<gh_stars>0
"""
CODE SOURCE adapted from https://github.com/mworchel/svbrdf-estimation

ADD CREDITS / REFERENCE BEFORE CODE RELEASE!!!
"""

from PIL import Image
import matplotlib.pyplot as plt
import os
import numpy as np
from pytorch_svbrdf_renderer import Renderer, Config, load_svbrdf_maps
import torch
import utils


class SvbrdfDataset(torch.utils.data.Dataset):
    """
    Class representing a collection of SVBRDF samples with corresponding input images (rendered or real views of the SVBRDF)
    """

    def __init__(
        self,
        data_directory,
        image_size,
        scale_mode,
        input_image_count,
        used_input_image_count,
        use_augmentation,
        mix_materials=False,
        no_svbrdf=False,
        is_linear=False,
        random_crop=False,
    ):
        self.data_directory = data_directory
        self.file_paths = [
            os.path.join(data_directory, f)
            for f in os.listdir(data_directory)
            if os.path.isfile(os.path.join(data_directory, f))
        ]

        self.image_size = image_size
        self.scale_mode = scale_mode
        self.input_image_count = input_image_count
        self.used_input_image_count = used_input_image_count
        # No augmentation means fixed view distance, light intensity, neutral whitebalance and constant flash falloff
        self.use_augmentation = use_augmentation

        # Material mixing augments the dataset by mixing together two materials
        self.mix_materials = mix_materials
        if self.mix_materials and self.input_image_count > 0:
            self.mix_materials = False
            print(
                "Warning: Material mixing is only supported for datasets without input images."
            )

        # The dataset does not contain any svbrdf maps
        self.no_svbrdf = no_svbrdf

        # The images in the dataset are already linear RGB
        self.is_linear = is_linear

        # If scale mode is 'crop', crop out a randomly placed window from the image
        self.random_crop = random_crop

    def __len__(self):
        return len(self.file_paths)

    def __getitem__(self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()

        input_images, svbrdf = self.read_sample(self.file_paths[idx])

        if self.mix_materials:
            import random

            other_index = random.randrange(0, self.__len__())
            _, other_svbrdf = self.read_sample(self.file_paths[other_index])
            svbrdf = self.mix(svbrdf, other_svbrdf)

        if self.scale_mode == "resize":
            width = input_images.shape[-1]
            height = input_images.shape[-2]

            # Crop out the center of the sample
            is_landscape = width > height
            crop_anchor = (
                torch.IntTensor([0, (width - height) // 2])
                if is_landscape
                else torch.IntTensor([(height - width) // 2, 0])
            )
            crop_size = height if is_landscape else width

            input_images = utils.crop_square(input_images, crop_anchor, crop_size)
            svbrdf = utils.crop_square(svbrdf, crop_anchor, crop_size)

            # Scale the images and the SVBRDF down to the desired size
            # Note: Do not use bicubic interpolation, as it might produce negative values or values > 1 which requires clamping
            input_images = torch.nn.functional.interpolate(
                input_images, size=(self.image_size, self.image_size), mode="bilinear"
            )
            svbrdf = torch.nn.functional.interpolate(
                svbrdf.unsqueeze(0),
                size=(self.image_size, self.image_size),
                mode="bilinear",
            ).squeeze(0)
        elif self.scale_mode == "crop":
            width = input_images.shape[-1]
            height = input_images.shape[-2]

            # Determine the top left point of the cropped image
            # TODO: For random jittering, the crop size would need to be a little bit larger than the final size
            #       and a final cropping stage would be needed after image generation, which produces slightly unaligned images.
            crop_anchor = torch.IntTensor([0, 0])
            if self.random_crop:
                crop_anchor = torch.IntTensor(
                    [
                        np.random.randint(0, height - self.image_size + 1),
                        np.random.randint(0, width - self.image_size + 1),
                    ]
                )

            # Crop down the svbrdf and the given input images
            svbrdf = utils.crop_square(svbrdf, crop_anchor, self.image_size)
            input_images = utils.crop_square(input_images, crop_anchor, self.image_size)
        else:
            raise ValueError("Unknown scale mode {}".format(self.scale_mode))

        # Transform to linear RGB
        if not self.is_linear:
            input_images = utils.gamma_decode(input_images)

        # Images which cannot be read must be generated artificially
        generated_input_image_count = (
            self.used_input_image_count - input_images.shape[0]
        )
        if generated_input_image_count > 0:
            renderings = self.render_inputs(svbrdf, generated_input_image_count)
            input_images = torch.cat([input_images, renderings], dim=0)

