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# import matplotlib.pyplot as plt
# import matplotlib.image as mpimg
import sys
sys.path.append(os.path.abspath(".")) # one level up
import numpy as np
# import cv2
# import open3d as o3d
# from scipy.spatial.transform import Rotation
# from utils.lidar import PointCloud
# from utils.camera import ImageData
# import utils.utils as utils
from utils.utils import get_all_corr_files
from FoL.reranking import run_rerank
from natsort import natsorted, index_natsorted
import torch
from tqdm import tqdm
from glob import glob
from math import floor
################## set device based on cuda availability #################
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print('CUDA availability: ' + str(torch.cuda.is_available()))
####################### Functions for matching using numpy on CPU or Pytorch on GPU ###################
def getMatchIndsCPU(ft_ref,ft_qry,topK=20,metric='cosine'):
"""
metric: 'euclidean' or 'cosine'
"""
# dMat = cdist(ft_ref,ft_qry,metric)
ft_qry_norm = ft_qry / np.linalg.norm(ft_qry, axis=1, keepdims=True) # Shape (M, N)
ft_ref_norm = ft_ref / np.linalg.norm(ft_ref, axis=1, keepdims=True) # Shape (C, N)
# Step 2: Compute cosine similarity
dMat = 1 - (ft_ref_norm @ ft_qry_norm.T)
mInds = np.argsort(dMat,axis=0)[:topK].squeeze() # shape: K x ft_qry.shape[0]
return mInds, dMat
def getMatchIndsGPU(ft_ref, ft_qry,topK=20, metric='cosine'):
# metric: 'euclidean' or 'cosine'
ft_qry_tensor = torch.Tensor(ft_qry).to(device)
ft_ref_tensor = torch.Tensor(ft_ref).to(device)
if metric == 'euclidean':
# Use torch's cdist for Euclidean distance
dMat = torch.cdist(ft_ref, ft_qry)
elif metric == 'cosine':
# # Normalize both the query and reference tensors
ft_qry_norm = ft_qry_tensor / ft_qry_tensor.norm(dim=1, keepdim=True)
ft_ref_norm = ft_ref_tensor / ft_ref_tensor.norm(dim=1, keepdim=True)
# Compute cosine similarity (1 - cosine similarity for distance)
dMat = 1 - ft_ref_norm @ ft_qry_norm.t()
# Get the indices of the top 5 closest matches
mInds = torch.argsort(dMat.cpu(), dim=0)[:topK].squeeze()
return mInds, dMat
qry_sets = [
'20210909_124816_v2',
]
ref_sets = [
'20230509_115540_v2',
]
vpr_descs = [
'FoL',
]
img_calib_file = f"./camera_calib.txt"
dist_tolerance = 10 # metres
# qry_idx = 4
slice_len = 1000
# User parameters
location = 'dalby-to-brigalow'
################ Reference filenames and directories #################################
ref_condition = ''
ref_camera_pos = 'front'
ref_timestamps = []
ref_utms = []
ref_img_filenames = []
ref_utm_filenames = []
for ref_set in ref_sets:
print(f"Loading {ref_set}")
ref_root_directory = f"../../Datasets/dalby/{location}"
ref_vpr_root = f"../../Datasets/dalby/{location}/vpr_ftrs/"
ref_image_dir = f"{ref_root_directory}/{ref_set}/{ref_camera_pos}-imgs/"
ref_utm_dir = f"{ref_root_directory}/{ref_set}/utm/"
this_ref_timestamp = [filename.split('.png')[0] for filename in natsorted(os.listdir(ref_image_dir)) if os.path.isfile(ref_image_dir+filename)]
ref_utms = ref_utms+[np.loadtxt(ref_utm_dir+filename) for filename in natsorted(os.listdir(ref_utm_dir)) if os.path.isfile(ref_utm_dir+filename)][55::]
ref_img_filenames = [filename for filename in natsorted(os.listdir(ref_image_dir)) if os.path.isfile(ref_image_dir+filename)]
ref_utm_filenames = np.array([filename for filename in natsorted(os.listdir(ref_utm_dir)) if os.path.isfile(ref_utm_dir+filename)])[:len(os.listdir(ref_utm_dir))-55]
ref_timestamps = ref_timestamps+this_ref_timestamp
ref_utms = np.array(ref_utms)
for vpr_desc in vpr_descs:
all_results = []
first = True
print(f"Loading references")
for ref_set in ref_sets:
