATE == False:
self.seat.x_offset = 0
self.seat.width = 0
self.seat.y_offset = 0
self.seat.height = 0
if t - self.time > 3:
self.time = t
self.seat_pos.publish(self.seat)
else:
self.seat.x_offset = self.sofa.xmin
self.seat.width = self.sofa.xmax - self.sofa.xmin
self.seat.y_offset = self.sofa.ymin
self.seat.height = self.sofa.ymax - self.sofa.ymin
if t - self.time > 3:
self.time = t
self.seat_pos.publish(self.seat)
else:
self.seat.x_offset = 0
self.seat.width = 0
self.seat.y_offset = 0
self.seat.height = 0
if t - self.time > 3:
self.time = t
self.seat_pos.publish(self.seat)
print(self.seat.width / 2 + self.seat.x_offset)
if 300 < self.seat.width / 2 + self.seat.x_offset < 340:
data = Bool()
data.data = True
self.seat_pub.publish(data)
else:
data = Bool()
data.data = False
self.seat_pub.publish(data)
# 人体数据转换
def msgtobody(self, image_msg):
# 转为base64存储
cv2.imwrite(self.filepath, image_msg)
with open(self.filepath, 'rb') as fp:
return fp.read()
# 打开文件
def fileopen(self, filepath):
with open(filepath, 'rb') as fp:
imgjson = base64.b64encode(fp.read())
data = str(imgjson).encode('utf-8')
return data
# 判断人脸已知
def judger(self, result):
if result['error_code'] == 0:
result_sc = result['result']['user_list'][0]['score']
# print(result_sc,type(result_sc))
if result_sc > 80:
# return result['result']['user_list'][0]['user_id']
return True
else:
return False
else:
return False
# 人脸库搜索 groupIdList="用户组名称"
def face_search(self, filepath, groupIdList='reception_group'):
image = self.fileopen(filepath)
imageType = "BASE64"
result = self.client_face.search(image, imageType, groupIdList)
print(result) # 打印出所有信息
print(result['result']['user_list'][0]['user_id'])
return self.judger(result)
# if result['error_code'] == 0:
# judger(result)
# elif result['result'] == None:
# return False
def imgCallback(self, image_msg):
if self.workstate == self.FindGuest:
try:
cv_image = self.bridge.imgmsg_to_cv2(image_msg, "bgr8")
except CvBridgeError as e:
print(e)
self.find_people(cv_image)
if self.workstate == self.FindJohn:
try:
cv_image = self.bridge.imgmsg_to_cv2(image_msg, "bgr8")
except CvBridgeError as e:
print(e)
self.can_speak(cv_image, self.groupIdList)
"""寻找人体并返回未知人员ROI"""
def find_people(self, image):
segment_data = self.msgtobody(image)
result = self.client_body.bodyAttr(segment_data, self.options_body)
if 'person_num' in result:
person_num = result['person_num']
# self.objpos_pub.publish()
else:
person_num = 0
print("The number of person is " + str(person_num))
if person_num == 0:
self.ROI.x_offset = 0
self.ROI.y_offset = 0
self.ROI.width = 0
self.ROI.height = 0
if t - self.time > 3:
self.time = t
self.roi_pub.publish(self.ROI)
for num in range(0, int(person_num)):
# 不认识的人则发布信息ROI
cv2.imwrite(self.filepath, image)
if self.face_search(self.filepath) == False:
location = result['person_info'][num]['location']
print(location)
# print('upper_color:',upper_color,' upper_wear_fg:',upper_wear_fg)
self.ROI.x_offset = int(location['left'])
self.ROI.y_offset = int(location['top'])
self.ROI.width = int(location['width'])
self.ROI.height = int(location['height'])
if t - self.time > 3:
self.time = t
self.roi_pub.publish(self.ROI)
print(self.ROI)
msg = reception()
msg.NowTask = msg.GuestRecognition
msg.NextTask = msg.Requesting
msg.FinishState = True
self.control_pub.publish(msg)
# 检查是否是被介绍者
"""判断是否为被介绍者"""
def can_speak(self, image, groupIdList='reception_group'):
# result = self.face_search(filepath,groupIdList)
# # 是已知人员可介绍
# if result['score']>80:
# self.introduce.publish(True)
cv2.imwrite(self.filepath, image)