        # TODO: For random jittering we would need to re-crop to the final size at this point
        #       with individual crop anchors for all the images.

        return {"inputs": input_images, "svbrdf": svbrdf}

    def read_sample(self, file_path):
        # Read full image
        image = load_svbrdf_maps([file_path])

        # 4 is the number of maps in the SVBRDF
        svbrdf_map_count = 0 if self.no_svbrdf else 4

        # Split the full image apart along the horizontal direction
        # image_parts.shape = (4 + n_input_images, channels, height, width)
        image_parts = torch.cat(image.chunk(self.input_image_count + svbrdf_map_count, dim=-1), dim=0)

        # Read the SVBRDF (dummy if there is none in the dataset)
        svbrdf = None
        if self.no_svbrdf:
            # If there are no SVBRDFs in the data, there must be images which we can use as size guide.
            width = image_parts[0].shape[-1]
            height = image_parts[0].shape[-2]

            normals = torch.cat(
                [torch.zeros((2, height, width)), torch.ones((1, height, width))], dim=0
            )
            diffuse = torch.zeros_like(normals)
            roughness = torch.zeros_like(normals)
            specular = torch.zeros_like(normals)
        else:
            normals = image_parts[self.input_image_count + 0].unsqueeze(0)
            normals = utils.decode_from_unit_interval(normals)
            diffuse = image_parts[self.input_image_count + 1].unsqueeze(0)
            roughness = image_parts[self.input_image_count + 2].unsqueeze(0)
            specular = image_parts[self.input_image_count + 3].unsqueeze(0)

        svbrdf = utils.pack_svbrdf(normals, diffuse, roughness, specular).squeeze(
            0
        )  # [12, 256, 256]

        # We read as many input images from the disk as we can
        # FIXME: This is a little bit counter-intuitive, as we are reading the last n images, not the first n
        read_input_image_count = min(
            self.input_image_count, self.used_input_image_count
        )
        input_images = image_parts[
            (self.input_image_count - read_input_image_count) : self.input_image_count
        ]  # [ni, 3, 256, 256]

        return input_images, svbrdf

    def mix(self, svbrdf_0, svbrdf_1, alpha=None):
        if alpha is None:
            alpha = torch.Tensor(1).uniform_(0.1, 0.9)

        normals_0, diffuse_0, roughness_0, specular_0 = utils.unpack_svbrdf(svbrdf_0)
        normals_1, diffuse_1, roughness_1, specular_1 = utils.unpack_svbrdf(svbrdf_1)

        # Reference "Project the normals to use the X and Y derivative"
        normals_0_projected = normals_0 / torch.max(
            torch.Tensor([0.01]), normals_0[2:3, :, :]
        )
        normals_1_projected = normals_1 / torch.max(
            torch.Tensor([0.01]), normals_1[2:3, :, :]
        )

        normals_mixed = (
            alpha * normals_0_projected + (1.0 - alpha) * normals_1_projected
        )
        normals_mixed = normals_mixed / torch.sqrt(
            torch.sum(normals_mixed ** 2, axis=0, keepdim=True)
        )  # Normalization

        diffuse_mixed = alpha * diffuse_0 + (1.0 - alpha) * diffuse_1
        roughness_mixed = alpha * roughness_0 + (1.0 - alpha) * roughness_1
        specular_mixed = alpha * specular_0 + (1.0 - alpha) * specular_1

        return utils.pack_svbrdf(
            normals_mixed, diffuse_mixed, roughness_mixed, specular_mixed
        )

    def render_inputs(self, svbrdf, count):
        # Constants as defined in the reference code
        min_eps = 0.001  # Reference: "allows near 90     degrees angles"
        max_eps = 0.02  # Reference: "removes all angles below 8.13 degrees."
        fixed_light_distance = 2.197
        fixed_view_distance = 2.75  # Reference: "39.98 degrees FOV"