print(f"Loading {ref_set} {vpr_desc} descriptors")
ref_root_directory = f"../../Datasets/dalby/{location}"
ref_vpr_root = f"../../Datasets/dalby/{location}/vpr_ftrs/"
ref_image_dir = f"{ref_root_directory}/{ref_set}/{ref_camera_pos}-imgs/"
# ref_name_sort_idx = index_natsorted(os.listdir(ref_image_dir))
if slice_len is None:
# Get the two orderings
glob_sorted_paths = sorted(glob(f"{ref_image_dir}/*.png"))
glob_sorted_filenames = [os.path.basename(p) for p in glob_sorted_paths]
# Get the indices that would sort glob_sorted_filenames into natsorted order
ref_name_sort_idx = index_natsorted(glob_sorted_filenames)
ref_ftr = np.load(f"{ref_vpr_root}/{ref_set}/{vpr_desc}/queries_descriptors.npy")
ref_local_ftr = np.load(f"{ref_vpr_root}/{ref_set}/{vpr_desc}/qry_local_feats.npy")
if first:
ref_ftrs = ref_ftr[ref_name_sort_idx]
ref_local_ftrs = ref_local_ftr[ref_name_sort_idx]
first = False
else:
ref_ftrs = np.vstack((ref_ftrs, ref_ftr[ref_name_sort_idx]))
ref_local_ftrs = np.vstack((ref_local_ftrs, ref_local_ftr[ref_name_sort_idx]))
else:
num_slices = floor(len(ref_img_filenames)/slice_len)
if len(ref_img_filenames) % slice_len > 0:
num_slices += 1
for idx in tqdm(range(num_slices)):
if idx == 0:
ref_ftrs = np.load(f"{ref_vpr_root}/{ref_set}/{vpr_desc}/sliced/queries_descriptors_slice_{idx:05d}.npy")
ref_local_ftrs = np.load(f"{ref_vpr_root}/{ref_set}/{vpr_desc}/sliced/qry_local_feats_slice_{idx:05d}.npy")
else:
ref_ftrs = np.vstack((ref_ftrs, np.load(f"{ref_vpr_root}/{ref_set}/{vpr_desc}/sliced/queries_descriptors_slice_{idx:05d}.npy")))
ref_local_ftrs = np.vstack((ref_local_ftrs, np.load(f"{ref_vpr_root}/{ref_set}/{vpr_desc}/sliced/qry_local_feats_slice_{idx:05d}.npy")))
print(f"Loaded ref ftr slices: {len(ref_ftrs)}")
print(f"Loaded ref local ftr slices: {len(ref_local_ftrs)}")
for qry_set in qry_sets:
################ Query filenames and directories #################################
qry_condition = ''
qry_camera_pos = 'front'
qry_root_directory = f"../../Datasets/dalby/{location}"
qry_vpr_root = f"../../Datasets/dalby/{location}/vpr_ftrs/"
qry_image_dir = f"{qry_root_directory}/{qry_set}/{qry_camera_pos}-imgs/"
qry_utm_dir = f"{qry_root_directory}/{qry_set}/utm/"
qry_timestamps = [filename.split('.png')[0] for filename in natsorted(os.listdir(qry_image_dir)) if os.path.isfile(qry_image_dir+filename)]
qry_utms = np.array([np.loadtxt(qry_utm_dir+filename) for filename in natsorted(os.listdir(qry_utm_dir)) if os.path.isfile(qry_utm_dir+filename)])
# qry_name_sort_idx = index_natsorted(os.listdir(qry_image_dir))
# if slice_len is None:
# # Get the two orderings
# glob_sorted_paths = sorted(glob(f"{qry_image_dir}/*.png"))
# glob_sorted_filenames = [os.path.basename(p) for p in glob_sorted_paths]
# # Get the indices that would sort glob_sorted_filenames into natsorted order
# qry_name_sort_idx = index_natsorted(glob_sorted_filenames)
# qry_ftrs = np.load(f"{qry_vpr_root}/{qry_set}/{vpr_desc}/queries_descriptors.npy")
# qry_local_ftrs = np.load(f"{qry_vpr_root}/{qry_set}/{vpr_desc}/qry_local_feats.npy")
# qry_ftrs = qry_ftrs[qry_name_sort_idx]
# qry_local_ftrs = qry_local_ftrs[qry_name_sort_idx]
# mInds, dMat = getMatchIndsGPU(ref_ftrs,qry_ftrs,topK=1)
# mInds = mInds.cpu().numpy()
if slice_len is None:
mInds = run_rerank(qry_ftrs, ref_ftrs, qry_local_ftrs, ref_local_ftrs, recall_values=[1, 5, 10, 20])[:,0] # 5, 10, 20
else:
print(f"Performing VPR on slices")
num_slices = floor(len(qry_timestamps)/slice_len)
if len(qry_timestamps) % slice_len > 0:
num_slices += 1
for idx in tqdm(range(num_slices)):
qry_ftrs = np.load(f"{qry_vpr_root}/{qry_set}/{vpr_desc}/sliced/queries_descriptors_slice_{idx:05d}.npy")
