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evaluate.py
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import tensorflow as tf
import numpy as np
np.set_printoptions(precision=2, linewidth=200)
import cv2
import os
import time
import sys
#import tf_nndistance
import argparse
import glob
import PIL
import scipy.ndimage as ndimage
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from utils import *
#from plane_utils import *
from modules import *
from train_planenet import build_graph
#from train_sample import build_graph as build_graph_sample
from planenet import PlaneNet
from RecordReaderAll import *
#from SegmentationRefinement import *
from crfasrnn.crfasrnn_layer import CrfRnnLayer
#ALL_TITLES = ['planenet', 'pixelwise', 'pixelwise+RANSAC', 'depth observation+RANSAC', 'pixelwise+semantics+RANSAC', 'gt']
#ALL_METHODS = [('bl2_ll1_bw0.5_pb_pp_sm0', ''), ('pb_pp', 'pixelwise_1'), ('pb_pp', 'pixelwise_2'), ('pb_pp', 'pixelwise_3'), ('pb_pp', 'semantics'), ('pb_pp', 'gt')]
ALL_TITLES = ['PlaneNet', 'Oracle NYU toolbox', 'NYU toolbox', 'Oracle Manhattan', 'Manhattan', 'Oracle Piecewise', 'Piecewise']
#ALL_TITLES = ['PlaneNet', '[25] + depth', '[25]', '[9] + depth', '[9]', '[26] + depth', '[26]']
#ALL_METHODS = [('bl0_dl0_bw0.5_pb_pp_ps_sm0', ''), ('ll1_pb_pp', ''), ('bl0_ll1_bw0.5_pb_pp_ps_sm0', ''), ('ll1_bw0.5_pb_pp_sm0', '')]
#ALL_METHODS = [('bl0_dl0_ll1_bw0.5_pb_pp_sm0', ''), ('bl0_dl0_ll1_pb_pp_sm0', ''), ('bl0_dl0_ll1_pb_pp_ps', ''), ('bl0_dl0_ll1_ds0_pb_pp', '')]
#ALL_METHODS = [('bl0_dl0_ll1_pb_pp_sm0', ''), ('bl0_dl0_ll1_pb_pp_sm0', 'pixelwise_2'), ('bl0_dl0_ll1_pb_pp_sm0', 'pixelwise_3'), ('bl0_dl0_ll1_pb_pp_sm0', 'pixelwise_6'), ('bl0_dl0_ll1_pb_pp_sm0', 'pixelwise_5')]
#ALL_METHODS = [('bl0_dl0_ll1_pb_pp_sm0', ''), ('bl0_dl0_crfrnn10_sm0', ''), ('bl0_dl0_ll1_pp_sm0', ''), ('bl0_dl0_ll1_pb_pp_sm0', ''), ('bl0_dl0_ll1_pb_pp_sm0', '')]
#ALL_METHODS = [('bl0_dl0_ll1_pb_pp_sm0', '', 0), ('bl0_dl0_ll1_pb_pp_sm0', 'crfrnn', 0), ('bl0_dl0_crfrnn10_sm0', '')]
#ALL_METHODS = [['planenet_hybrid3_bl0_dl0_ll1_pb_pp_sm0', '', 0, 0], ['planenet_hybrid3_bl0_dl0_ll1_pb_pp_ps_sm0', 'pixelwise_2', 1, 0], ['', 'pixelwise_3', 1, 0], ['', 'pixelwise_4', 1, 0], ['', 'pixelwise_5', 1, 0], ['', 'pixelwise_6', 1, 0], ['', 'pixelwise_7', 1, 0]]
#ALL_METHODS = [['planenet_hybrid3_bl0_dl0_ll1_pb_pp_sm0', '', 0, 0], ['planenet_hybrid3_bl0_dl0_ll1_pb_pp_ps_sm0', 'pixelwise_2', 1, 0], ['', 'pixelwise_3', 1, 0], ['', 'pixelwise_4', 1, 0], ['', 'pixelwise_5', 1, 0], ['', 'pixelwise_6', 1, 0], ['', 'pixelwise_7', 1, 0]]
ALL_METHODS = [['sample_np10_hybrid3_bl0_dl0_ds0_crfrnn5_sm0', '', 0, 0], ['planenet_hybrid3_bl0_dl0_ll1_pb_pp_ps_sm0', 'pixelwise_2', 1, 0], ['', 'pixelwise_3', 1, 0], ['', 'pixelwise_4', 1, 0], ['', 'pixelwise_5', 1, 0], ['', 'pixelwise_6', 1, 0], ['', 'pixelwise_7', 1, 0]]
#ALL_METHODS = [('ll1_pb_pp', 'pixelwise_1'), ('crf1_pb_pp', 'pixelwise_2'), ('bl0_ll1_bw0.5_pb_pp_ps_sm0', 'pixelwise_3'), ('ll1_bw0.5_pb_pp_sm0', 'pixelwise_4')]
#ALL_TITLES = ['planenet', 'pixelwise']
#ALL_METHODS = [('bl0_ll1_bw0.5_pb_pp_ps_sm0', ''), ('bl0_ll1_bw0.5_pb_pp_ps_sm0', 'pixelwise_1')]
#ALL_TITLES = ['crf', 'different matching']
#ALL_METHODS = [('pb_pp_sm0', 'crf'), ('pb_pp_sm0', '')]
def writeHTML(options):
from html import HTML
titles = options.titles
h = HTML('html')
h.p('Results')
h.br()
path = '.'
