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data.py
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data.py
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"""
Based on https://github.com/asanakoy/kaggle_carvana_segmentation
"""
import torch
import torch.utils.data as data
from torch.autograd import Variable as V
import cv2
import numpy as np
import os
def randomHueSaturationValue(image, hue_shift_limit=(-180, 180),
sat_shift_limit=(-255, 255),
val_shift_limit=(-255, 255), u=0.5):
if np.random.random() < u:
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(image)
hue_shift = np.random.randint(hue_shift_limit[0], hue_shift_limit[1]+1)
hue_shift = np.uint8(hue_shift)
h += hue_shift
sat_shift = np.random.uniform(sat_shift_limit[0], sat_shift_limit[1])
s = cv2.add(s, sat_shift)
val_shift = np.random.uniform(val_shift_limit[0], val_shift_limit[1])
v = cv2.add(v, val_shift)
image = cv2.merge((h, s, v))
#image = cv2.merge((s, v))
image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
return image
def randomShiftScaleRotate(image, mask,
shift_limit=(-0.0, 0.0),
scale_limit=(-0.0, 0.0),
rotate_limit=(-0.0, 0.0),
aspect_limit=(-0.0, 0.0),
borderMode=cv2.BORDER_CONSTANT, u=0.5):
if np.random.random() < u:
height, width, channel = image.shape
angle = np.random.uniform(rotate_limit[0], rotate_limit[1])
scale = np.random.uniform(1 + scale_limit[0], 1 + scale_limit[1])
aspect = np.random.uniform(1 + aspect_limit[0], 1 + aspect_limit[1])
sx = scale * aspect / (aspect ** 0.5)
sy = scale / (aspect ** 0.5)
dx = round(np.random.uniform(shift_limit[0], shift_limit[1]) * width)
dy = round(np.random.uniform(shift_limit[0], shift_limit[1]) * height)
cc = np.math.cos(angle / 180 * np.math.pi) * sx
ss = np.math.sin(angle / 180 * np.math.pi) * sy
rotate_matrix = np.array([[cc, -ss], [ss, cc]])
box0 = np.array([[0, 0], [width, 0], [width, height], [0, height], ])
box1 = box0 - np.array([width / 2, height / 2])
box1 = np.dot(box1, rotate_matrix.T) + np.array([width / 2 + dx, height / 2 + dy])
box0 = box0.astype(np.float32)
box1 = box1.astype(np.float32)
mat = cv2.getPerspectiveTransform(box0, box1)
image = cv2.warpPerspective(image, mat, (width, height), flags=cv2.INTER_LINEAR, borderMode=borderMode,
borderValue=(
0, 0,
0,))
mask = cv2.warpPerspective(mask, mat, (width, height), flags=cv2.INTER_LINEAR, borderMode=borderMode,
borderValue=(
0, 0,
0,))
return image, mask
def randomHorizontalFlip(image, mask, u=0.5):
if np.random.random() < u:
image = cv2.flip(image, 1)
mask = cv2.flip(mask, 1)
return image, mask
def randomVerticleFlip(image, mask, u=0.5):
if np.random.random() < u:
image = cv2.flip(image, 0)
mask = cv2.flip(mask, 0)
return image, mask
def randomRotate90(image, mask, u=0.5):
if np.random.random() < u:
image=np.rot90(image)
mask=np.rot90(mask)
return image, mask
def default_loader(id, root):
img = cv2.imread(os.path.join(root,'{}_sat.jpg').format(id))
mask = cv2.imread(os.path.join(root+'{}_mask.png').format(id), cv2.IMREAD_GRAYSCALE)
img = randomHueSaturationValue(img,
hue_shift_limit=(-30, 30),
sat_shift_limit=(-5, 5),
val_shift_limit=(-15, 15))
img, mask = randomShiftScaleRotate(img, mask,
shift_limit=(-0.1, 0.1),
scale_limit=(-0.1, 0.1),
aspect_limit=(-0.1, 0.1),
rotate_limit=(-0, 0))
img, mask = randomHorizontalFlip(img, mask)
img, mask = randomVerticleFlip(img, mask)
img, mask = randomRotate90(img, mask)
mask = np.expand_dims(mask, axis=2)
img = np.array(img, np.float32).transpose(2,0,1)/255.0 * 3.2 - 1.6
mask = np.array(mask, np.float32).transpose(2,0,1)/255.0
mask[mask>=0.5] = 1
mask[mask<=0.5] = 0
#mask = abs(mask-1)
return img, mask
class ImageFolder(data.Dataset):
def __init__(self, trainlist, root):
self.ids = trainlist
self.loader = default_loader
self.root = root
def __getitem__(self, index):
id = self.ids[index]
img, mask = self.loader(id, self.root)
img = torch.Tensor(img)
mask = torch.Tensor(mask)
return img, mask
def __len__(self):
return len(self.ids)