def bdcut(path):
try:
pic = Image.open(path)
if pic.mode == "P": pic = pic.convert('RGB') # 有些图片有P通道，base编码后会出问题
(wf,hf) = pic.size
BD_VIP='否'
BD_AI_client = AipBodyAnalysis('替换appid','替换apikey','替换secrekey')
with open(path, 'rb') as fp:
x_nose = int(BD_AI_client.bodyAnalysis(fp.read())["person_info"][0]['body_parts']['nose']['x']) # 返回鼻子横坐标
if BD_VIP == '否':
time.sleep(0.4) # 免费用户QPS=2
else:
time.sleep( 1/int(1.1*BD_VIP) )
if x_nose + 1/3*hf > wf: # 判断鼻子在图整体的位置
x_left = wf-2/3*hf # 以右为边
elif x_nose - 1/3*hf < 0:
x_left = 0 # 以左为边
else:
x_left = x_nose-1/3*hf # 以鼻子为中线向两边扩展
fixed_pic = pic.crop((x_left,0,x_left+2/3*hf,hf))
fixed_pic.save(path.replace('fanart','poster'), quality=95)
print('[+]百度AI裁剪操作成功')
except:
imagecut=1
print('[-] '+ path +' AI 分析失败，跳过 AI 直接裁剪。\n')
class BaiduAIP(object):
def __init__(self):
self.client = AipBodyAnalysis(cfg.APP_ID, cfg.API_KEY, cfg.SECRET_KEY)
def bodyAnalysis(self, img_jpg):
etval, buffer = cv2.imencode('.jpg', img_jpg)
result = self.client.bodyAnalysis(buffer) # 内部把buffer转换为base64了
return result
def fanHuiTuPianGuanDianX_Y(url): #从百度AI获取到图片关键点返回值，参数为图片路径
""" 你的 APPID AK SK """
APP_ID = '22952436'
API_KEY = 'a5UrzVfjQHyuK0GSCXk8QoQH'
SECRET_KEY = '8wguEEmbNTnMfAOOOigMr1cM1SZXvq1c'
client = AipBodyAnalysis(APP_ID, API_KEY, SECRET_KEY)
def get_file_content(filePath):
with open(filePath, 'rb') as fp:
return fp.read()
image = get_file_content(url)
return client.bodyAnalysis(image)
class BaiDuAPI(object):
#特殊 构造函数 初始化函数
def __init__(self, filePath):
target = configparser.ConfigParser()
target.read(filePath, encoding='utf-8-sig')
self.client = AipBodyAnalysis(APP_ID, API_KEY, SECRET_KEY)
""" 读取图片 """
def get_file_content(self, photoPath):
with open(photoPath, 'rb') as fp:
return fp.read()
""" 主函数 """
def file_main(self, photoPath):
#转码
#base64_data = base64.b64encode(photoPath)
# add by me
f = open(photoPath, 'rb')
img = base64.b64encode(f.read())
#img = self.get_file_content('{}'.format(photoPath))
""" 调用人体关键点识别 """
#此处只能对一个人进行关键点识别
#也就是说一个图片如果有好多人的话,只能标出一个人的关节特征
#此处可以做修改,即进行把一张图所有人的关节特征都表达出来
#------
print(self.client.bodyAnalysis(img))
result = self.client.bodyAnalysis(img)['person_info'][0]['body_parts']
jo = joint.Joint(result)
jo.xunhun(photoPath)
print(result)
def bodypart(filename):
# APP_ID = 'd90755ad2f2047dbabb12ad0adaa0b03'
# API_KEY = 'b7b0f4d01f7f4aef9b5453f6558c23b1'
# SECRET_KEY = '6ad666162ef24213b5bde7bdd904fcbe'
client = AipBodyAnalysis(APP_ID, API_KEY, SECRET_KEY)
# """ 读取图片 """
image = get_file_content(filename)
# """ 调用人体关键点识别 """
para = client.bodyAnalysis(image)
# print(para)
# time.sleep(2)
return para
def face_center(filename, model):
app_id = config.getInstance().conf.get("face", "appid")
api_key = config.getInstance().conf.get("face", "key")
app_secret = config.getInstance().conf.get("face", "secret")
client = AipBodyAnalysis(app_id, api_key, app_secret)
with open(filename, 'rb') as fp:
img = fp.read()
result = client.bodyAnalysis(img)
if 'error_code' in result:
raise ValueError(result['error_msg'])
