def upsample_data(self):
"""we generate some samples so that all classes will have equal number of training samples"""
dhl = DataHelper()
'''count samples & categorize their address based on their category'''
if self.ds_type == DatasetType.train:
sample_count_by_class = np.zeros([8])
img_addr_by_class = [[] for i in range(8)]
anno_addr_by_class = [[] for i in range(8)]
lnd_addr_by_class = [[] for i in range(8)]
else:
sample_count_by_class = np.zeros([7])
img_addr_by_class = [[] for i in range(7)]
anno_addr_by_class = [[] for i in range(7)]
lnd_addr_by_class = [[] for i in range(7)]
""""""
print("counting classes:")
for i, file in tqdm(enumerate(os.listdir(self.anno_path))):
if file.endswith("_exp.npy"):
exp = int(np.load(os.path.join(self.anno_path, file)))
sample_count_by_class[exp] += 1
'''adding ex'''
anno_addr_by_class[exp].append(
os.path.join(self.anno_path, file))
img_addr_by_class[exp].append(
os.path.join(self.img_path, file[:-8] + '.jpg'))
lnd_addr_by_class[exp].append(
os.path.join(self.anno_path, file[:-8] + '_slnd.npy'))
print("sample_count_by_category: ====>>")
print(sample_count_by_class)
'''calculate augmentation factor for each class:'''
aug_factor_by_class, aug_factor_by_class_freq = dhl.calculate_augmentation_rate(
sample_count_by_class=sample_count_by_class,
base_aug_factor=AffectnetConf.augmentation_factor)
'''after we have calculated those two array, we will augment samples '''
for i in range(len(anno_addr_by_class)):
dhl.do_random_augment(img_addrs=img_addr_by_class[i],
anno_addrs=anno_addr_by_class[i],
lnd_addrs=lnd_addr_by_class[i],
aug_factor=int(aug_factor_by_class[i]),
aug_factor_freq=int(
aug_factor_by_class_freq[i]),
img_save_path=self.img_path_aug,
anno_save_path=self.anno_path_aug,
class_index=i)
def upsample_data(self):
"""we generate some samples so that all classes will have equal number of training samples"""
dhl = DataHelper()
'''count samples & categorize their address based on their category'''
sample_count_by_class = np.zeros([7])
img_addr_by_class = [[] for i in range(7)]
anno_addr_by_class = [[] for i in range(7)]
lnd_addr_by_class = [[] for i in range(7)]
""""""
print("counting classes:")
count = 0
for i, file in tqdm(enumerate(os.listdir(self.anno_path))):
if file.endswith("_exp.npy"):
exp = int(np.load(os.path.join(self.anno_path, file)))
sample_count_by_class[exp] += 1
'''adding ex'''
anno_addr_by_class[exp].append(
os.path.join(self.anno_path, file))
img_addr_by_class[exp].append(
os.path.join(self.img_path, file[:-8] + '.jpg'))
lnd_addr_by_class[exp].append(
os.path.join(self.anno_path, file[:-8] + '_slnd.npy'))
count += 1
print("sample_count_by_category: ====>>")
print(sample_count_by_class)
# {Surprise 1290}===={ Fear 281.}===[Disgust 717}===[Happiness 4772]
# ==={ Sadness 1982}=={Anger 705}===.[ Neutral 2524}
'''calculate augmentation factor for each class:'''
aug_factor_by_class, aug_factor_by_class_freq = dhl.calculate_augmentation_rate(
sample_count_by_class=sample_count_by_class,
base_aug_factor=RafDBConf.augmentation_factor)
'''after we have calculated those two array, we will augment samples '''
for i in range(len(anno_addr_by_class)):
dhl.do_random_augment(img_addrs=img_addr_by_class[i],
anno_addrs=anno_addr_by_class[i],
lnd_addrs=lnd_addr_by_class[i],
aug_factor=int(aug_factor_by_class[i]),
aug_factor_freq=int(
aug_factor_by_class_freq[i]),
img_save_path=self.img_path_aug,
anno_save_path=self.anno_path_aug,
class_index=i)
