more versions, adding more images in the results plot (4b02b283) · Commits · Elphel / lwir-nn

explore_data15.py

+14 −3

Original line number	Diff line number	Diff line
		@@ -1755,7 +1755,7 @@ if __name__ == "__main__":
		test_corrs = []
		#1527257933_150165-ML_DATA-32B-O-FZ0.05-MAIN-RND2.00000.tiff
		#/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527257933_150165/v04/mlr32_18c/1527257933_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff

		'''
		test_sets = [
		"/data_ssd/lwir_sets/lwir_test2/1562390202_933097/v01/ml32", # andrey /empty
		"/data_ssd/lwir_sets/lwir_test2/1562390225_269784/v01/ml32", # andrey /empty
		@@ -1777,7 +1777,18 @@ if __name__ == "__main__":
		"/data_ssd/lwir_sets/lwir_test3/1562390409_661607/v01/ml32", # lena, 2 far moving cars
		"/data_ssd/lwir_sets/lwir_test3/1562390435_873048/v01/ml32", # 2 parked cars, lena
		"/data_ssd/lwir_sets/lwir_test3/1562390456_842237/v01/ml32", # near trees
		"/data_ssd/lwir_sets/lwir_test3/1562390460_261151/v01/ml32"] # near trees, olga
		"/data_ssd/lwir_sets/lwir_test3/1562390460_261151/v01/ml32", # near trees, olga
		]
		'''

		test_sets = [
		"/data_ssd/lwir_sets/lwir_test6/1562390317_693673/v01/ml32", # andrey + olga
		"/data_ssd/lwir_sets/lwir_test6/1562390318_833313/v01/ml32", # andrey + olga
		"/data_ssd/lwir_sets/lwir_test6/1562390326_354823/v01/ml32", # andrey + olga
		"/data_ssd/lwir_sets/lwir_test6/1562390331_483132/v01/ml32", # andrey + olga
		"/data_ssd/lwir_sets/lwir_test6/1562390333_192523/v01/ml32", # lena
		]


		#Parameters to generate neighbors data. Set radius to 0 to generate single-tile
		TEST_SAME_LENGTH_AS_TRAIN = False # True # make test to have same number of entries as train ones

explore_data16.py

+20 −19

Original line number	Diff line number	Diff line
		@@ -1657,14 +1657,15 @@ if __name__ == "__main__":
		test_corrs = []

		test_sets = [
		"/data_ssd/lwir_sets/lwir_test2/1562390202_933097/v01/ml32", # andrey /empty
		"/data_ssd/lwir_sets/lwir_test2/1562390225_269784/v01/ml32", # andrey /empty
		"/data_ssd/lwir_sets/lwir_test2/1562390225_839538/v01/ml32", # andrey /empty
		"/data_ssd/lwir_sets/lwir_test2/1562390243_047919/v01/ml32", # 2 trees
		"/data_ssd/lwir_sets/lwir_test2/1562390251_025390/v01/ml32", # empty space
		"/data_ssd/lwir_sets/lwir_test2/1562390257_977146/v01/ml32", # first 3
		"/data_ssd/lwir_sets/lwir_test2/1562390260_370347/v01/ml32", # all 3
		"/data_ssd/lwir_sets/lwir_test2/1562390260_940102/v01/ml32", # all 3
		"/data_ssd/lwir_sets/lwir_test2/1562390202_933097/v01/ml32b", # andrey /empty
		"/data_ssd/lwir_sets/lwir_test2/1562390225_269784/v01/ml32b", # andrey /empty
		"/data_ssd/lwir_sets/lwir_test2/1562390225_839538/v01/ml32b", # andrey /empty
		"/data_ssd/lwir_sets/lwir_test2/1562390243_047919/v01/ml32b", # 2 trees

		"/data_ssd/lwir_sets/lwir_test6/1562390251_025390/v01/ml32b", # empty space
		"/data_ssd/lwir_sets/lwir_test6/1562390257_977146/v01/ml32b", # first 3
		"/data_ssd/lwir_sets/lwir_test6/1562390260_370347/v01/ml32b", # all 3
		"/data_ssd/lwir_sets/lwir_test2/1562390260_940102/v01/ml32b", # all 3