#methods = ['planenet', 'pixelwise', 'pixelwise+RANSAC', 'GT+RANSAC', 'planenet+crf', 'pixelwise+semantics+RANSAC']
#methods = ['planenet', 'pixelwise', 'pixelwise+RANSAC', 'GT+RANSAC']
for index in xrange(options.numImages):
t = h.table(border='1')
r_inp = t.tr()
r_inp.td('input ' + str(index))
r_inp.td().img(src=path + '/' + str(index) + '_image.png')
r_inp.td().img(src=path + '/' + str(index) + '_depth_gt.png')
r_inp.td().img(src=path + '/' + str(index) + '_segmentation_gt.png')
r_inp.td().img(src=path + '/' + str(index) + '_semantics_gt.png')
r_inp.td().img(src=path + '/' + str(index) + '_depth_gt_plane.png')
r_inp.td().img(src=path + '/' + str(index) + '_depth_gt_diff.png')
# r = t.tr()
# r.td('PlaneNet prediction')
# r.td().img(src=firstFolder + '/' + str(index) + '_segmentation_pred.png')
# r.td().img(src=firstFolder + '/' + str(index) + '_depth_pred.png')
r = t.tr()
r.td('methods')
for method_index, method in enumerate(titles):
r.td(method)
continue
r = t.tr()
r.td('segmentation')
for method_index, method in enumerate(titles):
r.td().img(src=path + '/' + str(index) + '_segmentation_pred_' + str(method_index) + '.png')
continue
r = t.tr()
r.td('depth')
for method_index, method in enumerate(titles):
r.td().img(src=path + '/' + str(index) + '_depth_pred_' + str(method_index) + '.png')
continue
h.br()
continue
metric_titles = ['depth error 0.1', 'depth error 0.2', 'depth error 0.3', 'IOU 0.3', 'IOU 0.5', 'IOU 0.7']
h.p('Curves on plane accuracy')
for title in metric_titles:
h.img(src='curve_plane_' + title.replace(' ', '_') + '.png')
continue
h.p('Curves on pixel coverage')
for title in metric_titles:
h.img(src='curve_pixel_' + title.replace(' ', '_') + '.png')
continue
html_file = open(options.test_dir + '/index.html', 'w')
html_file.write(str(h))
html_file.close()
return
def evaluatePlanes(options):
#writeHTML(options)
#exit(1)
if not os.path.exists(options.test_dir):
os.system("mkdir -p %s"%options.test_dir)
pass
results = getResults(options)
gt_dict = results['gt']
predictions = results['pred']
saving = True
if gt_dict['image'].shape[0] != options.numImages or options.useCache == 1:
saving = False
pass
for key, value in gt_dict.iteritems():
if options.imageIndex >= 0:
gt_dict[key] = value[options.imageIndex:options.imageIndex + 1]
elif value.shape[0] > options.numImages:
gt_dict[key] = value[:options.numImages]
pass
continue
for pred_dict in predictions:
for key, value in pred_dict.iteritems():
if options.imageIndex >= 0:
pred_dict[key] = value[options.imageIndex:options.imageIndex + 1]
elif value.shape[0] > options.numImages:
pred_dict[key] = value[:options.numImages]
pass
continue
continue
#methods = ['planenet', 'pixelwise+RANSAC', 'GT+RANSAC']
#predictions[2] = predictions[3]
for image_index in xrange(options.visualizeImages):
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_image.png', gt_dict['image'][image_index])
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_gt.png', drawDepthImage(gt_dict['depth'][image_index]))
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_normal_gt.png', drawNormalImage(gt_dict['normal'][image_index]))
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_segmentation_gt.png', drawSegmentationImage(np.concatenate([gt_dict['segmentation'][image_index], 1 - np.expand_dims(gt_dict['plane_mask'][image_index], -1)], axis=2), blackIndex=options.numOutputPlanes))
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_semantics_gt.png', drawSegmentationImage(gt_dict['semantics'][image_index], blackIndex=0))
plane_depths = calcPlaneDepths(gt_dict['plane'][image_index], WIDTH, HEIGHT, gt_dict['info'][image_index])
all_depths = np.concatenate([plane_depths, np.expand_dims(gt_dict['depth'][image_index], -1)], axis=2)
depth = np.sum(all_depths * np.concatenate([gt_dict['segmentation'][image_index], 1 - np.expand_dims(gt_dict['plane_mask'][image_index], -1)], axis=2), axis=2)
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_gt_plane.png', drawDepthImage(depth))
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_gt_diff.png', drawMaskImage((depth - gt_dict['depth'][image_index]) * 5 + 0.5))
for method_index, pred_dict in enumerate(predictions):
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_pred_' + str(method_index) + '.png', drawDepthImage(pred_dict['depth'][image_index]))
#if 'pixelwise' in options.methods[method_index][1]:
#continue
segmentation = pred_dict['segmentation'][image_index]
#segmentation = np.concatenate([segmentation, pred_dict['np_mask'][image_index]], axis=2)
numPlanes = options.numOutputPlanes
if 'pixelwise' in options.methods[method_index][1]:
numPlanes = pred_dict['plane'][image_index].shape[0]
#print(numPlanes)
pass
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_segmentation_pred_' + str(method_index) + '.png', drawSegmentationImage(segmentation, blackIndex=numPlanes))
continue
continue
#post processing
for method_index, method in enumerate(options.methods):
if method[1] == '':
continue
if len(method) < 4 or method[3] == 0:
continue
if len(method) >= 3 and method[2] >= 0:
pred_dict = predictions[method[2]]
else:
pred_dict = predictions[method_index]
pass
if method[1] == 'graphcut':
#pred_dict = gt_dict
predSegmentations = []
predDepths = []
for image_index in xrange(options.numImages):
#if image_index != 3:
#continue
print('graph cut ' + str(image_index))
segmentation = np.argmax(np.concatenate([pred_dict['segmentation'][image_index], 1 - np.expand_dims(pred_dict['plane_mask'][image_index], -1)], axis=2), axis=2)
#pred_s = getSegmentationsGraphCut(pred_dict['plane'][image_index], gt_dict['image'][image_index], pred_dict['depth'][image_index], pred_dict['normal'][image_index], segmentation, pred_dict['semantics'][image_index], pred_dict['info'][image_index], gt_dict['num_planes'][image_index])
