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)
def upsample_data_fix_rate(self):
""" [1290. 281. 717. 4772. 1982. 705. 2524.]
Augmentation: sample_count_by_category: ====>>
[5, 20, 11, 1, 4, 11, 3] + 1 which i original
[6450, 5620, 7887, 4772, 7928, 7755, 7572]
"""
dhl = DataHelper()
''''''
aug_factor_by_class = [5, 20, 11, 1, 4, 11, 3]
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)]
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)
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=aug_factor_by_class[i],
aug_factor_freq=None,
img_save_path=self.img_path_aug,
anno_save_path=self.anno_path_aug,
class_index=i)
def upsample_data_fix_rate(self):
dhl = DataHelper()
if self.ds_type == DatasetType.train:
aug_factor_by_class = [0.5, 0.3, 1, 2, 4, 6, 1, 6]
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)]
elif self.ds_type == DatasetType.train_7:
aug_factor_by_class = [0.5, 0.3, 1, 2, 4, 6, 1]
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)]
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: ====>>")
# [ 74874. 134415. 25459. 14090. 6378. 3803. 24882.]
print(sample_count_by_class)
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=aug_factor_by_class[i],
aug_factor_freq=None,
img_save_path=self.img_path_aug,
anno_save_path=self.anno_path_aug,
class_index=i)
