Mercurial Hosting > traffic-intelligence

changeset 1228:5654c9173548

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
merged (bicycle)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 12 Jul 2023 13:21:08 -0400
parents eb3936809ea5 (diff) d478d3122804 (current diff)
children 759d76d6d20c
files scripts/classify-objects.py scripts/manual-video-analysis.py scripts/polytracktopdtv.py scripts/train-object-classification.py trafficintelligence/moving.py
diffstat 5 files changed, 20 insertions(+), 20 deletions(-) [+]
line wrap: on
line diff
--- a/scripts/classify-objects.py	Wed Jul 12 12:12:37 2023 -0400
+++ b/scripts/classify-objects.py	Wed Jul 12 13:21:08 2023 -0400
@@ -37,9 +37,9 @@
 pedNorm = norm(classifierParams.meanPedestrianSpeed, classifierParams.stdPedestrianSpeed)
 # numpy lognorm shape, loc, scale: shape for numpy is scale (std of the normal) and scale for numpy is exp(location) (loc=mean of the normal)
 bicLogNorm = lognorm(classifierParams.scaleCyclistSpeed, loc = 0., scale = np.exp(classifierParams.locationCyclistSpeed))
-speedProbabilities = {'car': lambda s: carNorm.pdf(s),
-                      'pedestrian': lambda s: pedNorm.pdf(s), 
-                      'cyclist': lambda s: bicLogNorm.pdf(s)}
+speedProbabilities = {moving.userTypeNames[1]: lambda s: carNorm.pdf(s),
+                      moving.userTypeNames[2]: lambda s: pedNorm.pdf(s), 
+                      moving.userTypeNames[4]: lambda s: bicLogNorm.pdf(s)}
 
 if args.plotSpeedDistribution:
     import matplotlib.pyplot as plt
--- a/scripts/manual-video-analysis.py	Wed Jul 12 12:12:37 2023 -0400
+++ b/scripts/manual-video-analysis.py	Wed Jul 12 13:21:08 2023 -0400
@@ -29,7 +29,7 @@
                  'car',
                  'pedestrian',
                  'motorcycle',
-                 'cyclist',
+                 'bicycle',
                  'bus',
                  'truck']
 class UserConfiguration(object):
--- a/scripts/polytracktopdtv.py	Wed Jul 12 12:12:37 2023 -0400
+++ b/scripts/polytracktopdtv.py	Wed Jul 12 13:21:08 2023 -0400
@@ -25,7 +25,7 @@
           "1"  -> "car"
           "2"  -> "pedestrians"
           "3"  -> "motorcycle"
-          "4"  -> "cyclist"
+          "4"  -> "bicycle"
           "5"  -> "bus"
           "6"  -> "truck"
           ... and other type if the objects_type table is defined in SQLite'''
@@ -38,7 +38,7 @@
         typeDict["1"] = "car"
         typeDict["2"] = "pedestrians"
         typeDict["3"] = "motorcycle"
-        typeDict["4"] = "cyclist"
+        typeDict["4"] = "bicycle"
         typeDict["5"] = "bus"
         typeDict["6"] = "truck"
         
--- a/scripts/train-object-classification.py	Wed Jul 12 12:12:37 2023 -0400
+++ b/scripts/train-object-classification.py	Wed Jul 12 13:21:08 2023 -0400
@@ -22,9 +22,9 @@
 args = parser.parse_args()
 classifierParams = storage.ClassifierParameters(args.configFilename)
 
-imageDirectories = {'pedestrian': args.directoryName + "/Pedestrians/",
-                    'cyclist': args.directoryName + "/Cyclists/",
-                    'car': args.directoryName + "/Vehicles/"}
+imageDirectories = {moving.userTypeNames[2]: args.directoryName + "/Pedestrians/",
+                    moving.userTypeNames[4]: args.directoryName + "/Cyclists/",
+                    moving.userTypeNames[1]: args.directoryName + "/Vehicles/"}
 
 trainingSamplesPBV = {}
 trainingLabelsPBV = {}
@@ -39,11 +39,11 @@
     print('Loading {} samples'.format(k))
     trainingSamples, trainingLabels = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogBlockNorm, classifierParams.hogNCellsPerBlock)
     trainingSamplesPBV[k], trainingLabelsPBV[k] = trainingSamples, trainingLabels
-    if k != 'pedestrian':
+    if k != moving.userTypeNames[2]:
 	trainingSamplesBV[k], trainingLabelsBV[k] = trainingSamples, trainingLabels
-    if k != 'car':
+    if k != moving.userTypeNames[1]:
 	trainingSamplesPB[k], trainingLabelsPB[k] = trainingSamples, trainingLabels
-    if k != 'cyclist':
+    if k != moving.userTypeNames[4]:
 	trainingSamplesPV[k], trainingLabelsPV[k] = trainingSamples, trainingLabels
 
 # Training the Support Vector Machine
--- a/trafficintelligence/moving.py	Wed Jul 12 12:12:37 2023 -0400
+++ b/trafficintelligence/moving.py	Wed Jul 12 13:21:08 2023 -0400
@@ -1371,19 +1371,19 @@
                  'car',
                  'pedestrian',
                  'motorcycle',
-                 'cyclist',
+                 'bicycle',
                  'bus',
                  'truck',
                  'automated']
 
 coco2Types = {0: 2, 1: 4, 2: 1, 5: 5, 7: 6}
 cocoTypeNames = {0: 'person',
-                     1: 'bicycle',
-	             2:	'car',
-                     3: 'motorcycle',
-                     5: 'bus',
-                     6: 'train',
-                     7: 'truck'}
+                 1: 'bicycle',
+	         2: 'car',
+                 3: 'motorcycle',
+                 5: 'bus',
+                 6: 'train',
+                 7: 'truck'}
 
 userType2Num = utils.inverseEnumeration(userTypeNames)
 
@@ -2073,7 +2073,7 @@
                     self.classifyUserTypeHoGSVMAtInstant(images[t], t, homography, width, height, px, py, minNPixels, rescaleSize, orientations, pixelsPerCell, cellsPerBlock)
         # compute P(Speed|Class)
         if speedProbabilities is None or self.aggregatedSpeed < minSpeedEquiprobable: # equiprobable information from speed
-            userTypeProbabilities = {userType2Num['car']: 1., userType2Num['pedestrian']: 1., userType2Num['cyclist']: 1.}
+            userTypeProbabilities = {userType2Num['car']: 1., userType2Num['pedestrian']: 1., userType2Num['bicycle']: 1.}
         else:
             userTypeProbabilities = {userType2Num[userTypename]: speedProbabilities[userTypename](self.aggregatedSpeed) for userTypename in speedProbabilities}
         # compute P(Class|Appearance)