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Running
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Zero
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"""
HPatches sequences dataset, to perform homography estimation and
evaluate basic line detection metrics.
"""
import os
import numpy as np
import torch
import cv2
from pathlib import Path
from torch.utils.data import Dataset, DataLoader
from ..config.project_config import Config as cfg
class HPatches(torch.utils.data.Dataset):
def __init__(self, mode='test', config=None):
assert mode in ['test', 'export']
self.conf = config
self.root_dir = Path(cfg.hpatches_dataroot)
folder_paths = [x for x in self.root_dir.iterdir() if x.is_dir()]
self.data = []
for path in folder_paths:
if config['alteration'] == 'i' and path.stem[0] != 'i':
continue
if config['alteration'] == 'v' and path.stem[0] != 'v':
continue
if mode == 'test':
for i in range(2, 7):
ref_path = Path(path, "1.ppm")
target_path = Path(path, str(i) + '.ppm')
self.data += [{
"ref_name": str(ref_path.parent.stem + "_" + ref_path.stem),
"ref_img_path": str(ref_path),
"target_name": str(target_path.parent.stem + "_" + target_path.stem),
"target_img_path": str(target_path),
"H": np.loadtxt(str(Path(path, "H_1_" + str(i)))),
}]
else:
for i in range(1, 7):
ref_path = Path(path, str(i) + '.ppm')
self.data += [{
"ref_name": str(ref_path.parent.stem + "_" + ref_path.stem),
"ref_img_path": str(ref_path)}]
def get_dataset(self):
return self
def __getitem__(self, idx):
img0_path = self.data[idx]['ref_img_path']
img0 = cv2.imread(img0_path, 0)
img_size = img0.shape
if max(img_size) > self.conf['max_side']:
s = self.conf['max_side'] / max(img_size)
h_s = int(img_size[0] * s)
w_s = int(img_size[1] * s)
img0 = cv2.resize(img0, (w_s, h_s), interpolation=cv2.INTER_AREA)
# Normalize the image in [0, 1]
img0 = img0.astype(float) / 255.
img0 = torch.tensor(img0[None], dtype=torch.float32)
outputs = {'image': img0, 'image_path': img0_path,
'name': self.data[idx]['ref_name']}
if 'target_name' in self.data[idx]:
img1_path = self.data[idx]['target_img_path']
img1 = cv2.imread(img1_path, 0)
H = self.data[idx]['H']
if max(img_size) > self.conf['max_side']:
img1 = cv2.resize(img1, (w_s, h_s),
interpolation=cv2.INTER_AREA)
H = self.adapt_homography_to_preprocessing(
H, img_size, img_size, (h_s, w_s))
# Normalize the image in [0, 1]
img1 = img1.astype(float) / 255.
img1 = torch.tensor(img1[None], dtype=torch.float)
H = torch.tensor(H, dtype=torch.float)
outputs['warped_image'] = img1
outputs['warped_image_path'] = img1_path
outputs['warped_name'] = self.data[idx]['target_name']
outputs['H'] = H
# root='/home/kezeran/code/hawpv4-dev/data-ssl/0images'
# try:
# cv2.imwrite(f'{root}/img_{idx}.png', cv2.imread(img0_path))
# cv2.imwrite(f'{root}/img_{idx}_w.png', cv2.imread(img1_path))
# except:
# pass
return outputs
def __len__(self):
return len(self.data)
def adapt_homography_to_preprocessing(self, H, img_shape1, img_shape2,
target_size):
source_size1 = np.array(img_shape1, dtype=float)
source_size2 = np.array(img_shape2, dtype=float)
target_size = np.array(target_size)
# Get the scaling factor in resize
scale1 = np.amax(target_size / source_size1)
scaling1 = np.diag([1. / scale1, 1. / scale1, 1.]).astype(float)
scale2 = np.amax(target_size / source_size2)
scaling2 = np.diag([scale2, scale2, 1.]).astype(float)
# Get the translation params in crop
pad_y1 = (source_size1[0] * scale1 - target_size[0]) / 2.
pad_x1 = (source_size1[1] * scale1 - target_size[1]) / 2.
translation1 = np.array([[1., 0., pad_x1],
[0., 1., pad_y1],
[0., 0., 1.]], dtype=float)
pad_y2 = (source_size2[0] * scale2 - target_size[0]) / 2.
pad_x2 = (source_size2[1] * scale2 - target_size[1]) / 2.
translation2 = np.array([[1., 0., -pad_x2],
[0., 1., -pad_y2],
[0., 0., 1.]], dtype=float)
return translation2 @ scaling2 @ H @ scaling1 @ translation1
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