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utils.py
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import numpy as np
import PIL.Image
import copy
import sys
import os
import cv2
import scipy.ndimage as ndimage
#import pydensecrf.densecrf as dcrf
#from pydensecrf.utils import compute_unary, create_pairwise_bilateral, \
#create_pairwise_gaussian, unary_from_softmax
from skimage import segmentation
#from skimage.future import graph
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from layers import PlaneDepthLayer
#from layers import PlaneNormalLayer
#from SegmentationRefinement import *
class ColorPalette:
def __init__(self, numColors):
#np.random.seed(2)
#self.colorMap = np.random.randint(255, size = (numColors, 3))
#self.colorMap[0] = 0
self.colorMap = np.array([[255, 0, 0],
[0, 255, 0],
[0, 0, 255],
[80, 128, 255],
[255, 230, 180],
[255, 0, 255],
[0, 255, 255],
[100, 0, 0],
[0, 100, 0],
[255, 255, 0],
[50, 150, 0],
[200, 255, 255],
[255, 200, 255],
[128, 128, 80],
[0, 50, 128],
[0, 100, 100],
[0, 255, 128],
[0, 128, 255],
[255, 0, 128],
[128, 0, 255],
[255, 128, 0],
[128, 255, 0],
])
if numColors > self.colorMap.shape[0]:
self.colorMap = np.random.randint(255, size = (numColors, 3))
pass
return
def getColorMap(self):
return self.colorMap
def getColor(self, index):
if index >= colorMap.shape[0]:
return np.random.randint(255, size = (3))
else:
return self.colorMap[index]
pass
def writePointCloud(filename, pointCloud, color = [255, 255, 255]):
with open(filename, 'w') as f:
header = """ply
format ascii 1.0
element vertex """
header += str(pointCloud.shape[0])
header += """
property float x
property float y
property float z
property uchar red { start of vertex color }
property uchar green
property uchar blue
end_header
"""
f.write(header)
for point in pointCloud:
for value in point:
f.write(str(value) + ' ')
continue
for value in color:
f.write(str(value) + ' ')
continue
f.write('\n')
continue
f.close()
pass
return
def writeClusteringPointCloud(filename, pointCloud, clusters):
with open(filename, 'w') as f:
header = """ply
format ascii 1.0
element vertex """
header += str(pointCloud.shape[0])
header += """
property float x
property float y
property float z
property uchar red { start of vertex color }
property uchar green
property uchar blue
end_header
"""
colorMap = np.random.randint(255, size = clusters.shape)
assignment = np.argmin(np.linalg.norm(pointCloud.reshape(-1, 1, 3).repeat(clusters.shape[0], 1)[:] - clusters, 2, 2), 1)
f.write(header)
for pointIndex, point in enumerate(pointCloud):
for value in point:
f.write(str(value) + ' ')
continue
color = colorMap[assignment[pointIndex]]
for value in color:
f.write(str(value) + ' ')
continue
f.write('\n')
continue
f.close()
pass
return
def writeNearestNeighbors(filename, pointCloudSource, pointCloudTarget):
with open(filename, 'w') as f:
header = """ply
format ascii 1.0
element vertex """
header += str((pointCloudSource.shape[0] + pointCloudTarget.shape[0] + pointCloudSource.shape[0]) * 4)
header += """
property float x
property float y
property float z
property uchar red
property uchar green
property uchar blue
element face """
header += str(pointCloudSource.shape[0] + pointCloudTarget.shape[0] + pointCloudSource.shape[0])
header += """
property list uchar int vertex_index
end_header
"""
f.write(header)
sourceColor = [0, 255, 0]
targetColor = [0, 0, 255]
colorMap = np.random.randint(255, size = pointCloudSource.shape)
# for pointIndex, point in enumerate(pointCloudSource):
# for value in point:
# f.write(str(value) + ' ')
# continue
# color = sourceColor
# for value in color:
# f.write(str(value) + ' ')
# continue
# f.write('\n')
# continue
# for pointIndex, point in enumerate(pointCloudTarget):
# for value in point:
# f.write(str(value) + ' ')
# continue
# color = targetColor
# for value in color:
# f.write(str(value) + ' ')
# continue
# f.write('\n')
# continue
planeSize = 0.1
for planeType, planes in enumerate([pointCloudSource, pointCloudTarget]):
for planeIndex, plane in enumerate(planes):
planeD = np.linalg.norm(plane)
planeNormal = -plane / planeD