qry_local_ftrs = np.load(f"{qry_vpr_root}/{qry_set}/{vpr_desc}/sliced/qry_local_feats_slice_{idx:05d}.npy")
if idx == 0:
mInds = run_rerank(qry_ftrs, ref_ftrs, qry_local_ftrs, ref_local_ftrs, recall_values=[1, 5, 10, 20])[:,0]
else:
mInds_slice = run_rerank(qry_ftrs, ref_ftrs, qry_local_ftrs, ref_local_ftrs, recall_values=[1, 5, 10, 20])[:,0]
mInds = np.vstack((np.expand_dims(mInds, axis=1), np.expand_dims(mInds_slice, axis=1))).squeeze()
del qry_ftrs
del qry_local_ftrs
print(f"VPR on query slices: {len(mInds)}")
np.save(f"{qry_vpr_root}/{qry_set}/{vpr_desc}/mInds.npy", mInds)
in_tol = []
dists = []
valid_qry = 0
qry_utm_timestamps, qry_utm_idxs = get_all_corr_files(qry_timestamps, [qry_utm_dir,])
ref_utm_timestamp, ref_utm_idxs = get_all_corr_files(ref_timestamps, [ref_utm_dir,])
for qry_idx in tqdm(range(len(qry_timestamps))):
qry_image_timestamp = qry_timestamps[qry_idx]
qry_image_filename = f"{qry_image_dir}/{qry_image_timestamp}.png"
qry_utm = qry_utms[qry_utm_idxs[qry_idx]]
diffs = ref_utms - qry_utm # shape (N, 2)
qry_dists = np.linalg.norm(diffs, axis=1) # shape (N,)
if qry_dists.min() > dist_tolerance:
continue
else:
valid_qry += 1
ref_utm = ref_utms[ref_utm_idxs[int(mInds[qry_idx])]]
diff = ref_utm - qry_utm # shape (N, 2)
dist = np.linalg.norm(diff) # shape (N,)
dists.append(dist)
if dist < dist_tolerance:
in_tol.append(1)
else:
in_tol.append(0)
# qry_image = ImageData(qry_image_filename, img_calib_file)
# fig, ax = plt.subplots(1, 2, figsize=(19.4, 6))
# ax[0].clear()
# ax[1].clear()
# ax[0].imshow(qry_image.image[:, :, ::-1])
# ax[0].set_title(f"{qry_image_timestamp}.png")
# ax[0].axis("off")
# # Show matching reference image
# # ref_img_timestamp = utils.get_corr_files(ref_timestamps[int(mInds[qry_idx])], [ref_image_dir,])
# ref_image = ImageData(f"{ref_image_dir}/{ref_timestamps[int(mInds[qry_idx])]}.png", img_calib_file)
# ax[1].imshow(ref_image.image[:, :, ::-1])
# ax[1].set_title(f"{ref_timestamps[int(mInds[qry_idx])]}\nDist={dist:.2f}m")
# ax[1].axis("off")
# fig.canvas.draw()
print(f"Recall for {qry_set} using {vpr_desc}: {np.sum(np.array(in_tol))/valid_qry:.02%}")
all_results.append(np.sum(np.array(in_tol))/valid_qry)
# plt.figure()
# plt.plot(np.clip(dists, 0, 30))
# plt.ylim((0,35))
print(f"All {vpr_desc} results:")
print(all_results)
# else:
# num_slices = floor(len(ref_img_filenames)/slice_len)
# if len(ref_img_filenames) % slice_len > 0:
# num_slices += 1
# for idx in range(num_slices):
# if idx == 0:
# ref_ftrs = np.load(f"{ref_vpr_root}/{ref_set}/{vpr_desc}/sliced/queries_descriptors_slice_{idx:05d}.npy")
# else:
# ref_ftrs = np.vstack((ref_ftrs, np.load(f"{ref_vpr_root}/{ref_set}/{vpr_desc}/sliced/queries_descriptors_slice_{idx:05d}.npy")))
# print(f"Loaded ref ftr slices: {len(ref_ftrs)}")
# if slice_len is None:
# mInds, dMat = getMatchIndsGPU(ref_ftrs,qry_ftrs,topK=1)
# mInds = mInds.cpu().numpy()
# else:
# print(f"Performing VPR on slices")
# num_slices = floor(len(qry_timestamps)/slice_len)
# if len(qry_timestamps) % slice_len > 0:
# num_slices += 1
# for idx in tqdm(range(num_slices)):
# qry_ftrs = np.load(f"{qry_vpr_root}/{qry_set}/{vpr_desc}/sliced/queries_descriptors_slice_{idx:05d}.npy")
# if idx == 0:
# mInds, dMat = getMatchIndsGPU(ref_ftrs,qry_ftrs,topK=1)
# mInds = mInds.cpu().numpy()
# else:
# mInds_slice, dMat = getMatchIndsGPU(ref_ftrs,qry_ftrs,topK=1)
# mInds_slice = mInds_slice.cpu().numpy()
# mInds = np.vstack((np.expand_dims(mInds, axis=1), np.expand_dims(mInds_slice, axis=1))).squeeze()
# print(f"VPR on query slices: {len(mInds)}")
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