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diffrender_analyze.py
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diffrender_analyze.py
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__author__ = 'pol'
import matplotlib
matplotlib.use('Qt4Agg')
import scene_io_utils
from math import radians
import timeit
import time
import opendr
import chumpy as ch
import geometry
import image_processing
import numpy as np
import cv2
import generative_models
import recognition_models
import matplotlib.pyplot as plt
from opendr_utils import *
from utils import *
import OpenGL.GL as GL
import glfw
useShapeModel = True
#########################################
# OpenDR Initialization starts here
#########################################
#Main script options:
glModes = ['glfw','mesa']
glMode = glModes[0]
width, height = (150, 150)
win = -1
if glMode == 'glfw':
#Initialize base GLFW context for the Demo and to share context among all renderers.
glfw.init()
glfw.window_hint(glfw.CONTEXT_VERSION_MAJOR, 3)
glfw.window_hint(glfw.CONTEXT_VERSION_MINOR, 3)
# glfw.window_hint(glfw.OPENGL_FORWARD_COMPAT, GL.GL_TRUE)
glfw.window_hint(glfw.OPENGL_PROFILE, glfw.OPENGL_CORE_PROFILE)
glfw.window_hint(glfw.DEPTH_BITS,32)
glfw.window_hint(glfw.VISIBLE, GL.GL_FALSE)
win = glfw.create_window(width, height, "Demo", None, None)
glfw.make_context_current(win)
angle = 60 * 180 / np.pi
clip_start = 0.05
clip_end = 10
frustum = {'near': clip_start, 'far': clip_end, 'width': width, 'height': height}
camDistance = 0.4
teapots = [line.strip() for line in open('teapots.txt')]
# renderTeapotsList = np.arange(len(teapots))
renderTeapotsList = np.arange(len(teapots))[0:1]
targetModels = []
v_teapots, f_list_teapots, vc_teapots, vn_teapots, uv_teapots, haveTextures_list_teapots, textures_list_teapots, vflat, varray, center_teapots = scene_io_utils.loadTeapotsOpenDRData(renderTeapotsList, False, False, targetModels)
azimuth = np.pi
chCosAz = ch.Ch([np.cos(azimuth)])
chSinAz = ch.Ch([np.sin(azimuth)])
chAz = 2*ch.arctan(chSinAz/(ch.sqrt(chCosAz**2 + chSinAz**2) + chCosAz))
chAz = ch.Ch([0])
chObjAz = ch.Ch([0])
chAzRel = chAz - chObjAz
elevation = 0
chLogCosEl = ch.Ch(np.log(np.cos(elevation)))
chLogSinEl = ch.Ch(np.log(np.sin(elevation)))
chEl = 2*ch.arctan(ch.exp(chLogSinEl)/(ch.sqrt(ch.exp(chLogCosEl)**2 + ch.exp(chLogSinEl)**2) + ch.exp(chLogCosEl)))
chEl = ch.Ch([0.95993109])
chDist = ch.Ch([camDistance])
chLightSHCoeffs = ch.Ch(np.array([2, 0.25, 0.25, 0.12,-0.17,0.36,0.1,0.,0.]))
clampedCosCoeffs = clampedCosineCoefficients()
chComponent = chLightSHCoeffs * clampedCosCoeffs
chPointLightIntensity = ch.Ch([1])
chLightAz = ch.Ch([0.0])
chLightEl = ch.Ch([np.pi/2])
chLightDist = ch.Ch([0.5])
light_color = ch.ones(3)*chPointLightIntensity
chVColors = ch.Ch([0.4,0.4,0.4])
chDisplacement = ch.Ch([0.0, 0.0,0.0])
chScale = ch.Ch([1.0,1.0,1.0])
# vcch[0] = np.ones_like(vcflat[0])*chVColorsGT.reshape([1,3])
renderer_teapots = []
for teapot_i in range(len(renderTeapotsList)):
vmod = v_teapots[teapot_i]
fmod_list = f_list_teapots[teapot_i]
vcmod = vc_teapots[teapot_i]
vnmod = vn_teapots[teapot_i]
uvmod = uv_teapots[teapot_i]
haveTexturesmod_list = haveTextures_list_teapots[teapot_i]
texturesmod_list = textures_list_teapots[teapot_i]
centermod = center_teapots[teapot_i]
vmod, vnmod, _ = transformObject(vmod, vnmod, chScale, chObjAz, ch.Ch([0]), ch.Ch([0]), np.array([0, 0, 0]))
renderer = createRendererTarget(glMode, chAz, chEl, chDist, centermod, vmod, vcmod, fmod_list, vnmod, light_color, chComponent, chVColors, 0, chDisplacement, width,height, uvmod, haveTexturesmod_list, texturesmod_list, frustum, win )
renderer.overdraw = True
renderer.nsamples = 8
renderer.msaa = False
renderer.initGL()
renderer.initGLTexture()
# renderer.initGL_AnalyticRenderer()
renderer.imageGT = None
renderer.r
renderer_teapots = renderer_teapots + [renderer]
currentTeapotModel = 0
center = center_teapots[currentTeapotModel]
if useShapeModel:
import shape_model
#%% Load data
filePath = 'data/teapotModel.pkl'
teapotModel = shape_model.loadObject(filePath)
faces = teapotModel['faces']
#%% Sample random shape Params
latentDim = np.shape(teapotModel['ppcaW'])[1]
shapeParams = np.random.randn(latentDim)
chShapeParams = ch.Ch(shapeParams)
meshLinearTransform=teapotModel['meshLinearTransform']
W=teapotModel['ppcaW']
b=teapotModel['ppcaB']
chVertices = shape_model.VerticesModel(chShapeParams=chShapeParams,meshLinearTransform=meshLinearTransform,W = W,b=b)
chVertices.init()
chVertices = ch.dot(geometry.RotateZ(-np.pi/2)[0:3,0:3],chVertices.T).T
chNormals = shape_model.chGetNormals(chVertices, faces)
smNormals = [chNormals]
smFaces = [[faces]]
smVColors = [chVColors*np.ones(chVertices.shape)]
smUVs = [ch.Ch(np.zeros([chVertices.shape[0],2]))]
smHaveTextures = [[False]]
