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Configuration error
Configuration error
| #from basemodels import VGGFace | |
| from deepface.basemodels import VGGFace | |
| import os | |
| from pathlib import Path | |
| import gdown | |
| import numpy as np | |
| from keras.models import Model, Sequential | |
| from keras.layers import Convolution2D, Flatten, Activation | |
| from keras.preprocessing import image | |
| import cv2 | |
| class Gender_Model(): | |
| def __init__(self): | |
| self.model = self.loadModel() | |
| def predict_gender(self, face_image): | |
| image_preprocesing = self.transform_face_array2gender_face(face_image) | |
| gender_predictions = self.model.predict(image_preprocesing )[0,:] | |
| if np.argmax(gender_predictions) == 0: | |
| result_gender = "Woman" | |
| elif np.argmax(gender_predictions) == 1: | |
| result_gender = "Man" | |
| return result_gender | |
| def loadModel(self): | |
| model = VGGFace.baseModel() | |
| #-------------------------- | |
| classes = 2 | |
| 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) | |
| #-------------------------- | |
| gender_model = Model(inputs=model.input, outputs=base_model_output) | |
| #-------------------------- | |
| #load weights | |
| home = str(Path.home()) | |
| if os.path.isfile(home+'/.deepface/weights/gender_model_weights.h5') != True: | |
| print("gender_model_weights.h5 will be downloaded...") | |
| url = 'https://drive.google.com/uc?id=1wUXRVlbsni2FN9-jkS_f4UTUrm1bRLyk' | |
| output = home+'/.deepface/weights/gender_model_weights.h5' | |
| gdown.download(url, output, quiet=False) | |
| gender_model.load_weights(home+'/.deepface/weights/gender_model_weights.h5') | |
| return gender_model | |
| #-------------------------- | |
| def transform_face_array2gender_face(self,face_array,grayscale=False,target_size = (224, 224)): | |
| detected_face = face_array | |
| if grayscale == True: | |
| detected_face = cv2.cvtColor(detected_face, cv2.COLOR_BGR2GRAY) | |
| detected_face = cv2.resize(detected_face, target_size) | |
| img_pixels = image.img_to_array(detected_face) | |
| img_pixels = np.expand_dims(img_pixels, axis = 0) | |
| #normalize input in [0, 1] | |
| img_pixels /= 255 | |
| return img_pixels |