maxNormalDim = np.argmax(np.abs(plane))
allDims = [0, 1, 2]
allDims.remove(maxNormalDim)
dim_1, dim_2 = allDims
for delta_1, delta_2 in [(-planeSize, -planeSize), (planeSize, -planeSize), (planeSize, planeSize), (-planeSize, planeSize)]:
point = copy.deepcopy(plane)
point[dim_1] += delta_1
point[dim_2] += delta_2
point[maxNormalDim] = (-planeD - planeNormal[dim_1] * point[dim_1] - planeNormal[dim_2] * point[dim_2]) / planeNormal[maxNormalDim]
for value in point:
f.write(str(value) + ' ')
continue
if planeType == 0:
color = sourceColor
else:
color = targetColor
pass
for value in color:
f.write(str(value) + ' ')
continue
f.write('\n')
continue
continue
continue
assignment = np.argmin(np.linalg.norm(pointCloudSource.reshape(-1, 1, 3).repeat(pointCloudTarget.shape[0], 1)[:] - pointCloudTarget, 2, 2), 1)
planeSize = 0.01
lineColor = [255, 0, 0]
for planeIndex, planeSource in enumerate(pointCloudSource):
planeD = np.linalg.norm(planeSource)
planeNormal = -planeSource / planeD
maxNormalDim = np.argmax(np.abs(planeSource))
allDims = [0, 1, 2]
allDims.remove(maxNormalDim)
dim_1, dim_2 = allDims
minNormalDim = np.argmin(np.abs(planeSource))
for delta in [-planeSize, planeSize]:
point = copy.deepcopy(planeSource)
point[minNormalDim] += delta
point[maxNormalDim] = (-planeD - planeNormal[dim_1] * point[dim_1] - planeNormal[dim_2] * point[dim_2]) / planeNormal[maxNormalDim]
for value in point:
f.write(str(value) + ' ')
continue
color = lineColor
for value in color:
f.write(str(value) + ' ')
continue
f.write('\n')
continue
planeTarget = pointCloudTarget[assignment[planeIndex]]
planeDTarget = np.linalg.norm(plane)
planeNormalTarget = -plane / planeD
planeD = np.linalg.norm(planeTarget)
planeNormal = -planeTarget / planeD
for delta in [planeSize, -planeSize]:
point = copy.deepcopy(planeTarget)
point[minNormalDim] += delta
point[maxNormalDim] = (-planeD - planeNormal[dim_1] * point[dim_1] - planeNormal[dim_2] * point[dim_2]) / planeNormal[maxNormalDim]
for value in point:
f.write(str(value) + ' ')
continue
color = lineColor
for value in color:
f.write(str(value) + ' ')
continue
f.write('\n')
continue
continue
for index in xrange(pointCloudSource.shape[0] + pointCloudTarget.shape[0] + pointCloudSource.shape[0]):
planeIndex = index * 4
f.write('4 ' + str(planeIndex + 0) + ' ' + str(planeIndex + 1) + ' ' + str(planeIndex + 2) + ' ' + str(planeIndex + 3) + '\n')
continue
# for pointIndexSource, point in enumerate(pointCloudSource):
# pointIndexTarget = assignment[pointIndexSource]
# f.write(str(pointIndexSource) + ' ' + str(pointIndexTarget + pointCloudSource.shape[0]) + ' ')
# color = colorMap[pointIndexSource]
# for value in color:
# f.write(str(value) + ' ')
# continue
# f.write('\n')
# continue
f.close()
pass
return
# def evaluatePlanes(planes, filename = None, depths = None, normals = None, invalidMask = None, outputFolder = None, outputIndex = 0, colorMap = None):
# if filename != None:
# if 'mlt' not in filename:
# filename = filename.replace('color', 'mlt')
# pass
# normalFilename = filename.replace('mlt', 'norm_camera')
# normals = np.array(PIL.Image.open(normalFilename)).astype(np.float) / 255 * 2 - 1
# norm = np.linalg.norm(normals, 2, 2)
# for c in xrange(3):
# normals[:, :, c] /= norm
# continue
# depthFilename = filename.replace('mlt', 'depth')
# depths = np.array(PIL.Image.open(depthFilename)).astype(np.float) / 1000
# # if len(depths.shape) == 3:
# # depths = depths.mean(2)
# # pass
# maskFilename = filename.replace('mlt', 'valid')
# invalidMask = np.array(PIL.Image.open(maskFilename))
# invalidMask = invalidMask < 128
# invalidMask += depths > 10
# pass
# height = normals.shape[0]
# width = normals.shape[1]
# focalLength = 517.97
# urange = np.arange(width).reshape(1, -1).repeat(height, 0) - width * 0.5
# vrange = np.arange(height).reshape(-1, 1).repeat(width, 1) - height * 0.5
# ranges = np.array([urange / focalLength, np.ones(urange.shape), -vrange / focalLength]).transpose([1, 2, 0])
# X = depths / focalLength * urange
# Y = depths
# Z = -depths / focalLength * vrange
# d = -(normals[:, :, 0] * X + normals[:, :, 1] * Y + normals[:, :, 2] * Z)
# normalDotThreshold = np.cos(np.deg2rad(30))
# distanceThreshold = 50000
# reconstructedNormals = np.zeros(normals.shape)
# reconstructedDepths = np.zeros(depths.shape)
# segmentationImage = np.zeros((height, width, 3))
# distanceMap = np.ones((height, width)) * distanceThreshold
# occupancyMask = np.zeros((height, width)).astype(np.bool)
# segmentationTest = np.zeros((height, width))
# y = 297
# x = 540
# for planeIndex, plane in enumerate(planes):
# planeD = np.linalg.norm(plane)
# planeNormal = -plane / planeD
# normalXYZ = np.dot(ranges, planeNormal)
# normalXYZ = np.reciprocal(normalXYZ)
# planeY = -normalXYZ * planeD
# distance = np.abs(planeNormal[0] * X + planeNormal[1] * Y + planeNormal[2] * Z + planeD) / np.abs(np.dot(normals, planeNormal))
# #distance = np.abs(planeY - depths)
# mask = (distance < distanceMap) * (planeY > 0) * (np.abs(np.dot(normals, planeNormal)) > normalDotThreshold) * (np.abs(planeY - depths) < 0.5)
# occupancyMask += mask
# reconstructedNormals[mask] = planeNormal
# #if planeNormal[2] > 0.9:
# #print(planeD)
# #print(planeNormal)
# # minDepth = depths.min()
# # maxDepth = depths.max()
# # print(depths[300][300])
# # print(planeY[300][300])
# # print(depths[350][350])
# # print(planeY[350][350])
# # PIL.Image.fromarray((np.maximum(np.minimum((planeY - minDepth) / (maxDepth - minDepth), 1), 0) * 255).astype(np.uint8)).save(outputFolder + '/plane.png')
# # exit(1)
# #pass
# reconstructedDepths[mask] = planeY[mask]
# if colorMap != None and planeIndex in colorMap:
# segmentationImage[mask] = colorMap[planeIndex]
# else:
# segmentationImage[mask] = np.random.randint(255, size=(3,))
# pass
# distanceMap[mask] = distance[mask]
# segmentationTest[mask] = planeIndex + 1
# #print((planeIndex, planeY[y][x], distance[y][x], np.abs(np.dot(normals, planeNormal))[y][x]))
# continue
# # print(distanceMap.mean())
# # print(distanceMap.max())
# # print(np.abs(reconstructedDepths - depths)[occupancyMask].max())
# # print(pow(reconstructedDepths - depths, 2)[True - invalidMask].mean())
# # exit(1)
# # planeIndex = segmentationTest[y][x]
# # print(normals[y][x])
# # plane = planes[int(planeIndex)]
# # planeD = np.linalg.norm(plane)
# # planeNormal = -plane / planeD
# # print((planeNormal, planeD))
# # print(depths[y][x])
# # print(reconstructedDepths[y][x])
# # print(segmentationTest[y][x])
# if outputFolder != None:
# depths[invalidMask] = 0
# normals[invalidMask] = 0
# reconstructedDepths[invalidMask] = 0
# reconstructedNormals[invalidMask] = 0
# minDepth = depths.min()
# maxDepth = depths.max()
# #print(minDepth)
# #print(maxDepth)
# PIL.Image.fromarray(((depths - minDepth) / (maxDepth - minDepth) * 255).astype(np.uint8)).save(outputFolder + '/' + str(outputIndex) + '_depth.png')
# PIL.Image.fromarray((np.maximum(np.minimum((reconstructedDepths - minDepth) / (maxDepth - minDepth), 1), 0) * 255).astype(np.uint8)).save(outputFolder + '/' + str(outputIndex) + '_depth_reconstructed.png')
# #PIL.Image.fromarray((np.maximum(np.minimum((reconstructedDepths - depths) / (distanceThreshold), 1), 0) * 255).astype(np.uint8)).save(outputFolder + '/depth_' + str(outputIndex) + '_diff.png')
# PIL.Image.fromarray(((normals + 1) / 2 * 255).astype(np.uint8)).save(outputFolder + '/' + str(outputIndex) + '_normal_.png')
# PIL.Image.fromarray(((reconstructedNormals + 1) / 2 * 255).astype(np.uint8)).save(outputFolder + '/' + str(outputIndex) + '_normal_reconstructed.png')
# PIL.Image.fromarray(segmentationImage.astype(np.uint8)).save(outputFolder + '/' + str(outputIndex) + '_plane_segmentation.png')
# #depthImage = ((depths - minDepth) / (maxDepth - minDepth) * 255).astype(np.uint8)
# #PIL.Image.fromarray((invalidMask * 255).astype(np.uint8)).save(outputFolder + '/mask.png')
# #exit(1)
# else:
# occupancy = (occupancyMask > 0.5).astype(np.float32).sum() / (1 - invalidMask).sum()
# invalidMask += np.invert(occupancyMask)
# #PIL.Image.fromarray(invalidMask.astype(np.uint8) * 255).save(outputFolder + '/mask.png')
# reconstructedDepths = np.maximum(np.minimum(reconstructedDepths, 10), 0)
# depthError = pow(reconstructedDepths - depths, 2)[np.invert(invalidMask)].mean()