result = self.face_search(self.filepath, groupIdList)
# 是已知人员可介绍
# if result:
if t - self.time > 3:
self.time = t
self.introduce.publish(result)
print('result:', result)
def face_add(self, filepath, image_msg, groupid, userid): # 人脸库增加 地址 组 用户
image = self.fileopen(filepath)
imageType = "BASE64"
result = self.client.addUser(image, imageType, groupid, userid)
if result['error_code'] == 0:
print("增加人脸成功")
else:
print("增加人脸失败")
print(result)
def get_user_facenum(self, userid):
if userid == 'john':
return self.john
else:
return self.guest1
def add_user(self, file_name, image_msg):
cv2.imwrite(file_name, image_msg)
search_result = self.face_search(file_name, 'reception_group')
curusr = user()
# 加入新人脸
if search_result['error_code'] == 222207:
userid = 'john'
self.john.facenum += 1
curusr = self.john
elif search_result['error_code'] == 0:
if search_result['result']['user_list'][0]['score'] < 80:
# ---------------------------------------
num = len(search_result['result']['user_list'][0])
if num == 0:
userid = 'john'
self.john.facenum += 1
curusr = self.john
elif num == 1:
userid = 'guest1'
self.guest1.facenum += 1
curusr = self.guest1
# 旧人脸
else:
userid = search_result['result']['user_list'][0]['user_id']
curusr = self.get_user_facenum(userid)
# 加入人脸张数信息 未达到上限20则持续添加，否则不作处理
if curusr.facenum == 0 and curusr.user_id:
self.face_add(file_name, image_msg, 'reception_group',
curusr.user_id)
print("curusr --------------------------")
# print(curusr.user_id , curusr.facenum)
else:
print('wrong')
import os
from aip import AipBodyAnalysis
""" 你的 APPID AK SK """
APP_ID = '11741060'
API_KEY = 'uXS3628lLLRGVGYNnqzNjksc'
SECRET_KEY = 'EU4Pykb3RbQllrulScrGizb6IYsmZcYy'
client = AipBodyAnalysis(APP_ID, API_KEY, SECRET_KEY)
""" 读取图片 """
filename = '2.jpg'
filepath = os.path.abspath('.') + '\\' + filename
with open(filepath, 'rb') as f: # TODO
img = f.read()
""" 百度手势识别SDK未开放 """
r = client.bodyAttr(img) # 人流量统计
print(r)
def detection(self):
self.start_detect = False
encode_image = self.image_encode()
print(type(encode_image))
image = cv2.imread(self.path_to_save_image)
#初始化aipfacce对象
client = AipBodyAnalysis(self.APP_ID, self.API_KEY, self.SECRET_KEY)
#接收返回的检测结果
result = client.bodyAttr(encode_image)
#print (result)
num = result["person_num"]
area = []
point = []
person_x = 0
height = 0
try:
if result["person_num"] == 0:
print("person_num =", result["person_num"])
rospy.loginfo("Start Turning robot")
self.turn_robot(0.7)
self.angle_count += 0.7
#self.find_person = False
#self.take_photo_signal = True
if result["person_num"] > 1:
print("person_num =", result["person_num"])
self.speech_pub.publish(
"I can see more than one person, I have found the target person"
)
self.speech_pub.publish(
"Please stand in front of me and lead me")
for i in range(num):
person_info = result["person_info"][i]
point1 = (person_info["location"]["left"],
person_info["location"]["top"])
point2 = (person_info["location"]["left"] +
person_info["location"]["width"],
person_info["location"]["top"] +
person_info["location"]["height"])
cv2.rectangle(image, point1, point2, (0, 0, 255), 2)
tmpPoint = (point1[0] + point2[0]) / 2
tmpArea = (point1[0] - point2[0]) * (point1[1] - point2[1])
point.append(tmpPoint)
area.append(tmpArea)
tmpArea = area[0]
person_x = point[0]
for i in range(len(area)):
if tmpArea < area[i]:
person_x = point[i]
tmpArea = area[i]
height = result["person_info"][i]["location"]["top"]
