Merge HDR stuff to main

nerfstudio/configs/method_configs.py

@@ -19,38 +19,44 @@ Put all the method implementations in one location.
 from __future__ import annotations
 
 from collections import OrderedDict
-from typing import Dict
 from dataclasses import dataclass, field
+from typing import Dict
 
 import tyro
+from lantern.config import lantern_config
 
 from nerfstudio.cameras.camera_optimizers import CameraOptimizerConfig
 from nerfstudio.configs.base_config import ViewerConfig
 from nerfstudio.configs.external_methods import get_external_methods
-
-from nerfstudio.data.datamanagers.random_cameras_datamanager import RandomCamerasDataManagerConfig
-from nerfstudio.data.datamanagers.base_datamanager import VanillaDataManager, VanillaDataManagerConfig, HDRVanillaDataManagerConfig
-
-from nerfstudio.data.dataparsers.blender_dataparser import BlenderDataParserConfig
+from nerfstudio.data.datamanagers.base_datamanager import (
+    HDRVanillaDataManagerConfig, VanillaDataManager, VanillaDataManagerConfig)
+from nerfstudio.data.datamanagers.random_cameras_datamanager import \
+    RandomCamerasDataManagerConfig
+from nerfstudio.data.dataparsers.blender_dataparser import \
+    BlenderDataParserConfig
 from nerfstudio.data.dataparsers.dnerf_dataparser import DNeRFDataParserConfig
-from nerfstudio.data.dataparsers.instant_ngp_dataparser import InstantNGPDataParserConfig
-from nerfstudio.data.dataparsers.nerfstudio_dataparser import NerfstudioDataParserConfig
-from nerfstudio.data.dataparsers.phototourism_dataparser import PhototourismDataParserConfig
-from nerfstudio.data.dataparsers.sdfstudio_dataparser import SDFStudioDataParserConfig
-from nerfstudio.data.dataparsers.sitcoms3d_dataparser import Sitcoms3DDataParserConfig
+from nerfstudio.data.dataparsers.instant_ngp_dataparser import \
+    InstantNGPDataParserConfig
+from nerfstudio.data.dataparsers.nerfstudio_dataparser import \
+    NerfstudioDataParserConfig
+from nerfstudio.data.dataparsers.phototourism_dataparser import \
+    PhototourismDataParserConfig
+from nerfstudio.data.dataparsers.sdfstudio_dataparser import \
+    SDFStudioDataParserConfig
+from nerfstudio.data.dataparsers.sitcoms3d_dataparser import \
+    Sitcoms3DDataParserConfig
 from nerfstudio.data.datasets.depth_dataset import DepthDataset
+from nerfstudio.data.datasets.hdr_dataset import HDRInputDataset
 from nerfstudio.data.datasets.sdf_dataset import SDFDataset
 from nerfstudio.data.datasets.semantic_dataset import SemanticDataset
-from nerfstudio.data.datasets.hdr_dataset import HDRInputDataset
-
-from nerfstudio.engine.optimizers import AdamOptimizerConfig, RAdamOptimizerConfig
-from nerfstudio.engine.schedulers import (
-    CosineDecaySchedulerConfig,
-    ExponentialDecaySchedulerConfig,
-    MultiStepSchedulerConfig,
-)
+from nerfstudio.engine.optimizers import (AdamOptimizerConfig,
+                                          RAdamOptimizerConfig)
+from nerfstudio.engine.schedulers import (CosineDecaySchedulerConfig,
+                                          ExponentialDecaySchedulerConfig,
+                                          MultiStepSchedulerConfig)
 from nerfstudio.engine.trainer import TrainerConfig
-from nerfstudio.field_components.temporal_distortions import TemporalDistortionKind
+from nerfstudio.field_components.temporal_distortions import \
+    TemporalDistortionKind
 from nerfstudio.fields.sdf_field import SDFFieldConfig
 from nerfstudio.models.depth_nerfacto import DepthNerfactoModelConfig
 from nerfstudio.models.generfacto import GenerfactoModelConfig
@@ -70,6 +76,7 @@ method_configs: Dict[str, TrainerConfig] = {}
 descriptions = {
     "nerfacto": "Recommended real-time model tuned for real captures. This model will be continually updated.",
     "nerfacto-HDR": "Nerfacto that take HDR as input, and output HDR field.",
+    "lantern-nerfacto": "Nerfacto that take HDR as input, and output HDR field.",
     "depth-nerfacto": "Nerfacto with depth supervision.",
     "volinga": "Real-time rendering model from Volinga. Directly exportable to NVOL format at https://volinga.ai/",
     "instant-ngp": "Implementation of Instant-NGP. Recommended real-time model for unbounded scenes.",
@@ -118,38 +125,8 @@ method_configs["nerfacto"] = TrainerConfig(
     vis="viewer",
 )
 
