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import numpy as np
from datasets.corners import CornersDataset
import os
import skimage
import cv2
import itertools
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
all_combibations = dict()
for length in range(2, 351):
ids = np.arange(length)
combs = np.array(list(itertools.combinations(ids, 2)))
all_combibations[length] = combs
class S3DFloorplanDataset(CornersDataset):
def __init__(self, data_path, phase='train', image_size=256, rand_aug=True, inference=False):
super(S3DFloorplanDataset, self).__init__(image_size, inference)
self.data_path = data_path
self.phase = phase
self.rand_aug = rand_aug
if phase == 'train':
datalistfile = os.path.join(data_path, 'train_list.txt')
self.training = True
elif phase == 'valid':
datalistfile = os.path.join(data_path, 'valid_list.txt')
self.training = False
else:
datalistfile = os.path.join(data_path, 'test_list.txt')
self.training = False
with open(datalistfile, 'r') as f:
self._data_names = f.readlines()
def __len__(self):
return len(self._data_names)
def __getitem__(self, idx):
data_name = self._data_names[idx][:-1]
annot_path = os.path.join(self.data_path, 'annot', data_name + '.npy')
annot = np.load(annot_path, allow_pickle=True, encoding='latin1').tolist()
density_path = os.path.join(self.data_path, 'density', data_name + '.png')
normal_path = os.path.join(self.data_path, 'normals', data_name + '.png')
density = cv2.imread(density_path)
normal = cv2.imread(normal_path)
rgb = np.maximum(density, normal)
if self.image_size != 256:
rgb, annot, det_corners = self.resize_data(rgb, annot, None)
if self.rand_aug:
image, annot, _ = self.random_aug_annot(rgb, annot, det_corners=None)
else:
image = rgb
rec_mat = None
corners = np.array(list(annot.keys()))[:, [1, 0]]
if not self.inference and len(corners) > 150:
new_idx = np.random.randint(0, len(self))
return self.__getitem__(new_idx)
if self.training:
# Add some randomness for g.t. corners
corners += np.random.normal(0, 0, size=corners.shape)
image = skimage.img_as_float(image)
# sort by the second value and then the first value, here the corners are in the format of (y, x)
sort_idx = np.lexsort(corners.T)
corners = corners[sort_idx]
corner_list = []
for corner_i in range(corners.shape[0]):
corner_list.append((corners[corner_i][1], corners[corner_i][0])) # to (x, y) format
raw_data = {
'name': data_name,
'corners': corner_list,
'annot': annot,
'image': image,
'rec_mat': rec_mat,
'annot_path': annot_path,
'img_path': density_path,
}
return self.process_data(raw_data)
def process_data(self, data):
img = data['image']
corners = data['corners']
annot = data['annot']
# pre-process the image to use ImageNet-pretrained backbones
img = img.transpose((2, 0, 1))
raw_img = img.copy()
img = (img - np.array(mean)[:, np.newaxis, np.newaxis]) / np.array(std)[:, np.newaxis, np.newaxis]
img = img.astype(np.float32)
corners = np.array(corners)
all_data = {
"annot": annot,
"name": data['name'],
'img': img,
'annot_path': data['annot_path'],
'img_path': data['img_path'],
'raw_img': raw_img,
}
# corner labels
if not self.inference:
pixel_labels, gauss_labels = self.get_corner_labels(corners)
all_data['pixel_labels'] = pixel_labels
all_data['gauss_labels'] = gauss_labels
return all_data
def random_aug_annot(self, img, annot, det_corners=None):
# do random flipping
img, annot, det_corners = self.random_flip(img, annot, det_corners)
# return img, annot, None
# prepare random augmentation parameters (only do random rotation for now)
theta = np.random.randint(0, 360) / 360 * np.pi * 2
r = self.image_size / 256
origin = [127 * r, 127 * r]
p1_new = [127 * r + 100 * np.sin(theta) * r, 127 * r - 100 * np.cos(theta) * r]
p2_new = [127 * r + 100 * np.cos(theta) * r, 127 * r + 100 * np.sin(theta) * r]
p1_old = [127 * r, 127 * r - 100 * r] # y_axis
p2_old = [127 * r + 100 * r, 127 * r] # x_axis
pts1 = np.array([origin, p1_old, p2_old]).astype(np.float32)
pts2 = np.array([origin, p1_new, p2_new]).astype(np.float32)
M_rot = cv2.getAffineTransform(pts1, pts2)
# Combine annotation corners and detection corners
all_corners = list(annot.keys())
if det_corners is not None:
for i in range(det_corners.shape[0]):
all_corners.append(tuple(det_corners[i]))
all_corners_ = np.array(all_corners)
# Do the per-corner transform
# Done in a big matrix transformation to save processing time.
corner_mapping = dict()
ones = np.ones([all_corners_.shape[0], 1])
all_corners_ = np.concatenate([all_corners_, ones], axis=-1)
aug_corners = np.matmul(M_rot, all_corners_.T).T
for idx, corner in enumerate(all_corners):
corner_mapping[corner] = aug_corners[idx]
# If the transformed geometry goes beyond image boundary, we simply re-do the augmentation
new_corners = np.array(list(corner_mapping.values()))
if new_corners.min() <= 0 or new_corners.max() >= (self.image_size - 1):
# return self.random_aug_annot(img, annot, det_corners)
return img, annot, None
# build the new annot dict
aug_annot = dict()
for corner, connections in annot.items():
new_corner = corner_mapping[corner]
tuple_new_corner = tuple(new_corner)
aug_annot[tuple_new_corner] = list()
for to_corner in connections:
aug_annot[tuple_new_corner].append(corner_mapping[tuple(to_corner)])
# Also transform the image correspondingly
rows, cols, ch = img.shape
new_img = cv2.warpAffine(img, M_rot, (cols, rows), borderValue=(255, 255, 255))
y_start = (new_img.shape[0] - self.image_size) // 2
x_start = (new_img.shape[1] - self.image_size) // 2
aug_img = new_img[y_start:y_start + self.image_size, x_start:x_start + self.image_size, :]
return aug_img, aug_annot, None
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