		"/data_ssd/lwir_sets/lwir_test6/1562390317_693673/v01/ml32", # andrey + olga
		"/data_ssd/lwir_sets/lwir_test6/1562390318_833313/v01/ml32", # andrey + olga
		@@ -1672,12 +1673,12 @@ if __name__ == "__main__":
		"/data_ssd/lwir_sets/lwir_test6/1562390331_483132/v01/ml32", # andrey + olga
		"/data_ssd/lwir_sets/lwir_test6/1562390333_192523/v01/ml32", # lena

		"/data_ssd/lwir_sets/lwir_test3/1562390402_254007/v01/ml32", # near moving car
		"/data_ssd/lwir_sets/lwir_test3/1562390407_382326/v01/ml32", # near moving car
		"/data_ssd/lwir_sets/lwir_test3/1562390409_661607/v01/ml32", # lena, 2 far moving cars
		"/data_ssd/lwir_sets/lwir_test3/1562390435_873048/v01/ml32", # 2 parked cars, lena
		"/data_ssd/lwir_sets/lwir_test3/1562390456_842237/v01/ml32", # near trees
		"/data_ssd/lwir_sets/lwir_test3/1562390460_261151/v01/ml32"] # near trees, olga
		"/data_ssd/lwir_sets/lwir_test6/1562390402_254007/v01/ml32b", # near moving car
		"/data_ssd/lwir_sets/lwir_test6/1562390407_382326/v01/ml32b", # near moving car
		"/data_ssd/lwir_sets/lwir_test6/1562390409_661607/v01/ml32b", # lena, 2 far moving cars
		"/data_ssd/lwir_sets/lwir_test6/1562390435_873048/v01/ml32b", # 2 parked cars, lena
		"/data_ssd/lwir_sets/lwir_test6/1562390456_842237/v01/ml32b", # near trees
		"/data_ssd/lwir_sets/lwir_test6/1562390460_261151/v01/ml32b"] # near trees, olga

		#Parameters to generate neighbors data. Set radius to 0 to generate single-tile
		TEST_SAME_LENGTH_AS_TRAIN = False # True # make test to have same number of entries as train ones

nn_ds_neibs31.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -3,7 +3,7 @@ __copyright__ = "Copyright 2018-2019, Elphel, Inc."
		__license__ = "GPL-3.0+"
		__email__ = "andrey@elphel.com"

		#python3 nn_ds_neibs31.py /data_ssd/lwir_sets/conf/qcstereo_lwir05.xml /data_ssd/lwir_sets/
		#python3 nn_ds_neibs31.py /data_ssd/lwir_sets/conf/qcstereo_lwir21.xml /data_ssd/lwir_sets/
		#tensorboard --logdir="nn_ds_neibs30_graph13-9RNSWLAM0.5SLAM0.1SCLP0.2_nG_nI_HF_CP0.3_S0.03" --port=7001

		import os

nn_eval_lwir.py

+140 −212

Original line number	Diff line number	Diff line
		@@ -16,7 +16,7 @@ import sys
		import imagej_tiffwriter

		import time

		import imagej_tiff as ijt
		import matplotlib.pyplot as plt
		from matplotlib.backends.backend_pdf import PdfPages
		import qcstereo_functions as qsf
		@@ -27,7 +27,8 @@ import numpy as np
		qsf.TIME_START = time.time()
		qsf.TIME_LAST = qsf.TIME_START

		IMG_WIDTH = 20 # 324 # tiles per image row Defined in config
		#IMG_WIDTH = 20 # 324 # tiles per image row Defined in config
		#IMG_HEIGHT = 15 # 324 # tiles per image row Defined in config

		DEBUG_LEVEL= 1

		@@ -41,6 +42,12 @@ try:
		except IndexError:
		root_dir = os.path.dirname(conf_file)

		try:
		modes = [sys.argv[3]] # train, infer
		except IndexError:
		modes = ['train']


		print ("Configuration file: " + conf_file)
		parameters, dirs, files, dbg_parameters = qsf.parseXmlConfig(conf_file, root_dir)
		"""
		@@ -53,6 +60,8 @@ if not "SLOSS_CLIP" in parameters:
		"""
		Defined in config file
		"""
		IMG_WIDTH = None # 20 # 324 # tiles per image row Defined in config
		IMG_HEIGHT = None # 15 # 324 # tiles per image row Defined in config
		TILE_SIDE, TILE_LAYERS, TWO_TRAINS, NET_ARCH1, NET_ARCH2 = [None]*5
		ABSOLUTE_DISPARITY,SYM8_SUB, WLOSS_LAMBDA, SLOSS_LAMBDA, SLOSS_CLIP = [None]*5
		SPREAD_CONVERGENCE, INTER_CONVERGENCE, HOR_FLIP, DISP_DIFF_CAP, DISP_DIFF_SLOPE = [None]*5
		@@ -111,14 +120,19 @@ qsf.prepareFiles(dirs, files, suffix = SUFFIX)