cv2.imwrite(self.path_to_save_result, image)
#self.find_person = True
#self.find_person = True
#msg = String()
#msg.data = "person_target_found"
#self.pub_result.publish(msg)
if result["person_num"] == 1:
print("person_num =", result["person_num"])
person_info = result["person_info"][0]
person_x = person_info["location"][
"left"] + person_info["location"]["width"] / 2
person_y = person_info["location"][
"top"] + person_info["location"]["height"] / 2
point1 = (person_info["location"]["left"],
person_info["location"]["top"])
point2 = (person_info["location"]["left"] +
person_info["location"]["width"],
person_info["location"]["top"] +
person_info["location"]["height"])
height = person_info["location"]["top"]
cv2.rectangle(image, point1, point2, (0, 0, 255), 2)
cv2.imwrite(self.path_to_save_result, image)
#self.find_person = True
except KeyError:
print("NO PERSON DETECTED")
pass
print("========================")
print("area =", area)
print("point =", point)
print("person_x =", person_x)
print("height =", height)
print("========================")
if person_x - 320 > 80:
self.turn_robot(-0.5)
print("turn right")
elif person_x - 320 < -80:
self.turn_robot(0.5)
print("turn left")
else:
print("Start going forward")
self.is_stop_turn = True
self.find_person = True
'''
while self.is_go_forward:
print("go_forward")
self.forward_robot(0.45)
self.find_person = True
rospy.loginfo("Person Target Reached!")
control_msg = String()
speech_msg = String()
control_msg.data = "person_target_found"
speech_msg.data = "I have reached the target person"
self.speech_pub.publish(speech_msg)
self.pub_result.publish(control_msg)
self.is_stop_turn = False
'''
'''
class Guest_Recognition:
def __init__(self):
APP_ID = '18721308'
API_KEY = 'lNQGdBNazTPv8LpSP4x0GQlI'
SECRET_KEY = 'nW8grONY777n4I2KvpOVuKGDNiY03omI'
self.client_face = AipFace(APP_ID, API_KEY, SECRET_KEY)
self.client_body = AipBodyAnalysis(APP_ID, API_KEY, SECRET_KEY)
self.image_type = "BASE64"
msg = reception_image()
self.guest = Guest()
# self.name = None
# self.age = None
# self.image = None
# self.gender = None
# self.drink = None
self.options_body = {}
self.options_body["type"] = "gender,upper_color,upper_wear_fg"
self.options_face = {}
self.options_face["face_field"] = "age,gender"
self.options_face["max_face_num"] = 3
self.options_face["face_type"] = "LIVE"
#ros params
self.sub_image_raw_topic_name = None
self.sub_destination_topic_name = None
self.pub_gender_recognition_topic_name = None
self.pub_object_position2d = None
# self.depth_img = np.array((480,640))
self.get_params()
#发布器
self.objpos_pub = rospy.Publisher('/image/object_position2d',
String,
queue_size=1)
#订阅器
self.control_sub = rospy.Subscriber("/control", reception,
self.controlCallback)
self.speech_sub = rospy.Subscriber('/speech/check_door', String,
self.find_people)
# --------------
def get_params(self):
self.sub_image_raw_topic_name = rospy.get_param(
'sub_image_raw_topic_name', '/camera/rgb/image_raw')
# self.sub_depth_image_topic_name = rospy.get_param('sub_depth_image_topic_name', '/camera/depth/image_raw')
self.pub_gender_recognition_topic_name = rospy.get_param(
'pub_gender_recognition_topic_name',
'/kamerider_image/gender_recognition')
self.objpos_pub = rospy.get_param('objpos_pub',
'/image/object_position2d')
#定义R发布器和订阅器，话题名通过ROS PARAM参数服务器获取
rospy.Subscriber(self.sub_image_raw_topic_name, Image,
self.imageCallback)
# rospy.Subscriber(self.sub_depth_image_topic_name, Image, self.depthCallback)
self.pub_result = rospy.Publisher(