        # Generate scenes (camera and light configurations)
        # The in the first configuration, the light and view direction are guaranteed to be perpendicular to the material sample.
        # For the remaining cases, both are randomly sampled from a hemisphere.
        light_poses = torch.cat(
            [
                torch.Tensor(2).uniform_(-0.75, 0.75),
                torch.ones(1) * fixed_light_distance,
            ],
            dim=-1,
        ).unsqueeze(0)
        if count > 1:
            light_poses_hemisphere = (
                utils.generate_normalized_random_direction(
                    count - 1, min_eps=min_eps, max_eps=max_eps
                )
                * fixed_light_distance
            )
            light_poses = torch.cat([light_poses, light_poses_hemisphere], dim=0)

        light_colors = torch.Tensor([35.0]).unsqueeze(-1)
        if self.use_augmentation:
            # Reference: "add a normal distribution to the stddev so that sometimes in a minibatch all the images are consistant and sometimes crazy".
            # NOTE: For us, this effect will not be batch-wide but only for this individual sample.
            # FIXME: Since our renderer is differently implemented, the color variations with the given standard deviations
            #        merely have an effect.
            std_deviation = torch.exp(
                torch.Tensor(1).normal_(mean=-2.0, std=0.5)
            ).numpy()[0]
            light_colors = torch.abs(
                torch.Tensor(count).normal_(mean=20.0, std=std_deviation)
            ).unsqueeze(-1)
        light_colors = light_colors.expand(count, 3)

        # Handle white balance by varying the light color not the camera properties
        if self.use_augmentation:
            white_balance = torch.abs(
                torch.Tensor(count, 3).normal_(mean=1.0, std=0.03)
            )
            light_colors = light_colors * white_balance

        if self.use_augmentation:
            # Reference: "Simulates a FOV between 30 degrees and 50 degrees centered around 40 degrees"
            # NOTE: This probably does not do what the reference code expects it to do.
            #       The uniform distribution generates view distances in [0.25, 2.75] which
            #       correspond to FOVs between roughly 150 degrees and 40 degrees.
            view_distance = torch.Tensor(count).uniform_(0.25, 2.75)
        else:
            view_distance = torch.ones(count) * fixed_view_distance

        view_poses = torch.cat(
            [torch.Tensor(2).uniform_(-0.25, 0.25), view_distance[:1]], dim=-1
        ).unsqueeze(0)
        if count > 1:
            view_poses_hemisphere = utils.generate_normalized_random_direction(
                count - 1, min_eps=min_eps, max_eps=max_eps
            ) * view_distance[1:].unsqueeze(-1)
            view_poses = torch.cat([view_poses, view_poses_hemisphere], dim=0)

        R = Renderer()
        # TODO: Add spotlight support to the renderer (currentConeTargetPos in the reference code)
        C = Config()
        C.light_position(light_poses)
        C.view_position(view_poses)
        C.light_color_intensity(30.0)

        R.set_config(C) # add decoder to renderer
        rendering = R.run(svbrdf.unsqueeze(0), stacked_channels=True)

        # Simulate noise
        std_deviation_noise = torch.exp(
            torch.Tensor(1).normal_(mean=np.log(0.005), std=0.3)
        ).numpy()[0]
        noise = torch.zeros_like(rendering).normal_(
            mean=0.0, std=std_deviation_noise
        )
        rendering = torch.clamp(rendering + noise, min=0.0, max=1.0)

        return rendering.squeeze(0).permute(0, 3, 1, 2)

"""
CODE SOURCE adapted from https://github.com/mworchel/svbrdf-estimation

ADD CREDITS / REFERENCE BEFORE CODE RELEASE!!!
"""

from PIL import Image
import matplotlib.pyplot as plt
import os
import numpy as np
from pytorch_svbrdf_renderer import Renderer, Config, load_svbrdf_maps
import torch
import utils


class SvbrdfDataset(torch.utils.data.Dataset):
    """
    Class representing a collection of SVBRDF samples with corresponding input images (rendered or real views of the SVBRDF)
    """

    def __init__(
        self,
        data_directory,
        image_size,
        scale_mode,
        input_image_count,
        used_input_image_count,
        use_augmentation,
        mix_materials=False,
        no_svbrdf=False,
        is_linear=False,
        random_crop=False,
    ):
        self.data_directory = data_directory
        self.file_paths = [
            os.path.join(data_directory, f)
            for f in os.listdir(data_directory)
            if os.path.isfile(os.path.join(data_directory, f))
        ]

        self.image_size = image_size
        self.scale_mode = scale_mode
        self.input_image_count = input_image_count
        self.used_input_image_count = used_input_image_count
        # No augmentation means fixed view distance, light intensity, neutral whitebalance and constant flash falloff
        self.use_augmentation = use_augmentation