		CONF_MAX = 0.7
		ERR_AMPL = 0.3
		ERR_AMPL = 0.4 # 0.3
		TIGHT_TOP = 0.95
		TIGHT_HPAD = 1.0
		TIGHT_WPAD = 1.0
		FIGSIZE = [8.5,11.0]
		WOI_COLOR = "red"
		X_COLOR = "grey"
		X_NEIBS = False
		TRANSPARENT = True # for export




		#dbg_parameters
		def get_fig_params(disparity_ranges):
		fig_params = []
		@@ -138,7 +152,14 @@ def get_fig_params(disparity_ranges):
		return fig_params

		#try:
		fig_params = get_fig_params(dbg_parameters['disparity_ranges'])
		#fig_params = get_fig_params(dbg_parameters['disparity_ranges'])
		extra_path = os.path.join(root_dir, dbg_parameters['extra'])
		eo_width = dbg_parameters['eo_params']['width']
		eo_height = dbg_parameters['eo_params']['height']
		eo_woi = dbg_parameters['eo_params']['woi'] # (x,y,width, height)
		eo_disparity_scale = 1.0/dbg_parameters['eo_params']['disparity_scale'] # 14.2
		image_sets = dbg_parameters['extra_paths'] # list of dictionaries


		pass

		@@ -162,10 +183,15 @@ index_gt = 2
		index_gt_weight = 3
		index_heur_err = 7
		index_nn_err = 6
		index_mm = 8 # max-min
		index_log = 9
		index_bad = 10
		index_num_neibs = 11
		index_fgbg_sngl = 10
		index_fgbg_neib = 11

		index_mm = 23 # 8 # max-min
		index_log = 24 # 9
		index_bad = 25 # 10
		index_num_neibs = 26 # 11

		index_fgbg = [index_fgbg_sngl,index_fgbg_neib][X_NEIBS]
		"""
		Debugging high 9-tile variations, removing error for all tiles with lower difference between max and min
		"""
		@@ -181,7 +207,21 @@ if not 'show' in FIGS_SAVESHOW:

		#for mode in ['train','infer']:
		#for mode in ['infer']:
		for mode in ['train']:
		def cross_out(
		plt,
		cross_out_mask):
		height = cross_out_mask.shape[0]
		width = cross_out_mask.shape[1]
		for row in range (height):
		for col in range(width):
		if cross_out_mask[row,col]:
		xdata = [col-0.3, col+0.3]
		ydata = [row-0.3, row+0.3]
		plt.plot(xdata,ydata,color=X_COLOR)
		ydata = [row+0.3, row-0.3]
		plt.plot(xdata,ydata,color=X_COLOR)

		for mode in modes: # ['train']:
		figs = []
		ffiles = [] # no ext
		def setlimsxy(lim_xy):
		@@ -189,218 +229,106 @@ for mode in ['train']:
		plt.xlim(min(lim_xy[:2]),max(lim_xy[:2]))
		plt.ylim(max(lim_xy[2:]),min(lim_xy[2:]))
		cumul_weights = None

		for nfile, fpars in enumerate(fig_params):
		if not fpars is None:
		cmap_disp=plt.get_cmap('viridis') # ('cividis')
		cmap_diff=plt.get_cmap('coolwarm') #('seismic') # ('viridis')
		for nfile, img_pars in enumerate(image_sets):
		if not img_pars is None:
		img_file = files['result'][nfile]
		if mode == 'infer':
		img_file = img_file.replace('.npy','-infer.npy')
		"""
		try:
		# data,_ = qsf.result_npy_prepare(img_file, ABSOLUTE_DISPARITY, fix_nan=True, insert_deltas=True)
		# data,_ = qsf.result_npy_prepare(img_file, ABSOLUTE_DISPARITY, fix_nan=True, insert_deltas=3)
		data,labels = qsf.result_npy_prepare(img_file, ABSOLUTE_DISPARITY, fix_nan=True, insert_deltas=3)
		except:
		print ("Image file does not exist:", img_file)
		continue
		"""
		pass
		data,labels = qsf.result_npy_prepare(img_file, ABSOLUTE_DISPARITY, fix_nan=True, insert_deltas=3)
		if True: #TIFF_ONLY:
		print ("Processing image set: "+img_file)

