--- a/scripts/classify-objects.py	Wed Jun 03 16:00:46 2015 +0200
+++ b/scripts/classify-objects.py	Fri Jun 05 02:25:30 2015 +0200
@@ -1,23 +1,85 @@
 #! /usr/bin/env python
 
+import cvutils, moving, ml, storage
+
 import numpy as np
 import sys, argparse
-from cv2 import SVM_RBF, SVM_C_SVC
+#from cv2 import SVM_RBF, SVM_C_SVC
+import cv2
+from scipy.stats import norm, lognorm
 
-import cvutils, moving, ml
+# TODO add mode detection live
 
 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene')
 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True)
-parser.add_argument('-d', dest = 'directoryName', help = 'name of the parent directory containing the videos and extracted trajectories to process', required = True)
-# parser.add_argument('-o', dest = 'homographyFilename', help = 'name of the image to world homography file')
-# need a classification config file for speed distribution parameters, svm models, frequency parameters, area parameters etc
-#parser.add_argument('--cfg', dest = 'svmType', help = 'SVM type', default = SVM_C_SVC, type = long)
-
-
-#parser.add_argument('-s', dest = 'rescaleSize', help = 'rescale size of image samples', default = 64, type = int)
-#parser.add_argument('-o', dest = 'nOrientations', help = 'number of orientations in HoG', default = 9, type = int)
-#parser.add_argument('-p', dest = 'nPixelsPerCell', help = 'number of pixels per cell', default = 8, type = int)
-#parser.add_argument('-c', dest = 'nCellsPerBlock', help = 'number of cells per block', default = 2, type = int)
+#parser.add_argument('-u', dest = 'undistort', help = 'undistort the video (because features have been extracted that way)', action = 'store_true')
+#parser.add_argument('-f', dest = 'firstFrameNum', help = 'number of first frame number to display', type = int)
+#parser.add_argument('--last-frame', dest = 'lastFrameNum', help = 'number of last frame number to save (for image saving, no display is made)', type = int)
+# parser.add_argument('--min-speed-equiprobable', dest = 'minSpeedEquiprobable', help = 'speed value below which all classes are equiprobable (distributions give odd values there) (km/h)', type = float, default = 3.33)
+# parser.add_argument('--speed-aggregation', dest = 'speedAggregationMethod', help = 'method to aggregate road user speed', type = str, choices = ['median', 'mean', 'quantile'], default = 'median')
+# parser.add_argument('--speed-aggregation-quantile', dest = 'speedAggregationQuantile', help = 'quantile for the speed aggregation, if quantile is chosen', type = int, default = 50)
 
 args = parser.parse_args()
 params = storage.ProcessParameters(args.configFilename)
+
+params.convertToFrames(3.6)
+invHomography = np.linalg.inv(params.homography)
+
+if params.speedAggregationMethod == 'median':
+    speedAggregationFunc = np.median
+elif params.speedAggregationMethod == 'mean':
+    speedAggregationFunc = np.mean
+elif params.speedAggregationMethod == 'quantile':
+    speedAggregationFunc = lambda speeds: np.percentile(speeds, args.speedAggregationQuantile)
+else:
+    print('Unknown speed aggregation method: {}. Exiting'.format(params.speedAggregationMethod))
+    from sys import exit
+    exit()
+
+pedBikeCarSVM = ml.SVM()
+pedBikeCarSVM.load(params.pedBikeCarSVMFilename)
+bikeCarSVM = ml.SVM()
+bikeCarSVM.load(params.bikeCarSVMFilename)
+
+# log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.)
+speedProbabilities = {'car': lambda s: norm(params.meanVehicleSpeed, params.stdVehicleSpeed).pdf(s),
+                      'pedestrian': lambda s: norm(params.meanPedestrianSpeed, params.stdPedestrianSpeed).pdf(s), 
+                      'bicycle': lambda s: lognorm(params.scaleCyclistSpeed, loc = 0., scale = np.exp(params.locationCyclistSpeed)).pdf(s)} # lognorm shape, loc, scale
+
+objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename, 'object')
+features = storage.loadTrajectoriesFromSqlite(params.databaseFilename, 'feature')
+intervals = []
+for obj in objects:
+    obj.setFeatures(features)
+    intervals.append(obj.getTimeInterval())
+timeInterval = moving.unionIntervals(intervals)
+
+capture = cv2.VideoCapture(params.videoFilename)
+width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
+height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
+
+if params.undistort: # setup undistortion
+    [map1, map2] = computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
+if capture.isOpened():
+    ret = True
+    frameNum = timeInterval.first
+    capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
+    lastFrameNum = timeInterval.last
+
+    while ret and frameNum <= lastFrameNum:
+        ret, img = capture.read()
+        if ret:
+            if frameNum%50 == 0:
+                print('frame number: {}'.format(frameNum))
+            if params.undistort:
+                img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
+            for obj in objects:
+                if obj.existsAtInstant(frameNum):
+                    if obj.getFirstInstant() == frameNum:
+                        print 'first frame for obj {}'.format(obj.getNum())
+                        obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, params.maxPedestrianSpeed, params.maxCyclistSpeed, params.nFramesIgnoreAtEnds)
+                    obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, 0.2, 0.2, 800) # px, py, pixelThreshold
+        frameNum += 1
+    
+    for obj in objects:
+        obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = params.minSpeedEquiprobable, speedProbabilities = speedProbabilities)