deepface表情识别转onnx摄像头推理代码

import numpy as np
import argparse
import matplotlib.pyplot as plt
import cv2
import onnxruntime as ort
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# command line argument
model_path = 'model.onnx'

session = ort.InferenceSession(model_path)

labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']


# emotions will be displayed on your face from the webcam feed

input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name
input_shape = session.get_inputs()[0].shape
# prevents openCL usage and unnecessary logging messages
cv2.ocl.setUseOpenCL(False)

# dictionary which assigns each label an emotion (alphabetical order)
emotion_dict = {
    
    0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 6: "Neutral", 4: "Sad", 5: "Surprised"}


# start the webcam feed
cap = cv2.VideoCapture(0)
while True:
    # Find haar cascade to draw bounding box around face
    ret, frame = cap.read()
    if not ret:
        break
    facecasc = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = facecasc.detectMultiScale(gray,scaleFactor=1.3, minNeighbors=5)

    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (255, 0, 0), 2)
        roi_gray = gray[y:y + h, x:x + w]
        cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray, (48, 48)), -1), 0).astype(np.float32)
        prediction = session.run([output_name], {
    
    input_name: cropped_img})[0]
        maxindex = int(np.argmax(prediction))
        cv2.putText(frame, emotion_dict[maxindex], (x+20, y-60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
        print("当前表情",emotion_dict[maxindex])
        # cv2.imwrite('frame.jpg',frame)
    cv2.imshow('Video', cv2.resize(frame,(1600,960),interpolation = cv2.INTER_CUBIC))
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()