人脸识别-脸部标志、表情、年龄/性别
from openvino.inference_engine import IECore
import numpy as np
import time
import cv2 as cv
emotions = ['neutral', 'happy', 'sad', 'surprise', 'anger']
genders = ['female', 'male']
def face_landmark_demo():
ie = IECore()
for device in ie.available_devices:
print(device)
model_xml = "/home/bhc/BHC/model/intel/face-detection-0200/FP16/face-detection-0200.xml"
model_bin = "/home/bhc/BHC/model/intel/face-detection-0200/FP16/face-detection-0200.bin"
net = ie.read_network(model=model_xml, weights=model_bin)
input_blob = next(iter(net.input_info))
out_blob = next(iter(net.outputs))
n, c, h, w = net.input_info[input_blob].input_data.shape #人脸识别模型输入(1,3,256,256)
print(n, c, h, w)
cap = cv.VideoCapture("1.mp4")
exec_net = ie.load_network(network=net, device_name="CPU")
# 加载人脸表情识别模型
em_xml = "/home/bhc/BHC/model/intel/facial-landmarks-35-adas-0002/FP16/facial-landmarks-35-adas-0002.xml"
em_bin = "/home/bhc/BHC/model/intel/facial-landmarks-35-adas-0002/FP16/facial-landmarks-35-adas-0002.bin"
em_net = ie.read_network(model=em_xml, weights=em_bin)
em_input_blob = next(iter(em_net.input_info))
em_out_blob = next(iter(em_net.outputs))
en, ec, eh, ew = em_net.input_info[em_input_blob].input_data.shape #人脸标志模型输入(1,3,60,60)
print(en, ec, eh, ew)
em_exec_net = ie.load_network(network=em_net, device_name="CPU")
while True:
ret, frame = cap.read()
if ret is not True:
break
image = cv.resize(frame, (w, h))
image = image.transpose(2, 0, 1)
inf_start = time.time()
res = exec_net.infer(inputs={input_blob: [image]})
inf_end = time.time() - inf_start
# print("infer time(ms)：%.3f"%(inf_end*1000))
ih, iw, ic = frame.shape
res = res[out_blob] #人脸识别模型输出(1, 1, N, 7)
for obj in res[0][0]:
if obj[2] > 0.75: #[image_id, label, conf, x_min, y_min, x_max, y_max],
xmin = int(obj[3] * iw)
ymin = int(obj[4] * ih)
xmax = int(obj[5] * iw)
ymax = int(obj[6] * ih)
if xmin < 0:
xmin = 0
if ymin < 0:
ymin = 0
if xmax >= iw:
xmax = iw - 1
if ymax >= ih:
ymax = ih - 1
roi = frame[ymin:ymax, xmin:xmax, :] #人脸识别模出的人脸数据，作为人脸标志模型的输入
rh, rw, rc = roi.shape
roi_img = cv.resize(roi, (ew, eh))
roi_img = roi_img.transpose(2, 0, 1)
em_res = em_exec_net.infer(inputs={em_input_blob: [roi_img]})
prob_landmarks = em_res[em_out_blob] #人脸标志模型输出(1, 70)
for index in range(0, len(prob_landmarks[0]), 2): #(x0, y0, x1, y1, …, x34, y34)
x = np.int(prob_landmarks[0][index] * rw)
y = np.int(prob_landmarks[0][index+1] * rh)
cv.circle(roi, (x, y), 3, (0, 0, 255), -1, 8, 0)
cv.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 255, 255), 2, 8)
cv.putText(frame, "infer time(ms): %.3f" % (inf_end * 1000), (50, 50), cv.FONT_HERSHEY_SIMPLEX, 1.0,
(255, 0, 255),
2, 8)
cv.imshow("Face+emotion Detection", frame)
c = cv.waitKey(1)
if c == 27:
break
cv.waitKey(0)
cv.destroyAllWindows()
def face_emotion_demo():
ie = IECore()
for device in ie.available_devices:
print(device)
model_xml = "/home/bhc/BHC/model/intel/face-detection-0200/FP16/face-detection-0200.xml"
model_bin = "/home/bhc/BHC/model/intel/face-detection-0200/FP16/face-detection-0200.bin"
net = ie.read_network(model=model_xml, weights=model_bin)
input_blob = next(iter(net.input_info))
out_blob = next(iter(net.outputs))
n, c, h, w = net.input_info[input_blob].input_data.shape
print(n, c, h, w)
cap = cv.VideoCapture("1.mp4")
exec_net = ie.load_network(network=net, device_name="CPU")
# 加载人脸表情识别模型
em_xml = "/home/bhc/BHC/model/intel/emotions-recognition-retail-0003/FP16/emotions-recognition-retail-0003.xml"
em_bin = "/home/bhc/BHC/model/intel/emotions-recognition-retail-0003/FP16/emotions-recognition-retail-0003.bin"
em_net = ie.read_network(model=em_xml, weights=em_bin)
em_input_blob = next(iter(em_net.input_info))
em_out_blob = next(iter(em_net.outputs))
en, ec, eh, ew = em_net.input_info[em_input_blob].input_data.shape
print(en, ec, eh, ew)
em_exec_net = ie.load_network(network=em_net, device_name="CPU")
while True:
ret, frame = cap.read()
if ret is not True:
break
image = cv.resize(frame, (w, h))
image = image.transpose(2, 0, 1)
inf_start = time.time()
res = exec_net.infer(inputs={input_blob: [image]})
inf_end = time.time() - inf_start
# print("infer time(ms)：%.3f"%(inf_end*1000))
ih, iw, ic = frame.shape
res = res[out_blob]
for obj in res[0][0]:
if obj[2] > 0.75:
xmin = int(obj[3] * iw)
ymin = int(obj[4] * ih)
xmax = int(obj[5] * iw)
ymax = int(obj[6] * ih)
if xmin < 0:
xmin = 0
if ymin < 0:
ymin = 0
if xmax >= iw:
xmax = iw - 1
if ymax >= ih:
ymax = ih - 1
roi = frame[ymin:ymax, xmin:xmax, :]
roi_img = cv.resize(roi, (ew, eh))
roi_img = roi_img.transpose(2, 0, 1)
em_res = em_exec_net.infer(inputs={em_input_blob: [roi_img]}) #人脸表情模型输出(1, 5, 1, 1)
prob_emotion = em_res[em_out_blob].reshape(1, 5)
label_index = np.argmax(prob_emotion, 1) #(0 - ‘neutral’, 1 - ‘happy’, 2 - ‘sad’, 3 - ‘surprise’, 4 - ‘anger’).
