Spaces:
Running
Running
File size: 4,417 Bytes
2c8b554 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 |
import os
from sys import exit, argv
import csv
import random
import joblib
import numpy as np
import cv2
from PIL import Image
from tqdm import tqdm
import torch
from torch.utils.data import Dataset
from lib.utils import preprocess_image, grid_positions, upscale_positions
np.random.seed(0)
class PhotoTourismIPR(Dataset):
def __init__(self, base_path, preprocessing, train=True, cropSize=256):
self.base_path = base_path
self.train = train
self.preprocessing = preprocessing
self.valid_images = []
self.cropSize=cropSize
def getImageFiles(self):
img_files = []
img_path = "dense/images"
if self.train:
print("Inside training!!")
with open(os.path.join("configs", "train_scenes_small.txt")) as f:
scenes = f.read().strip("\n").split("\n")
print("[INFO]",scenes)
for scene in scenes:
image_dir = os.path.join(self.base_path, scene, img_path)
img_names = os.listdir(image_dir)
img_files += [os.path.join(image_dir, img) for img in img_names]
return img_files
def imgCrop(self, img1):
w, h = img1.size
left = np.random.randint(low = 0, high = w - (self.cropSize))
upper = np.random.randint(low = 0, high = h - (self.cropSize))
cropImg = img1.crop((left, upper, left+self.cropSize, upper+self.cropSize))
return cropImg
def getGrid(self, im1, im2, H, scaling_steps=3):
h1, w1 = int(im1.shape[0]/(2**scaling_steps)), int(im1.shape[1]/(2**scaling_steps))
device = torch.device("cpu")
fmap_pos1 = grid_positions(h1, w1, device)
pos1 = upscale_positions(fmap_pos1, scaling_steps=scaling_steps).data.cpu().numpy()
pos1[[0, 1]] = pos1[[1, 0]]
ones = np.ones((1, pos1.shape[1]))
pos1Homo = np.vstack((pos1, ones))
pos2Homo = np.dot(H, pos1Homo)
pos2Homo = pos2Homo/pos2Homo[2, :]
pos2 = pos2Homo[0:2, :]
pos1[[0, 1]] = pos1[[1, 0]]
pos2[[0, 1]] = pos2[[1, 0]]
pos1 = pos1.astype(np.float32)
pos2 = pos2.astype(np.float32)
ids = []
for i in range(pos2.shape[1]):
x, y = pos2[:, i]
if(2 < x < (im1.shape[0]-2) and 2 < y < (im1.shape[1]-2)):
ids.append(i)
pos1 = pos1[:, ids]
pos2 = pos2[:, ids]
return pos1, pos2
def imgRotH(self, img1, min=0, max=360):
width, height = img1.size
theta = np.random.randint(low=min, high=max) * (np.pi / 180)
Tx = width / 2
Ty = height / 2
sx = random.uniform(-1e-2, 1e-2)
sy = random.uniform(-1e-2, 1e-2)
p1 = random.uniform(-1e-4, 1e-4)
p2 = random.uniform(-1e-4, 1e-4)
alpha = np.cos(theta)
beta = np.sin(theta)
He = np.matrix([[alpha, beta, Tx * (1 - alpha) - Ty * beta], [-beta, alpha, beta * Tx + (1 - alpha) * Ty], [0, 0, 1]])
Ha = np.matrix([[1, sy, 0], [sx, 1, 0], [0, 0, 1]])
Hp = np.matrix([[1, 0, 0], [0, 1, 0], [p1, p2, 1]])
H = He @ Ha @ Hp
return H, theta
def build_dataset(self):
print("Building Dataset.")
imgFiles = self.getImageFiles()
for idx in tqdm(range(len(imgFiles))):
img = imgFiles[idx]
img1 = Image.open(img)
if(img1.mode != 'RGB'):
img1 = img1.convert('RGB')
if(img1.size[0] < self.cropSize or img1.size[1] < self.cropSize):
continue
self.valid_images.append(img)
def __len__(self):
return len(self.valid_images)
def __getitem__(self, idx):
while 1:
try:
img = self.valid_images[idx]
img1 = Image.open(img)
img1 = self.imgCrop(img1)
width, height = img1.size
H, theta = self.imgRotH(img1, min=0, max=360)
img1 = np.array(img1)
img2 = cv2.warpPerspective(img1, H, dsize=(width,height))
img2 = np.array(img2)
pos1, pos2 = self.getGrid(img1, img2, H)
assert (len(pos1) != 0 and len(pos2) != 0)
break
except IndexError:
print("IndexError")
exit(1)
except:
del self.valid_images[idx]
img1 = preprocess_image(img1, preprocessing=self.preprocessing)
img2 = preprocess_image(img2, preprocessing=self.preprocessing)
return {
'image1': torch.from_numpy(img1.astype(np.float32)),
'image2': torch.from_numpy(img2.astype(np.float32)),
'pos1': torch.from_numpy(pos1.astype(np.float32)),
'pos2': torch.from_numpy(pos2.astype(np.float32)),
'H': np.array(H),
'theta': np.array([theta])
}
if __name__ == '__main__':
rootDir = argv[1]
training_dataset = PhotoTourismIPR(rootDir, 'caffe')
training_dataset.build_dataset()
data = training_dataset[0]
print(data['image1'].shape, data['image2'].shape, data['pos1'].shape, data['pos2'].shape, len(training_dataset))
|