print('[+]Found person ' + str(result['person_num']))
# 中心点取鼻子x坐标
maxRight = 0
maxTop = 0
for person_info in result["person_info"]:
x = int(person_info['body_parts']['nose']['x'])
top = int(person_info['location']['top'])
if x > maxRight:
maxRight = x
maxTop = top
return maxRight, maxTop
选择需要的功能
1.人体关键点识别
2.人体属性识别
3.人流量统计
''')
return int(i)
if __name__ == "__main__":
while(1):
a=input('请输入图片(小于4M)的位置：')
image = get_file_content(a)
i=select_power()
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)
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)
class BodyClient(object):
# APP_ID = '44eab39f0ed44844b94f487a6e88fdbc' # 'd90755ad2f2047dbabb12ad0adaa0b03'
# API_KEY = '55e735d6888b46908915f3533a6b7442' # '22f1025b88f54494bcf5d980697b4b83 '
# SECRET_KEY = '41213cbdaffa483d9ed9a59a24157d4b' # '4a4b41139c204905be1db659d751355f'
APP_ID = '18176250' # 'd90755ad2f2047dbabb12ad0adaa0b03'
API_KEY = 'jgfFvfLmKWBTuuIFdFgF4YXI' # '22f1025b88f54494bcf5d980697b4b83 '
SECRET_KEY = 'NCnPnPOcSRexlxoz9fI75fUmEGl3H15f' # '4a4b41139c204905be1db659d751355f'
def __init__(self):
self.client = AipBodyAnalysis(BodyClient.APP_ID, BodyClient.API_KEY, BodyClient.SECRET_KEY)
def _body_seg(self,filename):
image = get_file_content(filename)
res = self.client.bodySeg(image)
labelmap = base64.b64decode(res['labelmap'])
nparr_labelmap = np.fromstring(labelmap, np.uint8)
labelmapimg = cv2.imdecode(nparr_labelmap, 1)
im_new_labelmapimg = np.where(labelmapimg == 1, 255, labelmapimg)
return im_new_labelmapimg
def _canny_points(self,img,rect=None):
if rect:
x,y,w,h = rect
else:
x,y=0,0
h,w = img.shape[:2]
body = img[y:y + h, x:x + w]
edges = cv2.Canny(body, 10, 100)
edgesmat = np.mat(edges)
points = [(j + x, i + y) for i in range(h) for j in range(w) if edgesmat[i, j] == 255]
return points
def body_seg_points(self,filename):
img_seg=self._body_seg(filename)
return self._canny_points(img_seg)
def body_seg(self,filename):
img_seg = self._body_seg(filename)
h, w = img_seg.shape[:2]
return self._canny_points(img_seg),w,h
'''
{'person_num': 2, 'person_info': [{'body_parts': {'left_hip': {'y': 549.78125, 'x': 423.34375, 'score': 0.8641700744628906}, 'top_head': {'y': 295.46875, 'x': 394.0, 'score': 0.8867737650871277}, 'right_mouth_corner': {'y': 344.375, 'x': 384.21875, 'score': 0.8865712285041809}, 'neck': {'y': 363.9375, 'x': 394.0, 'score': 0.8912984728813171}, 'left_shoulder': {'y': 383.5, 'x': 442.90625, 'score': 0.8800243139266968}, 'left_knee': {'y': 657.375, 'x': 433.125, 'score': 0.8804177045822144}, 'left_ankle': {'y': 755.1875, 'x': 423.34375, 'score': 0.8549085855484009}, 'left_mouth_corner': {'y': 344.375, 'x': 403.78125, 'score': 0.8695278763771057}, 'right_elbow': {'y': 442.1875, 'x': 305.96875, 'score': 0.9053295850753784}, 'right_ear': {'y': 324.8125, 'x': 374.4375, 'score': 0.8913755416870117}, 'nose': {'y': 324.8125, 'x': 394.0, 'score': 0.8767616748809814}, 'left_eye': {'y': 315.03125, 'x': 403.78125, 'score': 0.8842508792877197}, 'right_eye': {'y': 315.03125, 'x': 384.21875, 'score': 0.872444748878479}, 'right_hip': {'y': 