smTexturesList = [[None]]
chVertices = chVertices - ch.mean(chVertices, axis=0)
minZ = ch.min(chVertices[:,2])
chMinZ = ch.min(chVertices[:,2])
zeroZVerts = chVertices[:,2]- chMinZ
chVertices = ch.hstack([chVertices[:,0:2] , zeroZVerts.reshape([-1,1])])
chVertices = chVertices*0.09
smCenter = ch.array([0,0,0.1])
smVertices = [chVertices]
chNormals = shape_model.chGetNormals(chVertices, faces)
smNormals = [chNormals]
smVertices, smNormals, _ = transformObject(smVertices, smNormals, chScale, chObjAz, ch.Ch([0]), ch.Ch([0]), np.array([0, 0, 0]))
renderer = createRendererTarget(glMode, chAz, chEl, chDist, smCenter, [smVertices], [smVColors], [smFaces], [smNormals], light_color, chComponent, chVColors, 0, chDisplacement, width,height, [smUVs], [smHaveTextures], [smTexturesList], frustum, win )
renderer.overdraw = True
renderer.nsamples = 8
renderer.msaa = False
renderer.initGL()
renderer.initGLTexture()
# renderer.initGL_AnalyticRenderer()
renderer.imageGT = None
renderer.r
chShapeParams[:] = np.zeros([latentDim])
chVerticesMean = chVertices.r.copy()
else:
renderer = renderer_teapots[0]
##GT Renderer
chObjAzGT = ch.Ch([0])
chAzGT = ch.Ch([0])
chAzRelGT = chAzGT - chObjAzGT
chElGT = ch.Ch(chEl.r[0])
chDistGT = ch.Ch([camDistance])
phiOffset = ch.Ch([0])
totalOffset = phiOffset + chObjAzGT
chVColorsGT = ch.Ch([0.8,0.8,0.8])
chAmbientIntensityGT = ch.Ch([0.025])
clampedCosCoeffs = clampedCosineCoefficients()
SHFilename = 'data/LightSHCoefficients.pickle'
with open(SHFilename, 'rb') as pfile:
envMapDic = pickle.load(pfile)
hdritems = list(envMapDic.items())
envMapCoeffs = ch.Ch(list(envMapDic.items())[0][1][1])
envMapCoeffsRotated = ch.Ch(np.dot(light_probes.chSphericalHarmonicsZRotation(totalOffset), envMapCoeffs[[0,3,2,1,4,5,6,7,8]])[[0,3,2,1,4,5,6,7,8]])
envMapCoeffsRotatedRel = ch.Ch(np.dot(light_probes.chSphericalHarmonicsZRotation(phiOffset), envMapCoeffs[[0,3,2,1,4,5,6,7,8]])[[0,3,2,1,4,5,6,7,8]])
shCoeffsRGB = envMapCoeffsRotated
shCoeffsRGBRel = envMapCoeffsRotatedRel
chShCoeffs = 0.3*shCoeffsRGB[:,0] + 0.59*shCoeffsRGB[:,1] + 0.11*shCoeffsRGB[:,2]
chShCoeffsRel = 0.3*shCoeffsRGBRel[:,0] + 0.59*shCoeffsRGBRel[:,1] + 0.11*shCoeffsRGBRel[:,2]
chAmbientSHGT = chShCoeffs.ravel() * chAmbientIntensityGT * clampedCosCoeffs
chAmbientSHGTRel = chShCoeffsRel.ravel() * chAmbientIntensityGT * clampedCosCoeffs
chComponentGT = chAmbientSHGT
chComponentGTRel = chAmbientSHGTRel
# shapeParams = np.random.randn(latentDim)
if useShapeModel:
shapeParams = np.random.randn(latentDim)
chShapeParamsGT = ch.Ch(shapeParams)
chVerticesGT = shape_model.VerticesModel(chShapeParams =chShapeParamsGT,meshLinearTransform=meshLinearTransform,W=W,b=b)
chVerticesGT.init()
chVerticesGT = ch.dot(geometry.RotateZ(-np.pi/2)[0:3,0:3],chVerticesGT.T).T
# chNormalsGT = shape_model.chShapeParamsToNormals(teapotModel['N'], landmarks, teapotModel['linT'])
# chNormalsGT = shape_model.shapeParamsToNormals(shapeParams, teapotModel)
chNormalsGT = shape_model.chGetNormals(chVerticesGT, faces)
smNormalsGT = [chNormalsGT]
smFacesGT = [[faces]]
smVColorsGT = [chVColorsGT*np.ones(chVerticesGT.shape)]
smUVsGT = [ch.Ch(np.zeros([chVerticesGT.shape[0],2]))]
smHaveTexturesGT = [[False]]
smTexturesListGT = [[None]]
smCenterGT = ch.mean(chVerticesGT, axis=0)
chVerticesGT = chVerticesGT - ch.mean(chVerticesGT, axis=0)
minZ = ch.min(chVerticesGT[:,2])
chMinZ = ch.min(chVerticesGT[:,2])
zeroZVerts = chVerticesGT[:,2]- chMinZ
chVerticesGT = ch.hstack([chVerticesGT[:,0:2] , zeroZVerts.reshape([-1,1])])
chVerticesGT = chVerticesGT*0.09
smCenterGT = ch.array([0,0,0.1])
smVerticesGT = [chVerticesGT]
#########################################
# Initialization ends here
#########################################
#########################################
# Generative model set up
#########################################
rendererGT = ch.Ch(renderer.r.copy())
numPixels = width*height
E_raw = renderer - rendererGT
SE_raw = ch.sum(E_raw*E_raw, axis=2)
SSqE_raw = ch.SumOfSquares(E_raw)/numPixels
initialPixelStdev = 0.01
reduceVariance = False
# finalPixelStdev = 0.05
stds = ch.Ch([initialPixelStdev])
variances = stds ** 2
globalPrior = ch.Ch([0.9])
negLikModel = -ch.sum(generative_models.LogGaussianModel(renderer=renderer, groundtruth=rendererGT, variances=variances))
negLikModelRobust = -ch.sum(generative_models.LogRobustModel(renderer=renderer, groundtruth=rendererGT, foregroundPrior=globalPrior, variances=variances))
pixelLikelihoodCh = generative_models.LogGaussianModel(renderer=renderer, groundtruth=rendererGT, variances=variances)
pixelLikelihoodRobustCh = generative_models.LogRobustModel(renderer=renderer, groundtruth=rendererGT, foregroundPrior=globalPrior, variances=variances)
