commited old modified files (d786da7b) · Commits · Elphel / python3-imagej-tiff

explore_data5m.py

0 → 100644

+1253 −0

File added.

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nn_eval_01.py

+189 −9

Original line number	Original line	Diff line number	Diff line
	@@ -13,11 +13,14 @@ import sys

	#import numpy as np		#import numpy as np

			import imagej_tiffwriter

	import time		import time

	import matplotlib.pyplot as plt		import matplotlib.pyplot as plt
	from matplotlib.backends.backend_pdf import PdfPages		from matplotlib.backends.backend_pdf import PdfPages
	import qcstereo_functions as qsf		import qcstereo_functions as qsf
			import numpy as np

	#import xml.etree.ElementTree as ET		#import xml.etree.ElementTree as ET

	@@ -132,27 +135,143 @@ fig_params = get_fig_params(dbg_parameters['disparity_ranges'])

	pass		pass

			#temporary:
			TIFF_ONLY = False # True
			#max_bad = 2.5 # excludes only direct bad
			max_bad = 2.5 #2.5 # 1.5 # excludes only direct bad
			max_diff = 1.5 # 2.0 # 5.0 # maximal max-min difference
			max_target_err = 1.0 # 0.5 # maximal max-min difference
			max_disp = 5.0

			min_strength = 0.18 #ignore tiles below
			min_neibs = 1
			max_log_to_mm = 0.5 # difference between center average and center should be under this fraction of max-min (0 - disables feature)


			#num_bins = 256 # number of histogram bins
			num_bins = 15 # 50 # number of histogram bins
			use_gt_weights = True # False # True
			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
			"""
			Debugging high 9-tile variations, removing error for all tiles with lower difference between max and min
			"""
			#min_diff = 0.25 # remove all flat tiles with spread less than this (do not show on heuristic/network disparity errors subplots
			min_diff = 0 # remove all flat tiles with spread less than this




			max_target_err2 = max_target_err * max_target_err
	if not 'show' in FIGS_SAVESHOW:		if not 'show' in FIGS_SAVESHOW:
	plt.ioff()		plt.ioff()

	for mode in ['train','infer']:		#for mode in ['train','infer']:
			for mode in ['infer']:
	figs = []		figs = []
	ffiles = [] # no ext		ffiles = [] # no ext
	def setlimsxy(lim_xy):		def setlimsxy(lim_xy):
	if not lim_xy is None:		if not lim_xy is None:
	plt.xlim(min(lim_xy[:2]),max(lim_xy[:2]))		plt.xlim(min(lim_xy[:2]),max(lim_xy[:2]))
	plt.ylim(max(lim_xy[2:]),min(lim_xy[2:]))		plt.ylim(max(lim_xy[2:]),min(lim_xy[2:]))
			cumul_weights = None

	for nfile, fpars in enumerate(fig_params):		for nfile, fpars in enumerate(fig_params):
	if not fpars is None:		if not fpars is None:
	img_file = files['result'][nfile]		img_file = files['result'][nfile]
	if mode == 'infer':		if mode == 'infer':
	img_file = img_file.replace('.npy','-infer.npy')		img_file = img_file.replace('.npy','-infer.npy')
			"""
	try:		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=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:		except:
	print ("Image file does not exist:", img_file)		print ("Image file does not exist:", img_file)
	continue		continue
			"""
			pass
			data,labels = qsf.result_npy_prepare(img_file, ABSOLUTE_DISPARITY, fix_nan=True, insert_deltas=3)
			if True: #TIFF_ONLY:


			tiff_path = img_file.replace('.npy','-test.tiff')

			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)

			if TIFF_ONLY:
			continue


	for subindex, rng in enumerate(fpars['ranges']):		for subindex, rng in enumerate(fpars['ranges']):
	lim_val = rng['lim_val']		lim_val = rng['lim_val']
	@@ -214,7 +333,68 @@ for mode in ['train','infer']:
	fb_noext+="-"+str(subindex)		fb_noext+="-"+str(subindex)
	ffiles.append(fb_noext)		ffiles.append(fb_noext)
	pass		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")

