175 lines
5.2 KiB
Python
175 lines
5.2 KiB
Python
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import os
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import glob
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import numpy as np
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def get_calibration(path_txt):
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"""Read calibration parameters from txt file:
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For the left color camera we use P2 which is K * [I|t]
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P = [fu, 0, x0, fu*t1-x0*t3
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0, fv, y0, fv*t2-y0*t3
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0, 0, 1, t3]
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check also http://ksimek.github.io/2013/08/13/intrinsic/
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Simple case test:
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xyz = np.array([2, 3, 30, 1]).reshape(4, 1)
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xyz_2 = xyz[0:-1] + tt
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uv_temp = np.dot(kk, xyz_2)
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uv_1 = uv_temp / uv_temp[-1]
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kk_1 = np.linalg.inv(kk)
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xyz_temp2 = np.dot(kk_1, uv_1)
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xyz_new_2 = xyz_temp2 * xyz_2[2]
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xyz_fin_2 = xyz_new_2 - tt
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"""
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with open(path_txt, "r") as ff:
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file = ff.readlines()
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p2_str = file[2].split()[1:]
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p2_list = [float(xx) for xx in p2_str]
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p2 = np.array(p2_list).reshape(3, 4)
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p3_str = file[3].split()[1:]
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p3_list = [float(xx) for xx in p3_str]
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p3 = np.array(p3_list).reshape(3, 4)
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kk, tt = get_translation(p2)
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kk_right, tt_right = get_translation(p3)
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return [kk, tt], [kk_right, tt_right]
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def get_translation(pp):
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"""Separate intrinsic matrix from translation and convert in lists"""
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kk = pp[:, :-1]
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f_x = kk[0, 0]
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f_y = kk[1, 1]
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x0, y0 = kk[2, 0:2]
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aa, bb, t3 = pp[0:3, 3]
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t1 = float((aa - x0*t3) / f_x)
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t2 = float((bb - y0*t3) / f_y)
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tt = [t1, t2, float(t3)]
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return kk.tolist(), tt
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def get_simplified_calibration(path_txt):
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with open(path_txt, "r") as ff:
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file = ff.readlines()
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for line in file:
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if line[:4] == 'K_02':
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kk_str = line[4:].split()[1:]
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kk_list = [float(xx) for xx in kk_str]
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kk = np.array(kk_list).reshape(3, 3).tolist()
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return kk
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raise ValueError('Matrix K_02 not found in the file')
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def check_conditions(line, category, method, thresh=0.3):
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"""Check conditions of our or m3d txt file"""
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check = False
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assert category in ['pedestrian', 'cyclist', 'all']
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if category == 'all':
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category = ['pedestrian', 'person_sitting', 'cyclist']
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if method == 'gt':
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if line.split()[0].lower() in category:
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check = True
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else:
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conf = float(line[15])
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if line[0].lower() in category and conf >= thresh:
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check = True
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return check
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def get_difficulty(box, trunc, occ):
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hh = box[3] - box[1]
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if hh >= 40 and trunc <= 0.15 and occ <= 0:
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cat = 'easy'
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elif trunc <= 0.3 and occ <= 1 and hh >= 25:
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cat = 'moderate'
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elif trunc <= 0.5 and occ <= 2 and hh >= 25:
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cat = 'hard'
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else:
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cat = 'excluded'
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return cat
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def split_training(names_gt, path_train, path_val):
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"""Split training and validation images"""
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set_gt = set(names_gt)
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set_train = set()
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set_val = set()
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with open(path_train, "r") as f_train:
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for line in f_train:
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set_train.add(line[:-1] + '.txt')
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with open(path_val, "r") as f_val:
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for line in f_val:
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set_val.add(line[:-1] + '.txt')
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set_train = set_gt.intersection(set_train)
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set_train.remove('000518.txt')
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set_train.remove('005692.txt')
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set_train.remove('003009.txt')
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set_train = tuple(set_train)
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set_val = tuple(set_gt.intersection(set_val))
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assert set_train and set_val, "No validation or training annotations"
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return set_train, set_val
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def factory_basename(dir_ann, dir_gt):
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""" Return all the basenames in the annotations folder corresponding to validation images"""
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# Extract ground truth validation images
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names_gt = tuple(os.listdir(dir_gt))
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path_train = os.path.join('splits', 'kitti_train.txt')
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path_val = os.path.join('splits', 'kitti_val.txt')
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_, set_val_gt = split_training(names_gt, path_train, path_val)
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set_val_gt = {os.path.basename(x).split('.')[0] for x in set_val_gt}
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# Extract pifpaf files corresponding to validation images
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list_ann = glob.glob(os.path.join(dir_ann, '*.json'))
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set_basename = {os.path.basename(x).split('.')[0] for x in list_ann}
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set_val = set_basename.intersection(set_val_gt)
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assert set_val, " Missing json annotations file to create txt files for KITTI datasets"
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return set_val
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def read_and_rewrite(path_orig, path_new):
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"""Read and write same txt file. If file not found, create open file"""
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try:
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with open(path_orig, "r") as f_gt:
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with open(path_new, "w+") as ff:
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for line_gt in f_gt:
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# if check_conditions(line_gt, category='all', method='gt'):
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line = line_gt.split()
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hwl = [float(x) for x in line[8:11]]
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hwl = " ".join([str(i)[0:4] for i in hwl])
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temp_1 = " ".join([str(i) for i in line[0: 8]])
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temp_2 = " ".join([str(i) for i in line[11:]])
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line_new = temp_1 + ' ' + hwl + ' ' + temp_2 + '\n'
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ff.write("%s" % line_new)
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except FileNotFoundError:
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with open(path_new, "a+"):
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pass
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def find_cluster(dd, clusters):
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"""Find the correct cluster. Above the last cluster goes into "excluded (together with the ones from kitti cat"""
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for idx, clst in enumerate(clusters[:-1]):
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if int(clst) < dd <= int(clusters[idx+1]):
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return clst
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return 'excluded'
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