def __getitem__(self, idx):
# BGR image
filename = self.image_files[idx]
print('filename = ', filename)
im = cv2.imread(filename)
if cfg.TRAIN.CHROMATIC and cfg.MODE == 'TRAIN' and np.random.rand(
1) > 0.1:
im = chromatic_transform(im)
if cfg.TRAIN.ADD_NOISE and cfg.MODE == 'TRAIN' and np.random.rand(
1) > 0.1:
im = add_noise(im)
im_tensor = torch.from_numpy(im) / 255.0
im_tensor_bgr = im_tensor.clone()
im_tensor_bgr = im_tensor_bgr.permute(2, 0, 1)
im_tensor -= self._pixel_mean
image_blob = im_tensor.permute(2, 0, 1)
# Label
labels_filename = filename.replace('image_color', 'annotation')
foreground_labels = util_.imread_indexed(labels_filename)
foreground_labels = self.process_label(foreground_labels)
label_blob = torch.from_numpy(foreground_labels).unsqueeze(0)
index = filename.find('OSD')
sample = {
'image_color': image_blob,
'image_color_bgr': im_tensor_bgr,
'label': label_blob,
'filename': filename[index + 4:]
}
# Depth image
if cfg.INPUT == 'DEPTH' or cfg.INPUT == 'RGBD':
pcd_filename = filename.replace('image_color', 'pcd')
pcd_filename = pcd_filename.replace('png', 'pcd')
print('pcd_filename = ', pcd_filename)
pcloud = pcl.load(pcd_filename).to_array()
pcloud[np.isnan(pcloud)] = 0
xyz_img = pcloud.reshape((self._height, self._width, 3))
depth_blob = torch.from_numpy(xyz_img).permute(2, 0, 1)
sample['depth'] = depth_blob
# # Depth image
# if cfg.INPUT == 'DEPTH' or cfg.INPUT == 'RGBD':
# pcd_filename = filename.replace('image_color', 'pcd')
# pcd_filename = pcd_filename.replace('png', 'pcd')
# # pcl replaced with open3d
# pcloud = o3d.io.read_point_cloud(pcd_filename)
# pcloud = np.asarray(pcloud)
# print(np.isnan(pcloud))
# pcloud[np.isnan(pcloud)] = 0
# xyz_img = pcloud.reshape((self._height, self._width, 3))
# depth_blob = torch.from_numpy(xyz_img).permute(2, 0, 1)
# sample['depth'] = depth_blob
return sample
def __getitem__(self, idx):
# BGR image
filename = str(self.image_paths[idx])
im = cv2.imread(filename)
if cfg.TRAIN.CHROMATIC and cfg.MODE == 'TRAIN' and np.random.rand(
1) > 0.1:
im = chromatic_transform(im)
if cfg.TRAIN.ADD_NOISE and cfg.MODE == 'TRAIN' and np.random.rand(
1) > 0.1:
im = add_noise(im)
im_tensor = torch.from_numpy(im) / 255.0
im_tensor_bgr = im_tensor.clone()
im_tensor_bgr = im_tensor_bgr.permute(2, 0, 1)
im_tensor -= self._pixel_mean
image_blob = im_tensor.permute(2, 0, 1)
# Label
labels_filename = filename.replace('rgb', 'label')
foreground_labels = util_.imread_indexed(labels_filename)
# mask table as background
foreground_labels[foreground_labels == 1] = 0
if 'table' in labels_filename:
foreground_labels[foreground_labels == 2] = 0
foreground_labels = self.process_label(foreground_labels)
label_blob = torch.from_numpy(foreground_labels).unsqueeze(0)
index = filename.find('OCID')
sample = {
'image_color': image_blob,
'image_color_bgr': im_tensor_bgr,
'label': label_blob,
'filename': filename[index + 5:]
}
# Depth image
if cfg.INPUT == 'DEPTH' or cfg.INPUT == 'RGBD':
pcd_filename = filename.replace('rgb', 'pcd')
pcd_filename = pcd_filename.replace('png', 'pcd')
pcloud = pcl.load(pcd_filename).to_array()
pcloud[np.isnan(pcloud)] = 0
xyz_img = pcloud.reshape((self._height, self._width, 3))
depth_blob = torch.from_numpy(xyz_img).permute(2, 0, 1)
sample['depth'] = depth_blob
return sample
from transforms3d.quaternions import mat2quat, quat2mat
import _init_paths
from datasets import OCIDObject, OSDObject
import matplotlib.pyplot as plt
from utils import mask as util_
if __name__ == '__main__':
dataset = OSDObject('test')
num = dataset._size
num_objects = []
for i in range(num):
filename = str(dataset.image_files[i])
# labels_filename = filename.replace('rgb', 'label')
labels_filename = filename.replace('image_color', 'annotation')
foreground_labels = util_.imread_indexed(labels_filename)
# mask table as background
foreground_labels[foreground_labels == 1] = 0
if 'table' in labels_filename:
foreground_labels[foreground_labels == 2] = 0