cv.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 255, 255), 2, 8)
cv.putText(frame, "infer time(ms): %.3f" % (inf_end * 1000), (50, 50), cv.FONT_HERSHEY_SIMPLEX, 1.0,
(255, 0, 255),
2, 8)
cv.putText(frame, emotions[np.int(label_index)], (xmin, ymin), cv.FONT_HERSHEY_SIMPLEX, 0.55,
(0, 0, 255),
2, 8)
cv.imshow("Face+emotion Detection", frame)
c = cv.waitKey(1)
if c == 27:
break
cv.waitKey(0)
cv.destroyAllWindows()
def face_age_gender_demo():
ie = IECore()
for device in ie.available_devices:
print(device)
model_xml = "/home/bhc/BHC/model/intel/face-detection-0200/FP16/face-detection-0200.xml"
model_bin = "/home/bhc/BHC/model/intel/face-detection-0200/FP16/face-detection-0200.bin"
net = ie.read_network(model=model_xml, weights=model_bin)
input_blob = next(iter(net.input_info))
out_blob = next(iter(net.outputs))
n, c, h, w = net.input_info[input_blob].input_data.shape
print(n, c, h, w)
cap = cv.VideoCapture("1.mp4")
exec_net = ie.load_network(network=net, device_name="CPU")
# 加载年龄性别模型
em_xml = "/home/bhc/BHC/model/intel/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013.xml"
em_bin = "/home/bhc/BHC/model/intel/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013.bin"
em_net = ie.read_network(model=em_xml, weights=em_bin)
em_input_blob = next(iter(em_net.input_info))
em_it = iter(em_net.outputs)
em_out_blob1 = next(em_it)
em_out_blob2 = next(em_it)
en, ec, eh, ew = em_net.input_info[em_input_blob].input_data.shape
print(en, ec, eh, ew)
em_exec_net = ie.load_network(network=em_net, device_name="CPU")
while True:
ret, frame = cap.read()
if ret is not True:
break
image = cv.resize(frame, (w, h))
image = image.transpose(2, 0, 1)
inf_start = time.time()
res = exec_net.infer(inputs={input_blob:[image]})
inf_end = time.time() - inf_start
# print("infer time(ms)：%.3f"%(inf_end*1000))
ih, iw, ic = frame.shape
res = res[out_blob]
for obj in res[0][0]:
if obj[2] > 0.75:
xmin = int(obj[3] * iw)
ymin = int(obj[4] * ih)
xmax = int(obj[5] * iw)
ymax = int(obj[6] * ih)
if xmin < 0:
xmin = 0
if ymin < 0:
ymin = 0
if xmax >= iw:
xmax = iw - 1
if ymax >= ih:
ymax = ih - 1
roi = frame[ymin:ymax,xmin:xmax,:]
roi_img = cv.resize(roi, (ew, eh))
roi_img = roi_img.transpose(2, 0, 1)
em_res = em_exec_net.infer(inputs={em_input_blob: [roi_img]})
age_conv3 = em_res[em_out_blob1].reshape(1, 1)[0][0] * 100 #age_conv3 （1, 1, 1, 1） age*100
prob_age = em_res[em_out_blob2].reshape(1, 2) #prob （1, 2, 1, 1） 0 - female, 1 - male
label_index = np.int(np.argmax(prob_age, 1))
age = np.int(age_conv3)
cv.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 255, 255), 2, 8)
cv.putText(frame, "infer time(ms): %.3f"%(inf_end*1000), (50, 50), cv.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 255),
2, 8)
cv.putText(frame, genders[label_index] + ', ' +str(age), (xmin, ymin), cv.FONT_HERSHEY_SIMPLEX, 0.55, (0, 0, 255),
2, 8)
cv.imshow("Face+emotion Detection", frame)
c = cv.waitKey(1)
if c == 27:
break
cv.waitKey(0)
cv.destroyAllWindows()
if __name__ == "__main__":
face_landmark_demo()
# face_emotion_demo()
# face_age_gender_demo()
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