File size: 5,301 Bytes
1eced3c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
from config import DatasetName, AffectnetConf, InputDataSize, LearningConfig, ExpressionCodesAffectnet
from config import LearningConfig, InputDataSize, DatasetName, AffectnetConf, DatasetType
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, savez
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 dataset_class import CustomDataset
from sklearn.metrics import precision_recall_fscore_support as score
class AffectNet:
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 = AffectnetConf.no_aug_train_img_path
self.anno_path = AffectnetConf.no_aug_train_annotation_path
self.img_path_aug = AffectnetConf.aug_train_img_path
self.masked_img_path = AffectnetConf.aug_train_masked_img_path
self.anno_path_aug = AffectnetConf.aug_train_annotation_path
elif ds_type == DatasetType.eval:
self.img_path_aug = AffectnetConf.eval_img_path
self.anno_path_aug = AffectnetConf.eval_annotation_path
self.img_path = AffectnetConf.eval_img_path
self.anno_path = AffectnetConf.eval_annotation_path
self.masked_img_path = AffectnetConf.eval_masked_img_path
elif ds_type == DatasetType.train_7:
self.img_path = AffectnetConf.no_aug_train_img_path_7
self.anno_path = AffectnetConf.no_aug_train_annotation_path_7
self.img_path_aug = AffectnetConf.aug_train_img_path_7
self.masked_img_path = AffectnetConf.aug_train_masked_img_path_7
self.anno_path_aug = AffectnetConf.aug_train_annotation_path_7
elif ds_type == DatasetType.eval_7:
self.img_path_aug = AffectnetConf.eval_img_path_7
self.anno_path_aug = AffectnetConf.eval_annotation_path_7
self.img_path = AffectnetConf.eval_img_path_7
self.anno_path = AffectnetConf.eval_annotation_path_7
self.masked_img_path = AffectnetConf.eval_masked_img_path_7
def test_accuracy(self, model, print_samples=False):
dhp = DataHelper()
batch_size = LearningConfig.batch_size
exp_pr_glob = []
exp_gt_glob = []
acc_per_label = []
'''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.affectnet)
batch_index = 0
for img_batch, exp_gt_b in ds:
'''predict on batch'''
exp_gt_b = exp_gt_b[:, -1]
img_batch = img_batch[:, -1, :, :]
# probab_exp_pr_b, _ = model.predict_on_batch([img_batch]) # with embedding
pr_data = model.predict_on_batch([img_batch])
probab_exp_pr_b = pr_data[0]
scores_b = np.array([tf.nn.softmax(probab_exp_pr_b[i]) for i in range(len(probab_exp_pr_b))])
exp_pr_b = np.array([np.argmax(scores_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.int64(np.array(exp_pr_lbl))
exp_gt_lbl = np.int64(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) / 500.0
# conf_mat = tf.math.confusion_matrix(exp_gt_lbl, exp_pr_lbl, num_classes=7)/500.0
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
global_bunch = None
upper_bunch = None
middle_bunch = None
bottom_bunch = None
return global_accuracy, conf_mat, avg_acc, precision, recall, fscore, support |