pred_p, pred_s, numPlanes = removeSmallSegments(pred_dict['plane'][image_index], gt_dict['image'][image_index], pred_dict['depth'][image_index], pred_dict['normal'][image_index], segmentation, pred_dict['semantics'][image_index], pred_dict['info'][image_index], gt_dict['num_planes'][image_index])
#pred_p, pred_s, numPlanes = pred_dict['plane'][image_index], segmentation, gt_dict['num_planes'][image_index]
print((gt_dict['num_planes'][image_index], numPlanes))
planeDepths = calcPlaneDepths(pred_p, WIDTH, HEIGHT, gt_dict['info'][image_index])
allDepths = np.concatenate([planeDepths, np.expand_dims(pred_dict['depth'][image_index], -1)], axis=2)
pred_d = allDepths.reshape(-1, options.numOutputPlanes + 1)[np.arange(WIDTH * HEIGHT), pred_s.reshape(-1)].reshape(HEIGHT, WIDTH)
predSegmentations.append(pred_s)
predDepths.append(pred_d)
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_pred_' + str(method_index) + '.png', drawDepthImage(pred_d))
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_segmentation_pred_' + str(method_index) + '.png', drawSegmentationImage(pred_s, blackIndex=options.numOutputPlanes))
continue
new_pred_dict = {}
for key, value in pred_dict.iteritems():
new_pred_dict[key] = value
continue
new_pred_dict['segmentation'] = np.array(predSegmentations)
if method_index < len(predictions):
predictions[method_index] = new_pred_dict
else:
predictions.append(new_pred_dict)
pass
if method[1] == 'crf_tf':
predSegmentations = []
predDepths = []
image_inp = tf.placeholder(tf.float32, shape=[1, HEIGHT, WIDTH, 3], name='image')
segmentation_inp = tf.placeholder(tf.float32, shape=[1, HEIGHT, WIDTH, options.numOutputPlanes + 1], name='segmentation')
plane_inp = tf.placeholder(tf.float32, shape=[1, options.numOutputPlanes, 3], name='plane')
non_plane_depth_inp = tf.placeholder(tf.float32, shape=[1, HEIGHT, WIDTH, 1], name='non_plane_depth')
info_inp = tf.placeholder(tf.float32, shape=[20], name='info')
plane_parameters = tf.reshape(plane_inp, (-1, 3))
plane_depths = planeDepthsModule(plane_parameters, WIDTH, HEIGHT, info_inp)
plane_depths = tf.transpose(tf.reshape(plane_depths, [HEIGHT, WIDTH, -1, options.numOutputPlanes]), [2, 0, 1, 3])
all_depths = tf.concat([plane_depths, non_plane_depth_inp], axis=3)
planesY = plane_inp[:, :, 1]
planesD = tf.maximum(tf.norm(plane_inp, axis=-1), 1e-4)
planesY /= planesD
planesY = tf.concat([planesY, tf.ones((1, 1))], axis=1)
#refined_segmentation = crfModule(segmentation_inp, plane_inp, non_plane_depth_inp, info_inp, numOutputPlanes = options.numOutputPlanes, numIterations=5)
imageDiff = calcImageDiff(image_inp)
#refined_segmentation, debug_dict = segmentationRefinementModule(segmentation_inp, all_depths, planesY, imageDiff, numOutputPlanes = options.numOutputPlanes + 1, numIterations=5)
refined_segmentation, debug_dict = meanfieldModule(segmentation_inp, all_depths, planesY, imageDiff, numOutputPlanes = options.numOutputPlanes + 1, maxDepthDiff=0.2, varDepthDiff=pow(0.2, 2))
config=tf.ConfigProto()
config.gpu_options.allow_growth=True
config.allow_soft_placement=True
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session(config=config) as sess:
sess.run(init_op)
for image_index in xrange(options.numImages):
#if image_index != 1:
#continue
print('crf tf ' + str(image_index))
allSegmentations = np.concatenate([pred_dict['segmentation'][image_index], pred_dict['np_mask'][image_index]], axis=2)
allSegmentations = softmax(allSegmentations)
pred_s, debug = sess.run([refined_segmentation, debug_dict], feed_dict={segmentation_inp: np.expand_dims(allSegmentations, 0), plane_inp: np.expand_dims(pred_dict['plane'][image_index], 0), non_plane_depth_inp: np.expand_dims(pred_dict['np_depth'][image_index], 0), info_inp: gt_dict['info'][image_index], image_inp: gt_dict['image'][image_index:image_index + 1]})
pred_s = pred_s[0]
planeDepths = calcPlaneDepths(pred_dict['plane'][image_index], WIDTH, HEIGHT, gt_dict['info'][image_index])
allDepths = np.concatenate([planeDepths, pred_dict['np_depth'][image_index]], axis=2)
pred_d = np.sum(allDepths * pred_s, axis=-1)
predSegmentations.append(pred_s)
predDepths.append(pred_d)
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_pred_' + str(method_index) + '.png', drawDepthImage(pred_d))
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_segmentation_pred_' + str(method_index) + '.png', drawSegmentationImage(pred_s, blackIndex=options.numOutputPlanes))
if 'diff' in debug:
segmentation = np.argmax(allSegmentations, axis=-1)
for planeIndex in xrange(options.numOutputPlanes + 1):
cv2.imwrite('test/mask_' + str(planeIndex) + '.png', drawMaskImage(allSegmentations[:, :, planeIndex]))
continue
for planeIndex in xrange(debug['diff'].shape[-1]):
cv2.imwrite('test/cost_mask_' + str(planeIndex) + '.png', drawMaskImage(debug['diff'][0, :, :, planeIndex] / 2))
continue
exit(1)
pass
continue
pass
new_pred_dict = {}
for key, value in pred_dict.iteritems():
new_pred_dict[key] = value
continue
segmentations = np.array(predSegmentations)
new_pred_dict['segmentation'] = segmentations[:, :, :, :options.numOutputPlanes]
new_pred_dict['non_plane_mask'] = segmentations[:, :, :, options.numOutputPlanes:options.numOutputPlanes + 1]
#new_pred_dict['non_plane_mask'] = segmentations[:, :, :, :options.numOutputPlanes]
new_pred_dict['depth'] = np.array(predDepths)
if method_index < len(predictions):
predictions[method_index] = new_pred_dict
else:
predictions.append(new_pred_dict)
pass
pass
if method[1] == 'crf':
predSegmentations = []
predDepths = []
for image_index in xrange(options.numImages):
print('crf ' + str(image_index))
boundaries = pred_dict['boundary'][image_index]
boundaries = sigmoid(boundaries)
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_boundary.png', drawMaskImage(np.concatenate([boundaries, np.zeros((HEIGHT, WIDTH, 1))], axis=2)))