549.78125, 'x': 374.4375, 'score': 0.8706536293029785}, 'left_wrist': {'y': 491.09375, 'x': 462.46875, 'score': 0.8681846857070923}, 'left_ear': {'y': 324.8125, 'x': 413.5625, 'score': 0.8833358883857727}, 'left_elbow': {'y': 432.40625, 'x': 491.8125, 'score': 0.8757244944572449}, 'right_shoulder': {'y': 383.5, 'x': 345.09375, 'score': 0.8604100942611694}, 'right_ankle': {'y': 755.1875, 'x': 364.65625, 'score': 0.883700966835022}, 'right_knee': {'y': 657.375, 'x': 364.65625, 'score': 0.8726198673248291}, 'right_wrist': {'y': 491.09375, 'x': 335.3125, 'score': 0.8524751663208008}}, 'location': {'height': 522.3967895507812, 'width': 213.4878540039062, 'top': 279.5125427246094, 'score': 0.9985131025314331, 'left': 288.0614013671875}}, {'body_parts': {'left_hip': {'y': 539.0, 'x': 413.25, 'score': 0.2676204741001129}, 'top_head': {'y': 741.5, 'x': 524.625, 'score': 0.0297189150005579}, 'right_mouth_corner': {'y': 478.25, 'x': 463.875, 'score': 0.009633682668209076}, 'neck': {'y': 852.875, 'x': 332.25, 'score': 0.01634016819298267}, 'left_shoulder': {'y': 377.0, 'x': 423.375, 'score': 0.0272684283554554}, 'left_knee': {'y': 650.375, 'x': 423.375, 'score': 0.3098172545433044}, 'left_ankle': {'y': 751.625, 'x': 433.5, 'score': 0.4415453672409058}, 'left_mouth_corner': {'y': 478.25, 'x': 463.875, 'score': 0.01229123119264841}, 'right_elbow': {'y': 427.625, 'x': 494.25, 'score': 0.08809270709753036}, 'right_ear': {'y': 680.75, 'x': 750, 'score': 0.02279716171324253}, 'nose': {'y': 488.375, 'x': 453.75, 'score': 0.02511453814804554}, 'left_eye': {'y': 488.375, 'x': 443.625, 'score': 0.02269705384969711}, 'right_eye': {'y': 751.625, 'x': 750, 'score': 0.02191649936139584}, 'right_hip': {'y': 539.0, 'x': 372.75, 'score': 0.1868444383144379}, 'left_wrist': {'y': 488.375, 'x': 474.0, 'score': 0.3365231156349182}, 'left_ear': {'y': 893.375, 'x': 403.125, 'score': 0.007937739603221416}, 'left_elbow': {'y': 437.75, 'x': 484.125, 'score': 0.1944440901279449}, 'right_shoulder': {'y': 377.0, 'x': 433.5, 'score': 0.02875573188066483}, 'right_ankle': {'y': 751.625, 'x': 423.375, 'score': 0.1604309529066086}, 'right_knee': {'y': 670.625, 'x': 372.75, 'score': 0.1398747861385345}, 'right_wrist': {'y': 488.375, 'x': 474.0, 'score': 0.07319473475217819}}, 'location': {'height': 539.47509765625, 'width': 126.1507263183594, 'top': 458.58251953125, 'score': 0.00636356882750988, 'left': 622.8492431640625}}], 'log_id': 1953527121404955486}
'''
def _body_part(self,filename):
image = get_file_content(filename)
para = self.client.bodyAnalysis(image)
if DEBUG:
print(para)
person_num=para.get('person_num',0)
if person_num < 1:
raise NoBodyException("文件%s 没有检测到人像，详细信息：%s"%(filename,para))
person=para['person_info'][0]
# score = person['location']['score']
# if score < 0.5:
# raise NoBodyException()
loc = person['location']
x_left = int(loc['left'])
y_top = int(loc['top'])
w = int(loc['width'])
h = int(loc['height'])
return person['body_parts'],(x_left,y_top,w,h)
#top_head, left_ankle, right_ankle
def body_points(self,filename):
