import sys, os, numpy, scipy.stats, sys
render_path = '/om/user/janner/mit/urop/picture/centos/'
divide_path = render_path + 'dDiff-dOccl/'
sys.path.append(render_path)
sys.path.append(divide_path)
from RenderLibrary import *
import divideRender
vector_base = '/om/user/janner/mit/urop/picture/centos/torch/cogsci_96/predictions/vector/unoccluded/'
save_path = '/om/user/janner/mit/urop/picture/centos/dDiff-dOccl/results/dms_96/unoccluded_predictions/'
dms_ids = range(96)
occLevels = [1, 3, 5]
imgs_per_job = 20
arr = []
for dms_id in dms_ids:
for occLevel in occLevels:
vector_path = vector_base + str(occLevel) + '/0_' + str(dms_id) + '_0.npy'
latents = numpy.load(vector_path)
name = str(occLevel) + '_' + str(dms_id) + '.png'
arr.append([latents, name, save_path, ('None', 0)])
numpy.save(divide_path + 'render.npy', arr)
div = [(i,i+imgs_per_job) for i in range(0, len(arr), imgs_per_job)]
print(len(arr), ' images')
print(len(div), ' jobs')
divideRender.divide(div)
# assert(False, 'Invalid direction: ' + direction)
OCCLUDERS = ["Bars", "Fence", "Curtain", "Door", "Blinds"]
currDir = os.path.dirname(os.path.realpath(__file__))
predPath = "preds/arrays/"
writePath = currDir + "/preds/images/"
print(writePath)
occLevels = [1, 3, 5]
directions = ["occ_un", "un_occ"]
numIDs = 96
render = []
for occ in occLevels:
for direction in directions:
preds = numpy.load(predPath + str(occ) + "_" + direction + "_0_latents.npy").tolist()
for idNum in range(numIDs):
occluder = getOccluder(direction, occ, idNum)
image = [preds[idNum], str(idNum) + ".png", writePath + str(occ) + "_" + direction + "/", occluder]
render.append(image)
# print(render)
numpy.save(RENDER_PATH + "render.npy", render)
div = [(i, i + 40) for i in range(0, len(render), 40)]
divide(div)
