教程 | 100行代码搞定实时视频人脸表情识别

OpenCV学堂

发布于 2020-07-08 15:27:14

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发布于 2020-07-08 15:27:14

文章被收录于专栏：贾志刚-OpenCV学堂贾志刚-OpenCV学堂

好就没有写点OpenCV4 + OpenVINO的应用了，前几天上课重新安装了一下最新OpenVINO2020.3版本，实现了一个基于OpenCV+OpenVINO的Python版本人脸表情识别。100行代码以内，简单好用！

人脸检测

人脸检测使用了OpenCV中基于深度学习的人脸检测算法，实现了一个实时人脸检测，该模型还支持OpenVINO加速，所以是非常好用的，之前写过一篇文章专门介绍OpenCV DNN的人脸检测的

OpenCV4.x中请别再用HAAR级联检测器检测人脸，有更好更准的方法

表情识别模型

使用OpenVINO模型库中的emotions-recognition-retail-0003人脸表情模型，该模型是基于全卷积神经网络训练完成，使用ResNet中Block结构构建卷积神经网络。数据集使用了AffectNet表情数据集，支持五种表情识别，分别是：

('neutral', 'happy', 'sad', 'surprise', 'anger')

输入格式：NCHW=1x3x64x64 输出格式：1x5x1x1

代码实现

首先基于OpenCV实现人脸检测，然后根据检测得到的人脸ROI区域，调用表情识别模型，完成人脸表情识别，整个代码基于Python语言完成。

加载表情识别模型并设置输入与输出的代码如下：

 1import cv2 as cv
 2import numpy as np
 3from openvino.inference_engine import IENetwork, IECore
 4
 5weight_pb = "D:/projects/opencv_tutorial/data/models/face_detector/opencv_face_detector_uint8.pb";
 6config_text = "D:/projects/opencv_tutorial/data/models/face_detector/opencv_face_detector.pbtxt";
 7
 8model_xml = "emotions-recognition-retail-0003.xml"
 9model_bin = "emotions-recognition-retail-0003.bin"
10
11labels = ['neutral', 'happy', 'sad', 'surprise', 'anger']
12emotion_labels = ["neutral","anger","disdain","disgust","fear","happy","sad","surprise"]
13
14emotion_net = IENetwork(model=model_xml, weights=model_bin)
15ie = IECore()
16versions = ie.get_versions("CPU")
17input_blob = next(iter(emotion_net.inputs))
18n, c, h, w = emotion_net.inputs[input_blob].shape
19print(emotion_net.inputs[input_blob].shape)
20
21output_info = emotion_net.outputs[next(iter(emotion_net.outputs.keys()))]
22output_info.precision = "FP32"
23exec_net = ie.load_network(network=emotion_net, device_name="CPU")
24root_dir = "D:/facedb/emotion_dataset/"

实现人脸检测与表情识别的代码如下：

 1def emotion_detect(frame):
 2    net = cv.dnn.readNetFromTensorflow(weight_pb, config=config_text)
 3    h, w, c = frame.shape
 4    blobImage = cv.dnn.blobFromImage(frame, 1.0, (300, 300), (104.0, 177.0, 123.0), False, False);
 5    net.setInput(blobImage)
 6    cvOut = net.forward()
 7
 8    # 绘制检测矩形
 9    for detection in cvOut[0,0,:,:]:
10        score = float(detection[2])
11        if score > 0.5:
12            left = detection[3]*w
13            top = detection[4]*h
14            right = detection[5]*w
15            bottom = detection[6]*h
16
17            # roi and detect landmark
18            y1 = np.int32(top) if np.int32(top) > 0 else 0
19            y2 = np.int32(bottom) if np.int32(bottom) < h else h-1
20            x1 = np.int32(left) if np.int32(left) > 0 else 0
21            x2 = np.int32(right) if np.int32(right) < w else w-1
22            roi = frame[y1:y2,x1:x2,:]
23            image = cv.resize(roi, (64, 64))
24            image = image.transpose((2, 0, 1))  # Change data layout from HWC to CHW
25            res = exec_net.infer(inputs={input_blob: [image]})
26            prob_emotion = res['prob_emotion']
27            probs = np.reshape(prob_emotion, (5))
28            txt = labels[np.argmax(probs)]
29            cv.putText(frame, txt, (np.int32(left), np.int32(top)), cv.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 0), 2)
30            cv.rectangle(frame, (np.int32(left), np.int32(top)),
31                         (np.int32(right), np.int32(bottom)), (0, 0, 255), 2, 8, 0)

打开摄像头或者视频文件，运行人脸表情识别的：

 1if __name__ == "__main__":
 2    capture = cv.VideoCapture("D:/images/video/Boogie_Up.mp4")
 3    while True:
 4        ret, frame = capture.read()
 5        if ret is not True:
 6            break
 7        emotion_detect(frame)
 8        cv.imshow("emotion-detect-demo", frame)
 9        c = cv.waitKey(1)
10        if c == 27:
11            break

运行截图如下：