parts,rect = self._body_part(filename)
points = {k: (v['x'], v['y']) for k, v in parts.items()}
return points,rect
def process_body(self,filename):
img = cv2.imread(filename)
height, width = img.shape[:2]
body_points, rect = self.body_points(filename)
x_left, y_top, w, h = rect
new_rect=expand_rect(x_left,y_top,w,h,width,height)
img_seg = self._body_seg(filename)
# cv2.imshow("seg",img_seg)
outline_points = self._canny_points(img_seg, new_rect)
# print(outline_points)
return body_points, outline_points, rect
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)
class BaiduApiUtil:
def __init__(self, configPath, configSection):
keyInfo = fu.readConfig(configPath, configSection)
self.appid = keyInfo['appid']
self.api_key = keyInfo['api_key']
self.secret_key = keyInfo['secret_key']
self.client = AipBodyAnalysis(self.appid, self.api_key, self.secret_key)
self.filename = None
self.picture = None
self.picture_size = None
self.picture_format = None
def upload(self, filename):
self.picture = fu.readPicture(filename)
self.filename = filename.split('.')[0]
img = Image.open(filename)
self.picture_size = img.size
self.picture_format = img.format
def getAccessToken(self, configPath, configSection):
keyInfo = fu.readConfig(configPath, configSection)
host = keyInfo['addr'] % (keyInfo['grant_type'], keyInfo['client_id'], keyInfo['client_secret'])
response = requests.post(host)
if response.status_code != 200:
print('Error Happened When Acquiring Access Token')
return -1
content = demjson.decode(response.text)
if 'error' in content.keys():
print('Invalid API Key or Secret Key')
return -1
return content['refresh_token']
def getBodyAnalysis(self):
response = self.client.bodyAnalysis(self.picture)
if 'error_code' in response.keys():
print(response['error_msg'])
exit(-1)
return response
def getBodySeg(self):
result = self.client.bodySeg(self.picture, {'type':'labelmap'})
# foreground = base64.b64decode(result['foreground'])
labelmap = base64.b64decode(result['labelmap'])
# scoremap = base64.b64decode(result['scoremap'])
# nparr_foreground = np.fromstring(foreground, np.uint8)
# foregroundimg = cv2.imdecode(nparr_foreground, 1)
# foregroundimg = cv2.resize(foregroundimg, self.picture_size, interpolation=cv2.INTER_NEAREST)
# im_new_foreground = np.where(foregroundimg == 1, 10, foregroundimg)
# cv2.imwrite(self.filename + '-foreground.png', im_new_foreground)
nparr_labelmap = np.fromstring(labelmap, np.uint8)
labelmapimg = cv2.imdecode(nparr_labelmap, 1)
labelmapimg = cv2.resize(labelmapimg, self.picture_size, interpolation=cv2.INTER_NEAREST)
im_new_labelmapimg = np.where(labelmapimg == 1, 255, labelmapimg)
cv2.imwrite(self.filename + '-labelmap.png', im_new_labelmapimg)
# nparr_scoremap = np.fromstring(scoremap, np.uint8)
# scoremapimg = cv2.imdecode(nparr_scoremap, 1)
# scoremapimg = cv2.resize(scoremapimg, self.picture_size, interpolation=cv2.INTER_NEAREST)
# im_new_scoremapimg = np.where(scoremapimg == 1, 255, scoremapimg)
# cv2.imwrite(self.filename + '-scoremap.png', im_new_scoremapimg)