		data,labels = qsf.result_npy_prepare(img_file, ABSOLUTE_DISPARITY, fix_nan=True, insert_deltas=3)
		cross_out_mask = data[...,index_fgbg] < 0.5 #data.shape = (15,20,27)
		# for subindex, rng in enumerate(fpars['ranges']):

		tiff_path = img_file.replace('.npy','-test.tiff')
		lim_val = img_pars['range'] # rng['lim_val']
		lim_val[0] -= ERR_AMPL
		lim_xy = [-0.5, IMG_WIDTH - 0.5, -0.5, IMG_HEIGHT - 0.5] # rng['lim_xy']

		data = data.transpose(2,0,1)
		print("Saving results to TIFF: "+tiff_path)
		imagej_tiffwriter.save(tiff_path,data,labels=labels)
		"""
		Calculate histograms
		"""
		err_heur2 = data[index_heur_err]*data[index_heur_err]
		err_nn2 = data[index_nn_err]* data[index_nn_err]
		diff_log2 = data[index_log]* data[index_log]
		weights = (
		(data[index_gt] < max_disp) &
		(err_heur2 < max_target_err2) &
		(data[index_bad] < max_bad) &
		(data[index_gt_weight] >= min_strength) &
		(data[index_num_neibs] >= min_neibs)&
		#max_log_to_mm = 0.5 # difference between center average and center should be under this fraction of max-min (0 - disables feature)
		(data[index_log] < max_log_to_mm * np.sqrt(data[index_mm]) )
		).astype(data.dtype) # 0.0/1.1
		#max_disp

		#max_target_err
		if use_gt_weights:
		weights *= data[index_gt_weight]
		mm = data[index_mm]
		weh = np.nan_to_num(weights*err_heur2)
		wen = np.nan_to_num(weights*err_nn2)
		wel = np.nan_to_num(weights*diff_log2)
		hist_weights,bin_vals = np.histogram(a=mm, bins = num_bins, range = (0.0, max_diff), weights = weights, density = False)
		hist_err_heur2,_ = np.histogram(a=mm, bins = num_bins, range = (0.0, max_diff), weights = weh, density = False)
		hist_err_nn2,_ = np.histogram(a=mm, bins = num_bins, range = (0.0, max_diff), weights = wen, density = False)
		hist_diff_log2,_ = np.histogram(a=mm, bins = num_bins, range = (0.0, max_diff), weights = wel, density = False)
		if cumul_weights is None:
		cumul_weights = hist_weights
		cumul_err_heur2 = hist_err_heur2
		cumul_err_nn2 = hist_err_nn2
		cumul_diff_log2 = hist_diff_log2
		else:
		cumul_weights += hist_weights
		cumul_err_heur2 += hist_err_heur2
		cumul_err_nn2 += hist_err_nn2
		cumul_diff_log2 += hist_diff_log2

		hist_err_heur2 = np.nan_to_num(hist_err_heur2/hist_weights)
		hist_err_nn2 = np.nan_to_num(hist_err_nn2/hist_weights)
		hist_gain2 = np.nan_to_num(hist_err_heur2/hist_err_nn2)
		hist_gain = np.sqrt(hist_gain2)
		hist_diff_log2 = np.nan_to_num(hist_diff_log2/hist_weights)

		print("hist_err_heur2", end = " ")
		print(np.sqrt(hist_err_heur2))
		print("hist_err_nn2", end = " ")
		print(np.sqrt(hist_err_nn2))
		print("hist_gain", end = " ")
		print(hist_gain)
		print("hist_diff_log2", end = " ")
		print(np.sqrt(hist_diff_log2))


		if min_diff> 0.0:
		pass
		good = (mm > min_diff).astype(mm.dtype)
		good /= good # good - 1, bad - nan
		data[index_heur_err] *= good
		data[index_nn_err] *= good
		data = data.transpose(1,2,0)
		#start new image page
		fig = plt.figure(figsize=FIGSIZE)
		fig.canvas.set_window_title(img_pars['title'])
		fig.suptitle(img_pars['title'])

