def __getitem__(self, idx):
image = Image.open(
os.path.join(self._configs["data_path"], self._stage,
self._pixels[idx]))
# print(image.size)
# pixels = self._pixels[idx]
# pixels = list(map(int, pixels.split(" ")))
image = np.asarray(image).reshape(48, 48)
image = image.astype(np.uint8)
image = cv2.resize(image, self._image_size)
image = np.dstack([image] * 3)
if self._stage == "train":
image = seg(image=image)
if self._stage == "test" and self._tta == True:
images = [seg(image=image) for i in range(self._tta_size)]
# images = [image for i in range(self._tta_size)]
images = list(map(self._transform, images))
# target = self._emotions.iloc[idx].idxmax()
return images
import torch
image = self._transform(image)
target = np.argmax(self._emotions.iloc[idx].values
) #self._emotions.iloc[idx]#.idxmax()
return image, target
def __getitem__(self, idx):
image_path, label = self._data[idx]
image_path = os.path.join(self._configs['data_path'], image_path)
image = cv2.imread(image_path)
image = cv2.resize(image, self._image_size)
if self._stage == 'train':
image = seg(image=image)
if self._stage == 'test' and self._tta == True:
images = [seg(image=image) for i in range(self._tta_size)]
images = list(map(self._transform, images))
return images, expression
image = self._transform(image)
return image, label
def __getitem__(self, idx):
path = self._path_list[idx]
image = cv2.imread(self._configs['data_path'] + '/' + path)
image = cv2.resize(image, (224, 224))
if self._stage == 'train':
image = seg(image=image)
if self._stage == 'test' and self._tta == True:
images = [seg(image=image) for i in range(self._tta_size)]
images = list(map(self._transform, images))
target = self._emotions.iloc[idx].idxmax()
return images, target
image = self._transform(image)
target = self._emotions.iloc[idx].idxmax()
return image, target
def __getitem__(self, idx):
data = self._data[idx]
data = cv2.imread(data)
image = cv2.resize(data,self._image_size)
image = image.astype(np.uint8)
if self._stage == 'train':
image = seg(image=image)
if self._stage == 'test' and self._tta == True:
images = [seg(image=image) for i in range(self._tta_size)]
# images = [image for i in range(self._tta_size)]
images = list(map(self._transform, images))
target = int(self._data[idx].split(os.path.sep)[-2])
return images, target
image = self._transform(image)
target = int(self._data[idx].split(os.path.sep)[-2])
return image, target
def __getitem__(self, idx):
image_name, image, label = self._data[idx]
image = cv2.resize(image, self._image_size)
assert image.shape[2] == 3
assert label <= 7 and label >= 0
if self._stage == "train":
image = seg(image=image)
return self._transform(image), label
def __getitem__(self, idx):
pixels = self._pixels[idx]
pixels = list(map(int, pixels.split(" ")))
image = np.asarray(pixels).reshape(48, 48)
image = image.astype(np.uint8)
image = cv2.resize(image, self._image_size)
image = np.dstack([image] * 3)
if self._stage == "train":
image = seg(image=image)
if self._stage == "test" and self._tta == True:
images = [seg(image=image) for i in range(self._tta_size)]
# images = [image for i in range(self._tta_size)]
images = list(map(self._transform, images))
target = self._emotions.iloc[idx].idxmax()
return images, target
image = self._transform(image)
target = self._emotions.iloc[idx].idxmax()
return image, target
def __getitem__(self, idx):
image_name = self._image_name[idx]
image_path = self._configs["data_path"] + "/" + image_name
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = cv2.resize(image, self._image_size)
image = np.dstack([image] * 3)
if self._stage == "train":
image = seg(image=image)
if self._stage == "test" and self._tta == True:
images = [seg(image=image) for i in range(self._tta_size)]
# images = [image for i in range(self._tta_size)]
images = list(map(self._transform, images))
target = self._emotions.iloc[idx].idxmax()
return images, target
image = self._transform(image)
target = self._emotions.iloc[idx].idxmax()
return image, target