allSegmentations = np.concatenate([pred_dict['segmentation'][image_index], pred_dict['np_mask'][image_index]], axis=2)
allSegmentations = softmax(allSegmentations)
planeDepths = calcPlaneDepths(pred_dict['plane'][image_index], WIDTH, HEIGHT, gt_dict['info'][image_index])
allDepths = np.concatenate([planeDepths, pred_dict['np_depth'][image_index]], axis=2)
#boundaries = np.concatenate([np.ones((allSegmentations.shape[0], allSegmentations.shape[1], 1)), -np.ones((allSegmentations.shape[0], allSegmentations.shape[1], 1))], axis=2)
#if options.imageIndex >= 0:
#boundaries = cv2.imread(options.test_dir + '/' + str(options.imageIndex) + '_boundary.png')
#else:
#boundaries = cv2.imread(options.test_dir + '/' + str(image_index) + '_boundary.png')
#pass
#boundaries = (boundaries > 128).astype(np.float32)[:, :, :2]
allDepths[:, :, options.numOutputPlanes] = 0
pred_s = refineSegmentation(gt_dict['image'][image_index], allSegmentations, allDepths, boundaries, numOutputPlanes = 20, numIterations=20, numProposals=5)
pred_d = allDepths.reshape(-1, options.numOutputPlanes + 1)[np.arange(WIDTH * HEIGHT), pred_s.reshape(-1)].reshape(HEIGHT, WIDTH)
predSegmentations.append(pred_s)
predDepths.append(pred_d)
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_pred_' + str(method_index) + '.png', drawDepthImage(pred_d))
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_segmentation_pred_' + str(method_index) + '.png', drawSegmentationImage(pred_s, blackIndex=options.numOutputPlanes))
#segmentation = np.argmax(allSegmentations, axis=-1)
# for planeIndex in xrange(options.numOutputPlanes + 1):
# cv2.imwrite('test/mask_' + str(planeIndex) + '.png', drawMaskImage(allSegmentations[:, :, planeIndex]))
# continue
#cv2.imwrite(options.test_dir + '/mask_' + str(21) + '.png', drawDepthImage(pred_dict['np_depth'][0]))
#for plane_index in xrange(options.numOutputPlanes + 1):
#cv2.imwrite(options.test_dir + '/mask_' + str(plane_index) + '.png', drawMaskImage(pred_s == plane_index))
#continue
#exit(1)
continue
new_pred_dict = {}
for key, value in pred_dict.iteritems():
new_pred_dict[key] = value
continue
new_pred_dict['segmentation'] = np.array(predSegmentations)
new_pred_dict['depth'] = np.array(predDepths)
if method_index < len(predictions):
predictions[method_index] = new_pred_dict
else:
predictions.append(new_pred_dict)
pass
pass
if 'pixelwise' in method[1]:
predPlanes = []
predSegmentations = []
predDepths = []
predNumPlanes = []
for image_index in xrange(options.numImages):
pred_d = pred_dict['np_depth'][image_index].squeeze()
pred_n = pred_dict['np_normal'][image_index].squeeze()
if '_1' in method[1]:
pred_s = np.zeros(pred_dict['segmentation'][image_index].shape)
pred_p = np.zeros(pred_dict['plane'][image_index].shape)
elif '_2' in method[1]:
parameters = {'distanceCostThreshold': 0.1, 'smoothnessWeight': 0.05, 'semantics': True}
pred_p, pred_s = fitPlanesNYU(gt_dict['image'], gt_dict['depth'][image_index].squeeze(), gt_dict['normal'][image_index], gt_dict['semantics'][image_index], gt_dict['info'][image_index], numOutputPlanes=options.numOutputPlanes, parameters=parameters)
elif '_3' in method[1]:
parameters = {'distanceCostThreshold': 0.1, 'smoothnessWeight': 0.03, 'semantics': True, 'distanceThreshold': 0.05}
pred_p, pred_s = fitPlanesNYU(gt_dict['image'], pred_d, pred_n, pred_dict['semantics'][image_index], gt_dict['info'][image_index], numOutputPlanes=options.numOutputPlanes, parameters=parameters)
elif '_4' in method[1]:
parameters = {'numProposals': 5, 'distanceCostThreshold': 0.1, 'smoothnessWeight': 30, 'dominantLineThreshold': 3, 'offsetGap': 0.1}
pred_p, pred_s = fitPlanesManhattan(gt_dict['image'][image_index], gt_dict['depth'][image_index].squeeze(), gt_dict['normal'][image_index], gt_dict['info'][image_index], numOutputPlanes=options.numOutputPlanes, parameters=parameters)
pred_d = np.zeros((HEIGHT, WIDTH))
elif '_5' in method[1]:
parameters = {'numProposals': 5, 'distanceCostThreshold': 0.1, 'smoothnessWeight': 100, 'dominantLineThreshold': 3, 'offsetGap': 0.6}
pred_p, pred_s = fitPlanesManhattan(gt_dict['image'][image_index], pred_d, pred_n, gt_dict['info'][image_index], numOutputPlanes=options.numOutputPlanes, parameters=parameters)
pred_d = np.zeros((HEIGHT, WIDTH))
elif '_6' in method[1]:
parameters = {'distanceCostThreshold': 0.1, 'smoothnessWeight': 300, 'numProposals': 5, 'normalWeight': 1, 'meanshift': 0.2}
pred_p, pred_s = fitPlanesPiecewise(gt_dict['image'][image_index], gt_dict['depth'][image_index].squeeze(), gt_dict['normal'][image_index], gt_dict['info'][image_index], numOutputPlanes=options.numOutputPlanes, parameters=parameters)
pred_d = np.zeros((HEIGHT, WIDTH))
elif '_7' in method[1]:
parameters = {'numProposals': 5, 'distanceCostThreshold': 0.1, 'smoothnessWeight': 300, 'normalWeight': 1, 'meanshift': 0.2}
pred_p, pred_s = fitPlanesPiecewise(gt_dict['image'][image_index], pred_d, pred_n, gt_dict['info'][image_index], numOutputPlanes=options.numOutputPlanes, parameters=parameters)
pred_d = np.zeros((HEIGHT, WIDTH))
pass
predPlanes.append(pred_p)
predSegmentations.append(pred_s)
predDepths.append(pred_d)
predNumPlanes.append(pred_p.shape[0])
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_pred_' + str(method_index) + '.png', drawDepthImage(pred_d))
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_segmentation_pred_' + str(method_index) + '.png', drawSegmentationImage(pred_s, blackIndex=pred_p.shape[0]))
#exit(1)
continue
new_pred_dict = {}
for key, value in pred_dict.iteritems():
new_pred_dict[key] = value
continue
new_pred_dict['plane'] = np.array(predPlanes)
new_pred_dict['segmentation'] = np.array(predSegmentations)
new_pred_dict['depth'] = np.array(predDepths)
new_pred_dict['num_planes'] = np.array(predNumPlanes)
if method_index < len(predictions):
predictions[method_index] = new_pred_dict