foreground_labels = dataset.process_label(foreground_labels)
n = len(np.unique(foreground_labels)) - 1
num_objects.append(n)
print(labels_filename, n)
nums = np.array(num_objects)
print('min: %d' % (np.min(nums)))
print('max: %d' % (np.max(nums)))
print('mean: %f' % (np.mean(nums)))
def __getitem__(self, idx):
# Get scene directory, crop dose not use background
scene_idx = idx // self.NUM_VIEWS_PER_SCENE
scene_dir = self.scene_dirs[scene_idx]
# Get view number
view_num = idx % self.NUM_VIEWS_PER_SCENE
if cfg.TRAIN.SYN_CROP:
view_num += 2
# Label
foreground_labels_filename = os.path.join(
scene_dir, 'segmentation_%05d.png' % view_num)
foreground_labels = util_.imread_indexed(foreground_labels_filename)
# mask table as background
foreground_labels[foreground_labels == 1] = 0
foreground_labels = self.process_label(foreground_labels)
# BGR image
filename = os.path.join(scene_dir, 'rgb_%05d.jpeg' % view_num)
im = cv2.imread(filename)
if cfg.INPUT == 'DEPTH' or cfg.INPUT == 'RGBD':
# Depth image
depth_img_filename = os.path.join(scene_dir,
'depth_%05d.png' % view_num)
depth_img = cv2.imread(
depth_img_filename, cv2.IMREAD_ANYDEPTH
) # This reads a 16-bit single-channel image. Shape: [H x W]
xyz_img = self.process_depth(depth_img)
else:
xyz_img = None
# crop
if cfg.TRAIN.SYN_CROP:
im, foreground_labels, xyz_img = self.pad_crop_resize(
im, foreground_labels, xyz_img)
foreground_labels = self.process_label(foreground_labels)
# sample labels
if cfg.TRAIN.EMBEDDING_SAMPLING:
foreground_labels = self.sample_pixels(
foreground_labels, cfg.TRAIN.EMBEDDING_SAMPLING_NUM)
label_blob = torch.from_numpy(foreground_labels).unsqueeze(0)
sample = {'label': label_blob}
if cfg.TRAIN.CHROMATIC and cfg.MODE == 'TRAIN' and np.random.rand(
1) > 0.1:
im = chromatic_transform(im)
if cfg.TRAIN.ADD_NOISE and cfg.MODE == 'TRAIN' and np.random.rand(
1) > 0.1:
im = add_noise(im)
im_tensor = torch.from_numpy(im) / 255.0
im_tensor -= self._pixel_mean
image_blob = im_tensor.permute(2, 0, 1)
sample['image_color'] = image_blob
if cfg.INPUT == 'DEPTH' or cfg.INPUT == 'RGBD':
depth_blob = torch.from_numpy(xyz_img).permute(2, 0, 1)
sample['depth'] = depth_blob
return sample
def __getitem__(self, idx):
sample = self.data[idx] # (idx: [rgb, d, seg])
rgb_path = sample[0]
depth_path = sample[1]
segmentation_path = sample[2]
# _, ax = plt.subplots(1, 3)
# ax[0].imshow(rgb)
# ax[1].imshow(depth)
# ax[2].imshow(segmentation)
# plt.show()
foreground_labels_filename = segmentation_path
foreground_labels = util_.imread_indexed(foreground_labels_filename)
# mask table as background
foreground_labels[foreground_labels == 1] = 0
foreground_labels = self.process_label(foreground_labels)
# BGR image
filename = rgb_path
im = cv2.imread(filename)
if cfg.INPUT == 'DEPTH' or cfg.INPUT == 'RGBD':
# Depth image
depth_img_filename = depth_path
depth_img = cv2.imread(
depth_img_filename, cv2.IMREAD_ANYDEPTH
) # This reads a 16-bit single-channel image. Shape: [H x W]
xyz_img = self.process_depth(depth_img)
else:
xyz_img = None
# crop
if cfg.TRAIN.SYN_CROP:
im, foreground_labels, xyz_img = self.pad_crop_resize(
im, foreground_labels, xyz_img)
foreground_labels = self.process_label(foreground_labels)
# sample labels
if cfg.TRAIN.EMBEDDING_SAMPLING:
foreground_labels = self.sample_pixels(
foreground_labels, cfg.TRAIN.EMBEDDING_SAMPLING_NUM)
label_blob = torch.from_numpy(foreground_labels).unsqueeze(0)
sample = {'label': label_blob}
if cfg.TRAIN.CHROMATIC and cfg.MODE == 'TRAIN' and np.random.rand(
1) > 0.1:
im = chromatic_transform(im)
if cfg.TRAIN.ADD_NOISE and cfg.MODE == 'TRAIN' and np.random.rand(
1) > 0.1:
im = add_noise(im)
im_tensor = torch.from_numpy(im) / 255.0
im_tensor -= self._pixel_mean
image_blob = im_tensor.permute(2, 0, 1)
sample['image_color'] = image_blob
if cfg.INPUT == 'DEPTH' or cfg.INPUT == 'RGBD':
depth_blob = torch.from_numpy(xyz_img).permute(2, 0, 1)
sample['depth'] = depth_blob
return sample