self.pub_gender_recognition_topic_name, String, queue_size=1)
# self.pub_person_pos = rospy.Publisher(self.pub_person_pos_topic_name, Pose,queue_size=1)
self.objpos_pub = rospy.Publisher('/image/object_position2d',
String,
queue_size=1)
def depthCallback(self, msg):
bridge = CvBridge()
try:
cv_image = bridge.imgmsg_to_cv2(msg, '16UC1')
self.depth_img = cv_image
except CvBridgeError as e:
print(e)
# -------------------------------------
# 人体数据转换
def msgtobody(self, image_msg, file_name='image_body.png'):
# 转为base64存储
cv2.imwrite(file_name, image_msg)
with open(file_name, 'rb') as fp:
return fp.read()
# 人脸库搜索 groupIdList="用户组名称"
def face_search(filepath, groupIdList):
image = fileopen(filepath)
imageType = "BASE64"
result = client.search(image, imageType, groupIdList)
# print(result) # 打印出所有信息
print(result['result']['user_list'][0]['user_id'])
# 判断人脸是否已知
def judger(result):
result_sc = result['result']['user_list'][0]['score']
if result_sc > 80:
return result['result']['user_list'][0]['user_id']
else:
return False
""" 调用人脸视频比对人脸库 """
def face_comparision(window_name='image', camera_idx=0):
cv2.namedWindow(window_name)
#视频来源，可以来自一段已存好的视频，也可以直接来自USB摄像头
cap = cv2.VideoCapture(camera_idx)
while cap.isOpened():
ok, frame = cap.read() #读取一帧数据
if not ok:
break
c = cv2.waitKey(100) #按q退出
if c == ord('q'):
break
cv2.imshow(window_name, frame)
base64_data = frame2base64(frame)
image = str(base64_data, 'utf-8')
imageType = "BASE64"
result = client.search(image, imageType, 'test_group')
if result['error_code'] == 0:
judger(result)
cap.release()
cv2.destroyAllWindows()
def find_people():
# msg_nav=msh
# bridge = CvBridge()
# try:
# cv_image = bridge.imgmsg_to_cv2(msg, 'bgr8')
# except CvBridgeError as e:
# print (e)
cap = cv2.ViseoCapture(0)
while cap.isOpened():
ret, imgraw0 = cap.read()
if ret:
if cnt % 10 == 0:
imgraw = cv2.resize(imgraw0, (512, 512))
cv2.imwrite(filepath, imgraw)
with open(filepath, "rb") as fp:
segment_data = fp.read()
result = client_body.bodyAttr(segment_data, options_body)
# print(result)
# 解析位置信息
if 'person_num' in result:
person_num = result['person_num']
else:
person_num = 0
for num in range(0, int(person_num)):
print(num)
location = result['person_info'][num - 1]['location']
print('upper_color:', upper_color, ' upper_wear_fg:',
upper_wear_fg)
A = (int(location['left']), int(location['top']))
B = (int(location['left']) + int(location['width']),
int(location['top']))
C = (int(location['left']) + int(location['width']),
int(location['top']) + int(location['height']))
D = (int(location['left']),
int(location['top']) + int(location['height']))
cv2.waitKey(1000)
# self.objpos_pub.publish()
cnt += 1
cv2.waitKey(0)
def imageCallback(self, msg):
if self.take_photo_signal:
print("[INFO] Start to take photo")
bridge = CvBridge()
self.take_photo_signal = False
try:
cv_image = bridge.imgmsg_to_cv2(msg, 'bgr8')
cv2.imwrite(self.path_to_save_image, cv_image)
except CvBridgeError as e:
print(e)
self.detection()
# 挥手检测
# image——输入的cv类型
# gender——是否检查性别
def detectWave(self, image, gender=False):
#if time.time()-self.time < self.tau:
# return None
# self.time = time.time()
print("CHECK")
data = self.msgtobody(image, "image_body.png")
# ----------挥手检测----------
result = self.client_body.bodyAnalysis(data)
wave = []
loaction = []
point_t = []
# 存在人
if result['person_num'] > 0:
id_ = -1
# 对每个人进行检查
for info in result['person_info']:
id_ += 1
keypoint = info['body_parts']
