--- a/scripts/extract-appearance-images.py	Thu Jun 22 17:02:03 2017 -0400
+++ b/scripts/extract-appearance-images.py	Thu Jun 22 18:08:46 2017 -0400
@@ -1,9 +1,9 @@
 #! /usr/bin/env python
 
-import numpy as np
-import argparse
-from cv2 import SVM_RBF, SVM_C_SVC
-#from cv2.ml import SVM_RBF, SVM_C_SVC, ROW_SAMPLE # row_sample for layout in cv2.ml.SVM_load
+import numpy as np, cv2
+import argparse, os
+from pandas import read_csv
+from matplotlib.pyplot import imsave
 
 import cvutils, moving, ml, storage
 
@@ -12,8 +12,12 @@
 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('--delimiter', dest = 'classificationAnnotationFilenameDelimiter', help = 'delimiter for the fields in the correct classification file', default= ' ')
 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('--extract-all', dest = 'extractAllObjectImages', help = 'extracts the images for all objects, well classified or not (otherwise, extracts only for the misclassified)', action = 'store_true')
+parser.add_argument('--prefix', dest = 'imagePrefix', help = 'image prefix', default = 'img')
+parser.add_argument('--ouput', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', default = '.')
 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')
 
 
@@ -23,34 +27,55 @@
 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
 classifierParams = storage.ClassifierParameters(params.classifierFilename)
 
-objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
+classificationAnnotations = read_csv(args.classificationAnnotationFilename, index_col=0, delimiter = args.classificationAnnotationFilenameDelimiter, names = ["object_num", "road_user_type"])
+annotatedObjectNumbers = classificationAnnotations.index.tolist()
+
+# objects has the objects for which we want to extract labeled images
+if args.extractAllObjectImages:
+    objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
+else:
+    if len(annotatedObjectNumbers) > args.nObjects:
+        classificationAnnotations = classificationAnnotations[:args.nObjects]
+        annotatedObjectNumbers = classificationAnnotations.index.tolist()
+    objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', annotatedObjectNumbers, withFeatures = True)
+for obj in objects:
+    if obj.getNum() in annotatedObjectNumbers:
+        obj.setUserType(classificationAnnotations.loc[obj.getNum(), 'road_user_type'])
 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
 
+for userType in classificationAnnotations['road_user_type'].unique():
+    if not os.path.exists(args.directoryName+os.sep+moving.userTypeNames[userType]):
+        os.mkdir(args.directoryName+os.sep+moving.userTypeNames[userType])
+
 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)
+
+print(timeInterval)
 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:
+    while ret and frameNum <= timeInterval.last:
         ret, img = capture.read()
         if ret:
             if frameNum%50 == 0:
                 print('frame number: {}'.format(frameNum))
-            if undistort:
+            if undistort: # undistort only if necessary
                 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
-
-
+            for obj in objects:
+                if obj.existsAtInstant(frameNum):
+                    if (obj.getFirstInstant()-frameNum)%args.nFramesStep == 0: # todo find next non zero image if none
+                        croppedImg = cvutils.imageBox(img, obj, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels)
+                        if croppedImg is not None:
+                            imsave(args.directoryName+os.sep+moving.userTypeNames[obj.getUserType()]+args.imagePrefix+'-{}-{}.png'.format(obj.getNum(), frameNum), croppedImg)
+                    elif obj.getLastInstant() == frameNum:
+                        objects.remove(obj)
         frameNum += 1
 
-
-
 # todo speed info: distributions AND min speed equiprobable
 
-# provide csv delimiter for the classification file as arg
-