		if TIFF_ONLY:
		continue
		# Create EO DSI image
		# load tiff image

		img_ds_main = ijt.imagej_tiff(os.path.join(extra_path,img_pars['dsi_path'] ))
		ds_main = img_ds_main.image[...,img_pars['dsi_slice']] * eo_disparity_scale
		ds_main = np.maximum(ds_main, lim_val[0])
		ds_main = np.minimum(ds_main, lim_val[1])
		ax_conf=plt.subplot(322)
		ax_conf.set_title("Hi-res camera disparity map")
		plt.imshow(ds_main, vmin=lim_val[0], vmax=lim_val[1], cmap=cmap_disp)
		setlimsxy([-0.5, eo_width-0.5, -0.5, eo_height - 0.5])
		if not eo_woi is None:
		pass # show frame
		xdata=[eo_woi['x'], eo_woi['x'] + eo_woi['width'], eo_woi['x'] + eo_woi['width'], eo_woi['x'], eo_woi['x']]
		ydata=[eo_woi['y'], eo_woi['y'], eo_woi['y'] + eo_woi['height'], eo_woi['y'] + eo_woi['height'], eo_woi['y']]
		plt.plot(xdata,ydata,color=WOI_COLOR)

		for subindex, rng in enumerate(fpars['ranges']):
		lim_val = rng['lim_val']
		lim_xy = rng['lim_xy']
		fig = plt.figure(figsize=FIGSIZE)
		fig.canvas.set_window_title(fpars['name'])
		fig.suptitle(fpars['name'])
		plt.colorbar(orientation='vertical') # location='bottom')
		'''
		# Ground truth confidence - to be replaced
		ax_conf=plt.subplot(322)
		ax_conf.set_title("Ground truth confidence")
		# fig.suptitle("Groud truth confidence")
		plt.imshow(data[...,qsf.GT_CONF], vmin=0, vmax=CONF_MAX, cmap='gray')
		if not lim_xy is None:
		pass # show frame
		xdata=[min(lim_xy[:2]),max(lim_xy[:2]),max(lim_xy[:2]),min(lim_xy[:2]),min(lim_xy[:2])]
		ydata=[min(lim_xy[2:]),min(lim_xy[2:]),max(lim_xy[2:]),max(lim_xy[2:]),min(lim_xy[2:])]
		plt.plot(xdata,ydata,color=WOI_COLOR)

		# setlimsxy(lim_xy)
		plt.colorbar(orientation='vertical') # location='bottom')
		'''

		ax_gtd=plt.subplot(321)
		ax_gtd.set_title("Ground truth disparity map")
		plt.imshow(data[...,qsf.GT_DISP], vmin=lim_val[0], vmax=lim_val[1])
		plt.imshow(data[...,qsf.GT_DISP], vmin=lim_val[0], vmax=lim_val[1], cmap=cmap_disp)
		setlimsxy(lim_xy)
		cross_out(plt, cross_out_mask)
		plt.colorbar(orientation='vertical') # location='bottom')

		ax_hed=plt.subplot(323)
		ax_hed.set_title("Heuristic disparity map")
		plt.imshow(data[...,qsf.HEUR_NAN], vmin=lim_val[0], vmax=lim_val[1])
		plt.imshow(data[...,qsf.HEUR_NAN], vmin=lim_val[0], vmax=lim_val[1], cmap=cmap_disp)
		setlimsxy(lim_xy)
		cross_out(plt, cross_out_mask)
		plt.colorbar(orientation='vertical') # location='bottom')

		ax_nnd=plt.subplot(325)
		ax_nnd.set_title("Network disparity output")
		plt.imshow(data[...,qsf.NN_NAN], vmin=lim_val[0], vmax=lim_val[1])
		plt.imshow(data[...,qsf.NN_NAN], vmin=lim_val[0], vmax=lim_val[1], cmap=cmap_disp)
		setlimsxy(lim_xy)
		cross_out(plt, cross_out_mask)
		plt.colorbar(orientation='vertical') # location='bottom')

		ax_hee=plt.subplot(324)
		ax_hee.set_title("Heuristic disparity error")
		plt.imshow(data[...,qsf.HEUR_DIFF], vmin=-ERR_AMPL, vmax=ERR_AMPL)
		cross_out(plt, cross_out_mask)
		plt.imshow(data[...,qsf.HEUR_DIFF], vmin=-ERR_AMPL, vmax=ERR_AMPL, cmap=cmap_diff)
		setlimsxy(lim_xy)
		cross_out(plt, cross_out_mask)
		plt.colorbar(orientation='vertical') # location='bottom')