# negLikModel = -generative_models.modelLogLikelihoodCh(rendererGT, renderer, np.array([]), 'FULL', variances)/numPixels
# negLikModelRobust = -generative_models.modelLogLikelihoodRobustCh(rendererGT, renderer, np.array([]), 'FULL', globalPrior, variances)/numPixels
# pixelLikelihoodCh = generative_models.logPixelLikelihoodCh(rendererGT, renderer, np.array([]), 'FULL', variances)
# pixelLikelihoodRobustCh = ch.log(generative_models.pixelLikelihoodRobustCh(rendererGT, renderer, np.array([]), 'FULL', globalPrior, variances))
post = generative_models.layerPosteriorsRobustCh(rendererGT, renderer, np.array([]), 'MASK', globalPrior, variances)[0]
# modelLogLikelihoodRobustRegionCh = -ch.sum(generative_models.LogRobustModelRegion(renderer=renderer, groundtruth=rendererGT, foregroundPrior=globalPrior, variances=variances))/numPixels
#
# pixelLikelihoodRobustRegionCh = generative_models.LogRobustModelRegion(renderer=renderer, groundtruth=rendererGT, foregroundPrior=globalPrior, variances=variances)
# models = [negLikModel, negLikModelRobust, hogError]
models = [negLikModel, negLikModelRobust]
pixelModels = [pixelLikelihoodCh, pixelLikelihoodRobustCh]
modelsDescr = ["Gaussian Model", "Outlier model" ]
model = 1
pixelErrorFun = pixelModels[model]
errorFun = models[model]
############
# Experiments
############
seed = 1
np.random.seed(seed)
gtPrefix = 'train4_occlusion_shapemodel_photorealistic_10K_test100-1100'
# gtPrefix = 'train4_occlusion_shapemodel'
gtDir = 'groundtruth/' + gtPrefix + '/'
featuresDir = gtDir
experimentPrefix = 'train4_occlusion_shapemodel_10k'
experimentDir = 'experiments/' + experimentPrefix + '/'
ignore = []
if os.path.isfile(gtDir + 'ignore.npy'):
ignore = np.load(gtDir + 'ignore.npy')
groundTruthFilename = gtDir + 'groundTruth.h5'
gtDataFile = h5py.File(groundTruthFilename, 'r')
rangeTests = np.arange(100,1100)
idsInRange = np.arange(len(rangeTests))
testSet = np.load(experimentDir + 'test.npy')[rangeTests][idsInRange]
shapeGT = gtDataFile[gtPrefix].shape
boolTestSet = np.array([np.any(num == testSet) for num in gtDataFile[gtPrefix]['trainIds']])
dataIds = gtDataFile[gtPrefix][boolTestSet]['trainIds']
dataIdsTestIndices = np.array([np.where(dataIds==num)[0][0] for num in testSet])
groundTruth = gtDataFile[gtPrefix][boolTestSet][dataIdsTestIndices]
dataTeapotIdsTest = groundTruth['trainTeapotIds']
test = np.arange(len(testSet))
testSet = testSet[test]
print("Reading experiment.")
dataAzsGT = groundTruth['trainAzsGT']
dataObjAzsGT = groundTruth['trainObjAzsGT']
dataElevsGT = groundTruth['trainElevsGT']
dataLightAzsGT = groundTruth['trainLightAzsGT']
dataLightElevsGT = groundTruth['trainLightElevsGT']
dataLightIntensitiesGT = groundTruth['trainLightIntensitiesGT']
dataVColorGT = groundTruth['trainVColorGT']
dataScenes = groundTruth['trainScenes']
dataTeapotIds = groundTruth['trainTeapotIds']
dataEnvMaps = groundTruth['trainEnvMaps']
dataOcclusions = groundTruth['trainOcclusions']
dataTargetIndices = groundTruth['trainTargetIndices']
dataLightCoefficientsGT = groundTruth['trainLightCoefficientsGT']
dataLightCoefficientsGTRel = groundTruth['trainLightCoefficientsGTRel']
dataEnvMapPhiOffsets = groundTruth['trainEnvMapPhiOffsets']
dataAmbientIntensityGT = groundTruth['trainAmbientIntensityGT']
dataIds = groundTruth['trainIds']
gtDtype = groundTruth.dtype
testSetFixed = testSet
whereBad = []
for test_it, test_id in enumerate(testSet):
if test_id in ignore:
bad = np.where(testSetFixed==test_id)
testSetFixed = np.delete(testSetFixed, bad)
whereBad = whereBad + [bad]
# testSet = testSetFixed
loadFromHdf5 = False
useShapeModel = True
syntheticGroundtruth = False
synthPrefix = '_cycles'
if syntheticGroundtruth:
synthPrefix = ''
if syntheticGroundtruth:
imagesDir = gtDir + 'images_opendr/'
else:
imagesDir = gtDir + 'images/'
images = readImages(imagesDir, dataIds, loadFromHdf5)
# testSet = np.arange(len(images))[0:10]
testAzsGT = dataAzsGT
testObjAzsGT = dataObjAzsGT
testElevsGT = dataElevsGT
testLightAzsGT = dataLightAzsGT
testLightElevsGT = dataLightElevsGT
testLightIntensitiesGT = dataLightIntensitiesGT
testVColorGT = dataVColorGT
testOcclusions = dataOcclusions
if useShapeModel:
dataShapeModelCoeffsGT = groundTruth['trainShapeModelCoeffsGT']
testShapeParamsGT = dataShapeModelCoeffsGT
testLightCoefficientsGTRel = dataLightCoefficientsGTRel * dataAmbientIntensityGT[:,None]
testAzsRel = np.mod(testAzsGT - testObjAzsGT, 2*np.pi)
##### Load Results data:
# testPrefix = 'train4_occlusion_shapemodel_10k_ECCVNEW-JOINED-ALL2018'
testPrefix = 'train4_occlusion_shapemodel_10k_ECCV-PHOTOREALISTIC-JOINT2018'
resultDir = 'results/' + testPrefix + '/'
with open(resultDir + 'experiment.pickle', 'rb') as pfile:
experimentDic = pickle.load(pfile)
testSet = experimentDic['testSet']
methodsPred = experimentDic['methodsPred']
testOcclusions = experimentDic['testOcclusions']
