[python][deepface][原创]使用deepface进行种族识别

Posted 2022-08-12 FL1623863129

from deepface.basemodels import VGGFace
import cv2
import os
from deepface.detectors import FaceDetector
from deepface.commons import functions
import numpy as np
import tensorflow as tf

tf_version = int(tf.__version__.split(".")[0])

if tf_version == 1:
    from keras.models import Model, Sequential
    from keras.layers import Convolution2D, Flatten, Activation
elif tf_version == 2:
    from tensorflow.keras.models import Model, Sequential
    from tensorflow.keras.layers import Convolution2D, Flatten, Activation


# url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMpO6smj'

def loadModel(weights='weights/race_model_single_batch.h5'):
    model = VGGFace.baseModel()
    # --------------------------
    classes = 6
    base_model_output = Sequential()
    base_model_output = Convolution2D(classes, (1, 1), name='predictions')(model.layers[-4].output)
    base_model_output = Flatten()(base_model_output)
    base_model_output = Activation('softmax')(base_model_output)
    # --------------------------
    race_model = Model(inputs=model.input, outputs=base_model_output)
    # --------------------------
    # load weights
    race_model.load_weights(weights)
    return race_model


detector_backend = 'opencv'
face_detector = FaceDetector.build_model(detector_backend)
race_model = loadModel()
race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
cap = cv2.VideoCapture('/firc/hdd/Videos/person.mp4')  # webcam
while True:
    ret, img = cap.read()
    #img=cv2.resize(img,(640,360))
    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]
            face_224 = functions.preprocess_face(img=roi_img, target_size=(224, 224), grayscale=False,
                                                 enforce_detection=False, detector_backend='opencv')
            predictions = race_model.predict(face_224)[0, :]
            cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 3)  # draw rectangle to main image
            cv2.putText(img, 'race='.format(race_labels[np.argmax(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()