def __call__(self, input_orig, target_orig):
# do something to both images
input = Resize(self.height, Image.BILINEAR)(input_orig)
target = Resize(self.height, Image.NEAREST)(target_orig)
if(self.augment):
# Random hflip
hflip = random.random()
if (hflip < 0.5):
input = input.transpose(Image.FLIP_LEFT_RIGHT)
target = target.transpose(Image.FLIP_LEFT_RIGHT)
#Random translation 0-2 pixels (fill rest with padding
transX = random.randint(-2, 2)
transY = random.randint(-2, 2)
input = ImageOps.expand(input, border=(transX,transY,0,0), fill=0)
target = ImageOps.expand(target, border=(transX,transY,0,0), fill=255) #pad label filling with 255
input = input.crop((0, 0, input.size[0]-transX, input.size[1]-transY))
target = target.crop((0, 0, target.size[0]-transX, target.size[1]-transY))
input = ToTensor()(input)
input_orig = ToTensor()(input_orig)
if (self.enc):
target = Resize(int(self.height/8), Image.NEAREST)(target)
target = ToLabel()(target)
target_orig = ToLabel()(target_orig)
target = Relabel(255, 1)(target)
target_orig = Relabel(255, 1)(target_orig)
return input, target, input_orig, target_orig
def __call__(self, input, target):
# do something to both images
input = cv2.resize(input, self.size, interpolation=cv2.INTER_LINEAR)
target = cv2.resize(target,self.size,interpolation=cv2.INTER_NEAREST)
if self.rescale:
input = (input/255.).astype(np.float32)
# target = target/255.
target = target.astype(np.float32)
if(self.augment):
# Random hflip
hflip = random.random()
if (hflip < 0.5):
input = cv2.flip(input,1)
target = cv2.flip(target, 1)
#Random translation 0-2 pixels (fill rest with padding
# transX = random.randint(-2, 2)
# transY = random.randint(-2, 2)
#
# input = ImageOps.expand(input, border=(transX,transY,0,0), fill=0)
# target = ImageOps.expand(target, border=(transX,transY,0,0), fill=255) #pad label filling with 255
# input = input.crop((0, 0, input.size[0]-transX, input.size[1]-transY))
# target = target.crop((0, 0, target.size[0]-transX, target.size[1]-transY))
input = ToTensor()(input)
target = ToLabel()(target)
target = Relabel(255, 3)(target)
return input, target
def __call__(self, input, target):
# do something to both images
input = Resize(self.height, Image.BILINEAR)(input)
target = Resize(self.height, Image.NEAREST)(target)
if(self.augment):
# Random hflip
hflip = random.random()
if (hflip < 0.5):
input = input.transpose(Image.FLIP_LEFT_RIGHT)
target = target.transpose(Image.FLIP_LEFT_RIGHT)
#Random translation 0-2 pixels (fill rest with padding
transX = random.randint(-2, 2)
transY = random.randint(-2, 2)
input = ImageOps.expand(input, border=(transX,transY,0,0), fill=0)
target = ImageOps.expand(target, border=(transX,transY,0,0), fill=255) #pad label filling with 255
input = input.crop((0, 0, input.size[0]-transX, input.size[1]-transY))
target = target.crop((0, 0, target.size[0]-transX, target.size[1]-transY))
#TODO future: additional augments
#CenterCrop(256)
#Normalize([.485, .456, .406], [.229, .224, .225]),
input = ToTensor()(input)
if (self.enc):
target = Resize(int(self.height/8), Image.NEAREST)(target)
target = ToLabel()(target)
target = Relabel(255, 19)(target)
return input, target
def __call__(self, input, tinput, target):
input = Resize(self.height, Image.BILINEAR)(input)
