def align_faces(): start = time.time() align_mtcnn('your_dataset', 'face_align') end = time.time() #total_minute = (end - start) / 60 #print('Aligning excecution time: ' + str(end - start) + ' second') #print('Total minutes:', total_minute) messagebox.showinfo( "Information", "The alignment is complete \n" + "Aligning excecution time: " + str(format(end - start, ".2f")) + "s")
model.fit(emb_array, labels) # Create a list of class names class_names = [cls.name.replace('_', ' ') for cls in dataset] # Saving classifier model with open(classifier_filename_exp, 'wb') as outfile: pickle.dump((model, class_names), outfile) print('Saved classifier model to file "%s"' % classifier_filename_exp) def split_dataset(dataset, min_nrof_images_per_class, nrof_train_images_per_class): train_set = [] test_set = [] for cls in dataset: paths = cls.image_paths # Remove classes with less than min_nrof_images_per_class if len(paths) >= min_nrof_images_per_class: np.random.shuffle(paths) train_set.append( ImageClass(cls.name, paths[:nrof_train_images_per_class])) test_set.append( ImageClass(cls.name, paths[nrof_train_images_per_class:])) return train_set, test_set if __name__ == '__main__': align_mtcnn('your_face', 'face_align') train('face_align/', 'models/20180402-114759.pb', 'models/your_model.pkl')
# Create a list of class names class_names = [cls.name.replace('_', ' ') for cls in dataset] # Saving classifier model with open(classifier_filename_exp, 'wb') as outfile: pickle.dump((model, class_names), outfile) print('Saved classifier model to file "%s"' % classifier_filename_exp) def split_dataset(dataset, min_nrof_images_per_class, nrof_train_images_per_class): train_set = [] test_set = [] for cls in dataset: paths = cls.image_paths # Remove classes with less than min_nrof_images_per_class if len(paths) >= min_nrof_images_per_class: np.random.shuffle(paths) train_set.append( ImageClass(cls.name, paths[:nrof_train_images_per_class])) test_set.append( ImageClass(cls.name, paths[nrof_train_images_per_class:])) return train_set, test_set if __name__ == '__main__': align_mtcnn('input_face', 'output_face') train('output_face/', 'models/20180402-114759.pb', 'models/your_model.pkl')
# Create a list of class names class_names = [cls.name.replace('_', ' ') for cls in dataset] # Saving classifier model with open(classifier_filename_exp, 'wb') as outfile: pickle.dump((model, class_names), outfile) print('Saved classifier model to file "%s"' % classifier_filename_exp) def split_dataset(dataset, min_nrof_images_per_class, nrof_train_images_per_class): train_set = [] test_set = [] for cls in dataset: paths = cls.image_paths # Remove classes with less than min_nrof_images_per_class if len(paths) >= min_nrof_images_per_class: np.random.shuffle(paths) train_set.append( ImageClass(cls.name, paths[:nrof_train_images_per_class])) test_set.append( ImageClass(cls.name, paths[nrof_train_images_per_class:])) return train_set, test_set if __name__ == '__main__': align_mtcnn('data_face', 'face_align') train('face_align/', 'models/20180402-114759.pb', 'models/your_model.pkl')
def TrainImages(): align_mtcnn('Datasets', 'face_align') train_data('face_align/', 'models/20180402-114759.pb', 'models/your_model.pkl') res = "Image Trained" message.configure(text=res)
model.fit(emb_array, labels) # Create a list of class names class_names = [cls.name.replace('_', ' ') for cls in dataset] # Saving classifier model with open(classifier_filename_exp, 'wb') as outfile: pickle.dump((model, class_names), outfile) print('Saved classifier model to file "%s"' % classifier_filename_exp) def split_dataset(dataset, min_nrof_images_per_class, nrof_train_images_per_class): train_set = [] test_set = [] for cls in dataset: paths = cls.image_paths # Remove classes with less than min_nrof_images_per_class if len(paths) >= min_nrof_images_per_class: np.random.shuffle(paths) train_set.append( ImageClass(cls.name, paths[:nrof_train_images_per_class])) test_set.append( ImageClass(cls.name, paths[nrof_train_images_per_class:])) return train_set, test_set if __name__ == '__main__': align_mtcnn('Datasets', 'face_align') train('face_align/', 'models/20180402-114759.pb', 'models/your_model.pkl')