# 腕高
if keypoint['right_elbow']['y'] > keypoint['right_wrist']['y']:
# 腕在外侧
if keypoint['right_wrist']['x'] < keypoint[
'right_shoulder']['x']:
wave.append(id_)
loc = []
loc.append(int(info['location']['left']))
loc.append(
int(info['location']['left'] +
info['location']['width']))
loc.append(int(info['location']['top']))
loc.append(
int(info['location']['top'] +
info['location']['height']))
loaction.append(copy.deepcopy(loc))
# 腕高
elif keypoint['left_elbow']['y'] > keypoint['left_wrist']['y']:
# 腕在外侧
if keypoint['left_wrist']['x'] > keypoint['left_shoulder'][
'x']:
wave.append(id_)
loc = []
loc.append(int(info['location']['left']))
loc.append(
int(info['location']['left'] +
info['location']['width']))
loc.append(int(info['location']['top']))
loc.append(
int(info['location']['top'] +
info['location']['height']))
loaction.append(copy.deepcopy(loc))
# 存在挥手
if len(loaction) > 0:
# 女性检测
# ----------性别检测----------
if gender:
options = {}
options["type"] = "gender"
# 保证存在挥手
for locate in loaction:
img = image[locate[2]:locate[3], locate[0]:locate[1]]
img = self.msgtobody(img, "try__.png")
result = self.client_body.bodyAttr(img, options)
try:
result['person_info'][0]['attributes'][
'gender'] == "女性"
except:
continue
# 女性则直接返回女性位置
if result['person_info'][0]['attributes'][
'gender'] == "女性":
loc = []
loc.append(locate[0])
loc.append(locate[1])
loc.append(locate[2])
loc.append(locate[3])
return locate
# 随机返回一个人
locate = loaction[0]
loc = []
loc.append(locate[0])
loc.append(locate[1])
loc.append(locate[2])
loc.append(locate[3])
return locate
return None
class BodyCheck:
def __init__(self):
self.time = time.time()
APP_ID = '18889374'
API_KEY = 'pUNweNaSK4rWz57vGs9KpuW1'
SECRET_KEY = 'ru5LqWM0lrcVYBh9cjd32fy951nagqcA'
self.imageType = "BASE64"
self.client_face = AipFace(APP_ID, API_KEY, SECRET_KEY)
self.client_body = AipBodyAnalysis(APP_ID, API_KEY, SECRET_KEY)
self.bridge = CvBridge()
##############人类数据
self.filepath = "/home/dell/img/"
self.option_face = {}
self.option_body = {}
self.option_face["face_field"] = "age,gender,glasses,race"
self.option_face["max_face_num"] = 1
self.option_body["type"] = "upper_wear,upper_color"
##############跟踪数据
self.roi = None
##############话题名称
# 接收器与发布器
self.sub_image_name = rospy.get_param('~image_raw_topic_name',
'/usb_cam/image_raw')
# 发布器
self.pub_pos_name = rospy.get_param('~object_view_topic_name', 'roi')
self.pub_img_name = rospy.get_param('~image_test_topic_name',
'/image/test')
self.pub_fet_name = rospy.get_param('~feature_topic_name',
'/image/feature')
##############发布器
self.img_pub = rospy.Publisher(self.pub_img_name, Image)
self.roi_pub = rospy.Publisher(self.pub_pos_name, RegionOfInterest)
self.fet_pub = rospy.Publisher(self.pub_fet_name, Description)
self.img_sub = rospy.Subscriber(self.sub_image_name, Image,
self.imgCallback)
print("============================================================")
# 人体数据转换
def msgtobody(self, image_msg, file_name='image_body.png'):
# 转为base64存储
cv2.imwrite(self.filepath + file_name, image_msg)
with open(self.filepath + file_name, 'rb') as fp:
return fp.read()
# 人脸数据转换
def msgtoface(self, image_msg, file_name='image_faces.png'):
cv2.imwrite(self.filepath + file_name, image_msg)
with open(self.filepath + file_name, 'rb') as fp:
data = base64.b64encode(fp.read())
# python2.7
data = str(data).encode('utf-8')
return data
# 挥手检测，返回一个挥手的人的方框xxyy数据
def detectWave(self, image, gender=False):
print("============================================================")
print("CHECK")
