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# -*- coding: UTF-8 -*-
'''=================================================
@Project -> File pram -> loc_by_rec
@IDE PyCharm
@Author fx221@cam.ac.uk
@Date 08/02/2024 15:26
=================================================='''
import torch
from torch.autograd import Variable
from localization.multimap3d import MultiMap3D
from localization.frame import Frame
import yaml, cv2, time
import numpy as np
import os.path as osp
import threading
import os
from tqdm import tqdm
from recognition.vis_seg import vis_seg_point, generate_color_dic
from tools.metrics import compute_iou, compute_precision
from localization.tracker import Tracker
from localization.utils import read_query_info
from localization.camera import Camera
def loc_by_rec_eval(rec_model, loader, config, local_feat, img_transforms=None):
n_epoch = int(config['weight_path'].split('.')[1])
save_fn = osp.join(config['localization']['save_path'],
config['weight_path'].split('/')[0] + '_{:d}'.format(n_epoch) + '_{:d}'.format(
config['feat_dim']))
tag = 'k{:d}_th{:d}_mm{:d}_mi{:d}'.format(config['localization']['seg_k'], config['localization']['threshold'],
config['localization']['min_matches'],
config['localization']['min_inliers'])
if config['localization']['do_refinement']:
tag += '_op{:d}'.format(config['localization']['covisibility_frame'])
if config['localization']['with_compress']:
tag += '_comp'
save_fn = save_fn + '_' + tag
save = config['localization']['save']
save = config['localization']['save']
if save:
save_dir = save_fn
os.makedirs(save_dir, exist_ok=True)
else:
save_dir = None
seg_color = generate_color_dic(n_seg=2000)
dataset_path = config['dataset_path']
show = config['localization']['show']
if show:
cv2.namedWindow('img', cv2.WINDOW_NORMAL)
locMap = MultiMap3D(config=config, save_dir=None)
# start tracker
mTracker = Tracker(locMap=locMap, matcher=locMap.matcher, config=config)
dataset_name = config['dataset'][0]
all_scene_query_info = {}
with open(osp.join(config['config_path'], '{:s}.yaml'.format(dataset_name)), 'r') as f:
scene_config = yaml.load(f, Loader=yaml.Loader)
scenes = scene_config['scenes']
for scene in scenes:
query_path = osp.join(config['dataset_path'], dataset_name, scene, scene_config[scene]['query_path'])
query_info = read_query_info(query_fn=query_path)
all_scene_query_info[dataset_name + '/' + scene] = query_info
# print(scene, query_info.keys())
tracking = False
full_log = ''
failed_cases = []
success_cases = []
poses = {}
err_ths_cnt = [0, 0, 0, 0]
seg_results = {}
time_results = {
'feat': [],
'rec': [],
'loc': [],
'ref': [],
'total': [],
}
n_total = 0
loc_scene_names = config['localization']['loc_scene_name']
# loader = loader[8990:]
for bid, pred in tqdm(enumerate(loader), total=len(loader)):
pred = loader[bid]
image_name = pred['file_name'] # [0]
scene_name = pred['scene_name'] # [0] # dataset_scene
if len(loc_scene_names) > 0:
skip = True
for loc_scene in loc_scene_names:
if scene_name.find(loc_scene) > 0:
skip = False
break
if skip:
continue
with torch.no_grad():
for k in pred:
if k.find('name') >= 0:
continue
