Updating for LWIR data (2c44046d) · Commits · Elphel / lwir-nn

explore_data6.py

0 → 100644

+1948 −0

File added.

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imagej_tiff.py

+497 −450

Original line number	Diff line number	Diff line
		@@ -55,6 +55,50 @@ class bcolors:
		BOLD = '\033[1m'
		BOLDWHITE = '\033[1;37m'
		UNDERLINE = '\033[4m'
		class IJML:
		# as devined in ImageDtt.java
		ML_OTHER_TARGET = 0 # Offset to target disparity data in ML_OTHER_INDEX layer tile
		ML_OTHER_GTRUTH = 2 # Offset to ground truth disparity data in ML_OTHER_INDEX layer tile
		ML_OTHER_GTRUTH_STRENGTH = 4 # Offset to ground truth confidence data in ML_OTHER_INDEX layer tile
		ML_OTHER_GTRUTH_RMS = 6 # Offset to ground truth RMS in ML_OTHER_INDEX layer tile
		ML_OTHER_GTRUTH_RMS_SPLIT = 8 # Offset to ground truth combined FG/BG RMS in ML_OTHER_INDEX layer tile
		ML_OTHER_GTRUTH_FG_DISP = 10 # Offset to ground truth FG disparity in ML_OTHER_INDEX layer tile
		ML_OTHER_GTRUTH_FG_STR = 12 # Offset to ground truth FG strength in ML_OTHER_INDEX layer tile
		ML_OTHER_GTRUTH_BG_DISP = 14 # Offset to ground truth BG disparity in ML_OTHER_INDEX layer tile
		ML_OTHER_GTRUTH_BG_STR = 16 # Offset to ground truth BG strength in ML_OTHER_INDEX layer tile
		ML_OTHER_AUX_DISP = 18 # Offset to AUX heuristic disparity in ML_OTHER_INDEX layer tile
		ML_OTHER_AUX_STR = 20 # Offset to AUX heuristic strength in ML_OTHER_INDEX layer tile

		# indices
		TARGET = ML_OTHER_TARGET // 2
		GTRUTH = ML_OTHER_GTRUTH // 2
		STRENGTH = ML_OTHER_GTRUTH_STRENGTH // 2
		RMS = ML_OTHER_GTRUTH_RMS // 2
		RMS_SPLIT = ML_OTHER_GTRUTH_RMS_SPLIT // 2
		FG_DISP = ML_OTHER_GTRUTH_FG_DISP // 2
		FG_STR = ML_OTHER_GTRUTH_FG_STR // 2
		BG_DISP = ML_OTHER_GTRUTH_BG_DISP // 2
		BG_STR = ML_OTHER_GTRUTH_BG_STR // 2
		AUX_DISP = ML_OTHER_AUX_DISP // 2
		AUX_STR = ML_OTHER_AUX_STR // 2

		SIGNED = (TARGET, GTRUTH, FG_DISP, BG_DISP)
		UNSIGNED_RMS = (RMS, RMS_SPLIT)

		NUM_VALUES = 11

		class IJFGBG:
		DSI_NAMES = ["disparity","strength","rms","rms-split","fg-disp","fg-str","bg-disp","bg-str","aux-disp","aux-str"]
		DISPARITY = 0
		STRENGTH = 1
		RMS = 2
		RMS_SPLIT = 3
		FG_DISP = 4
		FG_STR = 5
		BG_DISP = 6
		BG_STR = 7
		AUX_DISP = 8
		AUX_STR = 9

		# reshape to tiles
		def get_tile_images(image, width=8, height=8):
		@@ -95,7 +139,6 @@ Examples:

		'''
		class imagej_tiff:

		# imagej stores labels lengths in this tag
		__TIFF_TAG_LABELS_LENGTHS = 50838
		# imagej stores labels conents in this tag
		@@ -133,9 +176,7 @@ class imagej_tiff:
		tif.seek(i)
		a = np.array(tif)
		a = np.reshape(a,(a.shape[0],a.shape[1],1))

		#a = a[:,:,np.newaxis]