else:
predictions.append(new_pred_dict)
pass
#titles.append('pixelwise+semantics+RANSAC')
pass
if method[1] == 'crfrnn':
predSegmentations = []
predDepths = []
image_inp = tf.placeholder(tf.float32, shape=[1, HEIGHT, WIDTH, 3], name='image')
segmentation_inp = tf.placeholder(tf.float32, shape=[1, HEIGHT, WIDTH, options.numOutputPlanes + 1], name='segmentation')
refined_segmentation = CrfRnnLayer(image_dims=(HEIGHT, WIDTH), num_classes=21, theta_alpha=120., theta_beta=3., theta_gamma=3., num_iterations=10, name='crfrnn')([segmentation_inp, image_inp])
config=tf.ConfigProto()
config.gpu_options.allow_growth=True
config.allow_soft_placement=True
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session(config=config) as sess:
sess.run(init_op)
for image_index in xrange(options.numImages):
#if image_index != 1:
#continue
print('crf rnn ' + str(image_index))
allSegmentations = np.concatenate([pred_dict['segmentation'][image_index], pred_dict['np_mask'][image_index]], axis=2)
img = gt_dict['image'][image_index:image_index + 1].astype(np.float32) - 128
pred_s = sess.run(refined_segmentation, feed_dict={segmentation_inp: np.expand_dims(allSegmentations, 0), image_inp: img})
# print(pred_s.shape)
# print(pred_s[0].max())
# print(pred_s.sum(-1).max())
# exit(1)
pred_s = pred_s[0]
# print(allSegmentations.max())
# print(pred_s.max())
# print(img.max())
# print(img.min())
# print(np.abs(pred_s - allSegmentations).max())
# print(np.abs(np.argmax(pred_s, axis=-1) - np.argmax(allSegmentations, axis=-1)).max())
pred_s = one_hot(np.argmax(pred_s, axis=-1), options.numOutputPlanes + 1)
planeDepths = calcPlaneDepths(pred_dict['plane'][image_index], WIDTH, HEIGHT, gt_dict['info'][image_index])
allDepths = np.concatenate([planeDepths, pred_dict['np_depth'][image_index]], axis=2)
pred_d = np.sum(allDepths * pred_s, axis=-1)
predSegmentations.append(pred_s)
predDepths.append(pred_d)
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_pred_' + str(method_index) + '.png', drawDepthImage(pred_d))
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_segmentation_pred_' + str(method_index) + '.png', drawSegmentationImage(pred_s, blackIndex=options.numOutputPlanes))
continue
pass
new_pred_dict = {}
for key, value in pred_dict.iteritems():
new_pred_dict[key] = value
continue
segmentations = np.array(predSegmentations)
new_pred_dict['segmentation'] = segmentations[:, :, :, :options.numOutputPlanes]
new_pred_dict['non_plane_mask'] = segmentations[:, :, :, options.numOutputPlanes:options.numOutputPlanes + 1]
#new_pred_dict['non_plane_mask'] = segmentations[:, :, :, :options.numOutputPlanes]
new_pred_dict['depth'] = np.array(predDepths)
if method_index < len(predictions):
predictions[method_index] = new_pred_dict
else:
predictions.append(new_pred_dict)
pass
pass
if saving:
np.save(options.result_filename, {'gt': gt_dict, 'pred': predictions})
pass
continue
#exit(1)
#print(results)
# depth = gt_dict['depth'][4]
# cv2.imwrite(options.test_dir + '/test_depth_gt.png', drawDepthImage(depth))
# pred_p, pred_s, pred_d = fitPlanes(depth, getSUNCGCamera(), numPlanes=20, planeAreaThreshold=3*4, numIterations=100, distanceThreshold=0.05, local=0.2)
# cv2.imwrite(options.test_dir + '/test_depth.png', drawDepthImage(pred_d))
# cv2.imwrite(options.test_dir + '/test_segmentation.png', drawSegmentationImage(pred_s))
# exit(1)
#plotResults(gt_dict, predictions, options)
if options.numImages > gt_dict['image'].shape[0]:
plotAll(options)
else:
plotResults(gt_dict, predictions, options)
pass
writeHTML(options)
return
def plotAll(options):
result_filenames = glob.glob(options.test_dir + '/results_*.npy')
assert(len(result_filenames) > 0)
results = np.load(result_filenames[0])
results = results[()]
gt_dict = results['gt']
predictions = results['pred']
for index in xrange(1, len(result_filenames)):
other_results = np.load(result_filenames[index])
other_results = other_results[()]
other_gt_dict = other_results['gt']
other_predictions = other_results['pred']
for k, v in other_gt_dict.iteritems():
gt_dict[k] = np.concatenate([gt_dict[k], v], axis=0)
continue
for methodIndex, other_pred_dict in enumerate(other_predictions):
if methodIndex == 1:
continue
for k, v in other_pred_dict.iteritems():
print(methodIndex, k)
print(predictions[methodIndex][k].shape)
print(v.shape)
predictions[methodIndex][k] = np.concatenate([predictions[methodIndex][k], v], axis=0)
continue
continue
continue
plotResults(gt_dict, predictions, options)
return
def plotResults(gt_dict, predictions, options):
titles = options.titles
pixel_metric_curves = []
plane_metric_curves = []
for method_index, pred_dict in enumerate(predictions):
if titles[method_index] == 'pixelwise':
continue
segmentations = pred_dict['segmentation']
#if method_index == 0:
#segmentations = softmax(segmentations)
#pass
#pixel_curves, plane_curves = evaluatePlaneSegmentation(pred_dict['plane'], segmentations, gt_dict['plane'], gt_dict['segmentation'], gt_dict['num_planes'], numOutputPlanes = options.numOutputPlanes)
pixel_curves = np.zeros((6, 13))
plane_curves = np.zeros((6, 13, 3))
numImages = segmentations.shape[0]
for image_index in xrange(numImages):
gtDepths = calcPlaneDepths(gt_dict['plane'][image_index], WIDTH, HEIGHT, gt_dict['info'][image_index])
predDepths = calcPlaneDepths(pred_dict['plane'][image_index], WIDTH, HEIGHT, gt_dict['info'][image_index])
if 'num_planes' in pred_dict:
predNumPlanes = pred_dict['num_planes'][image_index]
else:
predNumPlanes = options.numOutputPlanes
pass
#if image_index != 2:
#continue
pixelStatistics, planeStatistics = evaluatePlanePrediction(predDepths, segmentations[image_index], predNumPlanes, gtDepths, gt_dict['segmentation'][image_index], gt_dict['num_planes'][image_index])