			#
	#whow to allow adjustment before applying tight_layout?		#whow to allow adjustment before applying tight_layout?
	pass		pass
	for fig in figs:		for fig in figs:
	@@ -229,9 +409,9 @@ for mode in ['train','infer']:
	pp=None		pp=None
	if 'pdf' in FIGS_EXTENSIONS:		if 'pdf' in FIGS_EXTENSIONS:
	if mode == 'infer':		if mode == 'infer':
	pdf_path = os.path.join(dirs['figures'],"figures-infer.pdf")		pdf_path = os.path.join(dirs['figures'],"figures-infer%s.pdf"%str(min_diff))
	else:		else:
	pdf_path = os.path.join(dirs['figures'],"figures-train.pdf")		pdf_path = os.path.join(dirs['figures'],"figures-train%s.pdf"%str(min_diff))
	pp= PdfPages(pdf_path)		pp= PdfPages(pdf_path)

	for fb_noext, fig in zip(ffiles,figs):		for fb_noext, fig in zip(ffiles,figs):
	@@ -259,8 +439,8 @@ if 'show' in FIGS_SAVESHOW:

	#FIGS_ESXTENSIONS		#FIGS_ESXTENSIONS

	#qsf.evaluateAllResults(result_files = files['result'],		qsf.evaluateAllResults(result_files = files['result'],
	# absolute_disparity = ABSOLUTE_DISPARITY,		absolute_disparity = ABSOLUTE_DISPARITY,
	# cluster_radius = CLUSTER_RADIUS)		cluster_radius = CLUSTER_RADIUS)
	print("All done")		print("All done")
	exit (0)		exit (0)

qcstereo_functions.py

+79 −10

Original line number	Original line	Diff line number	Diff line
	@@ -517,6 +517,7 @@ def result_npy_prepare(npy_path, absolute, fix_nan, insert_deltas=True,labels=No
	data will be written as 4-layer tiff, extension '.npy' replaced with '.tiff'		data will be written as 4-layer tiff, extension '.npy' replaced with '.tiff'
	@param absolute - True - the first layer contains absolute disparity, False - difference from target_disparity		@param absolute - True - the first layer contains absolute disparity, False - difference from target_disparity
	@param fix_nan - replace nan in target_disparity with 0 to apply offset, target_disparity will still contain nan		@param fix_nan - replace nan in target_disparity with 0 to apply offset, target_disparity will still contain nan
			@parame insert_deltas: +1 - add delta layers, +2 - add variance (max - min of this and 8 neighbors)
	"""		"""
	data = np.load(npy_path) #(324,242,4) [nn_disp, target_disp,gt_disp, gt_conf]		data = np.load(npy_path) #(324,242,4) [nn_disp, target_disp,gt_disp, gt_conf]
	if labels is None:		if labels is None:
	@@ -526,12 +527,17 @@ def result_npy_prepare(npy_path, absolute, fix_nan, insert_deltas=True,labels=No
	# target_disparity = 1		# target_disparity = 1
	gt_disparity = 2		gt_disparity = 2
	gt_strength = 3		gt_strength = 3
			heur_err = 7
			min_heur_err = 0.001
			height = data.shape[0]
			width = data.shape[1]
			nocenter9 = np.array([[[1,1,1,1,np.nan,1,1,1,1]]], dtype = data.dtype)
	if not absolute:		if not absolute:
	if fix_nan:		if fix_nan:
	data[...,nn_out] += np.nan_to_num(data[...,1], copy=True)		data[...,nn_out] += np.nan_to_num(data[...,1], copy=True)
	else:		else:
	data[...,nn_out] += data[...,1]		data[...,nn_out] += data[...,1]
	if insert_deltas:		if (insert_deltas & 1):
	np.nan_to_num(data[...,gt_strength], copy=False)		np.nan_to_num(data[...,gt_strength], copy=False)
	data = np.concatenate([data[...,0:4],data[...,0:2],data[...,0:2],data[...,4:]], axis = 2) # data[...,4:] may be empty		data = np.concatenate([data[...,0:4],data[...,0:2],data[...,0:2],data[...,4:]], axis = 2) # data[...,4:] may be empty
	labels = labels[:4]+["nn_out","hier_out","nn_err","hier_err"]+labels[4:]		labels = labels[:4]+["nn_out","hier_out","nn_err","hier_err"]+labels[4:]
	@@ -543,6 +549,69 @@ def result_npy_prepare(npy_path, absolute, fix_nan, insert_deltas=True,labels=No
	# All other layers - mast too		# All other layers - mast too
	for l in range(8,data.shape[2]):		for l in range(8,data.shape[2]):
	data[...,l] = np.select([data[...,gt_strength]==0.0, data[...,gt_strength]>0.0], [np.nan,data[...,l]])		data[...,l] = np.select([data[...,gt_strength]==0.0, data[...,gt_strength]>0.0], [np.nan,data[...,l]])
			"""
			Calculate bad tiles where ggt was used as a master, to remove them from the results (later versions add random error)
			"""
			bad1 = abs(data[...,heur_err]) < min_heur_err
			bad1_ext = np.concatenate([bad1 [0:1,:], bad1 [0:1,:], bad1[:,:], bad1 [-1:height,:], bad1 [-1:height,:]],axis = 0)
			bad1_ext = np.concatenate([bad1_ext[:,0:1], bad1_ext[:,0:1], bad1_ext[:,:], bad1_ext[:,-1:width], bad1_ext[:,-1:width]], axis = 1)
			bad25 = np.empty(shape=[height, width, 25], dtype=bad1.dtype)
			bm25=np.array([[[1,1,1,1,1, 1,1,1,1,1, 1,1,1,1,1, 1,1,1,1,1, 1,1,1,1,1]]])
			bm09=np.array([[[0,0,0,0,0, 0,1,1,1,0, 0,1,1,1,0, 0,1,1,1,0, 0,0,0,0,0]]])
			bm01=np.array([[[0,0,0,0,0, 0,0,0,0,0, 0,0,1,0,0, 0,0,0,0,0, 0,0,0,0,0]]])
			for row in range(5):
			for col in range(5):
			pass
			bad25 [...,row*5+col]= bad1_ext[row:height+row, col:width+col]