		# scale for 8-bits
		# exclude layer named 'other'
		if self.bpp==8:
		@@ -176,19 +217,19 @@ class imagej_tiff:
		num_layers = len(layers)
		tiles_corr = np.empty((num_tiles,num_layers,self.tileH*self.tileW),dtype=float)
		# tiles_other=np.empty((num_tiles,3),dtype=float)
		tiles_other=self.gettilesvalues(
		tiles_other=self.gettilesvalues( # returns nparray of 11 floats (was 3)
		tif = tif,
		tile_list=tile_list,
		label=other_label)
		for nl,label in enumerate(layers):
		tif.seek(self.labels.index(label))
		tif.seek(self.labels.index(label)) #'hor-pairs' is not in list
		layer = np.array(tif) # 8 or 32 bits
		tilesX = layer.shape[1]//self.tileW
		for nt,tl in enumerate(tile_list):
		ty = tl // tilesX
		tx = tl % tilesX
		# tiles_corr[nt,nl] = np.ravel(layer[self.tileHty:self.tileH(ty+1),self.tileWtx:self.tileW(tx+1)])
		a = np.ravel(layer[self.tileHty:self.tileH(ty+1),self.tileWtx:self.tileW(tx+1)])
		a = np.ravel(layer[self.tileH * ty : self.tileH * (ty+1),
		self.tileW * tx : self.tileW * (tx+1)])
		#convert from int8
		if self.bpp==8:
		a = a.astype(float)
		@@ -212,6 +253,7 @@ class imagej_tiff:
		self.corr2d = tiles_corr
		self.target_disparity = tiles_other[...,0]
		self.gt_ds = tiles_other[...,1:3]
		self.payload = tiles_other#[...,0:12]
		pass

		# init done, close the image
		@@ -220,26 +262,29 @@ class imagej_tiff:
		# label == tiff layer name
		def getvalues(self,label=""):
		l = self.getstack([label],shape_as_tiles=True)
		res = np.empty((l.shape[0],l.shape[1],3))
		res = np.empty((l.shape[0],l.shape[1], IJML.NUM_VALUES)) # was just 3

		for i in range(res.shape[0]):
		for j in range(res.shape[1]):
		# 9x9 -> 81x1
		m = np.ravel(l[i,j])
		if self.bpp==32:
		res[i,j,0] = m[0]
		res[i,j,1] = m[2]
		res[i,j,2] = m[4]
		for k in range(res.shape[2]):
		res[i,j,k] = m[k * 2]
		elif self.bpp==8:
		res[i,j,0] = ((m[0]-128)*256+m[1])/128
		res[i,j,1] = ((m[2]-128)*256+m[3])/128
		res[i,j,2] = (m[4]*256+m[5])/65536.0
		for k in range(res.shape[2]):
		if k in IJML.SIGNED:
		res[i,j,k] = ((m[2 * k] - 128) * 256 + m[2 * k + 1]) / 128
		elif k in IJML.UNSIGNED_RMS:
		res[i,j,k] = (m[2 * k]256+m[2 k + 1])/4096.0
		else:
		res[i,j,k] = (m[2 * k]256+m[2 k + 1])/65536.0
		else:
		res[i,j,0] = np.nan
		res[i,j,1] = np.nan
		res[i,j,2] = np.nan
		for k in range(res.shape[2]):
		res[i,j,k] = np.nan

		# NaNize
		# NaNize - TODO: update !
		if self.bpp==8:
		a = res[:,:,0]
		a[a==-256] = np.nan
		b = res[:,:,1]
		@@ -249,11 +294,12 @@ class imagej_tiff:
		return res

		# 3 values per tile: target disparity, GT disparity, GT confidence
		# With LWIR/aux there are more!
		def gettilesvalues(self,
		tif,
		tile_list,
		label=""):
		res = np.empty((len(tile_list),3),dtype=float)
		res = np.empty((len(tile_list), IJML.NUM_VALUES),dtype=float) # was only 3
		tif.seek(self.labels.index(label))
		layer = np.array(tif) # 8 or 32 bits
		tilesX = layer.shape[1]//self.tileW
		@@ -262,30 +308,29 @@ class imagej_tiff:
		tx = tl % tilesX
		m = np.ravel(layer[self.tileHty:self.tileH(ty+1),self.tileWtx:self.tileW(tx+1)])
		if self.bpp==32:
		res[i,0] = m[0]
		res[i,1] = m[2]
		res[i,2] = m[4]
		for k in range(res.shape[1]):
		res[i,k] = m[k * 2]
		elif self.bpp==8:
		res[i,0] = ((m[0]-128)*256+m[1])/128
		res[i,1] = ((m[2]-128)*256+m[3])/128
		res[i,2] = (m[4]*256+m[5])/65536.0
		for k in range(res.shape[1]):
		if k in IJML.SIGNED:
		res[i,k] = ((m[2 * k] - 128) * 256 + m[2 * k + 1]) / 128
		elif k in IJML.UNSIGNED_RMS:
		res[i,k] = (m[2 * k]256+m[2 k + 1])/4096.0
		else:
		res[i,k] = (m[2 * k]256+m[2 k + 1])/65536.0
		else:
		res[i,0] = np.nan
		res[i,1] = np.nan
		res[i,2] = np.nan
		# NaNize
		for k in range(res.shape[1]):
		res[i,k] = np.nan
		# NaNize update!
		if self.bpp==8:
		a = res[...,0]
		a[a==-256] = np.nan
		b = res[...,1]
		b[b==-256] = np.nan
		c = res[...,2]
		c[c==0] = np.nan

		return res




		# get ordered stack of images by provided items
		# by index or label name
		def getstack(self,items=[],shape_as_tiles=False):
		@@ -308,14 +353,13 @@ class imagej_tiff:
		return b

		# get np.array of a channel
		# * do not handle out of bounds
		# * does not handle out of bounds
		def channel(self,index):
		return self.image[:,:,index]


		# display images by index or label
		def show_images(self,items=[]):

		# show listed only
		if len(items)>0:
		for i in items:
		@@ -332,9 +376,7 @@ class imagej_tiff:

		# display single image
		def show_image(self,index):

		# display using matplotlib

		t = self.image[:,:,index]
		mytitle = "("+str(index+1)+" of "+str(self.nimages)+") "+self.labels[index]
		fig = plt.figure()
		@@ -355,7 +397,6 @@ class imagej_tiff:

		# puts etrees in infoss
		def __parse_info(self):

		infos = []
		for info in self.infos:
		infos.append(ET.fromstring(info))
		@@ -376,12 +417,12 @@ class imagej_tiff:
		# tiles are squares
		self.tileW = int(self.props['tileWidth'])
		self.tileH = int(self.props['tileWidth'])
		if self.bpp==8:
		self.data_min = float(self.props['data_min'])
		self.data_max = float(self.props['data_max'])

		# makes arrays of labels (strings) and unparsed xml infos
		def __split_labels(self,n,tag):

		# list
		tag_lens = tag[self.__TIFF_TAG_LABELS_LENGTHS]
		# string
		@@ -412,7 +453,8 @@ if __name__ == "__main__":
		try:
		fname = sys.argv[1]
		except IndexError:
		fname = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/train/1527182807_896892/v02/ml/1527182807_896892-ML_DATA-08B-O-FZ0.05-OFFS0.40000.tiff"
		fname = "/data_ssd/lwir3d/models/002/1562390096_605721/v01/ml32/1562390096_605721-ML_DATA-32B-AOT-FZ0.03-AG.tiff"
		# fname = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/train/1527182807_896892/v02/ml/1527182807_896892-ML_DATA-08B-O-FZ0.05-OFFS0.40000.tiff"
		# fname = "1521849031_093189-ML_DATA-32B-O-OFFS1.0.tiff"
		# fname = "1521849031_093189-ML_DATA-08B-O-OFFS1.0.tiff"

		@@ -437,8 +479,11 @@ if __name__ == "__main__":

		# needed properties:
		print("Tiles shape: "+str(ijt.tileW)+"x"+str(ijt.tileH))
		try:
		print("Data min: "+str(ijt.data_min))
		print("Data max: "+str(ijt.data_max))
		except:
		print (" No min/max are provided in 32-bit mode)")

		print(ijt.image.shape)

		@@ -448,7 +493,8 @@ if __name__ == "__main__":
		#tiles = get_tile_images(ijt.image,ijt.tileW,ijt.tileH)
		#print(tiles.shape)

		tiles = ijt.getstack(['diagm-pair','diago-pair','hor-pairs','vert-pairs'],shape_as_tiles=True)
		# tiles = ijt.getstack(['diagm-pair','diago-pair','hor-pairs','vert-pairs'],shape_as_tiles=True)
		tiles = ijt.getstack(['diagm-aux','diago-aux','hor-aux','vert-aux'],shape_as_tiles=True)
		print("Stack of images shape: "+str(tiles.shape))

		print(bcolors.BOLDWHITE+"time: "+str(time.time())+bcolors.ENDC)
		@@ -511,6 +557,7 @@ if __name__ == "__main__":

		ijt.show_images()
		plt.show()
		input("All done. Press ENTER to close images and exit...")

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