if k != 'image0' and k != 'image1' and k != 'depth0' and k != 'depth1':
if type(pred[k]) == np.ndarray:
pred[k] = Variable(torch.from_numpy(pred[k]).float().cuda())[None]
elif type(pred[k]) == torch.Tensor:
pred[k] = Variable(pred[k].float().cuda())
elif type(pred[k]) == list:
continue
else:
pred[k] = Variable(torch.stack(pred[k]).float().cuda())
print('scene: ', scene_name, image_name)
n_total += 1
with torch.no_grad():
img = pred['image']
while isinstance(img, list):
img = img[0]
new_im = torch.from_numpy(img).permute(2, 0, 1).cuda().float()
if img_transforms is not None:
new_im = img_transforms(new_im)[None]
else:
new_im = new_im[None]
img = (img * 255).astype(np.uint8)
fn = image_name
camera_model, width, height, params = all_scene_query_info[scene_name][fn]
camera = Camera(id=-1, model=camera_model, width=width, height=height, params=params)
curr_frame = Frame(image=img, camera=camera, id=0, name=fn, scene_name=scene_name)
gt_sub_map = locMap.sub_maps[curr_frame.scene_name]
if gt_sub_map.gt_poses is not None and curr_frame.name in gt_sub_map.gt_poses.keys():
curr_frame.gt_qvec = gt_sub_map.gt_poses[curr_frame.name]['qvec']
curr_frame.gt_tvec = gt_sub_map.gt_poses[curr_frame.name]['tvec']
t_start = time.time()
encoder_out = local_feat.extract_local_global(data={'image': new_im},
config=
{
# 'min_keypoints': 128,
'max_keypoints': config['eval_max_keypoints'],
}
)
t_feat = time.time() - t_start
# global_descriptors_cuda = encoder_out['global_descriptors']
# scores_cuda = encoder_out['scores'][0][None]
# kpts_cuda = encoder_out['keypoints'][0][None]
# descriptors_cuda = encoder_out['descriptors'][0][None].permute(0, 2, 1)
sparse_scores = pred['scores']
sparse_descs = pred['descriptors']
sparse_kpts = pred['keypoints']
gt_seg = pred['gt_seg']
curr_frame.add_keypoints(keypoints=np.hstack([sparse_kpts[0].cpu().numpy(),
sparse_scores[0].cpu().numpy().reshape(-1, 1)]),
descriptors=sparse_descs[0].cpu().numpy())
curr_frame.time_feat = t_feat
t_start = time.time()
_, seg_descriptors = local_feat.sample(score_map=encoder_out['score_map'],
semi_descs=encoder_out['mid_features'],
# kpts=kpts_cuda[0],
kpts=sparse_kpts[0],
norm_desc=config['norm_desc'])
rec_out = rec_model({'scores': sparse_scores,
'seg_descriptors': seg_descriptors[None].permute(0, 2, 1),
'keypoints': sparse_kpts,
'image': new_im})
t_rec = time.time() - t_start
curr_frame.time_rec = t_rec
pred = {
# 'scores': scores_cuda,
# 'keypoints': kpts_cuda,
# 'descriptors': descriptors_cuda,
# 'global_descriptors': global_descriptors_cuda,
'image_size': np.array([img.shape[1], img.shape[0]])[None],
}
pred = {**pred, **rec_out}
pred_seg = torch.max(pred['prediction'], dim=2)[1] # [B, N, C]
pred_seg = pred_seg[0].cpu().numpy()
kpts = sparse_kpts[0].cpu().numpy()
img_pred_seg = vis_seg_point(img=img, kpts=kpts, segs=pred_seg, seg_color=seg_color, radius=9)
show_text = 'kpts: {:d}'.format(kpts.shape[0])
img_pred_seg = cv2.putText(img=img_pred_seg, text=show_text,
org=(50, 30),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1, color=(0, 0, 255),
thickness=2, lineType=cv2.LINE_AA)
curr_frame.image_rec = img_pred_seg
if show:
cv2.imshow('img', img)
key = cv2.waitKey(1)
if key == ord('q'):
exit(0)
elif key == ord('s'):
show_time = -1
elif key == ord('c'):
show_time = 1
segmentations = pred['prediction'][0] # .cpu().numpy() # [N, C]
curr_frame.add_segmentations(segmentations=segmentations,
filtering_threshold=config['localization']['pre_filtering_th'])
# Step1: do tracker first
success = not mTracker.lost and tracking
if success:
success = mTracker.run(frame=curr_frame)
if not success:
success = locMap.run(q_frame=curr_frame)
if success:
curr_frame.update_point3ds()
if tracking:
mTracker.lost = False
mTracker.last_frame = curr_frame
# '''
pred_seg = torch.max(pred['prediction'], dim=-1)[1] # [B, N, C]
pred_seg = pred_seg[0].cpu().numpy()
gt_seg = gt_seg[0].cpu().numpy()
iou = compute_iou(pred=pred_seg, target=gt_seg, n_class=pred_seg.shape[0],
ignored_ids=[0]) # 0 - background
prec = compute_precision(pred=pred_seg, target=gt_seg, ignored_ids=[0])
kpts = sparse_kpts[0].cpu().numpy()
if scene not in seg_results.keys():
seg_results[scene] = {
'day': {
'prec': [],
'iou': [],
'kpts': [],
},
'night': {
'prec': [],
'iou': [],
'kpts': [],
}
}
if fn.find('night') >= 0:
seg_results[scene]['night']['prec'].append(prec)
seg_results[scene]['night']['iou'].append(iou)
seg_results[scene]['night']['kpts'].append(kpts.shape[0])
else:
seg_results[scene]['day']['prec'].append(prec)
seg_results[scene]['day']['iou'].append(iou)
seg_results[scene]['day']['kpts'].append(kpts.shape[0])
print_text = 'name: {:s}, kpts: {:d}, iou: {:.3f}, prec: {:.3f}'.format(fn, kpts.shape[0], iou,
prec)
print(print_text)
# '''
t_feat = curr_frame.time_feat
t_rec = curr_frame.time_rec
t_loc = curr_frame.time_loc
t_ref = curr_frame.time_ref
t_total = t_feat + t_rec + t_loc + t_ref
time_results['feat'].append(t_feat)
time_results['rec'].append(t_rec)
time_results['loc'].append(t_loc)
time_results['ref'].append(t_ref)
time_results['total'].append(t_total)
poses[scene + '/' + fn] = (curr_frame.qvec, curr_frame.tvec)
q_err, t_err = curr_frame.compute_pose_error()
if q_err <= 5 and t_err <= 0.05:
err_ths_cnt[0] = err_ths_cnt[0] + 1
if q_err <= 2 and t_err <= 0.25:
err_ths_cnt[1] = err_ths_cnt[1] + 1
if q_err <= 5 and t_err <= 0.5:
err_ths_cnt[2] = err_ths_cnt[2] + 1
if q_err <= 10 and t_err <= 5:
err_ths_cnt[3] = err_ths_cnt[3] + 1
if success:
success_cases.append(scene + '/' + fn)
print_text = 'qname: {:s} localization success {:d}/{:d}, q_err: {:.2f}, t_err: {:.2f}, {:d}/{:d}/{:d}/{:d}/{:d}, time: {:.2f}/{:.2f}/{:.2f}/{:.2f}/{:.2f}'.format(
scene + '/' + fn, len(success_cases), n_total, q_err, t_err, err_ths_cnt[0],
err_ths_cnt[1],
err_ths_cnt[2],
err_ths_cnt[3],
n_total,
t_feat, t_rec, t_loc, t_ref, t_total
)
else:
failed_cases.append(scene + '/' + fn)
print_text = 'qname: {:s} localization fail {:d}/{:d}, q_err: {:.2f}, t_err: {:.2f}, {:d}/{:d}/{:d}/{:d}/{:d}, time: {:.2f}/{:.2f}/{:.2f}/{:.2f}/{:.2f}'.format(
scene + '/' + fn, len(failed_cases), n_total, q_err, t_err, err_ths_cnt[0],
err_ths_cnt[1],
err_ths_cnt[2],
err_ths_cnt[3],
n_total, t_feat, t_rec, t_loc, t_ref, t_total)
print(print_text)
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