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database.py
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database.py
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from torch.utils.data import Dataset
import torch
import os
import numpy as np
import random
from matplotlib import pyplot as plt
import json
import random
class SigmoidDataset_eval(Dataset):
def __init__(self, sequs=['00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10'], neg_ratio=1, desc_folder="./data/desc_kitti", gt_folder="./data/gt_kitti", eva_ratio=0.1) -> None:
super().__init__()
print(sequs)
self.descs = []
self.gt_pos = []
self.gt_neg = []
self.pos_nums = [0]
self.neg_num = 0
self.pos_num = 0
for seq in sequs:
desc_file = os.path.join(desc_folder, seq+'.npy')
gt_file = os.path.join(gt_folder, seq+'.npz')
self.descs.append(np.load(desc_file))
gt = np.load(gt_file)
pos = gt['pos'][-int(len(gt['pos'])*eva_ratio):]
neg = gt['neg'][-int(len(gt['neg'])*eva_ratio):]
self.gt_pos.append(pos)
self.gt_neg.append(neg)
self.pos_num += len(self.gt_pos[-1])
self.pos_nums.append(self.pos_num)
self.neg_num = int(neg_ratio*self.pos_num)
def __len__(self):
return self.pos_num+self.neg_num
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
pair = [-1, -1, 0]
if idx >= self.pos_num:
id_seq = random.randint(0, len(self.gt_neg)-1)
id = random.randint(0, len(self.gt_neg[id_seq])-1)
pair = self.gt_neg[int(id_seq)][id]
out = {"desc1": self.descs[int(id_seq)][int(
pair[0])]/50., "desc2": self.descs[int(id_seq)][int(pair[1])]/50., 'label': pair[2]}
return out
for i in range(1, len(self.pos_nums)):
if self.pos_nums[i] > idx:
pair = self.gt_pos[i-1][idx-self.pos_nums[i-1]]
out = {"desc1": self.descs[i-1][int(
pair[0])]/50., "desc2": self.descs[i-1][int(pair[1])]/50., 'label': pair[2]}
return out
class SigmoidDataset_train(Dataset):
def __init__(self, sequs=['00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10'], neg_ratio=1, desc_folder="./data/desc_kitti", gt_folder="./data/gt_kitti", eva_ratio=0.1) -> None:
super().__init__()
print(sequs)
self.descs = []
self.gt_pos = []
self.gt_neg = []
self.pos_nums = [0]
self.neg_num = 0
self.pos_num = 0
for seq in sequs:
desc_file = os.path.join(desc_folder, seq+'.npy')
gt_file = os.path.join(gt_folder, seq+'.npz')
self.descs.append(np.load(desc_file))
gt = np.load(gt_file)
pos = gt['pos'][:-int(len(gt['pos'])*eva_ratio)]
neg = gt['neg'][:-int(len(gt['neg'])*eva_ratio)]
self.gt_pos.append(pos)
self.gt_neg.append(neg)
self.pos_num += len(self.gt_pos[-1])
self.pos_nums.append(self.pos_num)
self.neg_num = int(neg_ratio*self.pos_num)
def __len__(self):
return self.pos_num+self.neg_num
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
pair = [-1, -1, 0]
if idx >= self.pos_num:
id_seq = random.randint(0, len(self.gt_neg)-1)
id = random.randint(0, len(self.gt_neg[id_seq])-1)
pair = self.gt_neg[int(id_seq)][id]
out = {"desc1": self.descs[int(id_seq)][int(
pair[0])]/50., "desc2": self.descs[int(id_seq)][int(pair[1])]/50., 'label': pair[2]}
if random.randint(0, 1) > 0:
self.rand_occ(out["desc1"])
self.rand_occ(out["desc2"])
return out
for i in range(1, len(self.pos_nums)):
if self.pos_nums[i] > idx:
pair = self.gt_pos[i-1][idx-self.pos_nums[i-1]]
out = {"desc1": self.descs[i-1][int(
pair[0])]/50., "desc2": self.descs[i-1][int(pair[1])]/50., 'label': pair[2]}
if random.randint(0, 1) > 0:
self.rand_occ(out["desc1"])
self.rand_occ(out["desc2"])
return out
def rand_occ(self, in_desc):
n = random.randint(0, 60)
s = random.randint(0, 360-n)
in_desc[:, s:s+n] *= 0
class SigmoidDataset(Dataset):
def __init__(self, sequs=['00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10'], neg_ratio=1, desc_folder="./data/desc_kitti", gt_folder="./data/gt_kitti") -> None:
super().__init__()
print(sequs)
self.descs = []
self.gt_pos = []
self.gt_neg = []
self.pos_nums = [0]
self.neg_num = 0
self.pos_num = 0
for seq in sequs:
desc_file = os.path.join(desc_folder, seq+'.npy')
gt_file = os.path.join(gt_folder, seq+'.npz')
self.descs.append(np.load(desc_file))
gt = np.load(gt_file)
self.gt_pos.append(gt['pos'])
self.gt_neg.append(gt['neg'])
self.pos_num += len(self.gt_pos[-1])
self.pos_nums.append(self.pos_num)
self.neg_num = int(neg_ratio*self.pos_num)
def __len__(self):
return self.pos_num+self.neg_num
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
pair = [-1, -1, 0]
if idx >= self.pos_num:
id_seq = random.randint(0, len(self.gt_neg)-1)
id = random.randint(0, len(self.gt_neg[id_seq])-1)