tinput = Resize(self.height, Image.BILINEAR)(tinput)
target = Resize(self.height, Image.NEAREST)(target)
input = ToTensor()(input)
tinput = ToTensor()(tinput)
target = ToLabel()(target)
target = Relabel(255, 19)(target)
return input, tinput, target
def __call__(self, input, gray, target):
# do something to both images
if self.height != 8.1:
input = Resize(self.height, Image.BILINEAR)(input)
gray = Resize(self.height, Image.BILINEAR)(gray)
target = Resize(self.height, Image.NEAREST)(target)
# === Data Augmentation === #
if (self.dataAugment):
# 1. Random horizenal flip
hflip = random.random()
if (hflip < 0.5):
input = input.transpose(Image.FLIP_LEFT_RIGHT)
gray = gray.transpose(Image.FLIP_LEFT_RIGHT)
target = target.transpose(Image.FLIP_LEFT_RIGHT)
# 2. Random translation 0-2 pixels (fill rest with padding
transX = random.randint(-2, 2)
transY = random.randint(-2, 2)
input = ImageOps.expand(input,
border=(transX, transY, 0, 0),
fill=0)
gray = ImageOps.expand(gray, border=(transX, transY, 0, 0), fill=0)
target = ImageOps.expand(target,
border=(transX, transY, 0, 0),
fill=255) #pad label filling with 255
input = input.crop(
(0, 0, input.size[0] - transX, input.size[1] - transY))
gray = gray.crop(
(0, 0, gray.size[0] - transX, gray.size[1] - transY))
target = target.crop(
(0, 0, target.size[0] - transX, target.size[1] - transY))
input = ToTensor()(input)
gray = ToTensor()(gray)
if (self.encoderOnly):
target = Resize(int(self.height / 8), Image.NEAREST)(target)
target = ToLabel()(target)
target = Relabel(255, 19)(target) # ignored label 255 -> 19
input = torch.cat((input, gray), 0)
return input, target
def __call__(self, input, target):
# do something to both images
input = cv2.resize(input, (self.weight, self.height),
interpolation=cv2.INTER_LINEAR)
target = cv2.resize(target, (self.weight, self.height),
interpolation=cv2.INTER_NEAREST)
if self.rescale:
input = input / 255.
if (self.augment):
# Random hflip
hflip = random.random()
if (hflip < 0.5):
input = cv2.flip(input, 1)
target = cv2.flip(target, 1)
input = ToTensor()(input)
target = torch.from_numpy(np.array(target)).long().unsqueeze(0)
# target = Relabel(0, 1)(target) #障碍
# target = Relabel(35, 2)(target) #树
# target = Relabel(96, 2)(target) #树
# target = Relabel(100, 4)(target) #天
# target = Relabel(150, 3)(target) #草地
# target = Relabel(170, 6)(target) #沙地
# target = Relabel(255, 5)(target) # void
# 0->可通行，1->不可通行，2,3->待判断，4->天空
target = Relabel(0, 0)(target) #障碍
target = Relabel(35, 0)(target) #树
target = Relabel(96, 0)(target) #树
target = Relabel(99, 4)(target) #天
target = Relabel(149, 2)(target) #草地
target = Relabel(170, 3)(target) #沙地 2
target = Relabel(255, 0)(target) # void
return input, target
import visdom
NUM_CHANNELS = 3
NUM_CLASSES = 20
image_transform = ToPILImage()
input_transform_cityscapes = Compose([
Resize((512, 1024), Image.BILINEAR),
ToTensor(),
#Normalize([.485, .456, .406], [.229, .224, .225]),
])
target_transform_cityscapes = Compose([
Resize((512, 1024), Image.NEAREST),
ToLabel(),
Relabel(255, 19), #ignore label to 19
])
cityscapes_trainIds2labelIds = Compose([
Relabel(19, 255),
Relabel(18, 33),
Relabel(17, 32),
Relabel(16, 31),
Relabel(15, 28),
Relabel(14, 27),
Relabel(13, 26),
Relabel(12, 25),
