277 lines
12 KiB
Python
277 lines
12 KiB
Python
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# pylint: disable=too-many-branches
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"""
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Run MonoLoco/MonStereo and converts annotations into KITTI format
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"""
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import os
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import math
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from collections import defaultdict
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import torch
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from ..network import Loco
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from ..network.process import preprocess_pifpaf
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from .geom_baseline import geometric_coordinates
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from ..utils import get_keypoints, pixel_to_camera, factory_basename, make_new_directory, get_category, \
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xyz_from_distance, read_and_rewrite
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from .stereo_baselines import baselines_association
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from ..prep import factory_file
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from .reid_baseline import get_reid_features, ReID
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class GenerateKitti:
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dir_gt = os.path.join('data', 'kitti', 'gt')
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dir_gt_new = os.path.join('data', 'kitti', 'gt_new')
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dir_kk = os.path.join('data', 'kitti', 'calib')
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dir_byc = '/data/lorenzo-data/kitti/object_detection/left'
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monoloco_checkpoint = 'data/models/monoloco-190717-0952.pkl'
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baselines = {'mono': [], 'stereo': []}
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def __init__(self, args):
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# Load Network
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assert args.mode in ('mono', 'stereo'), "mode not recognized"
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self.mode = args.mode
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self.net = 'monstereo' if args.mode == 'stereo' else 'monoloco_pp'
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use_cuda = torch.cuda.is_available()
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device = torch.device("cuda" if use_cuda else "cpu")
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self.model = Loco(
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model=args.model,
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mode=args.mode,
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device=device,
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n_dropout=args.n_dropout,
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p_dropout=args.dropout,
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linear_size=args.hidden_size
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)
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# Extract list of pifpaf files in validation images
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self.dir_ann = args.dir_ann
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self.generate_official = args.generate_official
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assert os.listdir(self.dir_ann), "Annotation directory is empty"
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self.set_basename = factory_basename(args.dir_ann, self.dir_gt)
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# For quick testing
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# ------------------------------------------------------------------------------------------------------------
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# self.set_basename = ('001782',)
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# self.set_basename = ('002282',)
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# ------------------------------------------------------------------------------------------------------------
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# Add monocular and stereo baselines (they require monoloco as backbone)
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if args.baselines:
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# Load MonoLoco
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self.baselines['mono'] = ['monoloco', 'geometric']
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self.monoloco = Loco(
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model=self.monoloco_checkpoint,
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mode='mono',
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net='monoloco',
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device=device,
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n_dropout=args.n_dropout,
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p_dropout=args.dropout,
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linear_size=256
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)
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# Stereo baselines
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if args.mode == 'stereo':
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self.baselines['stereo'] = ['pose', 'reid']
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self.cnt_disparity = defaultdict(int)
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self.cnt_no_stereo = 0
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self.dir_images = os.path.join('data', 'kitti', 'images')
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self.dir_images_r = os.path.join('data', 'kitti', 'images_r')
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# ReID Baseline
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weights_path = 'data/models/reid_model_market.pkl'
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self.reid_net = ReID(weights_path=weights_path, device=device, num_classes=751, height=256, width=128)
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def run(self):
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"""Run Monoloco and save txt files for KITTI evaluation"""
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cnt_ann = cnt_file = cnt_no_file = 0
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# Prepare empty folder
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di = os.path.join('data', 'kitti', self.net)
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make_new_directory(di)