		ax_nne=plt.subplot(326)
		ax_nne.set_title("Network disparity error")
		plt.imshow(data[...,qsf.NN_DIFF], vmin=-ERR_AMPL, vmax=ERR_AMPL)
		plt.imshow(data[...,qsf.NN_DIFF], vmin=-ERR_AMPL, vmax=ERR_AMPL, cmap=cmap_diff)
		setlimsxy(lim_xy)
		cross_out(plt, cross_out_mask)
		plt.colorbar(orientation='vertical') # location='bottom')

		plt.tight_layout(rect =[0,0,1,TIGHT_TOP], h_pad = TIGHT_HPAD, w_pad = TIGHT_WPAD)

		figs.append(fig)
		fb_noext = os.path.splitext(os.path.basename(img_file))[0]#
		if subindex > 0:
		if subindex < 10:
		fb_noext+="abcdefghi"[subindex-1]
		else:
		fb_noext+="-"+str(subindex)
		# if subindex > 0:
		# if subindex < 10:
		# fb_noext+="abcdefghi"[subindex-1]
		# else:
		# fb_noext+="-"+str(subindex)
		ffiles.append(fb_noext)
		pass
		if True:
		cumul_err_heur2 = np.nan_to_num(cumul_err_heur2/cumul_weights)
		cumul_err_nn2 = np.nan_to_num(cumul_err_nn2/cumul_weights)
		cumul_gain2 = np.nan_to_num(cumul_err_heur2/cumul_err_nn2)
		cumul_gain = np.sqrt(cumul_gain2)
		cumul_diff_log2 = np.nan_to_num(cumul_diff_log2/cumul_weights)
		print("cumul_weights", end = " ")
		print(cumul_weights)
		print("cumul_err_heur", end = " ")
		print(np.sqrt(cumul_err_heur2))
		print("cumul_err_nn", end = " ")
		print(np.sqrt(cumul_err_nn2))
		print("cumul_gain", end = " ")
		print(cumul_gain)
		print("cumul_diff_log2", end = " ")
		print(np.sqrt(cumul_diff_log2))
		fig, ax1 = plt.subplots()
		ax1.set_xlabel('3x3 tiles ground truth disparity max-min (pix)')
		ax1.set_ylabel('RMSE\n(pix)', color='black', rotation='horizontal')
		ax1.yaxis.set_label_coords(-0.045,0.92)

		ax1.plot(bin_vals[0:-1], np.sqrt(cumul_err_nn2), 'tab:red',label="network disparity RMSE")
		ax1.plot(bin_vals[0:-1], np.sqrt(cumul_err_heur2), 'tab:green',label="heuristic disparity RMSE")
		ax1.plot(bin_vals[0:-1], np.sqrt(cumul_diff_log2), 'tab:cyan',label="ground truth LoG")

		ax1.tick_params(axis='y', labelcolor='black')

		ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
		ax2.set_ylabel('weight', color='black', rotation='horizontal') # we already handled the x-label with ax1
		ax2.yaxis.set_label_coords(1.06,1.0)



		ax2.plot(bin_vals[0:-1], cumul_weights,color='grey',dashes=[6, 2],label='weights = n_tiles * gt_confidence')
		ax1.legend(loc="upper left", bbox_to_anchor=(0.2,1.0))
		ax2.legend(loc="lower right", bbox_to_anchor=(1.0,0.1))

		"""

		fig = plt.figure(figsize=FIGSIZE)
		fig.canvas.set_window_title('Cumulative')
		fig.suptitle('Difference to GT')
		# ax_conf=plt.subplot(322)
		ax_conf=plt.subplot(211)
		ax_conf.set_title("RMS vs max9-min9")
		plt.plot(bin_vals[0:-1], np.sqrt(cumul_err_heur2),'red',
		bin_vals[0:-1], np.sqrt(cumul_err_nn2),'green',
		bin_vals[0:-1], np.sqrt(cumul_diff_log2),'blue')
		figs.append(fig)
		ffiles.append('cumulative')
		ax_conf=plt.subplot(212)
		ax_conf.set_title("weights vs max9-min9")
		plt.plot(bin_vals[0:-1], cumul_weights,'black')
		"""
		figs.append(fig)
		ffiles.append('cumulative')
		pass
		#bin_vals[0:-1]

		# fig.suptitle("Groud truth confidence")

		#
		#how to allow adjustment before applying tight_layout?

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