Mercurial > hg > nsaunier > traffic-intelligence

annotate scripts/nomad/site-parameters-optimization.py @ 1223:051cf5bddc1f

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work on optimization (improved for feature tracking)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 21 Jun 2023 23:57:54 -0400
parents 5a207c838323
children a09a80cdc118
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1 #! /usr/bin/env python3
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2 import os
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3 import sys
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4 import glob
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5 from trafficintelligence import storage, moving
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6 import subprocess
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7 import numpy as np
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8
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9
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10 def loadParameters(filename):
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11 # load initial parameters from x.txt
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12 f = open(filename, 'r+')
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13 l = f.readline()
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14 x = [s for s in l.strip().split(" ")]
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15 f.close()
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16
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17 # create para-value list
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18 return paraValueList(x)
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19
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20 def paraValueList(x):
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21 #create para-value list
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22 #list of the parameters and their values
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23 p = ['--feature-quality', #]0.-0.4]
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24 '--min-feature-distanceklt', #]0.-10]
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25 '--block-size', #[1-10]integer
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26 '--window-size', #[1-10]integer
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27 '--min-feature-displacement', #[0.0001-0.1] 0.05
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28 '--acceleration-bound', #[1.-5.] 3
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29 '--deviation-bound', #[-1, 1] 0.6
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30 #p[3] = '--min-tracking-error' #[0.01-0.3]
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31 '--min-feature-time', #[2-100]integer
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32 '--mm-connection-distance', #[0.5-100]
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33 '--mm-segmentation-distance', #[1-100] ~mm-connection-distance / 2.5
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34 '--min-nfeatures-group'] #[2-4]
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35
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36 para = []
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37 if len(x) == 4:
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38 for n in range(4):
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39 para = para + [p[-4+n],x[n]]
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40 else:
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41 for n in range(len(x)):
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42 para = para + [p[n],x[n]]
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43
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44 return para
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45
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46 def process(para, intersections, recursive):
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47 Mota = []
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48 gtDatabaseaAbsPaths = []
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49 configFileAbsPaths = []
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50
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51 cwd = os.getcwd()
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52 # move to the location of the intersection
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53 for intersectionPath in intersections:
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54 intersectionAbsPath = os.path.abspath(intersectionPath)
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55 os.chdir(intersectionAbsPath)
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56 # iterate through all the subdirectories to find ground truth sqlite files
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57 newPaths = [os.path.abspath(fn) for fn in glob.glob(intersectionAbsPath+'/*_gt.sqlite', recursive=recursive)]
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58 gtDatabaseaAbsPaths.extend(newPaths)
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59 configFilename = os.path.abspath(glob.glob(intersectionAbsPath+'/*.cfg', recursive=recursive)[0])
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60 configFileAbsPaths.extend([configFilename]*len(newPaths))
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61 os.chdir(cwd)
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62 for gtDatabaseAbsPath, configFileAbsPath in zip(gtDatabaseaAbsPaths, configFileAbsPaths):
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63 gtDatabaseBasename = gtDatabaseAbsPath[:-10]
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64 videoFilename = gtDatabaseBasename + ".avi" # careful, it may be necessary to check video type / extension
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65 if not os.path.exists(videoFilename):
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66 print('Video file {} does not exist'.format(videoFilename))
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67 databaseFilename = gtDatabaseBasename + ".sqlite"
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68 gtDatabaseDirname = os.path.dirname(gtDatabaseAbsPath)
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69 homographyFilename = gtDatabaseDirname + "/homography.txt"
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70 maskFilename = gtDatabaseDirname + "/mask.png"
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71 # Skip feature tracking if the user specified to optimize only grouping parameters
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72 if len(para) > 8:
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73 # Track features
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74 trackingFeature(para, configFileAbsPath, videoFilename, databaseFilename, homographyFilename, maskFilename)
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75 # Group features
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76 groupFeature(para, configFileAbsPath, videoFilename, databaseFilename, homographyFilename, maskFilename)
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77 #load trajectory
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78 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object')
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79 #load ground truth
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80 annotations = storage.loadTrajectoriesFromSqlite(gtDatabaseAbsPath, 'object')
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81 # Appending negative mota because nomad minimizes the output
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82 matchingDistance = 5
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83 inter = moving.TimeInterval.unionIntervals([a.getTimeInterval() for a in annotations])
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84 motp, mota, mt, mme, fpt, gt, gtMatches, toMatches = moving.computeClearMOT(annotations, objects, matchingDistance, inter.first, inter.last)
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85 Mota.append(-mota)
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86
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87 # Change to the previous directory
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88 os.chdir(cwd)
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89
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90 return np.mean(Mota)
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91
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92 def trackingFeature(para, config, video, db, homo, mask):
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93 # remove previous tracking
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94 if os.path.exists(db):
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95 os.remove(db)
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96 # trackingfeature command parameters
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97 tf = ['feature-based-tracking', config, '--tf', '--video-filename', video, '--database-filename', db, '--homography-filename', homo, '--mask-filename', mask]
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98 # run in command line and print directly
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99 subprocess.check_output(tf + para)
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100
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101 def groupFeature(para, config, video, db, homo, mask):
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102 #remove previous grouping
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103 storage.deleteFromSqlite(db, 'object')
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104 #groupfeature command parameters
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105 gf = ['feature-based-tracking', config, '--gf', '--video-filename', video, '--database-filename', db, '--homography-filename', homo, '--mask-filename', mask]
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106 #run in command line and print directly
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107 subprocess.check_output(gf + para) # ['--min-feature-time', 'x', '--mm-connection-distance', 'x', '--mm-segmentation-distance', 'x', '--min-nfeatures-group', 'x']
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108
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109
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110 if __name__ == "__main__":
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111 # Load args that were given with select-arguments.py
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112 # with open('arguments.txt', 'r') as f:
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113 # args = f.read().split('\n')
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114 # intersections = args[0]
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115 # optimizeGroupingOnly = eval(args[1])
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116 # intersections = eval(intersections)
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117
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118 # Just write the intersections to optimize here: do not use '~' for home directory
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119 intersections = ['/home/nicolas/Research/Data3/test-optimization/12-laurier']
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120 recursive = False
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121
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122 # first and only argument should be the
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123 if len(sys.argv) == 2:
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124 para = loadParameters(sys.argv[1])
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125 # run process including trackingfeature, groupfeature, load groundtruth, compute mota
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126 print(process(para, intersections, recursive))
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127 else:
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128 print('Usage: site-parameters-optimization.py [initial-parameters text file]')
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129 sys.exit(0)