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"""
"LiftFeat: 3D Geometry-Aware Local Feature Matching"
COCO_20k image augmentor
"""
import torch
from torch import nn
from torch.utils.data import Dataset
import torch.utils.data as data
from torchvision import transforms
import torch.nn.functional as F
import cv2
import kornia
import kornia.augmentation as K
from kornia.geometry.transform import get_tps_transform as findTPS
from kornia.geometry.transform import warp_points_tps, warp_image_tps
import glob
import random
import tqdm
import numpy as np
import pdb
import time
random.seed(0)
torch.manual_seed(0)
def generateRandomTPS(shape,grid=(8,6),GLOBAL_MULTIPLIER=0.3,prob=0.5):
h, w = shape
sh, sw = h/grid[0], w/grid[1]
src = torch.dstack(torch.meshgrid(torch.arange(0, h + sh , sh), torch.arange(0, w + sw , sw), indexing='ij'))
offsets = torch.rand(grid[0]+1, grid[1]+1, 2) - 0.5
offsets *= torch.tensor([ sh/2, sw/2 ]).view(1, 1, 2) * min(0.97, 2.0 * GLOBAL_MULTIPLIER)
dst = src + offsets if np.random.uniform() < prob else src
src, dst = src.view(1, -1, 2), dst.view(1, -1, 2)
src = (src / torch.tensor([h,w]).view(1,1,2) ) * 2 - 1.
dst = (dst / torch.tensor([h,w]).view(1,1,2) ) * 2 - 1.
weights, A = findTPS(dst, src)
return src, weights, A
def generateRandomHomography(shape,GLOBAL_MULTIPLIER=0.3):
#Generate random in-plane rotation [-theta,+theta]
theta = np.radians(np.random.uniform(-30, 30))
#Generate random scale in both x and y
scale_x, scale_y = np.random.uniform(0.35, 1.2, 2)
#Generate random translation shift
tx , ty = -shape[1]/2.0 , -shape[0]/2.0
txn, tyn = np.random.normal(0, 120.0*GLOBAL_MULTIPLIER, 2)
c, s = np.cos(theta), np.sin(theta)
#Affine coeffs
sx , sy = np.random.normal(0,0.6*GLOBAL_MULTIPLIER,2)
#Projective coeffs
p1 , p2 = np.random.normal(0,0.006*GLOBAL_MULTIPLIER,2)
# Build Homography from parmeterizations
H_t = np.array(((1,0, tx), (0, 1, ty), (0,0,1))) #t
H_r = np.array(((c,-s, 0), (s, c, 0), (0,0,1))) #rotation,
H_a = np.array(((1,sy, 0), (sx, 1, 0), (0,0,1))) # affine
H_p = np.array(((1, 0, 0), (0 , 1, 0), (p1,p2,1))) # projective
H_s = np.array(((scale_x,0, 0), (0, scale_y, 0), (0,0,1))) #scale
H_b = np.array(((1.0,0,-tx +txn), (0, 1, -ty + tyn), (0,0,1))) #t_back,
#H = H_e * H_s * H_a * H_p
H = np.dot(np.dot(np.dot(np.dot(np.dot(H_b,H_s),H_p),H_a),H_r),H_t)
return H
class COCOAugmentor(nn.Module):
def __init__(self,device,load_dataset=True,
img_dir="/home/yepeng_liu/code_python/dataset/coco_20k",
warp_resolution=(1200, 900),
out_resolution=(400, 300),
sides_crop=0.2,
max_num_imgs=50,
num_test_imgs=10,
batch_size=1,
photometric=True,
geometric=True,
reload_step=1_000
):
super(COCOAugmentor,self).__init__()
self.half=16
self.device=device
self.dims=warp_resolution
self.batch_size=batch_size
self.out_resolution=out_resolution
self.sides_crop=sides_crop
self.max_num_imgs=max_num_imgs
self.num_test_imgs=num_test_imgs
self.dims_t=torch.tensor([int(self.dims[0]*(1. - self.sides_crop)) - int(self.dims[0]*self.sides_crop) -1,
int(self.dims[1]*(1. - self.sides_crop)) - int(self.dims[1]*self.sides_crop) -1]).float().to(device).view(1,1,2)
self.dims_s=torch.tensor([self.dims_t[0,0,0] / out_resolution[0],
self.dims_t[0,0,1] / out_resolution[1]]).float().to(device).view(1,1,2)
self.all_imgs=glob.glob(img_dir+'/*.jpg')+glob.glob(img_dir+'/*.png')
self.photometric=photometric
self.geometric=geometric
self.cnt=1
self.reload_step=reload_step
list_augmentation=[
kornia.augmentation.ColorJitter(0.15,0.15,0.15,0.15,p=1.),
kornia.augmentation.RandomEqualize(p=0.4),
kornia.augmentation.RandomGaussianBlur(p=0.3,sigma=(2.0,2.0),kernel_size=(7,7))
]
if photometric is False:
list_augmentation = []
self.aug_list=kornia.augmentation.ImageSequential(*list_augmentation)
if len(self.all_imgs)<10:
raise RuntimeError('Couldnt find enough images to train. Please check the path: ',img_dir)
if load_dataset:
print('[COCO]: ',len(self.all_imgs),' images for training..')