# testOcclusions = testOcclusions[dataIdsTestIndices] #Change depending on the source of GT.
# import ipdb; ipdb.set_trace()
testPrefixBase = experimentDic[ 'testPrefixBase']
parameterRecognitionModels = experimentDic[ 'parameterRecognitionModels']
azimuths = experimentDic[ 'azimuths']
azimuths = [azimuths[method][dataIdsTestIndices] if azimuths[method] is not None else None for method in range(len(azimuths))]
elevations = experimentDic[ 'elevations']
elevations = [elevations[method][dataIdsTestIndices] if elevations[method] is not None else None for method in range(len(elevations))]
vColors = experimentDic[ 'vColors']
vColors = [vColors[method][dataIdsTestIndices] if vColors[method] is not None else None for method in range(len(vColors))]
lightCoeffs = experimentDic[ 'lightCoeffs']
lightCoeffs = [lightCoeffs[method][dataIdsTestIndices] if lightCoeffs[method] is not None else None for method in range(len(lightCoeffs))]
likelihoods = []
# likelihoods = experimentDic['likelihoods']
# likelihoods = [likelihoods[method][dataIdsTestIndices] if likelihoods[method] is not None else None for method in range(len(likelihoods))]
shapeParams = experimentDic['shapeParams']
shapeParams = [shapeParams[method][dataIdsTestIndices] if shapeParams[method] is not None else None for method in range(len(shapeParams))]
if os.path.isfile(resultDir + 'segmentations.pickle'):
with open(resultDir + 'segmentations.pickle', 'rb') as pfile:
segmentationsDic = pickle.load(pfile)
segmentations = segmentationsDic['segmentations']
segmentations = [segmentations[method][dataIdsTestIndices] if segmentations[method] is not None else None for method in range(len(segmentations))]
else:
segmentations = [None]*len(methodsPred)
testOcclusionsFull = testOcclusions.copy()
loadMask = True
if loadMask:
masksGT = loadMasks(gtDir + '/masks_occlusion/', dataIds)
#
# with open(resultDir + 'approxProjections.pickle', 'rb') as pfile:
# approxProjectionsDic = pickle.load(pfile)
#
# approxProjections = approxProjectionsDic['approxProjections']
# approxProjectionsGT = approxProjectionsDic['approxProjectionsGT']
#
# envMapTexture = np.zeros([180,360,3])
# approxProjections = []
# for method in range(len(methodsPred)):
# print("Approx projection on method " + str(method))
# approxProjectionsFittedList = []
# for test_i in range(len(testSet)):
# pEnvMap = SHProjection(envMapTexture, np.concatenate([lightCoeffs[4][test_i][:,None], lightCoeffs[4][test_i][:,None], lightCoeffs[4][test_i][:,None]], axis=1))
# approxProjection = np.sum(pEnvMap, axis=(2,3))
# approxProjectionsFittedList = approxProjectionsFittedList + [approxProjection[None,:]]
# approxProjections = approxProjections + [np.vstack(approxProjectionsFittedList)]
#
#
# approxProjectionsGTList = []
# for test_i in range(len(testSet)):
# pEnvMap = SHProjection(envMapTexture, np.concatenate([testLightCoefficientsGTRel[test_i][:,None], testLightCoefficientsGTRel[test_i][:,None], testLightCoefficientsGTRel[test_i][:,None]], axis=1))
# approxProjectionGT = np.sum(pEnvMap, axis=(2,3))
# approxProjectionsGTList = approxProjectionsGTList + [approxProjectionGT[None,:]]
# approxProjectionsGT = np.vstack(approxProjectionsGTList)
approxProjections = None
approxProjectionsGT = None
# #
# # ##### Load Results data:
# #
#
# testPrefix2 = 'train4_occlusion_shapemodel_10k_ECCV-PHOTREALISTIC-MEANBASELINE-2530-17944_predict_1000samples__method1errorFun1_std0.05_shapePen0'
# resultDir2 = 'results/' + testPrefix2 + '/'
# with open(resultDir2 + 'experiment.pickle', 'rb') as pfile:
# experimentDic2 = pickle.load(pfile)
# testSet2 = experimentDic2['testSet']
# methodsPred2 = experimentDic2['methodsPred']
# testOcclusions2 = experimentDic2[ 'testOcclusions']
# testPrefixBase2 = experimentDic2[ 'testPrefixBase']
# parameterRecognitionModels2 = experimentDic2[ 'parameterRecognitionModels']
# azimuths2 = experimentDic2['azimuths']
# elevations2 = experimentDic2[ 'elevations']
# vColors2 = experimentDic2[ 'vColors']
# lightCoeffs2 = experimentDic2[ 'lightCoeffs']
# likelihoods2 = experimentDic2['likelihoods']
# shapeParams2 = experimentDic2['shapeParams']
# if os.path.isfile(resultDir2 + 'segmentations.pickle'):
# with open(resultDir2 + 'segmentations.pickle', 'rb') as pfile:
# segmentationsDic2 = pickle.load(pfile)
# segmentations2 = segmentationsDic2['segmentations']
# else:
# segmentations2 = [None]*len(methodsPred2)
#
# testOcclusionsFull2 = testOcclusions2.copy()
#
#
# testPrefix3 = 'train4_occlusion_shapemodel_10k_ECCV-SYNTH-FIX-2530-17944_optimize_1000samples__method1errorFun1_std0.03_shapePen0'
# resultDir3 = 'results/' + testPrefix3 + '/'
# with open(resultDir3 + 'experiment.pickle', 'rb') as pfile:
# experimentDic3 = pickle.load(pfile)
# testSet3 = experimentDic3['testSet']
# methodsPred3 = experimentDic3['methodsPred']
# testOcclusions3 = experimentDic3[ 'testOcclusions']
# testPrefixBase3 = experimentDic3[ 'testPrefixBase']
# parameterRecognitionModels3 = experimentDic3[ 'parameterRecognitionModels']
# azimuths3 = experimentDic3['azimuths']
# elevations3 = experimentDic3[ 'elevations']
# vColors3 = experimentDic3[ 'vColors']
# lightCoeffs3 = experimentDic3[ 'lightCoeffs']
# likelihoods3 = experimentDic3['likelihoods']
# shapeParams3 = experimentDic3['shapeParams']
# if os.path.isfile(resultDir3 + 'segmentations.pickle'):
# with open(resultDir3 + 'segmentations.pickle', 'rb') as pfile:
# segmentationsDic3 = pickle.load(pfile)
# segmentations3 = segmentationsDic3['segmentations']
# else:
# segmentations3 = [None]*len(methodsPred3)
#
# testOcclusionsFull3 = testOcclusions3.copy()
#
# # with open(resultDir2 + 'approxProjections.pickle', 'rb') as pfile:
# # approxProjectionsDic2 = pickle.load(pfile)
# #
# # approxProjections2 = approxProjectionsDic2['approxProjections']
# # approxProjectionsGT2 = approxProjectionsDic2['approxProjectionsGT']
#
# approxProjections2 = None
# approxProjectionsGT2 = None
#
# testPrefix3 = 'train4_occlusion_shapemodel_10k_ECCV-SYNTHETIC-GAUSSIAN11664-17944_optimize_300samples__method1errorFun1_std0.05_shapePen0'
# resultDir3 = 'results/' + testPrefix3 + '/'
# with open(resultDir3 + 'experiment.pickle', 'rb') as pfile:
# experimentDic3 = pickle.load(pfile)
# testSet3 = experimentDic3['testSet']
# methodsPred3 = experimentDic3['methodsPred']
# testOcclusions3 = experimentDic3[ 'testOcclusions']
# testPrefixBase3 = experimentDic3[ 'testPrefixBase']
# parameterRecognitionModels3 = experimentDic3[ 'parameterRecognitionModels']
# azimuths3 = experimentDic3['azimuths']
# elevations3 = experimentDic3[ 'elevations']
# vColors3 = experimentDic3[ 'vColors']
# lightCoeffs3 = experimentDic3[ 'lightCoeffs']
# likelihoods3 = experimentDic3['likelihoods']
# shapeParams3 = experimentDic3['shapeParams']
# if os.path.isfile(resultDir3 + 'segmentations.pickle'):
# with open(resultDir3 + 'segmentations.pickle', 'rb') as pfile:
# segmentationsDic3 = pickle.load(pfile)
# segmentations3 = segmentationsDic3['segmentations']
# else:
# segmentations3 = [None]*len(methodsPred3)
#
# testOcclusionsFull3 = testOcclusions3.copy()
#
# # with open(resultDir3 + 'approxProjections.pickle', 'rb') as pfile:
# # approxProjectionsDic3 = pickle.load(pfile)
# #
# # approxProjections3 = approxProjectionsDic3['approxProjections']
# # approxProjectionsGT3 = approxProjectionsDic3['approxProjectionsGT']
#
# approxProjections3 = None
# approxProjectionsGT3 = None
#
# ipdb.set_trace()
#
# range1 = np.arange(len(azimuths2[3]))
# range2 = np.arange(-1000 + len(azimuths2[3]) + len(azimuths3[3]),len(azimuths3[3]))
#
# testSet, parameterRecognitionModels, testPrefixBase, methodsPred, testOcclusions, azimuths, elevations, vColors, lightCoeffs, shapeParams, likelihoods, segmentations, approxProjections, approxProjectionsGT = \
# joinExperiments(range1, range2, testSet2,methodsPred2,testOcclusions2,testPrefixBase2,parameterRecognitionModels2,azimuths2,elevations2,vColors2,lightCoeffs2,likelihoods2,shapeParams2,segmentations2, approxProjections2, approxProjectionsGT3, testSet3,methodsPred3,testOcclusions3,testPrefixBase3,parameterRecognitionModels3,azimuths3,elevations3,vColors3,lightCoeffs3,likelihoods3,shapeParams3,segmentations3, approxProjections3, approxProjectionsGT3)
#
# testSet = testSet2
# testOcclusions = testOcclusions2
# testPrefix = 'train4_occlusion_shapemodel_10k_ECCV-SYNTHETIC-GAUSSIAN2530-17944_optimize_1000samples__method1errorFun1_std0.05_shapePen0-FINAL'
# resultDir = 'results/' + testPrefix + '/'
#
# experimentDic = {'testSet':testSet, 'methodsPred':methodsPred, 'testOcclusions':testOcclusions, 'likelihoods':likelihoods, 'testPrefixBase':testPrefixBase, 'parameterRecognitionModels':parameterRecognitionModels, 'azimuths':azimuths, 'elevations':elevations, 'vColors':vColors, 'lightCoeffs':lightCoeffs, 'shapeParams':shapeParams}
#
# with open(resultDir + 'experiment.pickle', 'wb') as pfile:
# pickle.dump(experimentDic, pfile)
# #
# #
#
# methodsPred = methodsPred + ['Gaussian (OpenGL)']
# azimuths = azimuths + [azimuths2[3]]
# elevations = elevations + [elevations2[3]]
# vColors = vColors + [vColors2[3]]
# lightCoeffs = lightCoeffs + [lightCoeffs2[3]]
# likelihoods = likelihoods + [likelihoods2[3]]
# shapeParams = shapeParams + [shapeParams2[3]]
# segmentations = segmentations + [segmentations2[3]]
#
# methodsPred[0] = methodsPred2[0]
# azimuths[0] = azimuths2[0]
# elevations[0] = elevations2[0]
# vColors[0] = vColors2[0]
# lightCoeffs[0] = lightCoeffs2[0]
# likelihoods[0] = likelihoods2[0]
# shapeParams[0] = shapeParams2[0]
# segmentations[0] = segmentations2[0]
rendererGT = None
chPointLightIntensityGT = ch.Ch([1])
light_colorGT = ch.ones(3)*chPointLightIntensityGT
chDisplacementGT = ch.Ch([0.0,0.0,0.0])
chScaleGT = ch.Ch([1, 1.,1.])
replaceableScenesFile = '../databaseFull/fields/scene_replaceables_backup.txt'
# likelihoods = [np.array([]), np.array([])]
from OpenGL import contextdata