        # Material mixing augments the dataset by mixing together two materials
        self.mix_materials = mix_materials
        if self.mix_materials and self.input_image_count > 0:
            self.mix_materials = False
            print(
                "Warning: Material mixing is only supported for datasets without input images."
            )

        # The dataset does not contain any svbrdf maps
        self.no_svbrdf = no_svbrdf

        # The images in the dataset are already linear RGB
        self.is_linear = is_linear

        # If scale mode is 'crop', crop out a randomly placed window from the image
        self.random_crop = random_crop

    def __len__(self):
        return len(self.file_paths)

    def __getitem__(self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()

        input_images, svbrdf = self.read_sample(self.file_paths[idx])

        if self.mix_materials:
            import random

            other_index = random.randrange(0, self.__len__())
            _, other_svbrdf = self.read_sample(self.file_paths[other_index])
            svbrdf = self.mix(svbrdf, other_svbrdf)

        if self.scale_mode == "resize":
            width = input_images.shape[-1]
            height = input_images.shape[-2]

            # Crop out the center of the sample
            is_landscape = width > height
            crop_anchor = (
                torch.IntTensor([0, (width - height) // 2])
                if is_landscape
                else torch.IntTensor([(height - width) // 2, 0])
            )
            crop_size = height if is_landscape else width

            input_images = utils.crop_square(input_images, crop_anchor, crop_size)
            svbrdf = utils.crop_square(svbrdf, crop_anchor, crop_size)

            # Scale the images and the SVBRDF down to the desired size
            # Note: Do not use bicubic interpolation, as it might produce negative values or values > 1 which requires clamping
            input_images = torch.nn.functional.interpolate(
                input_images, size=(self.image_size, self.image_size), mode="bilinear"
            )
            svbrdf = torch.nn.functional.interpolate(
                svbrdf.unsqueeze(0),
                size=(self.image_size, self.image_size),
                mode="bilinear",
            ).squeeze(0)
        elif self.scale_mode == "crop":
            width = input_images.shape[-1]
            height = input_images.shape[-2]

            # Determine the top left point of the cropped image
            # TODO: For random jittering, the crop size would need to be a little bit larger than the final size
            #       and a final cropping stage would be needed after image generation, which produces slightly unaligned images.
            crop_anchor = torch.IntTensor([0, 0])
            if self.random_crop:
                crop_anchor = torch.IntTensor(
                    [
                        np.random.randint(0, height - self.image_size + 1),
                        np.random.randint(0, width - self.image_size + 1),
                    ]
                )

            # Crop down the svbrdf and the given input images
            svbrdf = utils.crop_square(svbrdf, crop_anchor, self.image_size)
            input_images = utils.crop_square(input_images, crop_anchor, self.image_size)
        else:
            raise ValueError("Unknown scale mode {}".format(self.scale_mode))

        # Transform to linear RGB
        if not self.is_linear:
            input_images = utils.gamma_decode(input_images)

        # Images which cannot be read must be generated artificially
        generated_input_image_count = (
            self.used_input_image_count - input_images.shape[0]
        )
        if generated_input_image_count > 0:
            renderings = self.render_inputs(svbrdf, generated_input_image_count)
            input_images = torch.cat([input_images, renderings], dim=0)

        # TODO: For random jittering we would need to re-crop to the final size at this point
        #       with individual crop anchors for all the images.

        return {"inputs": input_images, "svbrdf": svbrdf}

    def read_sample(self, file_path):
        # Read full image
        image = load_svbrdf_maps([file_path])

        # 4 is the number of maps in the SVBRDF
        svbrdf_map_count = 0 if self.no_svbrdf else 4

        # Split the full image apart along the horizontal direction
        # image_parts.shape = (4 + n_input_images, channels, height, width)
        image_parts = torch.cat(image.chunk(self.input_image_count + svbrdf_map_count, dim=-1), dim=0)

        # Read the SVBRDF (dummy if there is none in the dataset)
        svbrdf = None
        if self.no_svbrdf:
            # If there are no SVBRDFs in the data, there must be images which we can use as size guide.
            width = image_parts[0].shape[-1]
            height = image_parts[0].shape[-2]

            normals = torch.cat(
                [torch.zeros((2, height, width)), torch.ones((1, height, width))], dim=0
            )
            diffuse = torch.zeros_like(normals)
            roughness = torch.zeros_like(normals)
            specular = torch.zeros_like(normals)
        else:
            normals = image_parts[self.input_image_count + 0].unsqueeze(0)
            normals = utils.decode_from_unit_interval(normals)
            diffuse = image_parts[self.input_image_count + 1].unsqueeze(0)
            roughness = image_parts[self.input_image_count + 2].unsqueeze(0)
            specular = image_parts[self.input_image_count + 3].unsqueeze(0)

        svbrdf = utils.pack_svbrdf(normals, diffuse, roughness, specular).squeeze(
            0
        )  # [12, 256, 256]

