def __getitem__(self, idx):
size = self.input_size
img_path = self.excuse_list[idx]
img = Image.open(img_path)
if img.mode != 'RGB':
img = img.convert('RGB')
if self.att_flag:
att_map = self.get_att.get_att(img)
if self.align_flag:
img = self.alignment(img)
img = resize(img, size)
id_ = self.excuse_ids[idx]
#return self.transform(img), img_path, id_
if self.att_flag:
att_map = resize(att_map, size)
if self.flip_flag: img, att_map = random_flip([img, att_map])
att_map = self.transform_att(att_map)
att_map = torch.floor(100*att_map)
att_map = self.thresh(att_map)
img = self.transform(img)
return img, img_path, id_, att_map
else:
if self.flip_flag: img = random_flip(img)
img = self.transform(img)
return img, img_path, id_
def __getitem__(self, idx):
'''Load image.
Args:
idx: (int) image index.
Returns:
img: (tensor) image tensor.
loc_targets: (tensor) location targets.
cls_targets: (tensor) class label targets.
'''
# Load image and boxes.
fname = self.fnames[idx]
img = Image.open(os.path.join(self.root, fname))
if img.mode != 'RGB':
img = img.convert('RGB')
boxes = self.boxes[idx].clone()
labels = self.labels[idx]
size = self.input_size
# Data augmentation.
if self.train:
img, boxes = random_flip(img, boxes)
img, boxes = random_crop(img, boxes)
img, boxes = resize(img, boxes, (size, size))
else:
img, boxes = resize(img, boxes, size)
img, boxes = center_crop(img, boxes, (size, size))
img = self.transform(img)
return img, boxes, labels
def __getitem__(self, idx):
'''Load image.
Args:
idx: (int) image index.
Returns:
img: (tensor) image tensor.
loc_targets: (tensor) location targets.
cls_targets: (tensor) class label targets.
'''
# Load image and boxes.
fname = self.fnames[idx]
img = Image.open(os.path.join(self.root, fname))
if img.mode != 'RGB':
img = img.convert('RGB')
boxes = self.boxes[idx].clone()
labels = self.labels[idx]
size = self.input_size
# Data augmentation.
if self.train:
img, boxes = random_flip(img, boxes)
img, boxes = random_crop(img, boxes)
img, boxes = resize(img, boxes, (size,size))
else:
img, boxes = resize(img, boxes, size)
img, boxes = center_crop(img, boxes, (size,size))
img = self.transform(img)
return img, boxes, labels
def preprocess_image(img):
img = img_to_array(img)
img = image_crop(img, target_shape='random_square')
img = reshape(img, (256, 256))
img = random_flip(img, True, False)
img = standardize(img)
return img
def data_augmentation(img_name, idx):
'''
Data Augmentation on the segmented image. The file is from pytorch_pytorch_retinanet.retinanet_dataset.py
All transformation method are import from transform.py, which are from pytorch_retinanet.utils
Note:
Right Now, it is randomly augment the image
'''
img = Image.open(os.path.join(SEG_IMG_DIR, img_name))
if img.mode != 'RGB':
img = img.convert('RGB')
size = 600 # the desired image size
img = random_flip(img)
img = random_crop(img)
img = resize(img, (size, size))
if random.random() > 0.5:
img = ImageEnhance.Color(img).enhance(random.uniform(0, 1))
img = ImageEnhance.Brightness(img).enhance(random.uniform(0.5, 2))
img = ImageEnhance.Contrast(img).enhance(random.uniform(0.5, 1.5))
img = ImageEnhance.Sharpness(img).enhance(random.uniform(0.5, 1.5))
# im1 = img.filter(ImageFilter.BLUR) # Gaussian Blur
else:
img = resize(img, (size, size))
# img, boxes = center_crop(img, boxes, (size, size))
filename = img_name[:-4] + "_" + str(idx) + ".png"
img.save(os.path.join(SEG_IMG_DIR + "/transformed", filename), "PNG")
def __getitem__(self, index):
fname = os.path.join(self.im_pth, self.fnames[index])
img = Image.open(fname)