# #depthError = distanceMap.mean()
# normalError = np.arccos(np.maximum(np.minimum(np.sum(reconstructedNormals * normals, 2), 1), -1))[np.invert(invalidMask)].mean()
# #normalError = pow(np.linalg.norm(reconstructedNormals - normals, 2, 2), 2)[True - invalidMask].mean()
# #print((depthError, normalError, occupancy))
# # print(depths.max())
# # print(depths.min())
# # print(reconstructedDepths.max())
# # print(reconstructedDepths.min())
# # print(occupancy)
# # exit(1)
# #reconstructedDepths[np.invert(occupancyMask)] = depths[np.invert(occupancyMask)]
# return depthError, normalError, occupancy, segmentationTest, reconstructedDepths, occupancyMask
# return
# def evaluatePlanesSeparately(planes, filename, outputFolder = None, outputIndex = 0):
# if 'mlt' not in filename:
# filename = filename.replace('color', 'mlt')
# pass
# colorImage = np.array(PIL.Image.open(filename))
# normalFilename = filename.replace('mlt', 'norm_camera')
# normals = np.array(PIL.Image.open(normalFilename)).astype(np.float) / 255 * 2 - 1
# height = normals.shape[0]
# width = normals.shape[1]
# norm = np.linalg.norm(normals, 2, 2)
# for c in xrange(3):
# normals[:, :, c] /= norm
# continue
# depthFilename = filename.replace('mlt', 'depth')
# depths = np.array(PIL.Image.open(depthFilename)).astype(np.float) / 1000
# # if len(depths.shape) == 3:
# # depths = depths.mean(2)
# # pass
# focalLength = 517.97
# urange = np.arange(width).reshape(1, -1).repeat(height, 0) - width * 0.5
# vrange = np.arange(height).reshape(-1, 1).repeat(width, 1) - height * 0.5
# ranges = np.array([urange / focalLength, np.ones(urange.shape), -vrange / focalLength]).transpose([1, 2, 0])
# X = depths / focalLength * urange
# Y = depths
# Z = -depths / focalLength * vrange
# d = -(normals[:, :, 0] * X + normals[:, :, 1] * Y + normals[:, :, 2] * Z)
# maskFilename = filename.replace('mlt', 'valid')
# invalidMask = np.array(PIL.Image.open(maskFilename))
# # if len(invalidMask.shape) == 3:
# # invalidMask = invalidMask.mean(2)
# # pass
# invalidMask = invalidMask < 128
# invalidMask += depths > 10
# normalDotThreshold = np.cos(np.deg2rad(15))
# distanceThreshold = 0.15
# colorPalette = ColorPalette(len(planes))
# for planeIndex, plane in enumerate(planes):
# planeD = np.linalg.norm(plane)
# planeNormal = -plane / planeD
# distance = np.abs(planeNormal[0] * X + planeNormal[1] * Y + planeNormal[2] * Z + planeD)
# normalXYZ = np.dot(ranges, planeNormal)
# normalXYZ = np.reciprocal(normalXYZ)
# planeY = -normalXYZ * planeD
# mask = (planeY > 0) * (np.abs(np.dot(normals, planeNormal)) > normalDotThreshold) * (distance < distanceThreshold)
# maxDepth = 10
# minDepth = 0
# #PIL.Image.fromarray((np.minimum(np.maximum((planeY - minDepth) / (maxDepth - minDepth), 0), 1) * 255).astype(np.uint8)).save(outputFolder + '/plane_depth_' + str(planeIndex) + '.png')
# #PIL.Image.fromarray(((planeNormal.reshape(1, 1, 3).repeat(height, 0).repeat(width, 1) + 1) / 2 * 255).astype(np.uint8)).save(outputFolder + '/plane_normal_' + str(planeIndex) + '.png')
# planeImage = colorImage * 0.3
# planeImage[mask] += colorPalette.getColor(planeIndex) * 0.7
# PIL.Image.fromarray(planeImage.astype(np.uint8)).save(outputFolder + '/plane_mask_' + str(planeIndex) + '_' + str(outputIndex) + '.png')
# #PIL.Image.fromarray(mask.astype(np.uint8) * 255).save(outputFolder + '/mask_' + str(planeIndex) + '.png')
# continue
# return
def residual2Planes(residualPlanes, predefinedPlanes):
numClusters = predefinedPlanes.shape[0]
planes = []
for residualPlane in residualPlanes:
gridIndex = int(residualPlane[0]) / numClusters
planeIndex = int(residualPlane[0]) % numClusters
planes.append(predefinedPlanes[planeIndex] + residualPlane[1:])
continue
return planes
def residual2PlanesGlobal(residualPlanes, predefinedPlanes):
numClusters = predefinedPlanes.shape[0]
planes = []
for residualPlane in residualPlanes:
planeIndex = int(residualPlane[0])
planes.append(predefinedPlanes[planeIndex] + residualPlane[1:])
continue
return planes
def getPlaneInfo(planes):
imageWidth = 640
imageHeight = 480
focalLength = 517.97
