hist_analyzer_tmp.py File Reference

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Classes
class	pr2_playpen.hist_analyzer_tmp.HistAnalyzer
Namespaces
namespace	pr2_playpen::hist_analyzer_tmp
Variables
tuple	pr2_playpen::hist_analyzer_tmp.avg = np.mean(back_sum_ls)
tuple	pr2_playpen::hist_analyzer_tmp.back_sum_ls = deque()
tuple	pr2_playpen::hist_analyzer_tmp.background_noise = deque()
string	pr2_playpen::hist_analyzer_tmp.default = 'check for success or failure in batch mode with already stored data'
tuple	pr2_playpen::hist_analyzer_tmp.file_h = open(opt.batch_folder+'/object'+str(i).zfill(3)+'.pkl', 'r')
tuple	pr2_playpen::hist_analyzer_tmp.file_h2 = open(opt.batch_folder+'/object'+str(i).zfill(3)+'.pkl', 'w')
string	pr2_playpen::hist_analyzer_tmp.folder = '/background_noise/'
tuple	pr2_playpen::hist_analyzer_tmp.ha = HistAnalyzer(background_noise, mask)
tuple	pr2_playpen::hist_analyzer_tmp.Imask = cv.CreateImage(cv.GetSize(ha.background_noise[0]), cv.IPL_DEPTH_8U, 1)
tuple	pr2_playpen::hist_analyzer_tmp.img = cv.LoadImage(folder+'file'+str(i).zfill(3)+'.png')
	pr2_playpen::hist_analyzer_tmp.is_object = False
tuple	pr2_playpen::hist_analyzer_tmp.loc_sum = float(cv.Sum(result)[0])
tuple	pr2_playpen::hist_analyzer_tmp.mask = cv.LoadImage(folder+'mask.png', 0)
int	pr2_playpen::hist_analyzer_tmp.n = 0
tuple	pr2_playpen::hist_analyzer_tmp.p = optparse.OptionParser()
tuple	pr2_playpen::hist_analyzer_tmp.res_dict = cPickle.load(file_h)
tuple	pr2_playpen::hist_analyzer_tmp.result = ha.compare_imgs(img, ha.background_noise[0])
tuple	pr2_playpen::hist_analyzer_tmp.result2 = ha.compare_imgs(img, ha.background_noise[-1])
tuple	pr2_playpen::hist_analyzer_tmp.std = np.std(back_sum_ls)
int	pr2_playpen::hist_analyzer_tmp.sum_val = 0