if img.mode!='RGB':
img = img.convert('RGB')
boxes = self.boxes[index].clone()
size = self.size
#print(img.size)
if self.train:
img, boxes = random_flip(img, boxes)
img, boxes = random_crop(img, boxes)
img, boxes = resize(img, boxes, size)
else:
img, boxes = center_crop(img, boxes, size)
img, boxes = resize(img, boxes, size)
if self.transform is not None:
img = self.transform(img)
dense_map = torch.zeros([1, img.size()[1], img.size()[2]], dtype=torch.float32)
#print(dense_map.size())
box_num = 0
for box in boxes:
area = (box[2]-box[0])*(box[3]-box[1])
#print(box[0], box[1], box[2], box[3], area)
if area<100.:
continue
box_num += 1
try:
dense_map[:, box[1].type(torch.int32):box[3].type(torch.int32), box[0].type(torch.int32):box[2].type(torch.int32)] += 1/area
except:
print(fname, dense_map.size())
print(box[1].type(torch.int32), box[3].type(torch.int32), box[0].type(torch.int32), box[2].type(torch.int32), area)
return img, dense_map, box_num
def __getitem__(self, idx):
'''Load image.
Args:
idx: (int) image index.
Returns:
img: (tensor) image tensor.
loc_targets: (tensor) location targets.
cls_targets: (tensor) class label targets.
'''
# Load image and boxes.
fname = self.fnames[idx]
prefix_name = fname[:2]
if self.train:
image_path = self.root + '/' + prefix_name + '/' + fname
else:
image_path = self.root + '/' + prefix_name + '/' + fname
# img = Image.open(os.path.join(self.root, fname))
img_a = Image.open(image_path + '_a.jpg')
img_b = Image.open(image_path + '_b.jpg')
img_c = Image.open(image_path + '_c.jpg')
img = Image.merge('RGB', (img_a, img_b, img_c))
# if img.mode != 'RGB':
# img = img.convert('RGB')
boxes = self.boxes[idx].clone()
labels = self.labels[idx]
size = self.input_size
# Data augmentation.
if self.train:
img, boxes = random_flip(img, boxes)
img, boxes = random_crop(img, boxes)
img, boxes = resize(img, boxes, (size,size))
else:
img, boxes = resize(img, boxes, size)
img, boxes = center_crop(img, boxes, (size,size))
img = self.transform(img)
# if self.transforms is not None:
# # if img is a byte or uint8 array, it will convert from 0-255 to 0-1
# # this converts from (HxWxC) to (CxHxW) as well
# img_a, img_b, img_c = image
# img_a = self.transforms(img_a)
# img_b = self.transforms(img_b)
# img_c = self.transforms(img_c)
# img = (img_a, img_b, img_c)
return img, boxes, labels
def __getitem__(self, idx):
'''Load image.
Args:
idx: (int) image index.
Returns:
img: (tensor) image tensor.
loc_targets: (tensor) location targets.
cls_targets: (tensor) class label targets.
'''
# Load image and boxes.
fname = self.fnames[idx]
img = Image.open(os.path.join(self.root, fname))
if img.mode != 'RGB':
img = img.convert('RGB')
width, height = img.size
flabel = fname.replace('images/',
'labels/').replace('.jpg', '.txt').replace(
'.png', '.txt').replace('.jpeg', '.txt')
box = []
label = []
with open(flabel) as f:
lines = f.readlines()
for line in lines:
ls = line.strip().split()
x = float(ls[1]) * width
y = float(ls[2]) * height
w = float(ls[3]) * width
h = float(ls[4]) * height
box.append([x - w / 2, y - h / 2, x + w / 2, y + h / 2])
label.append(int(ls[0]))
boxes = torch.Tensor(box)
labels = torch.LongTensor(label)
size = self.input_size
# Data augmentation.
if self.train:
img, boxes = random_flip(img, boxes)
img, boxes = random_crop(img, boxes)
img, boxes = resize(img, boxes, (size, size))
else:
img, boxes = resize(img, boxes, size)
img, boxes = center_crop(img, boxes, (size, size))
img = self.transform(img)
return img, boxes, labels
def __getitem__(self, idx):
'''Load image.
