模型下载：
C:\OpenVINO\openvino_2020.4.287\deployment_tools\open_model_zoo\tools\downloader> python downloader.py --name age-gender-recognition-retail-0013
下面代码中，在循环中，先用人脸检测模型检测出人脸，得到人脸子图 roi_img(大小3, 62, 62)
1 roi = frame[ymin:ymax, xmin:xmax, :] 2 roi_img = cv.resize(roi, (ew, eh)) 3 roi_img = roi_img.transpose(2, 0, 1) # (3, 62, 62)
接着，将人脸子图输入到性别、年龄模型中进行识别：
1 em_res = em_exec_net.infer(inputs={em_input_blob: [roi_img]}) 2 # 年龄预测 3 age = em_res[em_out_blob1].reshape(1, 1)[0][0] * 100 4 # 性别预测 5 prob_sex = em_res[em_out_blob2].reshape(1, 2)
上面有对模型输出结果em_res进行解析，其中em_res是一个字典，和open VINO手册中一样，如下图：
其余的，没啥好说了。
1 import time 2 3 import cv2 as cv 4 import numpy as np 5 import numpy as py 6 from openvino.inference_engine import IECore 7 8 gengers = ["femal", "male"] 9 10 def age_sex_detection(): 11 ie = IECore() 12 for device in ie.available_devices: 13 print(device) 14 model_xml = "face-detection-0102.xml" 15 model_bin = "face-detection-0102.bin" 16 net = ie.read_network(model=model_xml, weights=model_bin) 17 input_blob = next(iter(net.input_info)) 18 out_blob = next(iter(net.outputs)) 19 n, c, h, w = net.input_info[input_blob].input_data.shape 20 print(n, c, h, w) # 1 3 384 384 21 22 cap = cv.VideoCapture("1.mp4") 23 exec_net = ie.load_network(network=net, device_name="CPU") 24 25 # 加载性别，年龄识别表情 26 em_xml = "age-gender-recognition-retail-0013.xml" 27 em_bin = "age-gender-recognition-retail-0013.bin" 28 29 em_net = ie.read_network(model=em_xml, weights=em_bin) 30 em_input_blob = next(iter(em_net.input_info)) # 类似于一个空的tensor 31 em_it = iter(em_net.outputs) 32 em_out_blob1 = next(em_it) 33 em_out_blob2 = next(em_it) 34 en, ec, eh, ew = em_net.input_info[em_input_blob].input_data.shape 35 print(en, ec, eh, ew) # 1 3 62 62 36 em_exec_net = ie.load_network(network=em_net, device_name="CPU") 37 38 while True: 39 ret, frame = cap.read() 40 if ret is not True: 41 break 42 image = cv.resize(frame, (w, h)) 43 image = image.transpose(2, 0, 1) 44 inf_start = time.time() 45 # 人脸检测 46 res = exec_net.infer(inputs={input_blob: [image]}) 47 inf_end = time.time() - inf_start 48 ih, iw, ic = frame.shape 49 res = res[out_blob] # (1, 1, 200, 7) 50 for obj in res[0][0]: 51 if obj[2] > 0.75: 52 xmin = int(obj[3] * iw) 53 ymin = int(obj[4] * ih) 54 xmax = int(obj[5] * iw) 55 ymax = int(obj[6] * ih) 56 if xmin < 0: 57 xmin = 0 58 if ymin < 0: 59 ymin = 0 60 if xmax >= iw: 61 xmax = iw - 1 62 if ymax >= ih: 63 ymax = ih - 1 64 roi = frame[ymin:ymax, xmin:xmax, :] 65 roi_img = cv.resize(roi, (ew, eh)) 66 roi_img = roi_img.transpose(2, 0, 1) # (3, 62, 62) 67 em_res = em_exec_net.infer(inputs={em_input_blob: [roi_img]}) 68 # 年龄预测 69 age = em_res[em_out_blob1].reshape(1, 1)[0][0] * 100 70 # 性别预测 71 prob_sex = em_res[em_out_blob2].reshape(1, 2) 72 label_index = np.int(np.argmax(prob_sex, 1)) 73 print(prob_sex, 1) 74 cv.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 255, 255), 2, 8) 75 cv.putText(frame, "infer time(ms): %.3f"%(inf_end*1000), (50, 50), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, 8) 76 cv.putText(frame, gengers[label_index] + ", " + str(int(age)), (xmin, ymin), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255),1, 8) 77 cv.imshow("Face+age+sex", frame) 78 cv.waitKey(1) 79 80 81 if __name__ == "__main__": 82 age_sex_detection()