# for planeIndex in xrange(options.numOutputPlanes):
# cv2.imwrite('test/mask_' + str(planeIndex) + '.png', drawMaskImage(gt_dict['segmentation'][image_index][:, :, planeIndex]))
# continue
# mask_1 = segmentations[image_index] == 8
# mask_2 = gt_dict['segmentation'][image_index][:, :, 3]
# print(mask_1.sum())
# print(mask_2.sum())
# cv2.imwrite('test/mask_pred.png', drawMaskImage(mask_1))
# cv2.imwrite('test/mask_gt.png', drawMaskImage(mask_2))
# cv2.imwrite('test/mask_intersection.png', drawMaskImage(mask_2 * mask_1 > 0.5))
# cv2.imwrite('test/mask_union.png', drawMaskImage((mask_2 + mask_1) > 0.5))
# print((mask_2 * mask_1 > 0.5).sum())
# print(((mask_2 + mask_1) > 0.5).sum())
#print(image_index, planeStatistics[4][5])
#exit(1)
# print(pred_dict['plane'][image_index])
# for planeIndex in xrange(options.numOutputPlanes):
# print((segmentations[image_index] == planeIndex).sum())
# continue
#exit(1)
pixel_curves += np.array(pixelStatistics)
plane_curves += np.array(planeStatistics)
continue
if len(pixel_metric_curves) == 0:
for metric_index, pixel_curve in enumerate(pixel_curves):
pixel_metric_curves.append([])
plane_metric_curves.append([])
continue
pass
for metric_index, pixel_curve in enumerate(pixel_curves):
pixel_metric_curves[metric_index].append(pixel_curve / numImages)
continue
for metric_index, plane_curve in enumerate(plane_curves):
#planeScore = plane_curve[:, 0] / plane_curve[:, 1]
plane_metric_curves[metric_index].append(plane_curve)
continue
continue
#np.save(options.test_dir + '/pixel_curves.npy', np.array(pixel_metric_curves))
#np.save(options.test_dir + '/plane_curves.npy', np.array(plane_metric_curves))
xs = []
xs.append((np.arange(13) * 0.1).tolist())
xs.append((np.arange(13) * 0.1).tolist())
xs.append((np.arange(13) * 0.1).tolist())
xs.append((np.arange(13) * 0.05).tolist())
xs.append((np.arange(13) * 0.05).tolist())
xs.append((np.arange(13) * 0.05).tolist())
xlabels = ['IOU threshold', 'IOU threshold', 'IOU threshold', 'depth threshold', 'depth threshold', 'depth threshold']
curve_titles = ['depth threshold 0.1', 'depth threshold 0.2', 'depth threshold 0.3', 'IOU 0.3', 'IOU 0.5', 'IOU 0.7']
curve_labels = [title for title in titles if title != 'pixelwise']
# metric_index = 4
# filename = options.test_dir + '/curves'
# pixel_curves = pixel_metric_curves[metric_index]
# plane_curves = plane_metric_curves[metric_index]
# plane_curves = [plane_curve[:, 0] / plane_curve[:, 1] for plane_curve in plane_curves]
# plotCurvesSubplot(xs[metric_index], [plane_curves, pixel_curves], filenames = [filename + '.png', filename + '_oracle.png'], xlabel=xlabels[metric_index], ylabels=['Per-plane recall', 'Per-pixel recall'], labels=curve_labels)
# return
for metric_index, curves in enumerate(pixel_metric_curves):
if metric_index not in [4]:
continue
#filename = options.test_dir + '/curve_pixel_' + curve_titles[metric_index].replace(' ', '_') + '.png'
#plotCurves(xs[metric_index], curves, filename = filename, xlabel=xlabels[metric_index], ylabel='pixel coverage', title=curve_titles[metric_index], labels=curve_labels)
filename = options.test_dir + '/curve_pixel_' + curve_titles[metric_index].replace(' ', '_').replace('.', '')
plotCurvesSplit(xs[metric_index], curves, filenames = [filename + '.png', filename + '_oracle.png'], xlabel=xlabels[metric_index], ylabel='Per-pixel recall', title=curve_titles[metric_index], labels=curve_labels)
#plotCurvesSubplot(xs[metric_index], curves, filename = filename + '.png', xlabel=xlabels[metric_index], ylabel='Per-pixel recall', title=curve_titles[metric_index], labels=curve_labels)
continue
for metric_index, curves in enumerate(plane_metric_curves):
if metric_index not in [4]:
continue
curves = [curve[:, 0] / curve[:, 1] for curve in curves]
#filename = options.test_dir + '/curve_plane_' + curve_titles[metric_index].replace(' ', '_') + '.png'
#plotCurves(xs[metric_index], curves, filename = filename, xlabel=xlabels[metric_index], ylabel='Per-plane recall', title=curve_titles[metric_index], labels=curve_labels)
filename = options.test_dir + '/curve_plane_' + curve_titles[metric_index].replace(' ', '_').replace('.', '')
plotCurvesSplit(xs[metric_index], curves, filenames = [filename + '.png', filename + '_oracle.png'], xlabel=xlabels[metric_index], ylabel='Per-plane recall', title=curve_titles[metric_index], labels=curve_labels)
continue
def gridSearch(options):
#writeHTML(options)
#exit(1)
if os.path.exists(options.result_filename):
results = np.load(options.result_filename)
results = results[()]
else:
assert(False)
pass
gt_dict = results['gt']
predictions = results['pred']
for key, value in gt_dict.iteritems():
if options.imageIndex >= 0:
gt_dict[key] = value[options.imageIndex:options.imageIndex + 1]
elif value.shape[0] > options.numImages:
gt_dict[key] = value[:options.numImages]
pass
continue
for pred_dict in predictions:
for key, value in pred_dict.iteritems():
if options.imageIndex >= 0:
pred_dict[key] = value[options.imageIndex:options.imageIndex + 1]
elif value.shape[0] > options.numImages:
pred_dict[key] = value[:options.numImages]
pass
continue
continue
#methods = ['planenet', 'pixelwise+RANSAC', 'GT+RANSAC']
titles = options.titles
for image_index in xrange(options.visualizeImages):
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_image.png', gt_dict['image'][image_index])
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_gt.png', drawDepthImage(gt_dict['depth'][image_index]))
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_normal_gt.png', drawNormalImage(gt_dict['normal'][image_index]))
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_segmentation_gt.png', drawSegmentationImage(np.concatenate([gt_dict['segmentation'][image_index], 1 - np.expand_dims(gt_dict['plane_mask'][image_index], -1)], axis=2), blackIndex=options.numOutputPlanes))
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_semantics_gt.png', drawSegmentationImage(gt_dict['semantics'][image_index], blackIndex=0))