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dir_out = {self.net: di}
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for _, names in self.baselines.items():
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for name in names:
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di = os.path.join('data', 'kitti', name)
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make_new_directory(di)
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dir_out[name] = di
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# Run the model
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for basename in self.set_basename:
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path_calib = os.path.join(self.dir_kk, basename + '.txt')
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annotations, kk, tt = factory_file(path_calib, self.dir_ann, basename)
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boxes, keypoints = preprocess_pifpaf(annotations, im_size=(1242, 374))
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cat = get_category(keypoints, os.path.join(self.dir_byc, basename + '.json'))
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if keypoints:
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annotations_r, _, _ = factory_file(path_calib, self.dir_ann, basename, ann_type='right')
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_, keypoints_r = preprocess_pifpaf(annotations_r, im_size=(1242, 374))
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if self.net == 'monstereo':
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dic_out = self.model.forward(keypoints, kk, keypoints_r=keypoints_r)
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elif self.net == 'monoloco_pp':
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dic_out = self.model.forward(keypoints, kk)
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all_outputs = {self.net: [dic_out['xyzd'], dic_out['bi'], dic_out['epi'],
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dic_out['yaw'], dic_out['h'], dic_out['w'], dic_out['l']]}
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zzs = [float(el[2]) for el in dic_out['xyzd']]
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# Save txt files
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params = [kk, tt]
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path_txt = os.path.join(dir_out[self.net], basename + '.txt')
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save_txts(path_txt, boxes, all_outputs[self.net], params, net=self.net, cat=cat)
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cnt_ann += len(boxes)
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cnt_file += 1
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# MONO (+ STEREO BASELINES)
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if self.baselines['mono']:
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# MONOLOCO
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dic_out = self.monoloco.forward(keypoints, kk)
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zzs_geom, xy_centers = geometric_coordinates(keypoints, kk, average_y=0.48)
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all_outputs['monoloco'] = [dic_out['d'], dic_out['bi'], dic_out['epi']] + [zzs_geom, xy_centers]
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all_outputs['geometric'] = all_outputs['monoloco']
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# monocular baselines
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for key in self.baselines['mono']:
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path_txt = {key: os.path.join(dir_out[key], basename + '.txt')}
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save_txts(path_txt[key], boxes, all_outputs[key], params, net=key, cat=cat)
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# stereo baselines
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if self.baselines['stereo']:
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all_inputs = {}
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dic_xyz = self._run_stereo_baselines(basename, boxes, keypoints, zzs, path_calib)
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for key in dic_xyz:
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all_outputs[key] = all_outputs['monoloco'].copy()
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all_outputs[key][0] = dic_xyz[key]
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all_inputs[key] = boxes
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path_txt[key] = os.path.join(dir_out[key], basename + '.txt')
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save_txts(path_txt[key], all_inputs[key], all_outputs[key], params,
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net='baseline',
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cat=cat)
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print("\nSaved in {} txt {} annotations. Not found {} images".format(cnt_file, cnt_ann, cnt_no_file))
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if self.baselines[self.mode] and self.net == 'monstereo':
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print("STEREO:")
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for key in self.baselines['stereo']:
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print("Annotations corrected using {} baseline: {:.1f}%".format(
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key, self.cnt_disparity[key] / cnt_ann * 100))
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print("Not found {}/{} stereo files".format(self.cnt_no_stereo, cnt_file))
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if self.generate_official:
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create_empty_files(dir_out, self.net) # Create empty files for official evaluation
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def _run_stereo_baselines(self, basename, boxes, keypoints, zzs, path_calib):
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annotations_r, _, _ = factory_file(path_calib, self.dir_ann, basename, ann_type='right')
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boxes_r, keypoints_r = preprocess_pifpaf(annotations_r, im_size=(1242, 374))
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_, kk, _ = factory_file(path_calib, self.dir_ann, basename)
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uv_centers = get_keypoints(keypoints, mode='bottom') # Kitti uses the bottom center to calculate depth
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xy_centers = pixel_to_camera(uv_centers, kk, 1)
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# Stereo baselines
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if keypoints_r:
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path_image = os.path.join(self.dir_images, basename + '.png')
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path_image_r = os.path.join(self.dir_images_r, basename + '.png')