data = self.msgtobody(image, "image_body.png")
# ----------挥手检测----------
result = self.client_body.bodyAnalysis(data)
wave = []
loaction = []
# 存在人
if result['person_num'] > 0:
id_ = -1
# 对每个人进行检查
for info in result['person_info']:
id_ += 1
keypoint = info['body_parts']
# 腕高
if keypoint['right_elbow']['y'] > keypoint['right_wrist']['y']:
# 腕在外侧
if keypoint['right_wrist']['x'] < keypoint[
'right_shoulder']['x']:
wave.append(id_)
loc = []
loc.append(int(info['location']['left']))
loc.append(
int(info['location']['left'] +
info['location']['width']))
loc.append(int(info['location']['top']))
loc.append(
int(info['location']['top'] +
info['location']['height']))
loaction.append(copy.deepcopy(loc))
# 腕高
elif keypoint['left_elbow']['y'] > keypoint['left_wrist']['y']:
# 腕在外侧
if keypoint['left_wrist']['x'] > keypoint['left_shoulder'][
'x']:
wave.append(id_)
loc = []
loc.append(int(info['location']['left']))
loc.append(
int(info['location']['left'] +
info['location']['width']))
loc.append(int(info['location']['top']))
loc.append(
int(info['location']['top'] +
info['location']['height']))
loaction.append(copy.deepcopy(loc))
if len(loaction) > 0:
# 返回挥手的随机一个人的位置
locate = loaction[0]
loc = []
loc.append(locate[0])
loc.append(locate[1])
loc.append(locate[2])
loc.append(locate[3])
return locate
return None
# 照片的回调函数，发布挥手人的位置
def imgCallback(self, image):
#try:
#cv_image = self.bridge.imgmsg_to_cv2(image, "bgr8")
#except CvBridgeError as e:
#print(e)
cv_image = self.bridge.imgmsg_to_cv2(image, "bgr8")
position = self.detectWave(cv_image)
if position is not None:
msg = Description()
msg.hair_style = "unknowm"
msg.pose = "unknowm"
# 特征检测——人脸和人体
img_body = cv_image[position[2]:position[3],
position[0]:position[1]]
face = self.msgtoface(img_body)
result1 = self.client_face.detect(face, self.imageType,
self.option_face)
print(result1["result"]["face_list"][0]["gender"]["type"])
print(result1["result"]["face_list"][0]["glasses"]["type"])
print(result1["result"]["face_list"][0]["race"]["type"])
print(str(result1["result"]["face_list"][0]["age"]))
data = result1["result"]["face_list"][0]
# 性别 + 眼睛 + 肤色 + 年龄
msg.gender = data["gender"]["type"]
msg.glasses = data["glasses"]["type"]
msg.skin_color = data["race"]["type"]
msg.age = str(data["age"])
# 颜色 + 服装
body = self.msgtobody(img_body)
result2 = self.client_body.bodyAttr(body, self.option_body)
print(
result2["person_info"][0]["attributes"]["upper_wear"]["name"])
data = result2["person_info"][0]["attributes"]
# 红、橙、黄、绿、蓝、紫、粉、黑、白、灰、棕
color = data["upper_color"]["name"]
if color == "红":
msg.clothes_color = "red"
elif color == "橙":
msg.clothes_color = "orange"
elif color == "黄":
msg.clothes_color = "yellow"
elif color == "绿":
msg.clothes_color = "green"
elif color == "蓝":
msg.clothes_color = "blue"
elif color == "紫":
msg.clothes_color = "purple"
elif color == "粉":
msg.clothes_color = "pink"
elif color == "黑":
msg.clothes_color = "black"
elif color == "白":
msg.clothes_color = "white"
elif color == "灰":
msg.clothes_color = "gray"
else:
msg.clothes_color = "brown"
type_ = data["upper_wear"]["name"]
if type_ == "长袖":
msg.clothes = "Coat"
else:
msg.clothes = "Short"
self.fet_pub.publish(msg)
# 如果存在人
try:
cv2.rectangle(cv_image, (position[1], position[3]),
(position[0], position[2]), (0, 0, 255))
roi = RegionOfInterest()
roi.x_offset = position[0]
roi.y_offset = position[2]
roi.width = position[1] - position[0]
roi.height = position[3] - position[2]
self.roi = roi
self.roi_pub.publish(roi)
print("One Wave!")