urange = np.arange(imageWidth).reshape(1, -1).repeat(imageHeight, 0) - imageWidth * 0.5
vrange = np.arange(imageHeight).reshape(-1, 1).repeat(imageWidth, 1) - imageHeight * 0.5
ranges = np.array([urange / focalLength, np.ones(urange.shape), -vrange / focalLength]).transpose([1, 2, 0])
planeDepths = PlaneDepthLayer(planes, ranges)
planeNormals = PlaneNormalLayer(planes, ranges)
return planeDepths, planeNormals
def getProbability(image, segmentation):
width = image.shape[1]
height = image.shape[0]
numPlanes = segmentation.shape[0]
probabilities = np.exp(segmentation)
probabilities = probabilities / probabilities.sum(0)
# The input should be the negative of the logarithm of probability values
# Look up the definition of the softmax_to_unary for more information
unary = unary_from_softmax(probabilities)
# The inputs should be C-continious -- we are using Cython wrapper
unary = np.ascontiguousarray(unary)
d = dcrf.DenseCRF(height * width, numPlanes)
d.setUnaryEnergy(unary)
# This potential penalizes small pieces of segmentation that are
# spatially isolated -- enforces more spatially consistent segmentations
# feats = create_pairwise_gaussian(sdims=(10, 10), shape=(height, width))
# d.addPairwiseEnergy(feats, compat=300,
# kernel=dcrf.DIAG_KERNEL,
# normalization=dcrf.NORMALIZE_SYMMETRIC)
# This creates the color-dependent features --
# because the segmentation that we get from CNN are too coarse
# and we can use local color features to refine them
feats = create_pairwise_bilateral(sdims=(50, 50), schan=(20, 20, 20),
img=image, chdim=2)
d.addPairwiseEnergy(feats, compat=10,
kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NORMALIZE_SYMMETRIC)
Q = d.inference(50)
inds = np.argmax(Q, axis=0).reshape((height, width))
probabilities = np.zeros((height * width, numPlanes))
probabilities[np.arange(height * width), inds.reshape(-1)] = 1
probabilities = probabilities.reshape([height, width, -1])
#print(res.shape)
return probabilities
def getProbabilityMax(segmentation):
width = segmentation.shape[2]
height = segmentation.shape[1]
numPlanes = segmentation.shape[0]
inds = np.argmax(segmentation.reshape([-1, height * width]), axis=0)
probabilities = np.zeros((height * width, numPlanes))
probabilities[np.arange(height * width), inds] = 1
probabilities = probabilities.reshape([height, width, -1])
return probabilities
def evaluateNormal(predNormal, gtSegmentations, numPlanes):
numImages = predNormal.shape[0]
normal = np.linalg.norm(predNormal, axis=-1)
errors = []
for imageIndex in xrange(numImages):
var = 0
count = 0
invalidNormalMask = np.linalg.norm(predNormal[imageIndex], axis=-1) > 1 - 1e-4
for planeIndex in xrange(numPlanes[imageIndex]):
#mask = gtSegmentations[imageIndex, :, :, planeIndex].astype(np.bool)
mask = gtSegmentations[imageIndex] == planeIndex
mask = cv2.erode(mask.astype(np.float32), np.ones((5, 5))) > 0.5
mask = np.logical_and(mask, invalidNormalMask)
if mask.sum() == 0:
continue
normals = predNormal[imageIndex][mask]
averageNormal = np.mean(normals, axis=0, keepdims=True)
averageNormal /= np.linalg.norm(averageNormal)
degrees = np.rad2deg(np.arccos(np.minimum(np.sum(normals * averageNormal, axis=-1), 1)))
#degrees = np.rad2deg(np.arccos(np.sum(normals * averageNormal, axis=-1)))
degrees = np.minimum(degrees, 180 - degrees)
var += degrees.sum()
count += degrees.shape[0]
#var += degrees.mean()
#totalNumPlanes += 1
#print(np.sum(normals * averageNormal, axis=-1))
print(planeIndex, degrees.sum() / degrees.shape[0])
cv2.imwrite('test/mask_' + str(planeIndex) + '.png', drawMaskImage(mask))
#print(normals)
# if planeIndex == 4:
# for normal in normals:
# print(normal)
# continue
# print(normals.mean(0))
# exit(1)
# print(degrees)
# print(planeIndex, averageNormal, degrees.mean())
# print(normals[degrees > 90])
continue
var /= count
errors.append(var)
#print(var / totalNumPlanes)
#exit(1)
continue
var = np.array(errors).mean()
print(var)
return var
def evaluateDepths(predDepths, gtDepths, validMasks, planeMasks=True, printInfo=True):
masks = np.logical_and(np.logical_and(validMasks, planeMasks), gtDepths > 1e-4)
numPixels = float(masks.sum())