Relabel(11, 24),
Relabel(10, 23),
Relabel(9, 22),
Relabel(8, 21),
from erfnet import ERFNet
from transform import Relabel, ToLabel, Colorize
from iouEval import iouEval, getColorEntry
NUM_CHANNELS = 3
NUM_CLASSES = 20
image_transform = ToPILImage()
input_transform_cityscapes = Compose([
Resize(512, Image.BILINEAR),
ToTensor(),
])
target_transform_cityscapes = Compose([
Resize(512, Image.NEAREST),
ToLabel(),
Relabel(255, 19), #ignore label to 19
])
def main(args):
modelpath = args.loadDir + args.loadModel
weightspath = args.loadDir + args.loadWeights
print("Loading model: " + modelpath)
print("Loading weights: " + weightspath)
model = ERFNet(NUM_CLASSES)
model = torch.nn.DataParallel(model)
if (not args.cpu):
NUM_CHANNELS = 3
NUM_CLASSES = 20 #pascal=22, cityscapes=20
color_transform = Colorize(NUM_CLASSES)
image_transform = ToPILImage()
input_transform = Compose([
CenterCrop(256),
ToTensor(),
Normalize([.485, .456, .406], [.229, .224, .225]),
])
target_transform = Compose([
CenterCrop(256),
ToLabel(),
Relabel(255, 21),
])
#Augmentations - different function implemented to perform random augments on both image and target
class MyCoTransform(object):
def __init__(self, enc, augment=True, height=512):
self.enc = enc
self.augment = augment
self.height = height
pass
def __call__(self, input, target):
# do something to both images
input = Resize(self.height, Image.BILINEAR)(input)
target = Resize(self.height, Image.NEAREST)(target)
from erfnet import ERFNet
from transform import Relabel, ToLabel, Colorize
NUM_CHANNELS = 3
NUM_CLASSES = 2
image_transform = ToPILImage()
input_transform_MAV = Compose([
Resize(512),
ToTensor(),
#Normalize([.485, .456, .406], [.229, .224, .225]),
])
target_transform_MAV = Compose([
Resize(512),
ToLabel(),
Relabel(101, 0),
Relabel(201, 0), #ignore label to 19
])
MAV_trainIds2labelIds = Compose([
Relabel(1, 1),
Relabel(0, 0),
ToPILImage(),
Resize(1024, Image.NEAREST),
])
def main(args):
modelpath = args.loadDir + args.loadModel
weightspath = args.loadDir + args.loadWeights
from transform import Relabel, ToLabel, Colorize
import visdom
NUM_CLASSES = 11
image_transform = ToPILImage()
input_transform_geoMat = Compose([
Resize((104, 104), Image.BILINEAR),
ToTensor(),
#Normalize([.485, .456, .406], [.229, .224, .225]),
])
target_transform_geoMat = Compose([
Resize((104, 104), Image.NEAREST),
ToLabel(),
Relabel(0, 0),
Relabel(25, 1),
Relabel(50, 2),
Relabel(75, 3),
Relabel(100, 4),
Relabel(125, 5),
Relabel(150, 6),
Relabel(175, 7),
Relabel(200, 8),
Relabel(225, 9),
Relabel(250, 10),
Relabel(255, 10),
])
def main(args):
import visdom
NUM_CHANNELS = 3
NUM_CLASSES = 4
image_transform = ToPILImage()
input_transform_cityscapes = Compose([
Resize((512, 1024), Image.BILINEAR),
ToTensor(),
#Normalize([.485, .456, .406], [.229, .224, .225]),
])
target_transform_cityscapes = Compose([
Resize((512, 1024), Image.NEAREST),
ToLabel(),
Relabel(0, 1),
Relabel(35, 2),
Relabel(96, 2),
Relabel(100, 4),
Relabel(150, 3),
Relabel(170, 6),
Relabel(255, 5),
])
def main(args):
modelpath = args.loadDir + args.loadModel
#weightspath = args.loadDir + args.loadWeights #TODO
weightspath = "../save/feriburgForest_3/model_best.pth"
print("Loading model: " + modelpath)
NUM_CHANNELS = 3