plane_depths = calcPlaneDepths(gt_dict['plane'][image_index], WIDTH, HEIGHT, gt_dict['info'][image_index])
all_depths = np.concatenate([plane_depths, np.expand_dims(gt_dict['depth'][image_index], -1)], axis=2)
depth = np.sum(all_depths * np.concatenate([gt_dict['segmentation'][image_index], 1 - np.expand_dims(gt_dict['plane_mask'][image_index], -1)], axis=2), axis=2)
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_gt_plane.png', drawDepthImage(depth))
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_gt_diff.png', drawMaskImage((depth - gt_dict['depth'][image_index]) * 5 + 0.5))
for method_index, pred_dict in enumerate(predictions):
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_pred_' + str(method_index) + '.png', drawDepthImage(pred_dict['depth'][image_index]))
if 'pixelwise' in options.methods[method_index][1]:
continue
segmentation = pred_dict['segmentation'][image_index]
segmentation = np.concatenate([segmentation, pred_dict['np_mask'][image_index]], axis=2)
cv2.imwrite(options.test_dir + '/' + str(image_index) + '_segmentation_pred_' + str(method_index) + '.png', drawSegmentationImage(segmentation, blackIndex=options.numOutputPlanes))
continue
continue
#post processing
for method_index, method in enumerate(options.methods):
if len(method) == 3:
pred_dict = predictions[method[2]]
else:
pred_dict = predictions[method_index]
pass
if 'pixelwise_2' in method[1] or 'pixelwise_3' in method[1]:
bestScore = 0
configurationIndex = 0
for distanceCostThreshold in [0.3]:
for smoothnessWeight in [0.01, 0.03, 0.05]:
for distanceThreshold in [0.2]:
parameters = {'distanceCostThreshold': distanceCostThreshold, 'smoothnessWeight': smoothnessWeight, 'distanceThreshold': distanceThreshold, 'semantics': True}
score = 0
for image_index in xrange(options.numImages):
#cv2.imwrite('test/normal.png', drawNormalImage(gt_dict['normal'][image_index]))
if '_2' in method[1]:
pred_p, pred_s = fitPlanesNYU(gt_dict['image'][image_index], gt_dict['depth'][image_index].squeeze(), gt_dict['normal'][image_index], gt_dict['semantics'][image_index], gt_dict['info'][image_index], numOutputPlanes=options.numOutputPlanes, parameters=parameters)
else:
pred_d = pred_dict['np_depth'][image_index].squeeze()
pred_n = pred_dict['np_normal'][image_index].squeeze()
pred_p, pred_s = fitPlanesNYU(gt_dict['image'][image_index], pred_d, pred_n, pred_dict['semantics'][image_index], gt_dict['info'][image_index], numOutputPlanes=options.numOutputPlanes, parameters=parameters)
pass
predNumPlanes = pred_p.shape[0]
gtDepths = calcPlaneDepths(gt_dict['plane'][image_index], WIDTH, HEIGHT, gt_dict['info'][image_index])
planeDepths = calcPlaneDepths(pred_p, WIDTH, HEIGHT, gt_dict['info'][image_index])
pixelStatistics, planeStatistics = evaluatePlanePrediction(planeDepths, pred_s, predNumPlanes, gtDepths, gt_dict['segmentation'][image_index], gt_dict['num_planes'][image_index])
#print(pixelStatistics)
#exit(1)
#planeStatistics = np.array(planeStatistics)[1]
#accuracy = (planeStatistics[3:8, 0].astype(np.float32) / np.maximum(planeStatistics[3:8, 1], 1e-4)).mean()
pixelStatistics = np.array(pixelStatistics)[1]
accuracy = pixelStatistics[3:8].mean()
score += accuracy
#cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_pred_' + str(method_index) + '.png', drawDepthImage(pred_d))
cv2.imwrite('test/segmentation_pred_' + str(image_index) + '.png', drawSegmentationImage(pred_s, blackIndex=options.numOutputPlanes))
#exit(1)
continue
score /= options.numImages
print(score, parameters)
configurationIndex += 1
#exit(1)
if score > bestScore:
bestScore = score
bestParameters = parameters
pass
continue
continue
continue
print(bestScore)
print(bestParameters)
exit(1)
if 'pixelwise_4' in method[1] or 'pixelwise_5' in method[1]:
bestScore = 0
configurationIndex = 0
for distanceCostThreshold in [0.05]:
for smoothnessWeight in [30]:
for offsetGap in [-0.1]:
parameters = {'distanceCostThreshold': distanceCostThreshold, 'smoothnessWeight': smoothnessWeight, 'offsetGap': abs(offsetGap), 'meanshift': offsetGap}
score = 0
for image_index in xrange(options.numImages):
if '_4' in method[1]:
pred_p, pred_s = fitPlanesManhattan(gt_dict['image'][image_index], gt_dict['depth'][image_index].squeeze(), gt_dict['normal'][image_index], gt_dict['info'][image_index], numOutputPlanes=options.numOutputPlanes, parameters=parameters)
else:
pred_d = pred_dict['np_depth'][image_index].squeeze()
pred_n = pred_dict['np_normal'][image_index].squeeze()
pred_p, pred_s = fitPlanesManhattan(gt_dict['image'][image_index], pred_d, pred_n, gt_dict['info'][image_index], numOutputPlanes=options.numOutputPlanes, parameters=parameters)
pass
predNumPlanes = pred_p.shape[0]
gtDepths = calcPlaneDepths(gt_dict['plane'][image_index], WIDTH, HEIGHT, gt_dict['info'][image_index])
planeDepths = calcPlaneDepths(pred_p, WIDTH, HEIGHT, gt_dict['info'][image_index])
pixelStatistics, planeStatistics = evaluatePlanePrediction(planeDepths, pred_s, predNumPlanes, gtDepths, gt_dict['segmentation'][image_index], gt_dict['num_planes'][image_index])
#print(pixelStatistics)
#exit(1)
#planeStatistics = np.array(planeStatistics)[1]
#accuracy = (planeStatistics[3:8, 0].astype(np.float32) / np.maximum(planeStatistics[3:8, 1], 1e-4)).mean()
pixelStatistics = np.array(pixelStatistics)[1]
accuracy = pixelStatistics[3:8].mean()
score += accuracy
#cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_pred_' + str(method_index) + '.png', drawDepthImage(pred_d))
#cv2.imwrite(options.test_dir + '/' + str(image_index) + '_segmentation_pred_' + str(method_index) + '.png', drawSegmentationImage(pred_s, blackIndex=options.numOutputPlanes))
#exit(1)
continue
score /= options.numImages
print(score, parameters)
configurationIndex += 1
#exit(1)
if score > bestScore:
bestScore = score