			bad_num1=(np.sum(bad25*bm25,axis=2) > 0).astype(data.dtype)
			bad_num2=(np.sum(bad25*bm09,axis=2) > 0).astype(data.dtype)
			bad_num3=(np.sum(bad25*bm01,axis=2) > 0).astype(data.dtype)
			bad_num = bad_num1 + bad_num2 + bad_num3
			if (insert_deltas & 2):
			wo = 0.7 # ortho
			wc = 0.5 #corner
			w8=np.array([wc,wo,wc,wo,0.0,wo,wc,wo,wc], dtype=data.dtype)
			w8/=np.sum(w8) #normalize

			gt_ext = np.concatenate([data[0:1,:,gt_disparity],data[:,:,gt_disparity],data[-1:height,:,gt_disparity]],axis = 0)
			gt_ext = np.concatenate([gt_ext[:,0:1], gt_ext[:,:], gt_ext[:,-1:width]],axis = 1)
			gs_ext = np.concatenate([data[0:1,:,gt_strength], data[:,:,gt_strength], data[-1:height,:,gt_strength]],axis = 0)
			gs_ext = np.concatenate([gs_ext[:,0:1], gs_ext[:,:], gs_ext[:,-1:width]],axis = 1)

			data9 = np.empty(shape=[height, width, 9], dtype=data.dtype)
			weight9 = np.empty(shape=[height, width, 9], dtype=data.dtype)
			for row in range(3):
			for col in range(3):
			pass
			data9 [...,row*3+col]= gt_ext[row:height+row, col:width+col]
			weight9[...,row*3+col]= gs_ext[row:height+row, col:width+col]

			data9 *= weight9/weight9 # make data=nan where wigth is 0

			# data = np.concatenate([data[...],np.empty_like(data[...,-1])], axis = 2) # data[...,4:] may be empty
			data = np.concatenate([data[...],np.empty(shape=[height,width,4],dtype=data.dtype)], axis = 2) # data[...,4:] may be empty
			data[...,-4] = np.nanmax(data9nocenter9, axis=2)-np.nanmin(data9nocenter9,axis=2)# will ignore nan

			np.nan_to_num(data9,copy=False) # replace all nan in data9 with 0.
			weight9 *= w8
			w_center = np.sum(weight9, axis=2)
			dw_center = np.sum(data9*weight9, axis=2)
			dw_center /= w_center # now dw_center - weighted average in the center

			data[...,-3] = np.abs(data[...,gt_disparity]- dw_center)

			# data[...,-2] = data[...,gt_disparity]- dw_center
			#data[...,-3] *= (data[...,-4] < 1.0) # just temporary
			#data[...,-3] *= (data[...,gt_disparity] < 5) #just temporary

			data[...,-2] =bad_num.astype(data.dtype)

			data [...,-1]= np.sum(np.nan_to_num(weight9/weight9),axis=2).astype(data.dtype)
			# data[...,-1] = dw_center
			labels +=["max-min","abs-center","badness","neibs"]
			#neib = np.concatenate([gt_ext[:height,:width,:],],axis = )
			pass
	return data, labels		return data, labels

	def result_npy_to_tiff(npy_path,		def result_npy_to_tiff(npy_path,

imagej_tiffwriter.py

+53 −53

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