[python][deepface][原创]使用deepface进行表情识别

# -*- coding: utf-8 -*-
# Copyright (C) 2022 FIRC. All Rights Reserved
# @Time    : 2022/8/10 下午5:41
# @Author  : FIRC
# @File    : emotion_detect.py
# @Software: PyCharm
# @ Function Description:
'''
function as follows:
'''
import numpy as np
from deepface.basemodels import VGGFace
import tensorflow as tf
import cv2
import os
from deepface.detectors import FaceDetector
from deepface.commons import functions
import tensorflow as tf
 
tf_version = int(tf.__version__.split(".")[0])
 
if tf_version == 1:
    import keras
    from keras.models import Model, Sequential
    from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense, Dropout
elif tf_version == 2:
    from tensorflow import keras
    from tensorflow.keras.models import Model, Sequential
    from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense, Dropout
 
 
def loadModel(weights='./weights/facial_expression_model_weights.h5'):
    num_classes = 7
    model = Sequential()
    # 1st convolution layer
    model.add(Conv2D(64, (5, 5), activation='relu', input_shape=(48, 48, 1)))
    model.add(MaxPooling2D(pool_size=(5, 5), strides=(2, 2)))
    # 2nd convolution layer
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(AveragePooling2D(pool_size=(3, 3), strides=(2, 2)))
    # 3rd convolution layer
    model.add(Conv2D(128, (3, 3), activation='relu'))
    model.add(Conv2D(128, (3, 3), activation='relu'))
    model.add(AveragePooling2D(pool_size=(3, 3), strides=(2, 2)))
    model.add(Flatten())
    # fully connected neural networks
    model.add(Dense(1024, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(1024, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(num_classes, activation='softmax'))
    # ----------------------------
    home = functions.get_deepface_home()
    model.load_weights(weights)
    return model
 
 
detector_backend = 'opencv'
face_detector = FaceDetector.build_model(detector_backend)
emotion_model = loadModel()
emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
cap = cv2.VideoCapture(0)  # webcam
while True:
    ret, img = cap.read()
    if img is None:
        break
    try:
        # faces store list of detected_face and region pair
        faces = FaceDetector.detect_faces(face_detector, detector_backend, img, align=False)
    except:  # to avoid exception if no face detected
        faces = []
    for face, (x, y, w, h) in faces:
        if w > 130:  # discard small detected faces
            roi_img = img[y:y + h, x:x + w]
            gray_img = functions.preprocess_face(img=roi_img, target_size=(48, 48), grayscale=True,
                                                 enforce_detection=False, detector_backend='opencv')
            emotion_predictions = emotion_model.predict(gray_img)[0, :]
            print(emotion_predictions)
            # print(apparent_age)
            cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 3)  # draw rectangle to main image
            cv2.putText(img, 'emotion={}'.format(emotion_labels[np.argmax(emotion_predictions)]), (x - 10, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255)
                        , 3)
        cv2.imshow('result', img)
        if cv2.waitKey(1) & 0xFF == ord('q'):  # press q to quit
            break
# kill open cv things
cap.release()
cv2.destroyAllWindows()