bestParameters = parameters
pass
continue
continue
continue
print(bestScore)
print(bestParameters)
exit(1)
if 'pixelwise_6' in method[1] or 'pixelwise_7' in method[1]:
bestScore = 0
configurationIndex = 0
for distanceCostThreshold in [0.1]:
for smoothnessWeight in [300]:
for normalWeight in [1]:
for offset in [0.2]:
parameters = {'distanceCostThreshold': distanceCostThreshold, 'smoothnessWeight': smoothnessWeight, 'numProposals': 5, 'normalWeight': normalWeight, 'offsetGap': abs(offset), 'meanshift': offset}
score = 0
for image_index in xrange(options.numImages):
if '_6' in method[1]:
pred_p, pred_s = fitPlanesPiecewise(gt_dict['image'][image_index], gt_dict['depth'][image_index].squeeze(), gt_dict['normal'][image_index], gt_dict['info'][image_index], numOutputPlanes=options.numOutputPlanes, parameters=parameters)
else:
pred_d = pred_dict['np_depth'][image_index].squeeze()
pred_n = pred_dict['np_normal'][image_index].squeeze()
pred_p, pred_s = fitPlanesPiecewise(gt_dict['image'][image_index], pred_d, pred_n, gt_dict['info'][image_index], numOutputPlanes=options.numOutputPlanes, parameters=parameters)
pass
predNumPlanes = pred_p.shape[0]
gtDepths = calcPlaneDepths(gt_dict['plane'][image_index], WIDTH, HEIGHT, gt_dict['info'][image_index])
planeDepths = calcPlaneDepths(pred_p, WIDTH, HEIGHT, gt_dict['info'][image_index])
pixelStatistics, planeStatistics = evaluatePlanePrediction(planeDepths, pred_s, predNumPlanes, gtDepths, gt_dict['segmentation'][image_index], gt_dict['num_planes'][image_index])
#print(pixelStatistics)
#exit(1)
#planeStatistics = np.array(planeStatistics)[1]
#accuracy = (planeStatistics[3:8, 0].astype(np.float32) / np.maximum(planeStatistics[3:8, 1], 1e-4)).mean()
pixelStatistics = np.array(pixelStatistics)[1]
accuracy = pixelStatistics[3:8].mean()
score += accuracy
#cv2.imwrite('test/depth_pred_' + str(configurationIndex) + '.png', drawDepthImage(pred_d))
cv2.imwrite('test/segmentation_pred_' + str(image_index) + '.png', drawSegmentationImage(pred_s, blackIndex=options.numOutputPlanes))
#exit(1)
continue
score /= options.numImages
print(score, parameters)
configurationIndex += 1
#exit(1)
if score > bestScore:
bestScore = score
bestParameters = parameters
pass
continue
continue
continue
continue
print(bestScore)
print(bestParameters)
exit(1)
if method[1] == 'crfrnn':
parameterConfigurations = []
for alpha in [15]:
for beta in [10]:
for gamma in [3]:
parameterConfigurations.append((alpha, beta, gamma))
continue
continue
continue
print(parameterConfigurations)
bestScore = 0
for parameters in parameterConfigurations:
tf.reset_default_graph()
image_inp = tf.placeholder(tf.float32, shape=[1, HEIGHT, WIDTH, 3], name='image')
segmentation_inp = tf.placeholder(tf.float32, shape=[1, HEIGHT, WIDTH, options.numOutputPlanes + 1], name='segmentation')
refined_segmentation = CrfRnnLayer(image_dims=(HEIGHT, WIDTH), num_classes=21, theta_alpha=parameters[0], theta_beta=parameters[1], theta_gamma=parameters[2], num_iterations=10, name='crfrnn')([segmentation_inp, image_inp])
config=tf.ConfigProto()
config.gpu_options.allow_growth=True
config.allow_soft_placement=True
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session(config=config) as sess:
sess.run(init_op)
score = 0.
for image_index in xrange(options.numImages):
#if image_index != 1:
#continue
print('crf as rnn', image_index)
allSegmentations = np.concatenate([pred_dict['segmentation'][image_index], pred_dict['np_mask'][image_index]], axis=2)
img = gt_dict['image'][image_index:image_index + 1].astype(np.float32) - 128
pred_s = sess.run(refined_segmentation, feed_dict={segmentation_inp: np.expand_dims(allSegmentations, 0), image_inp: img})
pred_s = pred_s[0]
#pred_s = allSegmentations
pred_s = one_hot(np.argmax(pred_s, axis=-1), options.numOutputPlanes + 1)
planeDepths = calcPlaneDepths(pred_dict['plane'][image_index], WIDTH, HEIGHT, gt_dict['info'][image_index])
allDepths = np.concatenate([planeDepths, pred_dict['np_depth'][image_index]], axis=2)
pred_d = np.sum(allDepths * pred_s, axis=-1)
# for planeIndex in xrange(options.numOutputPlanes):
# cv2.imwrite('test/mask_' + str(planeIndex) + '.png', drawMaskImage(pred_s[:, :, planeIndex]))
# cv2.imwrite('test/gt_mask_' + str(planeIndex) + '.png', drawMaskImage(gt_dict['segmentation'][image_index][:, :, planeIndex]))
# continue
# cv2.imwrite('test/depth_pred.png', drawDepthImage(pred_d))
# cv2.imwrite('test/depth_gt.png', drawDepthImage(gt_dict['depth'][image_index]))
# cv2.imwrite('test/depth_diff.png', drawMaskImage(np.abs(pred_d - gt_dict['depth'][image_index]) * 5))
# exit(1)
predNumPlanes = options.numOutputPlanes
gtDepths = calcPlaneDepths(gt_dict['plane'][image_index], WIDTH, HEIGHT, gt_dict['info'][image_index])
pixelStatistics, planeStatistics = evaluatePlanePrediction(planeDepths, pred_s[:, :, :options.numOutputPlanes], predNumPlanes, gtDepths, gt_dict['segmentation'][image_index], gt_dict['num_planes'][image_index])
#print(pixelStatistics)
#exit(1)
#planeStatistics = np.array(planeStatistics)[1]
#accuracy = (planeStatistics[3:8, 0].astype(np.float32) / np.maximum(planeStatistics[3:8, 1], 1e-4)).mean()
pixelStatistics = np.array(pixelStatistics)[1]
# print(pixelStatistics)
# pixelStatistics[3:8].mean()
# exit(1)
accuracy = pixelStatistics[3:8].mean()
score += accuracy
#cv2.imwrite(options.test_dir + '/' + str(image_index) + '_depth_pred_' + str(method_index) + '.png', drawDepthImage(pred_d))
#cv2.imwrite(options.test_dir + '/' + str(image_index) + '_segmentation_pred_' + str(method_index) + '.png', drawSegmentationImage(pred_s, blackIndex=options.numOutputPlanes))
#exit(1)
continue
score /= options.numImages
print(score, parameters)
#exit(1)
if score > bestScore:
bestScore = score
bestParameters = parameters
pass
pass
continue
print(bestScore, bestParameters)
pass
continue
return