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reid_features = get_reid_features(self.reid_net, boxes, boxes_r, path_image, path_image_r)
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dic_zzs, cnt = baselines_association(self.baselines['stereo'], zzs, keypoints, keypoints_r, reid_features)
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for key in cnt:
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self.cnt_disparity[key] += cnt[key]
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else:
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self.cnt_no_stereo += 1
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dic_zzs = {key: zzs for key in self.baselines['stereo']}
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# Combine the stereo zz with x, y from 2D detection (no MonoLoco involved)
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dic_xyz = defaultdict(list)
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for key in dic_zzs:
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for idx, zz_base in enumerate(dic_zzs[key]):
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xx = float(xy_centers[idx][0]) * zz_base
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yy = float(xy_centers[idx][1]) * zz_base
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dic_xyz[key].append([xx, yy, zz_base])
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return dic_xyz
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def save_txts(path_txt, all_inputs, all_outputs, all_params, net='monoloco', cat=None):
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assert net in ('monoloco', 'monstereo', 'geometric', 'baseline', 'monoloco_pp')
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if net in ('monstereo', 'monoloco_pp'):
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xyzd, bis, epis, yaws, hs, ws, ls = all_outputs[:]
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xyz = xyzd[:, 0:3]
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tt = [0, 0, 0]
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elif net in ('monoloco', 'geometric'):
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tt = [0, 0, 0]
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dds, bis, epis, zzs_geom, xy_centers = all_outputs[:]
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xyz = xyz_from_distance(dds, xy_centers)
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else:
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_, tt = all_params[:]
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xyz, bis, epis, zzs_geom, xy_centers = all_outputs[:]
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uv_boxes = all_inputs[:]
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assert len(uv_boxes) == len(list(xyz)), "Number of inputs different from number of outputs"
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with open(path_txt, "w+") as ff:
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for idx, uv_box in enumerate(uv_boxes):
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xx = float(xyz[idx][0]) - tt[0]
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yy = float(xyz[idx][1]) - tt[1]
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zz = float(xyz[idx][2]) - tt[2]
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if net == 'geometric':
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zz = zzs_geom[idx]
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cam_0 = [xx, yy, zz]
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bi = float(bis[idx])
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epi = float(epis[idx])
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if net in ('monstereo', 'monoloco_pp'):
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alpha, ry = float(yaws[0][idx]), float(yaws[1][idx])
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hwl = [float(hs[idx]), float(ws[idx]), float(ls[idx])]
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# scale to obtain (approximately) same recall at evaluation
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conf_scale = 0.035 if net == 'monoloco_pp' else 0.033
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else:
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alpha, ry, hwl = -10., -10., [0, 0, 0]
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conf_scale = 0.05
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conf = conf_scale * (uv_box[-1]) / (bi / math.sqrt(xx ** 2 + yy ** 2 + zz ** 2))
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output_list = [alpha] + uv_box[:-1] + hwl + cam_0 + [ry, conf, bi, epi]
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category = cat[idx]
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if category < 0.1:
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ff.write("%s " % 'Pedestrian')
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else:
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ff.write("%s " % 'Cyclist')
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ff.write("%i %i " % (-1, -1))
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for el in output_list:
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ff.write("%f " % el)
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ff.write("\n")
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def create_empty_files(dir_out, net):
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"""Create empty txt files to run official kitti metrics on MonStereo and all other methods"""
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methods = ['pseudo-lidar', 'monopsr', '3dop', 'm3d', 'oc-stereo', 'e2e', 'monodis', 'smoke']
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dirs = [os.path.join('data', 'kitti', method) for method in methods]
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dirs_orig = [os.path.join('data', 'kitti', method + '-orig') for method in methods]
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for di, di_orig in zip(dirs, dirs_orig):
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make_new_directory(di)
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for i in range(7481):
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name = "0" * (6 - len(str(i))) + str(i) + '.txt'
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path_orig = os.path.join(di_orig, name)
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path = os.path.join(di, name)
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# If the file exits, rewrite in new folder, otherwise create empty file
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read_and_rewrite(path_orig, path)
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for i in range(7481):
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name = "0" * (6 - len(str(i))) + str(i) + '.txt'
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with open(os.path.join(dir_out[net], name), "a+"): pass
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