def __getitem__(self, index):
image_path = self.images_path[index]
annocation_path = self.annocations_path[index]
# image = cv2.imread(image_path, cv2.IMREAD_COLOR)
# assert image is not None, 'cv2.imread({image_path}) fail'
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# org_size = image.shape[:2]
# annocation = np.loadtxt(annocation_path).reshape(-1, 6)
#
# target = []
# for c, i, x, y, w, h in annocation:
# target.append([0, c, i, x, y, w, h])
#
# target = torch.as_tensor(target, dtype=torch.float32, device=torch.device('cpu'))
# if target.size(0) == 0:
# target = torch.FloatTensor(0, 7)
from xxx import LoadImagesAndLabels
loader = LoadImagesAndLabels(augment=True)
image, labels, _, _ = loader.get_data(image_path, annocation_path)
target = []
for c, i, x, y, w, h in labels:
target.append([0, c, i, x, y, w, h])
target = torch.as_tensor(target,
dtype=torch.float32,
device=torch.device('cpu'))
if target.size(0) == 0:
target = torch.FloatTensor(0, 7)
return image, target
def __getitem__(self, index):
# Transpose global index to local index in dataset.
lid = index
ds_name = ''
for i, acc_im in enumerate(self.acc_ims):
if index >= acc_im:
ds_name = list(self.label_paths.keys())[i]
lid = index - acc_im
if not ds_name:
print('ERROR: index {} {}'.format(index, self.acc_ims))
image_path = self.image_paths[ds_name][lid]
label_path = self.label_paths[ds_name][lid]
# TODO: Load and augment image and labels.
image = None
targets = None
# TODO: Transpose local identifier in dataset to global identifier.
###################################################################
# Temporary solution
from xxx import LoadImagesAndLabels
if self.backbone is 'darknet':
loader = LoadImagesAndLabels()
else:
loader = LoadImagesAndLabels(transforms=None)
image, labels, _, _ = loader.get_data(image_path, label_path)
targets = []
for c, i, x, y, w, h in labels:
if i > -1:
targets.append([0, c, i + self.id_shifts[ds_name], x, y, w, h])
targets = torch.as_tensor(targets,
dtype=torch.float32,
device=torch.device('cpu'))
if targets.size(0) == 0:
targets = torch.FloatTensor(0, 7)
###################################################################
return image, targets
sys.path.append('.')
from xxx import LoadImagesAndLabels
parser = argparse.ArgumentParser()
parser.add_argument('--root',
type=str,
help='path to the dataset root directory')
parser.add_argument('--path', type=str, help='path to the dataset')
args = parser.parse_args()
im_paths = open(args.path, 'r').readlines()
im_paths = [path.strip() for path in im_paths]
im_paths = list(filter(lambda x: len(x) > 0, im_paths))
im_paths = [os.path.join(args.root, path) for path in im_paths]
loader = LoadImagesAndLabels(augment=False, transforms=None)
writer = None
for n, im_path in enumerate(im_paths):
lb_path = im_path.replace('images', 'labels_with_ids')
lb_path = lb_path.replace('.png', '.txt')
lb_path = lb_path.replace('.jpg', '.txt')
image, labels, _, _ = loader.get_data(im_path, lb_path)
image = image.numpy()
image = np.ascontiguousarray(image.transpose(1, 2, 0)).astype(np.uint8)
for c, i, x, y, w, h in labels:
x *= image.shape[1]
y *= image.shape[0]
w *= image.shape[1]
h *= image.shape[0]
np.random.seed(int(i))
color = np.random.randint(0, 256, size=(3, )).tolist()
def __init__(self, root, cfg='train.txt', backbone='shufflenetv2', augment=True):
'''Class initialization.
Args:
root : Datasets root directory.
cfg : Datasets configuration file. The content of cfg file like this:
-----------------------------
| ./data/caltech.train |
| ./data/citypersons.train |
| ./data/cuhksysu.train |
| ./data/eth.train |
| ./data/mot17.train |
| ./data/prw.train |
-----------------------------
backbone : Nerual network backbone architecture, 'darknet' or 'shufflenetv2'.
'''
self.root = root
self.cfg = cfg
self.backbone = backbone
# Read dataset files from configuration file.
self.datasets = open(self.cfg, 'r').readlines()
self.datasets = [ds.strip() for ds in self.datasets]
self.datasets = list(filter(lambda x: len(x) > 0, self.datasets))
# Read image paths from dataset files.
image_paths = OrderedDict()
label_paths = OrderedDict()
for ds in self.datasets:
ds_name = os.path.basename(ds) # With suffix
ds_name = os.path.splitext(ds_name)[0]
with open(ds, 'r') as file:
image_paths[ds_name] = file.readlines()
image_paths[ds_name] = [path.strip() for path in image_paths[ds_name]]
image_paths[ds_name] = [os.path.join(root, path) for path in image_paths[ds_name]]
image_paths[ds_name] = list(filter(lambda x: len(x) > 0, image_paths[ds_name]))
# Inference label paths from image paths
label_paths[ds_name] = []
for path in image_paths[ds_name]:
label_path = path.replace('images', 'labels_with_ids')
label_path = label_path.replace('.png', '.txt')
label_path = label_path.replace('.jpg', '.txt')
label_paths[ds_name].append(label_path)
self.image_paths = image_paths
self.label_paths = label_paths
# Count the number of training samples for each dataset.
self.num_ims = [len(paths) for paths in image_paths.values()]
# Accumulate total number of training samples by each dataset.
self.acc_ims = [sum(self.num_ims[:i]) for i in range(len(self.num_ims))]
self.total_ims = sum(self.num_ims)
# Find the number of identifiers for each dataset.
# The label format: class identifier centerx centery width height
self.num_ids = OrderedDict()
for ds_name, label_paths in self.label_paths.items():
ds_max_id = -1
for path in label_paths:
labels = np.loadtxt(path)
# Empty label file.
if len(labels) < 1:
continue
# Find the maximum identifier in current label file
if len(labels.shape) == 2:
file_max_id = np.max(labels[:, 1])
else: # Only one label in this file.
