OUTPUT_FOLDER = 'comparisonVideo1'
CAMERA_ORIGIN = [0, 0.8, -1.4] #will be normalized
FPS = 25
FRAMES_PER_ROTATION = 100
ROTATIONS_PER_EXAMPLE = 2
FRAME_DUPLICATION = 6
BACKGROUND = [1, 1, 1]
RESOLUTION = (1920, 1080)
# Load models
device = torch.device("cuda")
models = [None] * len(MODELS)
for i, m in enumerate(MODELS):
p = m['path']
if p is not MODEL_INPUT and p is not MODEL_GROUND_TRUTH:
models[i] = inference.LoadedModel(os.path.join(MODEL_DIR, p), device,
UPSCALING)
# create output folder
os.makedirs(OUTPUT_FOLDER, exist_ok=True)
# Render each dataset
for i in range(len(DATASETS)):
# create renderer
camera = inference.Camera(RESOLUTION[0], RESOLUTION[0], CAMERA_ORIGIN)
camera.currentDistance = DATASETS[i]['distance']
camera.currentPitch = DATASETS[i]['pitch']
camera.orientation = DATASETS[i]['orientation']
material = inference.Material(DATASETS[i]['iso'])
renderer_path = RENDERER_CPU if DATASETS[i]['file'].endswith(
'vdb') else RENDERER_GPU
data_dir = DATA_DIR_CPU if DATASETS[i]['file'].endswith(
renderer = inference.DirectRenderer(RENDERER)
CHANNEL_DEPTH = 0
CHANNEL_NORMAL = 1
CHANNEL_AO = 2
CHANNEL_COLOR_NOAO = 3
CHANNEL_COLOR_WITHAO = 4
CHANNEL_NAMES = ["depth", "normal", "ao", "colorNoAO", "colorWithAO"]
# load models
models = [None] * len(MODELS)
for i, m in enumerate(MODELS):
p = m['path']
if p.endswith('.pth'):
models[i] = inference.LoadedModel(
os.path.join(MODEL_DIR, p),
cpuDevice if CPU_SUPERRES else cudaDevice, UPSCALING)
# LOOP OVER ALL SCENES
for sceneFun in Scenes:
sceneName = sceneFun.__name__
print("====================================")
print(" Render", sceneName)
print("====================================")
# create output folder
outputFolder = os.path.join(OUTPUT_FOLDER, sceneName)
print("Write output to", outputFolder)
os.makedirs(outputFolder, exist_ok=True)
# open output files