Args:
idx: (int) image index.
Returns:
img: (tensor) image tensor.
loc_targets: (tensor) location targets.
cls_targets: (tensor) class label targets.
'''
# Load image and boxes.
fname = self.fnames[idx]
img_path = os.path.join(self.root, fname)
img = Image.open(img_path)
if img.mode != 'RGB':
img = img.convert('RGB')
boxes = self.boxes[idx].clone()
labels = self.labels[idx]
size = self.input_size
src_shape = self.shape_list[idx]
att_map = np.zeros([src_shape[0], src_shape[1]])
for att_box in boxes:
att_map[int(att_box[0]):int(att_box[2]),
int(att_box[1]):int(att_box[3])] = 1
# Data augmentation.
if self.train:
img, boxes = random_flip(img, boxes)
img, boxes = random_crop(img, boxes)
img, boxes = resize(img, boxes, (size, size))
else:
img, boxes = resize(img, boxes, size)
img, boxes = center_crop(img, boxes, (size, size))
att_map = Image.fromarray(att_map)
att_map = att_map.resize((size // 2, size // 2), Image.BILINEAR)
#img.save('test_in_datagen.jpg')
img = self.transform(img)
att_map = self.transform(att_map)
return img, boxes, labels, att_map, img_path
def __getitem__(self, idx):
'''Load image.
Args:
idx: (int) image index.
Returns:
img: (tensor) image tensor.
loc_targets: (tensor) location targets.
cls_targets: (tensor) class label targets.
'''
# Load image and boxes.
fname = self.fnames[idx]
img = Image.open(os.path.join(self.root, fname))
if img.mode != 'RGB':
img = img.convert('RGB')
boxes = self.boxes[idx].clone()
labels = self.labels[idx]
size = self.input_size
# Data augmentation.
if self.train:
img, boxes = random_flip(img, boxes)
img, boxes = random_crop(img, boxes)
img, boxes = resize(img, boxes, (size, size))
else:
img, boxes = resize(img, boxes, (size, size))
# img, boxes = center_crop(img, boxes, (size,size))
img = self.transform(img)
if self.is_debug:
filled_labels = np.zeros((self.max_objects, 4), dtype=np.float32)
if boxes is not None:
filled_labels[range(
len(boxes))[:self.max_objects]] = boxes[:self.max_objects]
else:
print('no object')
filled_labels = torch.from_numpy(filled_labels)
return img, filled_labels
else:
#error_indices_1 = np.where(labels<0)
#error_indices_2 = np.where(labels>19)
#print('_______:',labels[error_indices_1])
#print(labels.shape)
return img, boxes, labels, fname
def __getitem__(self, index):
'''Load image.
Args:
idx: (int) image index.
Returns:
img: (tensor) image tensor.
loc_targets: (tensor) location targets.
cls_targets: (tensor) class label targets.
'''
# Load image and boxes.
img_path = self._imgpath[index].rstrip()
fname = img_path.split('/')[-1].split('.')[0]
# print(img_path)
img = Image.open(img_path)
if img.mode != 'RGB':
img = img.convert('RGB')
label_path = self._labpath[index].rstrip()
# print(label_path)
targets = np.loadtxt(label_path).reshape(-1, 5)
# targets = np.array(targets)
# print(targets)
boxes = torch.Tensor(targets[:, 1:])
labels = torch.LongTensor(targets[:, 0])
size = self.input_size
# Data augmentation.
if self.train:
img, boxes = random_flip(img, boxes)
img, boxes = random_crop(img, boxes)
img, boxes = resize(img, boxes, (size, size))
else:
img, boxes = resize(img, boxes, (size, size))
# img, boxes = center_crop(img, boxes, (size,size))
img = self.transform(img)
return img, boxes, labels, fname