# #
# segmentations[2] = np.zeros([len(testSet), 150,150])
# segmentations[3] = np.zeros([len(testSet), 150,150])
# segmentations[4] = np.zeros([len(testSet), 150,150])
# segmentations[5] = np.zeros([len(testSet), 150,150])
#
#
# for test_i in range(len(testSet)):
#
# # sceneNumber = dataScenes[test_i]
# # sceneIdx = scene_io_utils.getSceneIdx(sceneNumber, replaceableScenesFile)
# # sceneNumber, sceneFileName, instances, roomName, roomInstanceNum, targetIndicesScene, targetPositions = scene_io_utils.getSceneInformation(sceneIdx, replaceableScenesFile)
# # # sceneNumber, sceneFileName, instances, roomName, roomInstanceNum, targetIndices, targetPositions = scene_io_utils.getSceneInformation(sceneIdx, replaceableScenesFile)
# #
# # targetIndex = dataTargetIndices[test_i]
# # sceneDicFile = 'data/scene' + str(sceneNumber) + '.pickle'
# # v, f_list, vc, vn, uv, haveTextures_list, textures_list = scene_io_utils.loadSavedScene(sceneDicFile, True)
# #
# # removeObjectData(len(v) -1 - targetIndex, v, f_list, vc, vn, uv, haveTextures_list, textures_list)
# #
# # addObjectData(v, f_list, vc, vn, uv, haveTextures_list, textures_list, smVerticesGT, smFacesGT, smVColorsGT, smNormalsGT, smUVsGT, smHaveTexturesGT, smTexturesListGT)
# #
# # if rendererGT is not None:
# # rendererGT.makeCurrentContext()
# # rendererGT.clear()
# # contextdata.cleanupContext(contextdata.getContext())
# # if glMode == 'glfw':
# # glfw.destroy_window(rendererGT.win)
# # del rendererGT
# #
# # targetPosition = targetPositions[np.where(targetIndex==np.array(targetIndicesScene))[0]]
# #
# # rendererGT = createRendererGT(glMode, chAzGT, chObjAzGT, chElGT, chDistGT, center, v, vc, f_list, vn, light_colorGT, chComponentGT, chVColorsGT, targetPosition.copy(), chDisplacementGT, chScaleGT, width,height, uv, haveTextures_list, textures_list, frustum, None )
# #
# # for hdrFile, hdrValues in hdritems:
# # hdridx = hdrValues[0]
# # envMapCoeffs = hdrValues[1]
# # if hdridx == dataEnvMaps[test_i]:
# # break
# # envMapFilename = hdrFile
# #
# # phiOffset[:] = dataEnvMapPhiOffsets[test_i]
# # chObjAzGT[:] = testObjAzsGT[test_i]
# # chAzGT[:] = testAzsGT[test_i]
# # chElGT[:] = testElevsGT[test_i]
# # chVColorsGT[:] = testVColorGT[test_i]
# # envMapCoeffsRotated[:] = np.dot(light_probes.chSphericalHarmonicsZRotation(totalOffset), envMapCoeffs[[0,3,2,1,4,5,6,7,8]])[[0,3,2,1,4,5,6,7,8]]
# # envMapCoeffsRotatedRel[:] = np.dot(light_probes.chSphericalHarmonicsZRotation(phiOffset), envMapCoeffs[[0,3,2,1,4,5,6,7,8]])[[0,3,2,1,4,5,6,7,8]]
# # chShapeParamsGT[:] = dataShapeModelCoeffsGT[test_i]
#
# im = images[test_i]
# rendererGT = srgb2lin(im.copy())
#
# chLightSHCoeffs[:] = testLightCoefficientsGTRel[test_i]
# chObjAz[:] = 0
# chAz[:] = testAzsRel[test_i]
# chEl[:] = testElevsGT[test_i]
# chVColors[:] = testVColorGT[test_i]
# chShapeParams[:] = testShapeParamsGT[test_i]
#
# stds[:] = 0.05
#
# negLikModelRobust = -ch.sum(generative_models.LogRobustModel(renderer=renderer, groundtruth=rendererGT, foregroundPrior=globalPrior, variances=variances))/numPixels
#
# # likelihoods[0] = np.append(likelihoods[0], negLikModelRobust.r)
#
# chLightSHCoeffs[:] = lightCoeffs[3][idsInRange[test_i]]
# chObjAz[:] = 0
# chAz[:] = azimuths[3][idsInRange[test_i]]
# chEl[:] = elevations[3][idsInRange[test_i]]
# chVColors[:] = vColors[3][idsInRange[test_i]]
# chShapeParams[:] = shapeParams[3][idsInRange[test_i]]
#
# # likelihoods[1] = np.append(likelihoods[1], negLikModelRobust.r)
#
# #masksGT
# #render[~mask*vis_im] = np.concatenate([np.ones([1000,1000])[:,:,None], np.zeros([1000,1000])[:,:,None],np.zeros([1000,1000])[:,:,None]], axis=2)[~mask*vis_im]
#
# vis_im = np.array(renderer.indices_image==1).copy().astype(np.bool)
# post = generative_models.layerPosteriorsRobustCh(rendererGT, renderer, vis_im, 'MASK', globalPrior, variances)[0].r>0.5
# render = ~post.copy()
# mask = masksGT[test_i]
# render[~vis_im] = 1
#
# segmentations[3][test_i] = post
#
#
# chLightSHCoeffs[:] = lightCoeffs[2][idsInRange[test_i]]
# chObjAz[:] = 0
# chAz[:] = azimuths[2][idsInRange[test_i]]
# chEl[:] = elevations[2][idsInRange[test_i]]
# chVColors[:] = vColors[2][idsInRange[test_i]]
# chShapeParams[:] = shapeParams[2][idsInRange[test_i]]
#
# # likelihoods[2] = np.append(likelihoods[2], negLikModelRobust.r)
#
# # masksGT
# # render[~mask*vis_im] = np.concatenate([np.ones([1000,1000])[:,:,None], np.zeros([1000,1000])[:,:,None],np.zeros([1000,1000])[:,:,None]], axis=2)[~mask*vis_im]
#
# post = np.array(renderer.indices_image == 1).copy().astype(np.bool)
#
# segmentations[2][test_i] = post
#
#
# chLightSHCoeffs[:] = lightCoeffs[5][idsInRange[test_i]]
# chObjAz[:] = 0
# chAz[:] = azimuths[5][idsInRange[test_i]]
# chEl[:] = elevations[5][idsInRange[test_i]]
# chVColors[:] = vColors[5][idsInRange[test_i]]
# chShapeParams[:] = shapeParams[5][idsInRange[test_i]]
#
# # likelihoods[1] = np.append(likelihoods[1], negLikModelRobust.r)
#
# # masksGT
# # render[~mask*vis_im] = np.concatenate([np.ones([1000,1000])[:,:,None], np.zeros([1000,1000])[:,:,None],np.zeros([1000,1000])[:,:,None]], axis=2)[~mask*vis_im]
#
# post = np.array(renderer.indices_image == 1).copy().astype(np.bool)
#
# segmentations[5][test_i] = post
# #
# # chLightSHCoeffs[:] = lightCoeffs[4][idsInRange[test_i]]
# # chObjAz[:] = 0
# # chAz[:] = azimuths[4][idsInRange[test_i]]
# # chEl[:] = elevations[4][idsInRange[test_i]]
# # chVColors[:] = vColors[4][idsInRange[test_i]]
# # chShapeParams[:] = shapeParams[4][idsInRange[test_i]]
#
# # likelihoods[1] = np.append(likelihoods[1], negLikModelRobust.r)
#
# # masksGT
# # render[~mask*vis_im] = np.concatenate([np.ones([1000,1000])[:,:,None], np.zeros([1000,1000])[:,:,None],np.zeros([1000,1000])[:,:,None]], axis=2)[~mask*vis_im]
#
# # vis_im = np.array(renderer.indices_image == 1).copy().astype(np.bool)
# # post = generative_models.layerPosteriorsRobustCh(rendererGT, renderer, vis_im, 'MASK', globalPrior, variances)[0].r > 0.5
# # render = ~post.copy()