if len(self.all_imgs) - num_test_imgs < max_num_imgs:
raise RuntimeError('Error: test set overlaps with training set! Decrease number of test imgs')
self.load_imgs()
self.TPS = True
def load_imgs(self):
random.shuffle(self.all_imgs)
train = []
for p in tqdm.tqdm(self.all_imgs[:self.max_num_imgs],desc='loading train'):
im=cv2.imread(p)
halfH,halfW=im.shape[0]//2,im.shape[1]//2
if halfH>halfW:
im=np.rot90(im)
halfH,halfW=halfW,halfH
if im.shape[0]!=self.dims[1] or im.shape[1]!=self.dims[0]:
im = cv2.resize(im, self.dims)
train.append(np.copy(im))
self.train=train
self.test=[
cv2.resize(cv2.imread(p),self.dims)
for p in tqdm.tqdm(self.all_imgs[-self.num_test_imgs:],desc='loading test')
]
def norm_pts_grid(self, x):
if len(x.size()) == 2:
return (x.view(1,-1,2) * self.dims_s / self.dims_t) * 2. - 1
return (x * self.dims_s / self.dims_t) * 2. - 1
def denorm_pts_grid(self, x):
if len(x.size()) == 2:
return ((x.view(1,-1,2) + 1) / 2.) / self.dims_s * self.dims_t
return ((x+1) / 2.) / self.dims_s * self.dims_t
def rnd_kps(self, shape, n = 256):
h, w = shape
kps = torch.rand(size = (3,n)).to(self.device)
kps[0,:]*=w
kps[1,:]*=h
kps[2,:] = 1.0
return kps
def warp_points(self, H, pts):
scale = self.dims_s.view(-1,2)
offset = torch.tensor([int(self.dims[0]*self.sides_crop), int(self.dims[1]*self.sides_crop)], device = pts.device).float()
pts = pts*scale + offset
pts = torch.vstack( [pts.t(), torch.ones(1, pts.shape[0], device = pts.device)])
warped = torch.matmul(H, pts)
warped = warped / warped[2,...]
warped = warped.t()[:, :2]
return (warped - offset) / scale
@torch.inference_mode()
def forward(self, x, difficulty = 0.3, TPS = False, prob_deformation = 0.5, test = False):
"""
Perform augmentation to a batch of images.
input:
x -> torch.Tensor(B, C, H, W): rgb images
difficulty -> float: level of difficulty, 0.1 is medium, 0.3 is already pretty hard
tps -> bool: Wether to apply non-rigid deformations in images
prob_deformation -> float: probability to apply a deformation
return:
'output' -> torch.Tensor(B, C, H, W): rgb images
Tuple:
'H' -> torch.Tensor(3,3): homography matrix
'mask' -> torch.Tensor(B, H, W): mask of valid pixels after warp
(deformation only)
src, weights, A are parameters from a TPS warp (all torch.Tensors)
"""
if self.cnt % self.reload_step == 0:
self.load_imgs()
if self.geometric is False:
difficulty = 0.
with torch.no_grad():
x = (x/255.).to(self.device)
b, c, h, w = x.shape
shape = (h, w)
######## Geometric Transformations
H = torch.tensor(np.array([generateRandomHomography(shape,difficulty) for b in range(self.batch_size)]),dtype=torch.float32).to(self.device)
output = kornia.geometry.transform.warp_perspective(x,H,dsize=shape,padding_mode='zeros')
#crop % of image boundaries each side to reduce invalid pixels after warps
low_h = int(h * self.sides_crop); low_w = int(w*self.sides_crop)
high_h = int(h*(1. - self.sides_crop)); high_w= int(w * (1. - self.sides_crop))
output = output[..., low_h:high_h, low_w:high_w]
x = x[..., low_h:high_h, low_w:high_w]
#apply TPS if desired:
if TPS:
src, weights, A = None, None, None
for b in range(self.batch_size):
b_src, b_weights, b_A = generateRandomTPS(shape, (8,6), difficulty, prob = prob_deformation)
b_src, b_weights, b_A = b_src.to(self.device), b_weights.to(self.device), b_A.to(self.device)
if src is None:
src, weights, A = b_src, b_weights, b_A
else:
src = torch.cat((b_src, src))
weights = torch.cat((b_weights, weights))
A = torch.cat((b_A, A))
output = warp_image_tps(output, src, weights, A)
output = F.interpolate(output, self.out_resolution[::-1], mode = 'nearest')
x = F.interpolate(x, self.out_resolution[::-1], mode = 'nearest')
mask = ~torch.all(output == 0, dim=1, keepdim=True)
mask = mask.expand(-1,3,-1,-1)
# Make-up invalid regions with texture from the batch
rv = 1 if not TPS else 2
output_shifted = torch.roll(x, rv, 0)
output[~mask] = output_shifted[~mask]
mask = mask[:, 0, :, :]
######## Photometric Transformations
output = self.aug_list(output)
b, c, h, w = output.shape
#Correlated Gaussian Noise
if np.random.uniform() > 0.5 and self.photometric:
noise = F.interpolate(torch.randn_like(output)*(10/255), (h//2, w//2))
noise = F.interpolate(noise, (h, w), mode = 'bicubic')
output = torch.clip( output + noise, 0., 1.)
#Random shadows
if np.random.uniform() > 0.6 and self.photometric:
noise = torch.rand((b, 1, h//64, w//64), device = self.device) * 1.3
noise = torch.clip(noise, 0.25, 1.0)
noise = F.interpolate(noise, (h, w), mode = 'bicubic')
noise = noise.expand(-1, 3, -1, -1)
output *= noise
output = torch.clip( output, 0., 1.)
self.cnt+=1
if TPS:
return output, (H, src, weights, A, mask)
else:
return output, (H, mask)
def get_correspondences(self, kps_target, T):
H, H2, src, W, A = T
undeformed = self.denorm_pts_grid(
warp_points_tps(self.norm_pts_grid(kps_target),
src, W, A) ).view(-1,2)
warped_to_src = self.warp_points([email protected](H2), undeformed)
return warped_to_src |