|
from config import DatasetName, AffectnetConf, InputDataSize, LearningConfig, ExpressionCodesAffectnet |
|
from config import LearningConfig, InputDataSize, DatasetName, RafDBConf, DatasetType |
|
from sklearn.metrics import precision_recall_fscore_support as score |
|
|
|
import numpy as np |
|
import os |
|
import matplotlib.pyplot as plt |
|
import math |
|
from datetime import datetime |
|
from sklearn.utils import shuffle |
|
from sklearn.model_selection import train_test_split |
|
from numpy import save, load, asarray, savez_compressed |
|
import csv |
|
from skimage.io import imread |
|
import pickle |
|
import csv |
|
from tqdm import tqdm |
|
from PIL import Image |
|
from skimage.transform import resize |
|
import tensorflow as tf |
|
import random |
|
import cv2 |
|
from skimage.feature import hog |
|
from skimage import data, exposure |
|
from matplotlib.path import Path |
|
from scipy import ndimage, misc |
|
from data_helper import DataHelper |
|
from sklearn.metrics import accuracy_score |
|
from sklearn.metrics import confusion_matrix |
|
from shutil import copyfile |
|
from dataset_class import CustomDataset |
|
|
|
|
|
class RafDB: |
|
def __init__(self, ds_type): |
|
"""we set the parameters needed during the whole class: |
|
""" |
|
self.ds_type = ds_type |
|
if ds_type == DatasetType.train: |
|
self.img_path = RafDBConf.no_aug_train_img_path |
|
self.anno_path = RafDBConf.no_aug_train_annotation_path |
|
self.img_path_aug = RafDBConf.aug_train_img_path |
|
self.anno_path_aug = RafDBConf.aug_train_annotation_path |
|
self.masked_img_path = RafDBConf.aug_train_masked_img_path |
|
|
|
elif ds_type == DatasetType.test: |
|
self.img_path = RafDBConf.test_img_path |
|
self.anno_path = RafDBConf.test_annotation_path |
|
self.img_path_aug = RafDBConf.test_img_path |
|
self.anno_path_aug = RafDBConf.test_annotation_path |
|
self.masked_img_path = RafDBConf.test_masked_img_path |
|
|
|
def test_accuracy(self, model, print_samples=False): |
|
dhp = DataHelper() |
|
batch_size = LearningConfig.batch_size |
|
|
|
|
|
|
|
'''create batches''' |
|
img_filenames, exp_filenames = dhp.create_generator_full_path( |
|
img_path=self.img_path, |
|
annotation_path=self.anno_path, label=None) |
|
print(len(img_filenames)) |
|
step_per_epoch = int(len(img_filenames) // batch_size) |
|
exp_pr_lbl = [] |
|
exp_gt_lbl = [] |
|
|
|
cds = CustomDataset() |
|
ds = cds.create_dataset(img_filenames=img_filenames, |
|
anno_names=exp_filenames, |
|
is_validation=True, |
|
ds=DatasetName.rafdb) |
|
|
|
batch_index = 0 |
|
for img_batch, exp_gt_b in tqdm(ds): |
|
'''predict on batch''' |
|
exp_gt_b = exp_gt_b[:, -1] |
|
img_batch = img_batch[:, -1, :, :] |
|
|
|
pr_data = model.predict_on_batch([img_batch]) |
|
|
|
probab_exp_pr_b = pr_data[0] |
|
exp_pr_b = np.array([np.argmax(probab_exp_pr_b[i]) for i in range(len(probab_exp_pr_b))]) |
|
|
|
if print_samples: |
|
for i in range(len(exp_pr_b)): |
|
dhp.test_image_print_exp(str(i) + str(batch_index + 1), np.array(img_batch[i]), |
|
np.int8(exp_gt_b[i]), np.int8(exp_pr_b[i])) |
|
|
|
exp_pr_lbl += np.array(exp_pr_b).tolist() |
|
exp_gt_lbl += np.array(exp_gt_b).tolist() |
|
batch_index += 1 |
|
exp_pr_lbl = np.float64(np.array(exp_pr_lbl)) |
|
exp_gt_lbl = np.float64(np.array(exp_gt_lbl)) |
|
|
|
global_accuracy = accuracy_score(exp_gt_lbl, exp_pr_lbl) |
|
precision, recall, fscore, support = score(exp_gt_lbl, exp_pr_lbl) |
|
|
|
conf_mat = confusion_matrix(exp_gt_lbl, exp_pr_lbl, normalize='true') |
|
avg_acc = np.mean([conf_mat[i, i] for i in range(7)]) |
|
|
|
ds = None |
|
face_img_filenames = None |
|
eyes_img_filenames = None |
|
nose_img_filenames = None |
|
mouth_img_filenames = None |
|
exp_filenames = None |
|
|
|
return global_accuracy, conf_mat, avg_acc, precision, recall, fscore, support |
