[Anaconda][OpenVINO] expression detection

Original link: Application | OpenCV + OpenVINO to realize facial expression recognition_Intel Edge Computing Community-CSDN Blog

1. Environment preparation

先安装python 3.6.1，再在cmd里运行下面指令
python -m pip install --upgrade pip
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple opencv-python
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple openvino
python main.py

@2022.1.18 Add description

        If following the above steps, the "from .ie_api import *" module is not found, it may be that your VS environment is not installed properly. It is recommended to install the VC operating environment from 2005 to 2019

2.python code

import cv2 as cv
import numpy as np
from openvino.inference_engine import IENetwork, IECore

weight_pb = "./opencv/opencv_face_detector_uint8.pb";
config_text = "./opencv/opencv_face_detector.pbtxt";

model_xml = "./openvino/emotions-recognition-retail-0003/emotions-recognition-retail-0003.xml"
model_bin = "./openvino/emotions-recognition-retail-0003/emotions-recognition-retail-0003.bin"

labels = ['neutral', 'happy', 'sad', 'surprise', 'anger']
emotion_labels = ["neutral", "anger", "disdain", "disgust", "fear", "happy", "sad", "surprise"]

emotion_net = IENetwork(model=model_xml, weights=model_bin)
ie = IECore()
versions = ie.get_versions("CPU")
input_blob = next(iter(emotion_net.inputs))
n, c, h, w = emotion_net.inputs[input_blob].shape
print(emotion_net.inputs[input_blob].shape)

output_info = emotion_net.outputs[next(iter(emotion_net.outputs.keys()))]
output_info.precision = "FP32"
exec_net = ie.load_network(network=emotion_net, device_name="CPU")
root_dir = "D:/facedb/emotion_dataset/"
count_ = 1

def emotion_detect(frame):
    net = cv.dnn.readNetFromTensorflow(weight_pb, config=config_text)
    h, w, c = frame.shape
    blobImage = cv.dnn.blobFromImage(frame, 1.0, (300, 300), (104.0, 177.0, 123.0), False, False);
    net.setInput(blobImage)
    cvOut = net.forward()

    # 绘制检测矩形
    for detection in cvOut[0,0,:,:]:
        score = float(detection[2])
        if score > 0.5:
            left = detection[3]*w
            top = detection[4]*h
            right = detection[5]*w
            bottom = detection[6]*h

            # roi and detect landmark
            y1 = np.int32(top) if np.int32(top) > 0 else 0
            y2 = np.int32(bottom) if np.int32(bottom) < h else h-1
            x1 = np.int32(left) if np.int32(left) > 0 else 0
            x2 = np.int32(right) if np.int32(right) < w else w-1
            roi = frame[y1:y2,x1:x2,:]
            image = cv.resize(roi, (64, 64))
            image = image.transpose((2, 0, 1))  # Change data layout from HWC to CHW
            res = exec_net.infer(inputs={input_blob: [image]})
            prob_emotion = res['prob_emotion']
            probs = np.reshape(prob_emotion, (5))
            txt = labels[np.argmax(probs)]
            cv.putText(frame, txt, (np.int32(left), np.int32(top)), cv.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 0), 2)
            cv.rectangle(frame, (np.int32(left), np.int32(top)),
                         (np.int32(right), np.int32(bottom)), (0, 0, 255), 2, 8, 0)

if __name__ == "__main__":
    capture = cv.VideoCapture(0)
    while True:
        ret, frame = capture.read()
        if ret is not True:
            continue
        emotion_detect(frame)
        cv.imshow("emotion-detect-demo", frame)
        c = cv.waitKey(30)
        if c == 27:
            break

 Project link: Alibaba cloud disk sharing

Note: This project uses PyCharm Community Edition 2021.2.3