file_max_id = labels[1]
if file_max_id > ds_max_id:
ds_max_id = file_max_id
# The valid identifier is begin with 0.
self.num_ids[ds_name] = ds_max_id + 1
# Calculate identifier shift for each dataset.
# We will calculate global identifier based on the shift.
last_id = 0
self.id_shifts = OrderedDict()
for ds_name, num_id in self.num_ids.items():
self.id_shifts[ds_name] = last_id
last_id += num_id
self._max_id = last_id - 1
if self.backbone is 'darknet':
self.loader = LoadImagesAndLabels(augment=augment)
else:
self.loader = LoadImagesAndLabels(augment=augment, transforms=None)
class HotchpotchDataset(object):
'''Hotchpotch dataset for Caltech, Citypersons, CUHK-SYSU, ETHZ, PRW, MOT, and so on.
'''
def __init__(self, root, cfg='train.txt', backbone='shufflenetv2', augment=True):
'''Class initialization.
Args:
root : Datasets root directory.
cfg : Datasets configuration file. The content of cfg file like this:
-----------------------------
| ./data/caltech.train |
| ./data/citypersons.train |
| ./data/cuhksysu.train |
| ./data/eth.train |
| ./data/mot17.train |
| ./data/prw.train |
-----------------------------
backbone : Nerual network backbone architecture, 'darknet' or 'shufflenetv2'.
'''
self.root = root
self.cfg = cfg
self.backbone = backbone
# Read dataset files from configuration file.
self.datasets = open(self.cfg, 'r').readlines()
self.datasets = [ds.strip() for ds in self.datasets]
self.datasets = list(filter(lambda x: len(x) > 0, self.datasets))
# Read image paths from dataset files.
image_paths = OrderedDict()
label_paths = OrderedDict()
for ds in self.datasets:
ds_name = os.path.basename(ds) # With suffix
ds_name = os.path.splitext(ds_name)[0]
with open(ds, 'r') as file:
image_paths[ds_name] = file.readlines()
image_paths[ds_name] = [path.strip() for path in image_paths[ds_name]]
image_paths[ds_name] = [os.path.join(root, path) for path in image_paths[ds_name]]
image_paths[ds_name] = list(filter(lambda x: len(x) > 0, image_paths[ds_name]))
# Inference label paths from image paths
label_paths[ds_name] = []
for path in image_paths[ds_name]:
label_path = path.replace('images', 'labels_with_ids')
label_path = label_path.replace('.png', '.txt')
label_path = label_path.replace('.jpg', '.txt')
label_paths[ds_name].append(label_path)
self.image_paths = image_paths
self.label_paths = label_paths
# Count the number of training samples for each dataset.
self.num_ims = [len(paths) for paths in image_paths.values()]
# Accumulate total number of training samples by each dataset.
self.acc_ims = [sum(self.num_ims[:i]) for i in range(len(self.num_ims))]
self.total_ims = sum(self.num_ims)
# Find the number of identifiers for each dataset.
# The label format: class identifier centerx centery width height
self.num_ids = OrderedDict()
for ds_name, label_paths in self.label_paths.items():
ds_max_id = -1
for path in label_paths:
labels = np.loadtxt(path)
# Empty label file.
if len(labels) < 1:
continue
# Find the maximum identifier in current label file
if len(labels.shape) == 2:
file_max_id = np.max(labels[:, 1])
else: # Only one label in this file.
file_max_id = labels[1]
if file_max_id > ds_max_id:
ds_max_id = file_max_id
# The valid identifier is begin with 0.
self.num_ids[ds_name] = ds_max_id + 1
# Calculate identifier shift for each dataset.
# We will calculate global identifier based on the shift.
last_id = 0
self.id_shifts = OrderedDict()
for ds_name, num_id in self.num_ids.items():
self.id_shifts[ds_name] = last_id
last_id += num_id
self._max_id = last_id - 1
if self.backbone is 'darknet':
self.loader = LoadImagesAndLabels(augment=augment)
else:
self.loader = LoadImagesAndLabels(augment=augment, transforms=None)
def __getitem__(self, index):
# Transform global index to local index in dataset.
lid = index
ds_name = ''
for i, acc_im in enumerate(self.acc_ims):
if index >= acc_im:
ds_name = list(self.label_paths.keys())[i]
lid = index - acc_im
if not ds_name:
print('ERROR: index {} {}'.format(index, self.acc_ims))
image_path = self.image_paths[ds_name][lid]
label_path = self.label_paths[ds_name][lid]
# TODO: Load and augment image and labels.
image = None
targets = None
###################################################################
# Temporary solution
image, labels, _, _ = self.loader.get_data(image_path, label_path)
###################################################################
# Transform local identifier in dataset to global identifier.
targets = []
for c, i, x, y, w, h in labels:
if i > -1:
targets.append([0, c, i + self.id_shifts[ds_name], x, y, w, h])
else: # Only have bounding box annotations.
targets.append([0, c, i, x, y, w, h])
targets = torch.as_tensor(targets, dtype=torch.float32, device=torch.device('cpu'))
if targets.size(0) == 0:
targets = torch.FloatTensor(0, 7)
return image, targets
def __len__(self):
return self.total_ims
@property
def max_id(self):
return self._max_id