--- a/scripts/extract-appearance-images.py	Mon Jun 26 00:10:35 2017 -0400
+++ b/scripts/extract-appearance-images.py	Mon Jun 26 17:45:32 2017 -0400
@@ -3,7 +3,7 @@
 import numpy as np, cv2
 import argparse, os
 from pandas import read_csv
-from matplotlib.pyplot import imsave, imshow, figure
+from matplotlib.pyplot import imshow, figure
 
 import cvutils, moving, ml, storage
 
@@ -15,14 +15,12 @@
 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('-o', dest = 'overlap', help = 'maximum intersection over union of the features nFramesStep apart to save image', type = float, default = 0.2)
 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')
 
-
-#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, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
 classifierParams = storage.ClassifierParameters(params.classifierFilename)
@@ -62,19 +60,22 @@
     lastFrameNum = timeInterval.last
     while ret and frameNum <= timeInterval.last:
         ret, img = capture.read()
+        distorted = True
         if ret:
             if frameNum%50 == 0:
                 print('frame number: {}'.format(frameNum))
-            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 (10+frameNum-obj.getFirstInstant())%args.nFramesStep == 0:
-                        # todo find next non zero image if none
-                        # todo get several images if different features (measure of similarity)
-                        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()]+os.sep+args.imagePrefix+'-{}-{}.png'.format(obj.getNum(), frameNum), croppedImg)
+                        currentImageFeatures = set([f.num for f in obj.getFeatures() if f.existsAtInstant(frameNum)])
+                        if not hasattr(obj, 'lastImageFeatures') or len(currentImageFeatures.intersection(obj.lastImageFeatures))/len(currentImageFeatures.union(obj.lastImageFeatures)) < args.overlap:
+                            obj.lastImageFeatures = currentImageFeatures
+                            if undistort and distorted: # undistort only if necessary
+                                img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
+                                distorted = False
+                            croppedImg = cvutils.imageBox(img, obj, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels)
+                            if croppedImg is not None:
+                                cv2.imwrite(args.directoryName+os.sep+moving.userTypeNames[obj.getUserType()]+os.sep+args.imagePrefix+'-{}-{}.png'.format(obj.getNum(), frameNum), croppedImg)
                     elif obj.getLastInstant() == frameNum:
                         objects.remove(obj)
         frameNum += 1