def train_transform(rgb, depth):
s = np.random.uniform(1.0, 1.5) # random scaling
# print("scale factor s={}".format(s))
depth_np = depth / s
angle = np.random.uniform(-5.0, 5.0) # random rotation degrees
do_flip = np.random.uniform(0.0, 1.0) < 0.5 # random horizontal flip
# perform 1st part of data augmentation
transform = transforms.Compose([
transforms.Resize(
250.0 / iheight
), # this is for computational efficiency, since rotation is very slow
transforms.Rotate(angle),
transforms.Resize(s),
transforms.CenterCrop((oheight, owidth)),
transforms.HorizontalFlip(do_flip)
])
rgb_np = transform(rgb)
# random color jittering
rgb_np = color_jitter(rgb_np)
rgb_np = np.asfarray(rgb_np, dtype='float') / 255
depth_np = transform(depth_np)
return rgb_np, depth_np
def train_transform(rgb, depth):
s = np.random.uniform(1.0, 1.5) # random scaling
# print("scale factor s={}".format(s))
depth_np = depth / s
angle = np.random.uniform(-5.0, 5.0) # random rotation degrees
do_flip = np.random.uniform(0.0, 1.0) < 0.5 # random horizontal flip
# perform 1st part of data augmentation
transform = transforms.Compose([
transforms.Crop(130, 10, 240, 1200),
transforms.Rotate(angle),
transforms.Resize(s),
transforms.CenterCrop((oheight, owidth)),
transforms.HorizontalFlip(do_flip)
])
rgb_np = transform(rgb)
# random color jittering
rgb_np = color_jitter(rgb_np)
rgb_np = np.asfarray(rgb_np, dtype='float') / 255
# Scipy affine_transform produced RuntimeError when the depth map was
# given as a 'numpy.ndarray'
depth_np = np.asfarray(depth_np, dtype='float32')
depth_np = transform(depth_np)
return rgb_np, depth_np
def train_transform(rgb, depth):
s = np.random.uniform(1.0, 1.5) # random scaling
# print("scale factor s={}".format(s))
depth_np = depth / s
angle = np.random.uniform(-5.0, 5.0) # random rotation degrees
do_flip = np.random.uniform(0.0, 1.0) < 0.5 # random horizontal flip
# set zeros in depth as NaN
depth_np[depth_np == 0] = np.nan
# perform 1st part of data augmentation
transform = transforms.Compose([
transforms.Resize(
float(image_size) / iheight
), # this is for computational efficiency, since rotation is very slow
transforms.Rotate(angle),
transforms.Resize(s),
transforms.CenterCrop((oheight, owidth)),
transforms.HorizontalFlip(do_flip),
])
rgb_np = transform(rgb)
# random color jittering
rgb_np = color_jitter(rgb_np)
rgb_np = np.asfarray(rgb_np, dtype='float') / 255
rgb_np = normalize(rgb_np) # from [0,1] to [-1,1]
depth_np = transform(depth_np)
depth_np[np.isnan(depth_np)] = 0
depth_np = depth_np / 10.0
return rgb_np, depth_np
def train_transform(self, rgb: np.ndarray, depth_raw: np.ndarray,
depth_fix: np.ndarray) -> TNpData:
s = np.random.uniform(1.0, 1.5) # random scaling
depth_raw = depth_raw / s
depth_fix = depth_fix / s
angle = np.random.uniform(-5.0, 5.0) # random rotation degrees
do_flip = np.random.uniform(0.0, 1.0) < 0.5 # random horizontal flip
# perform 1st part of data augmentation
transform = transforms.Compose([
transforms.Resize(
250.0 / self.iheight
), # this is for computational efficiency, since rotation is very slow
transforms.Rotate(angle),
transforms.Resize(s),
transforms.CenterCrop((self.oheight, self.owidth)),
transforms.HorizontalFlip(do_flip)
])
rgb = transform(rgb)
# random color jittering
rgb = color_jitter(rgb)
rgb = np.asfarray(rgb, dtype='float') / 255
depth_raw = transform(depth_raw)
depth_fix = transform(depth_fix)
return rgb, depth_raw, depth_fix
def train_transform(rgb, depth):
s = np.random.uniform(1.0, 1.5) # random scaling
# print("scale factor s={}".format(s))
depth_np = depth / s
angle = np.random.uniform(-5.0, 5.0) # random rotation degrees
do_flip = np.random.uniform(0.0, 1.0) < 0.5 # random horizontal flip
# perform 1st part of data augmentation
transform = transforms.Compose([
#transforms.Resize(530 / iheight), # this is for computational efficiency, since rotation is very slow
transforms.Resize(250 / iheight),
transforms.Rotate(angle),
transforms.Resize(s),
transforms.CenterCrop((oheight, owidth)),
transforms.HorizontalFlip(do_flip)
])
rgb_np = transform(rgb)
# 自己添加
# rgb_np = Transform.resize(rgb_np, [512, 512])
rgb_np = cv2.resize(rgb_np, (512, 512), interpolation=cv2.INTER_NEAREST)
###########
# random color jittering
rgb_np = color_jitter(rgb_np)
rgb_np = np.asfarray(rgb_np, dtype='float') / 255
depth_np = transform(depth_np)
#自己添加
depth_np = cv2.resize(depth_np, (512, 512),
interpolation=cv2.INTER_NEAREST)
#depth_np=Transform.resize(depth_np,[512,512])
###########
#data=rgb_np*255
#data=Image.fromarray(data.astype(np.uint8))
#data.show()
return rgb_np, depth_np