ids[temp == l] = self.valid_classes[l]
return ids
def encode_segmap(self, mask):
# Put all void classes to zero
for _voidc in self.void_classes:
mask[mask == _voidc] = self.ignore_index
for _validc in self.valid_classes:
mask[mask == _validc] = self.class_map[_validc]
return mask
if __name__ == "__main__":
import matplotlib.pyplot as plt
augmentations = Compose([Scale(2048), RandomRotate(10), RandomHorizontallyFlip(0.5)])
local_path = "/datasets01/cityscapes/112817/"
dst = cityscapesLoader(local_path, is_transform=True, augmentations=augmentations)
bs = 4
trainloader = data.DataLoader(dst, batch_size=bs, num_workers=0)
for i, data_samples in enumerate(trainloader):
imgs, labels = data_samples
import pdb
pdb.set_trace()
imgs = imgs.numpy()[:, ::-1, :, :]
imgs = np.transpose(imgs, [0, 2, 3, 1])
f, axarr = plt.subplots(bs, 2)
for j in range(bs):
axarr[j][0].imshow(imgs[j])
for l in range(0, n_classes):
r[temp == l] = label_colours[l, 0]
g[temp == l] = label_colours[l, 1]
b[temp == l] = label_colours[l, 2]
rgb = np.zeros((temp.shape[0], temp.shape[1], 3))
rgb[:, :, 0] = r / 255.0
rgb[:, :, 1] = g / 255.0
rgb[:, :, 2] = b / 255.0
return rgb
if __name__ == "__main__":
local_path = "/home/meetshah1995/datasets/segnet/CamVid"
augmentations = Compose([RandomRotate(10), RandomHorizontallyFlip()])
dst = camvidLoader(local_path,
is_transform=True,
augmentations=augmentations)
bs = 4
trainloader = data.DataLoader(dst, batch_size=bs)
for i, data_samples in enumerate(trainloader):
imgs, labels = data_samples
imgs = imgs.numpy()[:, ::-1, :, :]
imgs = np.transpose(imgs, [0, 2, 3, 1])
f, axarr = plt.subplots(bs, 2)
for j in range(bs):
axarr[j][0].imshow(imgs[j])
axarr[j][1].imshow(dst.decode_segmap(labels.numpy()[j]))
plt.show()
g = temp.copy()
b = temp.copy()
for l in range(0, self.n_classes):
r[temp == l] = self.class_colors[l][0]
g[temp == l] = self.class_colors[l][1]
b[temp == l] = self.class_colors[l][2]
rgb = np.zeros((temp.shape[0], temp.shape[1], 3))
rgb[:, :, 0] = r / 255.0
rgb[:, :, 1] = g / 255.0
rgb[:, :, 2] = b / 255.0
return rgb
if __name__ == "__main__":
augment = Compose([RandomHorizontallyFlip(), RandomRotate(6)])
local_path = "/private/home/meetshah/datasets/seg/vistas/"
dst = mapillaryVistasLoader(local_path,
img_size=(512, 1024),
is_transform=True,
augmentations=augment)
bs = 8
trainloader = data.DataLoader(dst,
batch_size=bs,
num_workers=4,
shuffle=True)
for i, data_samples in enumerate(trainloader):
x = dst.decode_segmap(data_samples[1][0].numpy())
print("batch :", i)
ids[temp == l] = self.valid_classes[l]
return ids
def encode_segmap(self, mask):
# Put all void classes to zero
for _voidc in self.void_classes:
mask[mask == _voidc] = self.ignore_index
for _validc in self.valid_classes:
mask[mask == _validc] = self.class_map[_validc]
return mask
if __name__ == "__main__":
import matplotlib.pyplot as plt
augmentations = Compose([RandomHorizontallyFlip(0.5)])
local_path = "/datasets01/cityscapes/112817/"
dst = tempestLoader(local_path,
is_transform=True,
augmentations=augmentations)
bs = 4
trainloader = data.DataLoader(dst, batch_size=bs, num_workers=0)
for i, data_samples in enumerate(trainloader):
imgs, labels = data_samples
import pdb
pdb.set_trace()
imgs = imgs.numpy()[:, ::-1, :, :]
imgs = np.transpose(imgs, [0, 2, 3, 1])
f, axarr = plt.subplots(bs, 2)
return img, lbl
def decode_segmap(self, temp):
r = temp.copy()
g = temp.copy()
b = temp.copy()
for l in range(0, self.n_classes):
r[temp == l] = self.class_colors[l][0]
g[temp == l] = self.class_colors[l][1]
b[temp == l] = self.class_colors[l][2]
rgb = np.zeros((temp.shape[0], temp.shape[1], 3))
rgb[:, :, 0] = r / 255.0
rgb[:, :, 1] = g / 255.0
rgb[:, :, 2] = b / 255.0
return rgb
if __name__ == "__main__":
augment = Compose([RandomRotate(6)])
local_path = "/home/ruslan/datasets"
dst = mapillaryVistasLoader(
local_path, img_size=(512, 1024), is_transform=True, augmentations=augment
)
bs = 8
trainloader = data.DataLoader(dst, batch_size=bs, num_workers=4, shuffle=True)
for i, data_samples in enumerate(trainloader):
x = dst.decode_segmap(data_samples[1][0].numpy())
print("batch :", i)
def encode_segmap(self, mask):
# Put all void classes to zero
for _voidc in self.void_classes:
mask[mask == _voidc] = self.ignore_index
for _validc in self.valid_classes:
mask[mask == _validc] = self.class_map[_validc]
#print(np.unique(mask))
return mask
if __name__ == "__main__":
import matplotlib.pyplot as plt
#augmentations = Compose([Scale(800), RandomRotate(180), RandomHorizontallyFlip(0.5)])
augmentations = Compose([Scale(800), RandomHorizontallyFlip(0.5)])
local_path = "/data0/qilei_chen/old_alien/AI_EYE_IMGS/ROP_DATASET_with_label/9LESIONS"
dst = ROPRidge_loader(local_path,
is_transform=True,
augmentations=augmentations)
bs = 4
trainloader = data.DataLoader(dst, batch_size=bs, num_workers=0)
for i, data_samples in enumerate(trainloader):
imgs, labels = data_samples
#import pdb
#pdb.set_trace()
imgs = imgs.numpy()[:, ::-1, :, :]
imgs = np.transpose(imgs, [0, 2, 3, 1])
f, axarr = plt.subplots(bs, 2)