NUM_CLASSES = 1 # 20
image_transform = ToPILImage()
input_transform_lot = Compose([
Resize((512, 1024), Image.BILINEAR),
ToTensor(),
#Normalize([.485, .456, .406], [.229, .224, .225]),
])
label_transform_lot = Compose([
Resize((512, 1024), Image.NEAREST),
ToLabel(),
])
pred_transform_lot = Compose([
# Resize((512,1024),Image.NEAREST),
Relabel(1, 255),
ToPILImage(),
Resize((480, 640), Image.NEAREST),
])
def main(args):
modelpath = args.loadDir + args.loadModel
weightspath = args.loadDir + args.loadWeights
print("Loading model: " + modelpath)
print("Loading weights: " + weightspath)
#Import ERFNet model from the folder
#Net = importlib.import_module(modelpath.replace("/", "."), "ERFNet")
from dataset import camvid
from transform import Relabel, ToLabel, Colorize
from iouEval import iouEval, getColorEntry
from FSFNet_Camvid import FSFNet
NUM_CHANNELS = 3
NUM_CLASSES = 12
image_transform = ToPILImage()
input_transform_camvid = Compose([
ToTensor(),
])
target_transform_camvid = Compose([
ToLabel(),
Relabel(255, 11), #ignore label to 19
])
def main(args):
modelpath = args.loadDir + args.loadModel
weightspath = args.loadDir + args.loadWeights
print("Loading model: " + modelpath)
print("Loading weights: " + weightspath)
model = FSFNet(NUM_CLASSES)
#model = torch.nn.DataParallel(model)
if (not args.cpu):
def __call__(self, input, target):
# do something to both images
input = Resize(self.height, Image.BILINEAR)(input)
target = Resize(self.height, Image.NEAREST)(target)
if(self.augment):
# Random hflip
hflip = random.random()
if (hflip < 0.5):
input = input.transpose(Image.FLIP_LEFT_RIGHT)
target = target.transpose(Image.FLIP_LEFT_RIGHT)
#Random translation 0-2 pixels (fill rest with padding
transX = random.randint(-2, 2)
transY = random.randint(-2, 2)
input = ImageOps.expand(input, border=(transX,transY,0,0), fill=0)
target = ImageOps.expand(target, border=(transX,transY,0,0), fill=255) #pad label filling with 255
input = input.crop((0, 0, input.size[0]-transX, input.size[1]-transY))
target = target.crop((0, 0, target.size[0]-transX, target.size[1]-transY))
input = ToTensor()(input)
target = ToLabel()(target)
#target = Relabel(255, 19)(target) #TODO
target = Relabel(0, 0)(target)
target = Relabel(25, 1)(target)
target = Relabel(50, 2)(target)
target = Relabel(75, 3)(target)
target = Relabel(100, 4)(target)
target = Relabel(125, 5)(target)
target = Relabel(150, 6)(target)
target = Relabel(175, 7)(target)
target = Relabel(200, 8)(target)
target = Relabel(225, 9)(target)
target = Relabel(250, 10)(target)
target = Relabel(255, 10)(target)
return input, target
from erfnet import ERFNet
from transform import Relabel, ToLabel, Colorize
from iouEval import iouEval, getColorEntry
NUM_CHANNELS = 3
NUM_CLASSES = 2
image_transform = ToPILImage()
input_transform_MAV = Compose([
Resize(512, Image.BILINEAR),
ToTensor(),
])
target_transform_MAV = Compose([
Resize(512, Image.NEAREST),
ToLabel(),
Relabel(101, 0),
Relabel(201, 0), #ignore label to 19
])
def main(args):
modelpath = args.loadDir + args.loadModel
weightspath = args.loadDir + args.loadWeights
print ("Loading model: " + modelpath)
print ("Loading weights: " + weightspath)
model = ERFNet(NUM_CLASSES)
#model = torch.nn.DataParallel(model)
if (not args.cpu):