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from pca_utilities import PCAUtility |
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from config import DatasetName, DatasetType, W300Conf, InputDataSize, LearningConfig, CofwConf |
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import tensorflow as tf |
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import numpy as np |
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import os |
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from skimage.transform import resize |
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import csv |
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import sys |
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from PIL import Image |
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from pathlib import Path |
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import sqlite3 |
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import cv2 |
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import os.path |
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from keras import backend as K |
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from scipy import misc |
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from scipy.ndimage import gaussian_filter, maximum_filter |
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from numpy import save, load, asarray |
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from tqdm import tqdm |
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import pickle |
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import PIL.ImageDraw as ImageDraw |
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import math |
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class DataUtil: |
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def __init__(self, number_of_landmark): |
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self.number_of_landmark = number_of_landmark |
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def get_asm(self, input, dataset_name, accuracy, alpha=1.0): |
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pca_utils = PCAUtility() |
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eigenvalues = load('pca_obj/' + dataset_name + pca_utils.eigenvalues_prefix + str(accuracy) + ".npy") |
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eigenvectors = load('pca_obj/' + dataset_name + pca_utils.eigenvectors_prefix + str(accuracy) + ".npy") |
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meanvector = load('pca_obj/' + dataset_name + pca_utils.meanvector_prefix + str(accuracy) + ".npy") |
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b_vector_p = pca_utils.calculate_b_vector(input, True, eigenvalues, eigenvectors, meanvector) |
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out = alpha * meanvector + np.dot(eigenvectors, b_vector_p) |
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return out |
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def create_image_and_labels_name(self, img_path, annotation_path): |
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img_filenames = [] |
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lbls_filenames = [] |
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for file in os.listdir(img_path): |
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if file.endswith(".jpg") or file.endswith(".png"): |
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lbl_file = str(file)[:-3] + "npy" |
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if os.path.exists(annotation_path + lbl_file): |
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img_filenames.append(str(file)) |
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lbls_filenames.append(lbl_file) |
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return np.array(img_filenames), np.array(lbls_filenames) |
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