        # We read as many input images from the disk as we can
        # FIXME: This is a little bit counter-intuitive, as we are reading the last n images, not the first n
        read_input_image_count = min(
            self.input_image_count, self.used_input_image_count
        )
        input_images = image_parts[
            (self.input_image_count - read_input_image_count) : self.input_image_count
        ]  # [ni, 3, 256, 256]

        return input_images, svbrdf

    def mix(self, svbrdf_0, svbrdf_1, alpha=None):
        if alpha is None:
            alpha = torch.Tensor(1).uniform_(0.1, 0.9)

        normals_0, diffuse_0, roughness_0, specular_0 = utils.unpack_svbrdf(svbrdf_0)
        normals_1, diffuse_1, roughness_1, specular_1 = utils.unpack_svbrdf(svbrdf_1)

        # Reference "Project the normals to use the X and Y derivative"
        normals_0_projected = normals_0 / torch.max(
            torch.Tensor([0.01]), normals_0[2:3, :, :]
        )
        normals_1_projected = normals_1 / torch.max(
            torch.Tensor([0.01]), normals_1[2:3, :, :]
        )

        normals_mixed = (
            alpha * normals_0_projected + (1.0 - alpha) * normals_1_projected
        )
        normals_mixed = normals_mixed / torch.sqrt(
            torch.sum(normals_mixed ** 2, axis=0, keepdim=True)
        )  # Normalization

        diffuse_mixed = alpha * diffuse_0 + (1.0 - alpha) * diffuse_1
        roughness_mixed = alpha * roughness_0 + (1.0 - alpha) * roughness_1
        specular_mixed = alpha * specular_0 + (1.0 - alpha) * specular_1

        return utils.pack_svbrdf(
            normals_mixed, diffuse_mixed, roughness_mixed, specular_mixed
        )

    def render_inputs(self, svbrdf, count):
        # Constants as defined in the reference code
        min_eps = 0.001  # Reference: "allows near 90     degrees angles"
        max_eps = 0.02  # Reference: "removes all angles below 8.13 degrees."
        fixed_light_distance = 2.197
        fixed_view_distance = 2.75  # Reference: "39.98 degrees FOV"

        # Generate scenes (camera and light configurations)
        # The in the first configuration, the light and view direction are guaranteed to be perpendicular to the material sample.
        # For the remaining cases, both are randomly sampled from a hemisphere.
        light_poses = torch.cat(
            [
                torch.Tensor(2).uniform_(-0.75, 0.75),
                torch.ones(1) * fixed_light_distance,
            ],
            dim=-1,
        ).unsqueeze(0)
        if count > 1:
            light_poses_hemisphere = (
                utils.generate_normalized_random_direction(
                    count - 1, min_eps=min_eps, max_eps=max_eps
                )
                * fixed_light_distance
            )
            light_poses = torch.cat([light_poses, light_poses_hemisphere], dim=0)

        light_colors = torch.Tensor([35.0]).unsqueeze(-1)
        if self.use_augmentation:
            # Reference: "add a normal distribution to the stddev so that sometimes in a minibatch all the images are consistant and sometimes crazy".
            # NOTE: For us, this effect will not be batch-wide but only for this individual sample.
            # FIXME: Since our renderer is differently implemented, the color variations with the given standard deviations
            #        merely have an effect.
            std_deviation = torch.exp(
                torch.Tensor(1).normal_(mean=-2.0, std=0.5)
            ).numpy()[0]
            light_colors = torch.abs(
                torch.Tensor(count).normal_(mean=20.0, std=std_deviation)
            ).unsqueeze(-1)
        light_colors = light_colors.expand(count, 3)