pair = self.gt_neg[int(id_seq)][id]
out = {"desc1": self.descs[int(id_seq)][int(
pair[0])]/50., "desc2": self.descs[int(id_seq)][int(pair[1])]/50., 'label': pair[2]*1.}
if random.randint(0, 2) > 1:
self.rand_occ(out["desc1"])
self.rand_occ(out["desc2"])
return out
for i in range(1, len(self.pos_nums)):
if self.pos_nums[i] > idx:
pair = self.gt_pos[i-1][idx-self.pos_nums[i-1]]
out = {"desc1": self.descs[i-1][int(pair[0])]/50., "desc2": self.descs[i-1][int(
pair[1])]/50., 'label': pair[2]*1.}
if random.randint(0, 2) > 1:
self.rand_occ(out["desc1"])
self.rand_occ(out["desc2"])
return out
def rand_occ(self, in_desc):
n = random.randint(0, 60)
s = random.randint(0, 360-n)
in_desc[:, s:s+n] *= 0
class evalDataset(Dataset):
def __init__(self, seq="00", desc_folder="./data/desc_kitti", gt_folder="./data/pairs_kitti/neg_100") -> None:
super().__init__()
self.descs = []
self.pairs = []
self.num = 0
desc_file = os.path.join(desc_folder, seq+'.npy')
pair_file = os.path.join(gt_folder, seq+'.txt')
self.descs = np.load(desc_file)
self.pairs = np.genfromtxt(pair_file, dtype='int32')
self.num = len(self.pairs)
def __len__(self):
return self.num
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
pair = self.pairs[idx]
out = {"desc1": self.descs[int(
pair[0])]/50., "desc2": self.descs[int(pair[1])]/50., 'label': pair[2]}
angle1 = np.random.randint(0, 359)
angle2 = np.random.randint(0, 359)
out["desc1"] = np.roll(out["desc1"], angle1, axis=1)
out["desc2"] = np.roll(out["desc2"], angle2, axis=1)
return out
class SigmoidDataset_kitti360(Dataset):
def __init__(self, sequs=['0000', '0002', '0003', '0004', '0005', '0006', '0007', '0009', '0010'], neg_ratio=1, desc_folder="./data/desc_kitti360", gt_folder="./data/gt_kitti360") -> None:
super().__init__()
print(sequs)
self.descs = []
self.gt_pos = []
self.gt_neg = []
self.key_map = []
self.pos_nums = [0]
self.neg_num = 0
self.pos_num = 0
for seq in sequs:
desc_file = os.path.join(desc_folder, seq+'.npy')
gt_file = os.path.join(gt_folder, seq+'.npz')
self.descs.append(np.load(desc_file))
self.key_map.append(
json.load(open(os.path.join(desc_folder, seq+'.json'))))
gt = np.load(gt_file)
self.gt_pos.append(gt['pos'])
self.gt_neg.append(gt['neg'])
self.pos_num += len(self.gt_pos[-1])
self.pos_nums.append(self.pos_num)
self.neg_num = int(neg_ratio*self.pos_num)
def __len__(self):
return self.pos_num+self.neg_num
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
pair = [-1, -1, 0]
if idx >= self.pos_num:
id_seq = random.randint(0, len(self.gt_neg)-1)
id = random.randint(0, len(self.gt_neg[id_seq])-1)
pair = self.gt_neg[int(id_seq)][id]
out = {"desc1": self.descs[int(id_seq)][self.key_map[int(id_seq)][str(int(
pair[0]))]]/50., "desc2": self.descs[int(id_seq)][self.key_map[int(id_seq)][str(int(pair[1]))]]/50., 'label': pair[2]}
if random.randint(0, 1) > 0:
self.rand_occ(out["desc1"])
self.rand_occ(out["desc2"])
return out
for i in range(1, len(self.pos_nums)):
if self.pos_nums[i] > idx:
pair = self.gt_pos[i-1][idx-self.pos_nums[i-1]]
out = {"desc1": self.descs[i-1][self.key_map[i-1][str(int(
pair[0]))]]/50., "desc2": self.descs[i-1][self.key_map[i-1][str(int(pair[1]))]]/50., 'label': pair[2]}
if random.randint(0, 1) > 0:
self.rand_occ(out["desc1"])
self.rand_occ(out["desc2"])
return out
def rand_occ(self, in_desc):
n = random.randint(0, 60)
s = random.randint(0, 360-n)
in_desc[:, s:s+n] *= 0
class evalDataset_kitti360(Dataset):
def __init__(self, seq="0000", desc_folder="./data/desc_kitti360", gt_folder="./data/pairs_kitti360/neg10") -> None:
super().__init__()
self.descs = []
self.pairs = []
self.num = 0
desc_file = os.path.join(desc_folder, seq+'.npy')
pair_file = os.path.join(gt_folder, seq+'.txt')
self.descs = np.load(desc_file)
self.pairs = np.genfromtxt(pair_file, dtype='int32')
self.key_map = json.load(open(os.path.join(desc_folder, seq+'.json')))
self.num = len(self.pairs)
def __len__(self):
return self.num
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
pair = self.pairs[idx]
out = {"desc1": self.descs[self.key_map[str(int(
pair[0]))]]/50., "desc2": self.descs[self.key_map[str(int(pair[1]))]]/50., 'label': pair[2]}
return out
if __name__ == '__main__':
database = SigmoidDataset_train(
['00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10'], 2)
print(len(database))
for i in range(0, len(database)):
idx = random.randint(0, len(database)-1)
d = database[idx]
print(i, d['label'])
plt.subplot(2, 1, 1)
plt.imshow(d['desc1'])
plt.subplot(2, 1, 2)
plt.imshow(d['desc2'])
plt.show()