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dataset.py
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dataset.py
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import os
import cv2
import glob
import numpy as np
from PIL import Image, ImageEnhance
from megengine.data.dataset import Dataset
class Augmentor:
def __init__(
self,
image_height=384,
image_width=512,
max_disp=256,
scale_min=0.6,
scale_max=1.0,
seed=0,
):
super().__init__()
self.image_height = image_height
self.image_width = image_width
self.max_disp = max_disp
self.scale_min = scale_min
self.scale_max = scale_max
self.rng = np.random.RandomState(seed)
def chromatic_augmentation(self, img):
random_brightness = np.random.uniform(0.8, 1.2)
random_contrast = np.random.uniform(0.8, 1.2)
random_gamma = np.random.uniform(0.8, 1.2)
img = Image.fromarray(img)
enhancer = ImageEnhance.Brightness(img)
img = enhancer.enhance(random_brightness)
enhancer = ImageEnhance.Contrast(img)
img = enhancer.enhance(random_contrast)
gamma_map = [
255 * 1.0 * pow(ele / 255.0, random_gamma) for ele in range(256)
] * 3
img = img.point(gamma_map) # use PIL's point-function to accelerate this part
img_ = np.array(img)
return img_
def __call__(self, left_img, right_img, left_disp):
# 1. chromatic augmentation
left_img = self.chromatic_augmentation(left_img)
right_img = self.chromatic_augmentation(right_img)
# 2. spatial augmentation
# 2.1) rotate & vertical shift for right image
if self.rng.binomial(1, 0.5):
angle, pixel = 0.1, 2
px = self.rng.uniform(-pixel, pixel)
ag = self.rng.uniform(-angle, angle)
image_center = (
self.rng.uniform(0, right_img.shape[0]),
self.rng.uniform(0, right_img.shape[1]),
)
rot_mat = cv2.getRotationMatrix2D(image_center, ag, 1.0)
right_img = cv2.warpAffine(
right_img, rot_mat, right_img.shape[1::-1], flags=cv2.INTER_LINEAR
)
trans_mat = np.float32([[1, 0, 0], [0, 1, px]])
right_img = cv2.warpAffine(
right_img, trans_mat, right_img.shape[1::-1], flags=cv2.INTER_LINEAR
)
# 2.2) random resize
resize_scale = self.rng.uniform(self.scale_min, self.scale_max)
left_img = cv2.resize(
left_img,
None,
fx=resize_scale,
fy=resize_scale,
interpolation=cv2.INTER_LINEAR,
)
right_img = cv2.resize(
right_img,
None,
fx=resize_scale,
fy=resize_scale,
interpolation=cv2.INTER_LINEAR,
)
disp_mask = (left_disp < float(self.max_disp / resize_scale)) & (left_disp > 0)
disp_mask = disp_mask.astype("float32")
disp_mask = cv2.resize(
disp_mask,
None,
fx=resize_scale,
fy=resize_scale,
interpolation=cv2.INTER_LINEAR,
)
left_disp = (
cv2.resize(
left_disp,
None,
fx=resize_scale,
fy=resize_scale,
interpolation=cv2.INTER_LINEAR,
)
* resize_scale
)
# 2.3) random crop
h, w, c = left_img.shape
dx = w - self.image_width
dy = h - self.image_height
dy = self.rng.randint(min(0, dy), max(0, dy) + 1)
dx = self.rng.randint(min(0, dx), max(0, dx) + 1)
M = np.float32([[1.0, 0.0, -dx], [0.0, 1.0, -dy]])
left_img = cv2.warpAffine(
left_img,
M,
(self.image_width, self.image_height),
flags=cv2.INTER_LINEAR,
borderValue=0,
)
right_img = cv2.warpAffine(
right_img,
M,
(self.image_width, self.image_height),
flags=cv2.INTER_LINEAR,
borderValue=0,
)
left_disp = cv2.warpAffine(
left_disp,
M,
(self.image_width, self.image_height),
flags=cv2.INTER_LINEAR,
borderValue=0,
)
disp_mask = cv2.warpAffine(
disp_mask,
M,
(self.image_width, self.image_height),
flags=cv2.INTER_LINEAR,
borderValue=0,
)
# 3. add random occlusion to right image
if self.rng.binomial(1, 0.5):
sx = int(self.rng.uniform(50, 100))
sy = int(self.rng.uniform(50, 100))
cx = int(self.rng.uniform(sx, right_img.shape[0] - sx))
cy = int(self.rng.uniform(sy, right_img.shape[1] - sy))
right_img[cx - sx : cx + sx, cy - sy : cy + sy] = np.mean(
np.mean(right_img, 0), 0
)[np.newaxis, np.newaxis]
return left_img, right_img, left_disp, disp_mask
class CREStereoDataset(Dataset):
def __init__(self, root):
super().__init__()
self.imgs = glob.glob(os.path.join(root, "**/*_left.jpg"), recursive=True)
self.augmentor = Augmentor(
image_height=384,
image_width=512,
max_disp=256,
scale_min=0.6,
scale_max=1.0,
seed=0,
)
self.rng = np.random.RandomState(0)
def get_disp(self, path):
disp = cv2.imread(path, cv2.IMREAD_UNCHANGED)
return disp.astype(np.float32) / 32
def __getitem__(self, index):
# find path
left_path = self.imgs[index]
prefix = left_path[: left_path.rfind("_")]
right_path = prefix + "_right.jpg"
left_disp_path = prefix + "_left.disp.png"
right_disp_path = prefix + "_right.disp.png"
# read img, disp
left_img = cv2.imread(left_path, cv2.IMREAD_COLOR)
right_img = cv2.imread(right_path, cv2.IMREAD_COLOR)
left_disp = self.get_disp(left_disp_path)
right_disp = self.get_disp(right_disp_path)
if self.rng.binomial(1, 0.5):
left_img, right_img = np.fliplr(right_img), np.fliplr(left_img)
left_disp, right_disp = np.fliplr(right_disp), np.fliplr(left_disp)
left_disp[left_disp == np.inf] = 0
# augmentaion
left_img, right_img, left_disp, disp_mask = self.augmentor(
left_img, right_img, left_disp
)
left_img = left_img.transpose(2, 0, 1).astype("uint8")
right_img = right_img.transpose(2, 0, 1).astype("uint8")
return {
"left": left_img,
"right": right_img,
"disparity": left_disp,
"mask": disp_mask,
}
def __len__(self):
return len(self.imgs)