colored displaying of weights (39c85178) · Commits · Elphel / python3-imagej-tiff

nn_ds_neibs1_tmp.py

+29 −25

Original line number	Diff line number	Diff line
		@@ -250,10 +250,10 @@ files_train_hvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/
		"/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train007_R1_GT_1.5.tfrecords",
		]

		files_train_lvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train000_R1_LE_1.5.tfrecords",
		]
		files_train_hvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train000_R1_GT_1.5.tfrecords",
		]
		#files_train_lvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train000_R1_LE_1.5.tfrecords",
		# ]
		#files_train_hvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train000_R1_GT_1.5.tfrecords",
		#]
		#files_train_hvar = []
		#file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3a/train000_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
		file_test_lvar= "/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/testTEST_R1_LE_1.5.tfrecords"
		@@ -855,18 +855,18 @@ with tf.Session() as sess:

		merged = tf.summary.merge_all()

		vis_placeholder = tf.placeholder(tf.float32, [1,32,325,3])
		some_image2 = tf.summary.image('custom_test', vis_placeholder)
		# display weights, part 1 begin
		import numpy_image_test as nit

		l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1)))
		l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2)))
		with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE):
		w = tf.get_variable('weights',shape=[325,32])
		wd = w[...,tf.newaxis]
		wds = tf.stack([wd]*3,axis=0)
		#print(wd.shape)
		#some_image = tf.summary.image("tfsi_test",wds.eval(),max_outputs=1)
		some_image = tf.summary.image("tfsi_test",wds,max_outputs=1)

		wimg1_placeholder = tf.placeholder(tf.float32, [1,160,80,3])
		wimg1 = tf.summary.image('weights/sub_'+str(l1), wimg1_placeholder)

		wimg2_placeholder = tf.placeholder(tf.float32, [1,120,60,3])
		wimg2 = tf.summary.image('weights/inter_'+str(l2), wimg2_placeholder)
		# display weights, part 1 end

		train_writer = tf.summary.FileWriter(TRAIN_PATH, sess.graph)
		test_writer = tf.summary.FileWriter(TEST_PATH, sess.graph)
		@@ -970,23 +970,27 @@ with tf.Session() as sess:
		# _,_=sess.run([tf_ph_G_loss,tf_ph_sq_diff],feed_dict={tf_ph_G_loss:test_avg, tf_ph_sq_diff:test2_avg})

		#train_writer.add_summary(some_image.eval(), epoch)

		# display weights, part 2 begin
		l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1)))
		l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2)))
		with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE):
		w = tf.get_variable('weights',shape=[325,32])
		wd = w[tf.newaxis,...]
		wds = tf.stack([wd]*3,axis=-1)

		timg_min = tf.reduce_min(w).eval()
		timg_max = tf.reduce_max(w).eval()
		with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE):
		w = tf.get_variable('weights',shape=[325,NN_LAYOUT1[l1]])
		w = tf.transpose(w,(1,0))
		img1 = nit.tiles(nit.coldmap(w.eval(),zero_span=0.0002),(1,4,9,9),tiles_per_line=2,borders=True)
		img1 = img1[np.newaxis,...]

		timg = wds.eval()
		train_writer.add_summary(wimg1.eval(feed_dict={wimg1_placeholder: img1}), epoch)

		timg[:,:,:,0] = timg_min
		timg[:,:,:,1] = timg_min
		timg = np.transpose(timg,(0,2,1,3))
		with tf.variable_scope('g_fc_inter'+str(l2),reuse=tf.AUTO_REUSE):
		w = tf.get_variable('weights',shape=[144,NN_LAYOUT1[l2]])
		w = tf.transpose(w,(1,0))
		img2 = nit.tiles(nit.coldmap(w.eval(),zero_span=0.0002),(3,3,4,4),tiles_per_line=4,borders=True)
		img2 = img2[np.newaxis,...]

		train_writer.add_summary(some_image2.eval(feed_dict={vis_placeholder: timg}), epoch)
		train_writer.add_summary(wimg2.eval(feed_dict={wimg2_placeholder: img2}), epoch)
		# display weights, part 2 end

		train_writer.add_summary(train_summary, epoch)
		test_writer.add_summary(test_summaries[0], epoch)

numpy_image_test.py

0 → 100644

+192 −0

Original line number	Diff line number	Diff line
		#!/usr/bin/env python3

		import numpy as np
		import matplotlib.pyplot as plt
		import math

		def hamming_window(x,N):
		y = 0.2 - 0.46math.cos(2math.pi*x/(N-1))
		return y

		# input: np.array(a,b) - 1 channel
		# output: np.array(a,b,3) - 3 color channels
		def coldmap(img,zero_span=0.2):

		out = np.dstack(3*[img])

		img_min = np.nanmin(img)
		img_max = np.nanmax(img)

		#print("min: "+str(img_min)+", max: "+str(img_max))

		ch_r = out[...,0]
		ch_g = out[...,1]
		ch_b = out[...,2]

		# blue for <0
		ch_r[img<0] = 0
		ch_g[img<0] = 0
		ch_b[img<0] = -ch_b[img<0]

		# red for >0
		ch_r[img>0] = ch_b[img>0]
		ch_g[img>0] = 0
		ch_b[img>0] = 0

		# green for 0
		ch_r[img==0] = 0
		ch_g[img==0] = img_max
		ch_b[img==0] = 0

		# green for zero vicinity
		ch_r[abs(img)<zero_span/2] = 0
		ch_g[abs(img)<zero_span/2] = img_max/2
		ch_b[abs(img)<zero_span/2] = 0

		return out

		# has to be pre transposed
		# it just suppose to match
		def tiles(img,shape,tiles_per_line=1,borders=True):

		# shape is (n0,n1,n2,n3)
		# n0n1n2*n3 = img.shape[1]
		img_min = np.nanmin(img)
		img_max = np.nanmax(img)

		outer_color = [img_max,img_max,img_min]
		outer_color = [img_max,img_max,img_max]

		inner_color = [img_max/4,img_max/4,img_min]
		inner_color = [img_min,img_min,img_min]
		inner_color = [img_max,img_max,img_min]

		group_h = shape[0]
		group_w = shape[1]
		group_size = group_h*group_w

		tile_h = shape[2]
		tile_w = shape[3]
		tile_size = tile_h*tile_w

		tpl = tiles_per_line

		# main

		tmp1 = []

		for i in range(img.shape[0]):

		if i%tpl==0:
		tmp2 = []

		tmp3 = []

		for igh in range(group_h):

		tmp4 = []

		for igw in range(group_w):

		si = (group_wigh + igw + 0)tile_size
		ei = (group_wigh + igw + 1)tile_size

		tile = img[i,si:ei]
		tile = np.reshape(tile,(tile_h,tile_w,tile.shape[1]))

		if borders:

		if igw==group_w-1:

		b_h_inner = [[inner_color](tile_w+0)]( 1)
		b_h_outer = [[outer_color](tile_w+0)]( 1)
		b_v_outer = [[outer_color]( 1)](tile_h+1)

		# outer hor
		if igh==group_h-1:
		tile = np.concatenate([tile,b_h_outer],axis=0)
		# inner hor
		else:
		tile = np.concatenate([tile,b_h_inner],axis=0)
		# outer vert
		tile = np.concatenate([tile,b_v_outer],axis=1)

		else:

		b_v_inner = [[inner_color]( 1)](tile_h+0)
		b_h_inner = [[inner_color](tile_w+1)]( 1)
		b_h_outer = [[outer_color](tile_w+1)]( 1)

		# inner vert
		tile = np.concatenate([tile,b_v_inner],axis=1)

		# outer hor
		if igh==group_h-1:
		tile = np.concatenate([tile,b_h_outer],axis=0)
		# inner hor
		else:
		tile = np.concatenate([tile,b_h_inner],axis=0)

		tmp4.append(tile)

		tmp3.append(np.concatenate(tmp4,axis=1))

		tmp2.append(np.concatenate(tmp3,axis=0))

		if i%tpl==(tpl-1):
		tmp1.append(np.concatenate(tmp2,axis=1))

		out = np.concatenate(tmp1,axis=0)
		#out = img
		return out

		if __name__=="__main__":
		#
		hw = hamming_window
		#
		image = np.array([[1hw(i,512)hw(j,512) for i in range(512)] for j in range(512)],np.float32)
		zeros = np.zeros((512,512))

		# 32x324

		#image2 = np.zeros((32,324))
		#rgb_img_0 = tiles(image2,(1,4,9,9),tiles_per_line=2,borders=True)

		#image2 = np.zeros((32,144))
		image2 = np.array([[1hw(i,144)hw(j,32) for i in range(144)] for j in range(32)],np.float32)
		#image3 = coldmap(image2)
		rgb_img_0 = tiles(coldmap(image2),(3,3,4,4),tiles_per_line=8,borders=True)

		fig = plt.figure()
		fig.suptitle("HaWi")
		plt.imshow(rgb_img_0)

		rgb_img = coldmap(image)

		#print(rgb_img)

		'''
		for i in range(512):
		for j in range(512):
		if image[i,j]<0:
		rgb_img[i,j,0] = 0
		rgb_img[i,j,1] = 0
		#rgb_img[i,j,2] = 255

		if image[i,j]>0:
		#rgb_img[i,j,0] = 255
		rgb_img[i,j,1] = 0
		rgb_img[i,j,2] = 0

		if image[i,j]==0:
		rgb_img[i,j,0] = 0
		rgb_img[i,j,1] = 255
		rgb_img[i,j,2] = 0
		'''
		print(rgb_img.shape)

		fig = plt.figure()
		fig.suptitle("HamWindow")
		plt.imshow(rgb_img)
		#plt.colorbar()
		plt.show()
		No newline at end of file

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