rmse = np.sqrt((pow(predDepths - gtDepths, 2) * masks).sum() / numPixels)
rmse_log = np.sqrt((pow(np.log(predDepths) - np.log(gtDepths), 2) * masks).sum() / numPixels)
log10 = (np.abs(np.log10(np.maximum(predDepths, 1e-4)) - np.log10(np.maximum(gtDepths, 1e-4))) * masks).sum() / numPixels
rel = (np.abs(predDepths - gtDepths) / np.maximum(gtDepths, 1e-4) * masks).sum() / numPixels
rel_sqr = (pow(predDepths - gtDepths, 2) / np.maximum(gtDepths, 1e-4) * masks).sum() / numPixels
deltas = np.maximum(predDepths / np.maximum(gtDepths, 1e-4), gtDepths / np.maximum(predDepths, 1e-4)) + (1 - masks.astype(np.float32)) * 10000
accuracy_1 = (deltas < 1.25).sum() / numPixels
accuracy_2 = (deltas < pow(1.25, 2)).sum() / numPixels
accuracy_3 = (deltas < pow(1.25, 3)).sum() / numPixels
recall = float(masks.sum()) / validMasks.sum()
#print((rms, recall))
if printInfo:
print(('evaluate', rel, rel_sqr, log10, rmse, rmse_log, accuracy_1, accuracy_2, accuracy_3, recall))
pass
return rel, rel_sqr, log10, rmse, rmse_log, accuracy_1, accuracy_2, accuracy_3, recall
#return rel, log10, rms, accuracy_1, accuracy_2, accuracy_3, recall
def drawDepthImage(depth):
#return cv2.applyColorMap(np.clip(depth / 10 * 255, 0, 255).astype(np.uint8), cv2.COLORMAP_JET)
return 255 - np.clip(depth / 5 * 255, 0, 255).astype(np.uint8)
def drawDepthImageOverlay(image, depth):
#return cv2.applyColorMap(np.clip(depth / 10 * 255, 0, 255).astype(np.uint8), cv2.COLORMAP_JET)
depth = np.clip(depth / min(np.max(depth), 10) * 255, 0, 255).astype(np.uint8)
imageOverlay = np.stack([image[:, :, 1], depth, image[:, :, 2]], axis=2)
return imageOverlay
def drawNormalImage(normal):
return ((normal + 1) / 2 * 255).astype(np.uint8)
def drawSegmentationImage(segmentations, randomColor=None, numColors=22, blackIndex=-1):
if segmentations.ndim == 2:
numColors = max(numColors, segmentations.max() + 2, blackIndex + 1)
else:
numColors = max(numColors, segmentations.shape[2] + 2, blackIndex + 1)
pass
randomColor = ColorPalette(numColors).getColorMap()
if blackIndex >= 0:
randomColor[blackIndex] = 0
pass
width = segmentations.shape[1]
height = segmentations.shape[0]
if segmentations.ndim == 3:
#segmentation = (np.argmax(segmentations, 2) + 1) * (np.max(segmentations, 2) > 0.5)
segmentation = np.argmax(segmentations, 2)
else:
segmentation = segmentations
pass
segmentation = segmentation.astype(np.int)
return randomColor[segmentation.reshape(-1)].reshape((height, width, 3))
def drawMaskImage(mask):
return (np.clip(mask * 255, 0, 255)).astype(np.uint8)
def drawDiffImage(values_1, values_2, threshold):
#return cv2.applyColorMap(np.clip(np.abs(values_1 - values_2) / threshold * 255, 0, 255).astype(np.uint8), cv2.COLORMAP_JET)
return np.clip(np.abs(values_1 - values_2) / threshold * 255, 0, 255).astype(np.uint8)
def getSuperpixels(depth, normal, width, height, numPlanes=50, numGlobalPlanes = 10):
depth = np.expand_dims(depth, -1)
urange = (np.arange(width, dtype=np.float32) / (width + 1) - 0.5) / focalLength * 641
urange = np.tile(np.reshape(urange, [1, -1]), [height, 1])
vrange = (np.arange(height, dtype=np.float32) / (height + 1) - 0.5) / focalLength * 481
vrange = np.tile(np.reshape(vrange, [-1, 1]), [1, width])
ranges = np.stack([urange, np.ones([height, width]), -vrange], axis=2)
#ranges = np.expand_dims(ranges, 0)
planeImage = np.sum(normal * ranges, axis=2, keepdims=True) * depth * normal
planeImage = planeImage / 10 * 1000
superpixels = segmentation.slic(planeImage, compactness=30, n_segments=400)
g = graph.rag_mean_color(planeImage, superpixels, mode='similarity')
planeSegmentation = graph.cut_normalized(superpixels, g)
return planeSegmentation, superpixels
def calcPlaneDepths(planes, width, height, info):
urange = np.arange(width, dtype=np.float32).reshape(1, -1).repeat(height, 0) / (width + 1) * (info[16] + 1) - info[2]
vrange = np.arange(height, dtype=np.float32).reshape(-1, 1).repeat(width, 1) / (height + 1) * (info[17] + 1) - info[6]
ranges = np.array([urange / info[0], np.ones(urange.shape), -vrange / info[5]]).transpose([1, 2, 0])
planeDepths = PlaneDepthLayer(planes, ranges)
return planeDepths
def calcPlaneNormals(planes, width, height):