# # mask = masksGT[test_i]
# # render[~vis_im] = 1
# #
# # segmentations[4][test_i] = post
#
# # renderRGB = np.concatenate([render[:,:,None], render[:,:,None], render[:,:,None]], axis=2)
#
# # cv2.imwrite('tmp/SH/renderergt' + str(test_i) + '.jpeg' , 255*lin2srgb(rendererGT[:,:,[2,1,0]]), [int(cv2.IMWRITE_JPEG_QUALITY), 100])
# # plt.imsave('tmp/SH/mask' + str(test_i) + '.jpeg', mask)
# # cv2.imwrite('tmp/SH/post' + str(test_i) + '.jpeg' , 255*post, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
# # cv2.imwrite('tmp/SH/post' + str(test_i) + '.jpeg' , 255*render, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
# # plt.imsave('tmp/SH/renderer' + str(test_i) + '.png' , lin2srgb(renderer.r.copy()))
# # ipdb.set_trace()
# # # #
#
# methodsPred[3] = 'Robust Fit'
#
# methodsPred[4] = 'Robust (OpenGL)'
#
# methodsPred[5] = 'Recognition (OpenGL)'
#
# methodsPred[6] = 'Gaussian Fit'
#
# segmentations[4] = segmentations3[3]
#
# errorsPosePredList, errorsLightCoeffsList, errorsShapeParamsList, errorsShapeVerticesList, errorsEnvMapList, errorsLightCoeffsCList, errorsVColorsEList, errorsVColorsCList, errorsVColorsSList, errorsSegmentationList \
# = computeErrors(np.arange(len(rangeTests)), azimuths, testAzsRel, elevations, testElevsGT, vColors, testVColorGT, lightCoeffs, testLightCoefficientsGTRel, approxProjections, approxProjectionsGT, shapeParams, testShapeParamsGT, useShapeModel, chShapeParams, chVertices, segmentations, masksGT)
# envMapTexture = np.zeros([180,360,3])
# approxProjectionsFittedList = []
# for test_i in range(len(testSet)):
# pEnvMap = SHProjection(envMapTexture, np.concatenate([lightCoeffs[4][test_i][:,None], lightCoeffs[4][test_i][:,None], lightCoeffs[4][test_i][:,None]], axis=1))
# approxProjection = np.sum(pEnvMap, axis=(2,3))
# approxProjectionsFittedList = approxProjectionsFittedList + [approxProjection[None,:]]
# approxProjectionsFitted = np.vstack(approxProjectionsFittedList)
#
# envMapTexture = np.zeros([180,360,3])
# approxProjectionsGTList = []
# for test_i in range(len(testSet)):
# pEnvMap = SHProjection(envMapTexture, np.concatenate([testLightCoefficientsGTRel[test_i][:,None], testLightCoefficientsGTRel[test_i][:,None], testLightCoefficientsGTRel[test_i][:,None]], axis=1))
# approxProjectionGT = np.sum(pEnvMap, axis=(2,3))
# approxProjectionsGTList = approxProjectionsGTList + [approxProjectionGT[None,:]]
# approxProjectionsGT = np.vstack(approxProjectionsGTList)
# with open(resultDir + 'experiment_errors.pickle', 'rb') as pfile:
# experimentErrorsDic = pickle.load(pfile)
#
# errorsPosePredList = experimentErrorsDic['errorsPosePredList']
# errorsLightCoeffsList = experimentErrorsDic['errorsLightCoeffsList']
# errorsShapeParamsList = experimentErrorsDic['errorsShapeParamsLis']
# errorsShapeVerticesList = experimentErrorsDic['errorsShapeVerticesList']
# errorsEnvMapList = experimentErrorsDic['errorsEnvMapList']
# errorsLightCoeffsCList = experimentErrorsDic['errorsLightCoeffsCList']
# errorsVColorsEList = experimentErrorsDic['errorsVColorsEList']
# errorsVColorsCList = experimentErrorsDic['errorsVColorsCList']
# errorsVColorsSList = experimentErrorsDic['errorsVColorsSList']
# errorsSegmentationList = experimentErrorsDic['errorsSegmentationList']
testPrefix = 'train4_occlusion_shapemodel_10k_ECCV-PHOTOREALISTIC-JOINT2018'
resultDir = 'results/' + testPrefix + '/'
# experimentDic = {'testSet':testSet, 'methodsPred':methodsPred, 'testOcclusions':testOcclusions, 'likelihoods':likelihoods, 'testPrefixBase':testPrefixBase, 'parameterRecognitionModels':parameterRecognitionModels, 'azimuths':azimuths, 'elevations':elevations, 'vColors':vColors, 'lightCoeffs':lightCoeffs, 'shapeParams':shapeParams}
#
# with open(resultDir + 'experiment.pickle', 'wb') as pfile:
# pickle.dump(experimentDic, pfile)
# #
# experimentErrorsDic = {'errorsPosePredList':errorsPosePredList, 'errorsLightCoeffsList':errorsLightCoeffsList, 'errorsShapeParamsLis':errorsShapeParamsList, 'errorsShapeVerticesList':errorsShapeVerticesList, 'errorsEnvMapList':errorsEnvMapList, 'errorsLightCoeffsCList':errorsLightCoeffsCList, 'errorsVColorsEList':errorsVColorsEList, 'errorsVColorsCList':errorsVColorsCList, 'errorsVColorsSList':errorsVColorsSList,'errorsSegmentationList':errorsSegmentationList}
#
#
# with open(resultDir + 'experiment_errors.pickle', 'wb') as pfile:
# pickle.dump(experimentErrorsDic, pfile)
with open(resultDir + 'experiment_errors.pickle', 'rb') as pfile:
experimentErrorsDic = pickle.load(pfile)
errorsPosePredList = experimentErrorsDic['errorsPosePredList']
errorsLightCoeffsList = experimentErrorsDic['errorsLightCoeffsList']
errorsShapeParamsList = experimentErrorsDic['errorsShapeParamsLis']
errorsShapeVerticesList = experimentErrorsDic['errorsShapeVerticesList']
errorsEnvMapList = experimentErrorsDic['errorsEnvMapList']
errorsLightCoeffsCList = experimentErrorsDic['errorsLightCoeffsCList']
errorsVColorsEList = experimentErrorsDic['errorsVColorsEList']
errorsVColorsCList = experimentErrorsDic['errorsVColorsCList']
errorsVColorsSList = experimentErrorsDic['errorsVColorsSList']
errorsSegmentationList = experimentErrorsDic['errorsSegmentationList']
