###################################################
## test_summaries_img = [0.0]*len(ind_img) # datasets_img)
disp_out = np.empty((WIDTH * HEIGHT), dtype=np.float32)
dbg_cost_nw = np.empty((WIDTH * HEIGHT), dtype=np.float32)
dbg_cost_w = np.empty((WIDTH * HEIGHT), dtype=np.float32)
dbg_d = np.empty((WIDTH * HEIGHT), dtype=np.float32)
dbg_avg_disparity = np.empty((WIDTH * HEIGHT), dtype=np.float32)
dbg_gt_disparity = np.empty((WIDTH * HEIGHT), dtype=np.float32)
dbg_offs = np.empty((WIDTH * HEIGHT), dtype=np.float32)
for ntest in ind_img: # datasets_img):
dataset_img = qsf.readImageData(image_data=image_data,
files=files,
indx=ntest,
cluster_radius=CLUSTER_RADIUS,
tile_layers=TILE_LAYERS,
tile_side=TILE_SIDE,
width=IMG_WIDTH,
replace_nans=True)
sess.run(iterator_tt.initializer,
feed_dict={
corr2d_train_placeholder:
dataset_img['corr2d'],
target_disparity_train_placeholder:
dataset_img['target_disparity'],
gt_ds_train_placeholder:
dataset_img['gt_ds']
})
for start_offs in range(0, disp_out.shape[0], BATCH_SIZE):
end_offs = min(start_offs + BATCH_SIZE, disp_out.shape[0])
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter(ROOT_PATH, sess.graph)
lf = None
if LOGPATH:
lf=open(LOGPATH,"w") #overwrite previous (or make it "a"?
for nimg,_ in enumerate(image_data):
dataset_img = qsf.readImageData(
image_data = image_data,
files = files,
indx = nimg,
cluster_radius = 0, # CLUSTER_RADIUS,
tile_layers = TILE_LAYERS,
tile_side = TILE_SIDE,
width = IMG_WIDTH,
replace_nans = True,
infer = True,
keep_gt = True) # to generate same output files
img_corr2d = dataset_img['corr2d'] # (?,324)
img_target = dataset_img['target_disparity'] # (?,1)
img_ntile = dataset_img['ntile'].reshape([-1]) # (?) - 0...78k int32
#run first stage network
qsf.print_time("Running inferred model, stage1", end=" ")
_ = sess.run([stage1done],
feed_dict={ph_corr2d: img_corr2d,
ph_target_disparity: img_target,
ph_ntile: img_ntile })
qsf.print_time("Done.")