        # Handle white balance by varying the light color not the camera properties
        if self.use_augmentation:
            white_balance = torch.abs(
                torch.Tensor(count, 3).normal_(mean=1.0, std=0.03)
            )
            light_colors = light_colors * white_balance

        if self.use_augmentation:
            # Reference: "Simulates a FOV between 30 degrees and 50 degrees centered around 40 degrees"
            # NOTE: This probably does not do what the reference code expects it to do.
            #       The uniform distribution generates view distances in [0.25, 2.75] which
            #       correspond to FOVs between roughly 150 degrees and 40 degrees.
            view_distance = torch.Tensor(count).uniform_(0.25, 2.75)
        else:
            view_distance = torch.ones(count) * fixed_view_distance

        view_poses = torch.cat(
            [torch.Tensor(2).uniform_(-0.25, 0.25), view_distance[:1]], dim=-1
        ).unsqueeze(0)
        if count > 1:
            view_poses_hemisphere = utils.generate_normalized_random_direction(
                count - 1, min_eps=min_eps, max_eps=max_eps
            ) * view_distance[1:].unsqueeze(-1)
            view_poses = torch.cat([view_poses, view_poses_hemisphere], dim=0)

        R = Renderer()
        # TODO: Add spotlight support to the renderer (currentConeTargetPos in the reference code)
        C = Config()
        C.light_position(light_poses)
        C.view_position(view_poses)
        C.light_color_intensity(30.0)

        R.set_config(C) # add decoder to renderer
        rendering = R.run(svbrdf.unsqueeze(0), stacked_channels=True)

        # Simulate noise
        std_deviation_noise = torch.exp(
            torch.Tensor(1).normal_(mean=np.log(0.005), std=0.3)
        ).numpy()[0]
        noise = torch.zeros_like(rendering).normal_(
            mean=0.0, std=std_deviation_noise
        )
        rendering = torch.clamp(rendering + noise, min=0.0, max=1.0)

        return rendering.squeeze(0).permute(0, 3, 1, 2)
CODE SOURCE adapted from https://github.com/mworchel/svbrdf-estimation  ADD CREDITS / REFERENCE BEFORE CODE RELEASE!!! Class representing a collection of SVBRDF samples with corresponding input images (rendered or real views of the SVBRDF) # No augmentation means fixed view distance, light intensity, neutral whitebalance and constant flash falloff # Material mixing augments the dataset by mixing together two materials # The dataset does not contain any svbrdf maps # The images in the dataset are already linear RGB # If scale mode is 'crop', crop out a randomly placed window from the image # Crop out the center of the sample # Scale the images and the SVBRDF down to the desired size # Note: Do not use bicubic interpolation, as it might produce negative values or values > 1 which requires clamping # Determine the top left point of the cropped image # TODO: For random jittering, the crop size would need to be a little bit larger than the final size #       and a final cropping stage would be needed after image generation, which produces slightly unaligned images. # Crop down the svbrdf and the given input images # Transform to linear RGB # Images which cannot be read must be generated artificially # TODO: For random jittering we would need to re-crop to the final size at this point #       with individual crop anchors for all the images. # Read full image # 4 is the number of maps in the SVBRDF # Split the full image apart along the horizontal direction # image_parts.shape = (4 + n_input_images, channels, height, width) # Read the SVBRDF (dummy if there is none in the dataset) # If there are no SVBRDFs in the data, there must be images which we can use as size guide. # [12, 256, 256] # We read as many input images from the disk as we can # FIXME: This is a little bit counter-intuitive, as we are reading the last n images, not the first n # [ni, 3, 256, 256] # Reference "Project the normals to use the X and Y derivative" # Normalization # Constants as defined in the reference code # Reference: "allows near 90     degrees angles" # Reference: "removes all angles below 8.13 degrees." # Reference: "39.98 degrees FOV" # Generate scenes (camera and light configurations) # The in the first configuration, the light and view direction are guaranteed to be perpendicular to the material sample. # For the remaining cases, both are randomly sampled from a hemisphere. # Reference: "add a normal distribution to the stddev so that sometimes in a minibatch all the images are consistant and sometimes crazy". # NOTE: For us, this effect will not be batch-wide but only for this individual sample. # FIXME: Since our renderer is differently implemented, the color variations with the given standard deviations #        merely have an effect. # Handle white balance by varying the light color not the camera properties # Reference: "Simulates a FOV between 30 degrees and 50 degrees centered around 40 degrees" # NOTE: This probably does not do what the reference code expects it to do. #       The uniform distribution generates view distances in [0.25, 2.75] which #       correspond to FOVs between roughly 150 degrees and 40 degrees. # TODO: Add spotlight support to the renderer (currentConeTargetPos in the reference code) # add decoder to renderer # Simulate noise