meanAbsErrAzsList, meanAbsErrElevsList, meanErrorsLightCoeffsList, meanErrorsShapeParamsList, meanErrorsShapeVerticesList, meanErrorsLightCoeffsCList, meanErrorsEnvMapList, meanErrorsVColorsEList, meanErrorsVColorsCList, meanErrorsVColorsCList, meanErrorsSegmentation \
= computeErrorAverages(np.mean, np.arange(len(rangeTests)), useShapeModel, errorsPosePredList, errorsLightCoeffsList, errorsShapeParamsList, errorsShapeVerticesList, errorsEnvMapList, errorsLightCoeffsCList, errorsVColorsEList, errorsVColorsCList,errorsVColorsSList, errorsSegmentationList)
nearestNeighbours = False
if 'Nearest Neighbours' in set(methodsPred):
nearestNeighbours = True
plotColors = ['k']
if nearestNeighbours:
# methodsPred = methodsPred + ["Nearest Neighbours"]
plotColors = plotColors + ['m']
plotColors = plotColors + ['b']
plotColors = plotColors + ['r']
plotColors = plotColors + ['r']
plotColors = plotColors + ['b']
plotColors = plotColors + ['g']
plotColors = plotColors + ['g']
plotStyles = ['solid']
if nearestNeighbours:
# methodsPred = methodsPred + ["Nearest Neighbours"]
plotStyles = plotStyles + ['solid']
plotStyles = plotStyles + ['solid']
plotStyles = plotStyles + ['solid']
plotStyles = plotStyles + ['dashed']
plotStyles = plotStyles + ['dashed']
plotStyles = plotStyles + ['solid']
plotStyles = plotStyles + ['dashed']
plotMethodsIndices = [2,5,6, 7, 3,4]
# plotMethodsIndices = [0,2,6,3]
import ipdb; ipdb.set_trace()
recognitionIdx = 2
robustIdx = 3
errorsAzimuthList = [errorsPosePredList[i][0] if errorsPosePredList[i] is not None else None for i in range(len(methodsPred))]
errorsElevationList = [errorsPosePredList[i][1] if errorsPosePredList[i] is not None else None for i in range(len(methodsPred))]
variablesDescr = ['Azimuth', 'Elevation', 'VColor', 'Illumination', 'Shape', 'Segmentation']
errorsList = [errorsAzimuthList,errorsElevationList, errorsVColorsCList, errorsLightCoeffsCList, errorsShapeVerticesList, errorsSegmentationList]
saveConditionalHistograms(resultDir, testOcclusions, methodsPred, variablesDescr, plotMethodsIndices, errorsList)
print("Printing occlusin-likelihood plots!")
# meanLikelihoodArr = [np.array([]), np.array([]), np.array([]), np.array([])]
# occlusions = []
# for occlusionLevel in range(100):
#
# setUnderOcclusionLevel = testOcclusionsFull * 100 < occlusionLevel
#
# if np.any(setUnderOcclusionLevel):
# occlusions = occlusions + [occlusionLevel]
# testOcclusions = testOcclusionsFull[setUnderOcclusionLevel]
#
# if likelihoods[0] is not None:
# meanLikelihoodArr[0] = np.append(meanLikelihoodArr[0], np.mean(likelihoods[0][setUnderOcclusionLevel]))
# if likelihoods[1] is not None:
# meanLikelihoodArr[1] = np.append(meanLikelihoodArr[1], np.mean(likelihoods[1][setUnderOcclusionLevel]))
# if likelihoods[2] is not None:
# meanLikelihoodArr[2] = np.append(meanLikelihoodArr[2], np.mean(likelihoods[2][setUnderOcclusionLevel]))
# if likelihoods[3] is not None:
# meanLikelihoodArr[3] = np.append(meanLikelihoodArr[3], np.mean(likelihoods[3][setUnderOcclusionLevel]))
# saveLikelihoodPlots(resultDir, occlusions, methodsPred, plotColors, plotMethodsIndices, meanLikelihoodArr)
#
# print("Computing means!")
#
meanAbsErrAzsArr = []
meanAbsErrElevsArr = []
meanErrorsLightCoeffsArr = []
meanErrorsEnvMapArr = []
meanErrorsShapeParamsArr = []
meanErrorsShapeVerticesArr = []
meanErrorsLightCoeffsCArr = []
meanErrorsVColorsEArr = []
meanErrorsVColorsCArr = []
meanErrorsVColorsSArr = []
meanErrorsSegmentationArr = []
for method_i in range(len(methodsPred)):
meanAbsErrAzsArr = meanAbsErrAzsArr + [np.array([])]
meanAbsErrElevsArr = meanAbsErrElevsArr + [np.array([])]
meanErrorsLightCoeffsArr = meanErrorsLightCoeffsArr + [np.array([])]
meanErrorsShapeParamsArr = meanErrorsShapeParamsArr + [np.array([])]
meanErrorsShapeVerticesArr = meanErrorsShapeVerticesArr + [np.array([])]
meanErrorsLightCoeffsCArr = meanErrorsLightCoeffsCArr + [np.array([])]
meanErrorsVColorsEArr = meanErrorsVColorsEArr + [np.array([])]
meanErrorsVColorsCArr = meanErrorsVColorsCArr + [np.array([])]
meanErrorsVColorsSArr = meanErrorsVColorsSArr + [np.array([])]
meanErrorsEnvMapArr = meanErrorsEnvMapArr + [np.array([])]
meanErrorsSegmentationArr = meanErrorsSegmentationArr + [np.array([])]
occlusions = []
print("Printing occlusin-error plots!")
for occlusionLevel in range(100):
setUnderOcclusionLevel = testOcclusionsFull * 100 < occlusionLevel
if np.any(setUnderOcclusionLevel):
occlusions = occlusions + [occlusionLevel]
testOcclusions = testOcclusionsFull[setUnderOcclusionLevel]
colors = matplotlib.cm.plasma(testOcclusions)
for method_i in range(len(methodsPred)):
if errorsPosePredList[method_i] is not None:
meanAbsErrAzsArr[method_i] = np.append(meanAbsErrAzsArr[method_i], np.mean(np.abs(errorsPosePredList[method_i][0][setUnderOcclusionLevel])))
meanAbsErrElevsArr[method_i] = np.append(meanAbsErrElevsArr[method_i], np.mean(np.abs(errorsPosePredList[method_i][1][setUnderOcclusionLevel])))
if errorsLightCoeffsList[method_i] is not None:
meanErrorsLightCoeffsArr[method_i] = np.append(meanErrorsLightCoeffsArr[method_i],np.mean(np.mean(errorsLightCoeffsList[method_i][setUnderOcclusionLevel], axis=1), axis=0))
if errorsLightCoeffsCList[method_i] is not None:
meanErrorsLightCoeffsCArr[method_i] = np.append(meanErrorsLightCoeffsCArr[method_i],np.mean(np.mean(errorsLightCoeffsCList[method_i][setUnderOcclusionLevel], axis=1), axis=0))
if useShapeModel: