--- a/scripts/extract-appearance-images.py	Thu Jun 22 12:02:34 2017 -0400
+++ b/scripts/extract-appearance-images.py	Thu Jun 22 16:57:34 2017 -0400
@@ -9,14 +9,46 @@
 
 parser = argparse.ArgumentParser(description='The program extracts labeled image patches to train the HoG-SVM classifier, and optionnally speed information')
 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True)
+parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)')
+parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)')
+parser.add_argument('--gt', dest = 'classificationAnnotationFilename', help = 'name of the file containing the correct classes (user types)', required = True)
+parser.add_argument('-s', dest = 'nFramesStep', help = 'number of frames between each saved patch', default = 50, type = int)
+parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to use to extract patches from', type = int, default = None)
+parser.add_argument('--compute-speed-distributions', dest = 'computeSpeedDistribution', help = 'computes the distribution of the road users of each type and fits parameters to each', action = 'store_true')
+
 
 #parser.add_argument('-d', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', required = True)
 
 args = parser.parse_args()
-params = storage.ProcessParameters(args.configFilename)
+params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
 classifierParams = storage.ClassifierParameters(params.classifierFilename)
 
-# need all info as for classification (image info)
+objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
+timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
+
+capture = cv2.VideoCapture(videoFilename)
+width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
+height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
+
+if undistort: # setup undistortion
+    [map1, map2] = cvutils.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 undistort:
+                img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
+
+
+        frameNum += 1
+
+
 
 # todo speed info: distributions AND min speed equiprobable