except:
pass
cv_image = self.bridge.cv2_to_imgmsg(cv_image, "bgr8")
self.img_pub.publish(cv_image)
from aip import AipBodyAnalysis
import matplotlib.pyplot as plt # plt 用于显示图片
import matplotlib.image as mpimg # mpimg 用于读取图片
import numpy as np
""" 你的 APPID AK SK """
APP_ID = '22952720'
API_KEY = 'inMk4SrpYUxPmSUGmqGw89Ow'
SECRET_KEY = '7ecoXWQBLwwEjwp83Od0jV5EZM166oG7'
client = AipBodyAnalysis(APP_ID, API_KEY, SECRET_KEY)
""" 读取图片 """
def get_file_content(filePath):
with open(filePath, 'rb') as fp:
return fp.read()
image = get_file_content('post_img.jpg')
""" 调用人体检测与属性识别 """
resp = client.bodyAttr(image)
# print(resp)
if resp['person_info'] and resp['person_info'][0]:
attributes = resp['person_info'][0]['attributes']
print('识别成功: \n衣服：{}\n裤子：{} \n年纪：{} \n动作：{}'.format(
attributes['upper_wear']['name'], attributes['lower_wear']['name'],
attributes['age']['name'], attributes['cellphone']['name']))
else:
print('识别失败')
class BodyCheck:
def __init__(self):
self.time = time.time()
APP_ID = '18889374'
API_KEY = 'pUNweNaSK4rWz57vGs9KpuW1'
SECRET_KEY = 'ru5LqWM0lrcVYBh9cjd32fy951nagqcA'
self.image_type = "BASE64"
self.client_face = AipFace(APP_ID, API_KEY, SECRET_KEY)
self.client_body = AipBodyAnalysis(APP_ID, API_KEY, SECRET_KEY)
self.client_body.setConnectionTimeoutInMillis(2000)
self.client_body.setSocketTimeoutInMillis(2000)
self.client_face.setConnectionTimeoutInMillis(2000)
self.client_face.setSocketTimeoutInMillis(2000)
self.bridge = CvBridge()
self.ispub = False
##############人类数据
self.filepath = "/home/qian/catkin_ws/src/fare_src/kamerider_image/kamerider_image_api/imgfile/"
##############跟踪数据
self.roi = None
self.depth_array = None
self.target_pos = Pose()
##############话题名称
# 接收器与发布器
self.check_gender = rospy.get_param('~check_gender', 'False')
if (type(self.check_gender) == type('text')):
self.check_gender = False
##############发布器
self.img_pub = rospy.Publisher("/image/test", Image, queue_size=1)
self.roi_pub = rospy.Publisher("roi", RegionOfInterest, queue_size=1)
self.img_sub = rospy.Subscriber("/usb_cam/image_raw",
Image,
self.imgCallback,
queue_size=1)
self.word_pub = rospy.Publisher("/xfwords", String, queue_size=1)
self.face_pub = rospy.Publisher("/start_recognize_faces",
String,
queue_size=1)
print("============================================================")
# 人体数据转换
def msgtobody(self, image_msg, file_name='image_body.png'):
# 转为base64存储
cv2.imwrite(self.filepath + file_name, image_msg)
with open(self.filepath + file_name, 'rb') as fp:
return fp.read()
# 人脸数据转换
def msgtoface(self, image_msg, file_name='image_faces.png'):
cv2.imwrite(self.filepath + file_name, image_msg)
with open(self.filepath + file_name, 'rb') as fp:
data = base64.b64encode(fp.read())
# python2.7
data = str(data).encode('utf-8')
return data
# 挥手检测，返回一个挥手的人的方框xxyy数据
def detectWave(self, image, gender=False):
print("CHECK")
data = self.msgtobody(image, "image_body.png")
# ----------挥手检测----------
result = self.client_body.bodyAnalysis(data)
wave = []
loaction = []
point_t = []
# 存在人
if result['person_num'] > 0:
id_ = -1
# 对每个人进行检查
for info in result['person_info']:
id_ += 1
keypoint = info['body_parts']
# 腕高
if keypoint['right_elbow']['y'] > keypoint['right_wrist']['y']:
# 腕在外侧
if keypoint['right_wrist']['x'] < keypoint[
'right_shoulder']['x']:
wave.append(id_)
loc = []
loc.append(int(info['location']['left']))
loc.append(
int(info['location']['left'] +
info['location']['width']))
loc.append(int(info['location']['top']))
loc.append(
int(info['location']['top'] +
info['location']['height']))
loaction.append(copy.deepcopy(loc))
# 腕高
elif keypoint['left_elbow']['y'] > keypoint['left_wrist']['y']:
# 腕在外侧
if keypoint['left_wrist']['x'] > keypoint['left_shoulder'][
'x']:
wave.append(id_)
loc = []
loc.append(int(info['location']['left']))
loc.append(
int(info['location']['left'] +
info['location']['width']))
loc.append(int(info['location']['top']))
loc.append(
int(info['location']['top'] +
info['location']['height']))
loaction.append(copy.deepcopy(loc))
# 存在挥手
if len(loaction) > 0:
# 女性检测
# ----------性别检测----------
if gender:
options = {}
options["type"] = "gender"
# 保证存在挥手
for locate in loaction:
img = image[locate[2]:locate[3], locate[0]:locate[1]]
img = self.msgtobody(img, "image_face.png")
result = self.client_body.bodyAttr(img, options)
try:
result['person_info'][0]['attributes'][
'gender'] == "女性"
except:
continue
# 女性则直接返回女性位置
if result['person_info'][0]['attributes'][
'gender'] == "女性":
loc = []
loc.append(locate[0])
loc.append(locate[1])
loc.append(locate[2])
loc.append(locate[3])
return locate
# 随机返回一个人
locate = loaction[0]
loc = []
loc.append(locate[0])
loc.append(locate[1])
loc.append(locate[2])
loc.append(locate[3])
return locate
return None
# 照片的回调函数，发布挥手人的位置
def imgCallback(self, image):
num = 0
try:
cv_image = self.bridge.imgmsg_to_cv2(image, "bgr8")
except CvBridgeError as e:
print(e)
# 如果存在人
roi = RegionOfInterest()
try:
position = self.detectWave(cv_image, self.check_gender)
cv2.rectangle(cv_image, (position[1], position[3]),
(position[0], position[2]), (0, 0, 255))
roi.x_offset = position[0]
roi.y_offset = position[2]
roi.width = position[1] - position[0]
roi.height = position[3] - position[2]
self.roi = roi
self.roi_pub.publish(roi)
if self.ispub == False:
stringq = "I can tell one wave. Now I will recognize people. "
self.word_pub.publish(stringq)
stringq = "ok "
self.face_pub.publish(stringq)
self.ispub = True
rospy.loginfo("One Wave!")
num = 1
except:
self.roi = roi
if num == 0:
self.roi_pub.publish(roi)
cv_image = self.bridge.cv2_to_imgmsg(cv_image, "bgr8")
self.img_pub.publish(cv_image)