planeNormals = -planes / np.maximum(np.linalg.norm(planes, axis=-1, keepdims=True), 1e-4)
return np.tile(planeNormals.reshape([1, 1, -1, 3]), [height, width, 1, 1])
def writePLYFile(folder, index, image, depth, segmentation, planes, info):
imageFilename = str(index) + '_model_texture.png'
cv2.imwrite(folder + '/' + imageFilename, image)
width = image.shape[1]
height = image.shape[0]
numPlanes = planes.shape[0]
camera = getCameraFromInfo(info)
#camera = getNYURGBDCamera()
#camera = getSUNCGCamera()
urange = (np.arange(width, dtype=np.float32) / width * camera['width'] - camera['cx']) / camera['fx']
urange = urange.reshape(1, -1).repeat(height, 0)
vrange = (np.arange(height, dtype=np.float32) / height * camera['height'] - camera['cy']) / camera['fy']
vrange = vrange.reshape(-1, 1).repeat(width, 1)
X = depth * urange
Y = depth
Z = -depth * vrange
XYZ = np.stack([X, Y, Z], axis=2)
#focalLength = 517.97
faces = []
#minDepthDiff = 0.15
#maxDepthDiff = 0.3
#occlusionBoundary = boundaries[:, :, 1]
betweenRegionThreshold = 0.1
nonPlanarRegionThreshold = 0.02
planesD = np.linalg.norm(planes, axis=1, keepdims=True)
planeNormals = -planes / np.maximum(planesD, 1e-4)
croppingRatio = -0.05
dotThreshold = np.cos(np.deg2rad(30))
for y in xrange(height - 1):
for x in xrange(width - 1):
if y < height * croppingRatio or y > height * (1 - croppingRatio) or x < width * croppingRatio or x > width * (1 - croppingRatio):
continue
segmentIndex = segmentation[y][x]
if segmentIndex == -1:
continue
point = XYZ[y][x]
#neighborPixels = []
validNeighborPixels = []
for neighborPixel in [(x, y + 1), (x + 1, y), (x + 1, y + 1)]:
neighborSegmentIndex = segmentation[neighborPixel[1]][neighborPixel[0]]
if neighborSegmentIndex == segmentIndex:
if segmentIndex < numPlanes:
validNeighborPixels.append(neighborPixel)
else:
neighborPoint = XYZ[neighborPixel[1]][neighborPixel[0]]
if np.linalg.norm(neighborPoint - point) < nonPlanarRegionThreshold:
validNeighborPixels.append(neighborPixel)
pass
pass
else:
neighborPoint = XYZ[neighborPixel[1]][neighborPixel[0]]
if segmentIndex < numPlanes and neighborSegmentIndex < numPlanes:
if (abs(np.dot(planeNormals[segmentIndex], neighborPoint) + planesD[segmentIndex]) < betweenRegionThreshold or abs(np.dot(planeNormals[neighborSegmentIndex], point) + planesD[neighborSegmentIndex]) < betweenRegionThreshold) and np.abs(np.dot(planeNormals[segmentIndex], planeNormals[neighborSegmentIndex])) < dotThreshold:
validNeighborPixels.append(neighborPixel)
pass
else:
if np.linalg.norm(neighborPoint - point) < betweenRegionThreshold:
validNeighborPixels.append(neighborPixel)
pass
pass
pass
continue
if len(validNeighborPixels) == 3:
faces.append((x, y, x + 1, y + 1, x + 1, y))
faces.append((x, y, x, y + 1, x + 1, y + 1))
elif len(validNeighborPixels) == 2 and segmentIndex < numPlanes:
faces.append((x, y, validNeighborPixels[0][0], validNeighborPixels[0][1], validNeighborPixels[1][0], validNeighborPixels[1][1]))
pass
continue
continue
with open(folder + '/' + str(index) + '_model.ply', 'w') as f:
header = """ply
format ascii 1.0
comment VCGLIB generated
comment TextureFile """
header += imageFilename
header += """
element vertex """
header += str(width * height)
header += """
property float x
property float y
property float z
element face """
header += str(len(faces))
header += """
property list uchar int vertex_indices
property list uchar float texcoord
end_header
"""
f.write(header)
for y in xrange(height):
for x in xrange(width):
segmentIndex = segmentation[y][x]
if segmentIndex == -1:
f.write("0.0 0.0 0.0\n")
continue
point = XYZ[y][x]
X = point[0]
Y = point[1]
Z = point[2]
#Y = depth[y][x]
#X = Y / focalLength * (x - width / 2) / width * 640
#Z = -Y / focalLength * (y - height / 2) / height * 480
f.write(str(X) + ' ' + str(Z) + ' ' + str(-Y) + '\n')
continue
continue
for face in faces:
f.write('3 ')
for c in xrange(3):
f.write(str(face[c * 2 + 1] * width + face[c * 2]) + ' ')
continue
f.write('6 ')
for c in xrange(3):
f.write(str(float(face[c * 2]) / width) + ' ' + str(1 - float(face[c * 2 + 1]) / height) + ' ')
continue
f.write('\n')
continue
f.close()
pass
return
def writePLYFileDepth(folder, index, image, depth, segmentation, planes, info):
imageFilename = str(index) + '_segmentation_pred_blended_0.png'
#cv2.imwrite(folder + '/' + imageFilename, image)
numPlanes = planes.shape[0]
camera = getCameraFromInfo(info)
#camera = getNYURGBDCamera()
#camera = getSUNCGCamera()
urange = (np.arange(width, dtype=np.float32) / width * camera['width'] - camera['cx']) / camera['fx']
urange = urange.reshape(1, -1).repeat(height, 0)
vrange = (np.arange(height, dtype=np.float32) / height * camera['height'] - camera['cy']) / camera['fy']
vrange = vrange.reshape(-1, 1).repeat(width, 1)
X = depth * urange
Y = depth
Z = -depth * vrange
XYZ = np.stack([X, Y, Z], axis=2).reshape(-1, 3)
#focalLength = 517.97
width = image.shape[1]
height = image.shape[0]
faces = []
minDepthDiff = 0.15
maxDepthDiff = 0.3
#occlusionBoundary = boundaries[:, :, 1]
for y in xrange(height - 1):
for x in xrange(width - 1):
segmentIndex = segmentation[y][x]
if segmentIndex == -1:
continue
#if segmentIndex == 0:
#continue
#depths = [depth[y][x], depth[y + 1][x], depth[y + 1][x + 1]]
# if segmentIndex >= 0 or (max([occlusionBoundary[y][x], occlusionBoundary[y + 1][x], occlusionBoundary[y + 1][x + 1]]) < 0.5 and (max(depths) - min(depths)) < maxDepthDiff):
# if segmentation[y + 1][x] == segmentIndex and segmentation[y + 1][x + 1] == segmentIndex:
# if min(depths) > 0 and max(depths) < 10:
# faces.append((x, y, x, y + 1, x + 1, y + 1))
# pass
# pass
# elif max(depths) - min(depths) < minDepthDiff:
# faces.append((x, y, x, y + 1, x + 1, y + 1))
# pass
# pass
if segmentation[y + 1][x] == segmentIndex and segmentation[y + 1][x + 1] == segmentIndex and min(depths) > 0 and max(depths) < 10:
faces.append((x, y, x, y + 1, x + 1, y + 1))
pass
depths = [depth[y][x], depth[y][x + 1], depth[y + 1][x + 1]]
# if segmentIndex > 0 or (max([occlusionBoundary[y][x], occlusionBoundary[y][x + 1], occlusionBoundary[y + 1][x + 1]]) < 0.5 and (max(depths) - min(depths)) < maxDepthDiff):
# if segmentation[y][x + 1] == segmentIndex and segmentation[y + 1][x + 1] == segmentIndex:
# if min(depths) > 0 and max(depths) < 10:
# faces.append((x, y, x + 1, y + 1, x + 1, y))
# pass
# pass
# elif max(depths) - min(depths) < minDepthDiff:
# faces.append((x, y, x + 1, y + 1, x + 1, y))
# pass
# pass
if segmentation[y][x + 1] == segmentIndex and segmentation[y + 1][x + 1] == segmentIndex and min(depths) > 0 and max(depths) < 10:
faces.append((x, y, x + 1, y + 1, x + 1, y))
pass
continue
continue
#print(len(faces))
print(folder)
with open(folder + '/' + str(index) + '_model.ply', 'w') as f:
header = """ply
format ascii 1.0
comment VCGLIB generated
comment TextureFile """
header += imageFilename
header += """
element vertex """
header += str(width * height)
header += """
property float x
property float y
property float z
element face """
header += str(len(faces))
header += """
property list uchar int vertex_indices
property list uchar float texcoord
end_header
"""
f.write(header)
for y in xrange(height):
for x in xrange(width):
segmentIndex = segmentation[y][x]
if segmentIndex == -1:
f.write("0.0 0.0 0.0\n")
continue
Y = depth[y][x]
X = Y / focalLength * (x - width / 2) / width * 640
Z = -Y / focalLength * (y - height / 2) / height * 480
f.write(str(X) + ' ' + str(Z) + ' ' + str(-Y) + '\n')
continue
continue
for face in faces:
f.write('3 ')
for c in xrange(3):
f.write(str(face[c * 2 + 1] * width + face[c * 2]) + ' ')
continue
f.write('6 ')
for c in xrange(3):
f.write(str(float(face[c * 2]) / width) + ' ' + str(1 - float(face[c * 2 + 1]) / height) + ' ')
continue
f.write('\n')
continue
f.close()
pass
return
def writeHTML(folder, numImages):
from html import HTML
h = HTML('html')
h.p('Results')
h.br()
#suffixes = ['', '_crf_1']
#folders = ['test_all_resnet_v2' + suffix + '/' for suffix in suffixes]
for index in xrange(numImages):
t = h.table(border='1')
r_inp = t.tr()
r_inp.td('input')
r_inp.td().img(src=str(index) + '_image.png')
r_inp.td().img(src='one.png')
r_inp.td().img(src='one.png')
#r_inp.td().img(src='one.png')
r_inp.td().img(src=str(index) + '_model.png')