-method_configs["nerfacto-HDR"] = TrainerConfig(
-    method_name="nerfacto-HDR",
-    steps_per_eval_batch=500,
-    steps_per_save=2000,
-    max_num_iterations=30000,
-    mixed_precision=True,
-    pipeline=VanillaPipelineConfig(
-            datamanager=HDRVanillaDataManagerConfig(
-            dataparser=NerfstudioDataParserConfig(),
-            train_num_rays_per_batch=4096,
-            eval_num_rays_per_batch=4096,
-            camera_optimizer=CameraOptimizerConfig(
-                mode="SO3xR3",
-                optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2),
-                scheduler=ExponentialDecaySchedulerConfig(lr_final=6e-6, max_steps=200000),
-            ),
-        ),
-        model=NerfactoModelConfig(eval_num_rays_per_chunk=1 << 15),
-    ),
-    optimizers={
-        "proposal_networks": {
-            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
-            "scheduler": ExponentialDecaySchedulerConfig(lr_final=0.0001, max_steps=200000),
-        },
-        "fields": {
-            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
-            "scheduler": ExponentialDecaySchedulerConfig(lr_final=0.0001, max_steps=200000),
-        },
-    },
-    viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
-    vis="viewer",
-)
+method_configs["lantern-nerfacto"] = lantern_config
+
 method_configs["nerfacto-big"] = TrainerConfig(
     method_name="nerfacto",
     steps_per_eval_batch=500,

nerfstudio/lantern/config.py

@@ -0,0 +1,55 @@
+"""
+Nerfstudio Config
+
+Define custom method here that registers with Nerfstudio CLI.
+"""
+
+from __future__ import annotations
+
+from nerfstudio.cameras.camera_optimizers import CameraOptimizerConfig
+from nerfstudio.configs.base_config import ViewerConfig
+from nerfstudio.data.dataparsers.nerfstudio_dataparser import \
+    NerfstudioDataParserConfig
+from nerfstudio.engine.optimizers import (AdamOptimizerConfig,
+                                          RAdamOptimizerConfig)
+from nerfstudio.engine.schedulers import ExponentialDecaySchedulerConfig
+from nerfstudio.engine.trainer import TrainerConfig
+from nerfstudio.lantern.datamanager import HDRVanillaDataManagerConfig
+from nerfstudio.lantern.model import LanternModelConfig
+from nerfstudio.pipelines.base_pipeline import VanillaPipelineConfig
+from nerfstudio.plugins.types import MethodSpecification
+
+
+def Get_lantern_config():
+    return TrainerConfig(
+    method_name="Lantern-nerfacto",
+    steps_per_eval_batch=500,
+    steps_per_save=2000,
+    max_num_iterations=30000,
+    mixed_precision=True,
+    pipeline=VanillaPipelineConfig(
+            datamanager=HDRVanillaDataManagerConfig(
+            dataparser=NerfstudioDataParserConfig(),
+            train_num_rays_per_batch=4096,
+            eval_num_rays_per_batch=4096,
+            camera_optimizer=CameraOptimizerConfig(
+                mode="SO3xR3",
+                optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2),
+                scheduler=ExponentialDecaySchedulerConfig(lr_final=6e-6, max_steps=200000),
+            ),
+        ),
+        model=LanternModelConfig(eval_num_rays_per_chunk=1 << 15),
+    ),
+    optimizers={
+        "proposal_networks": {
+            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
+            "scheduler": ExponentialDecaySchedulerConfig(lr_final=0.0001, max_steps=200000),
+        },
+        "fields": {
+            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
+            "scheduler": ExponentialDecaySchedulerConfig(lr_final=0.0001, max_steps=200000),
+        },
+    },
+    viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
+    vis="viewer",
+    )

nerfstudio/lantern/datamanager.py

@@ -0,0 +1,22 @@
+"""
+Lantern HDR DataManager
+"""
+
+from dataclasses import dataclass, field
+from typing import Dict, Literal, Tuple, Type, Union
+
+import torch
+
+from nerfstudio.cameras.rays import RayBundle
+from nerfstudio.data.datamanagers.base_datamanager import (
+    VanillaDataManager, VanillaDataManagerConfig)
+from nerfstudio.data.datasets.hdr_dataset import HDRInputDataset
+
+
+@dataclass
+class HDRVanillaDataManagerConfig(VanillaDataManagerConfig):
+    """A HDR data manager, based on VanillaDataManager"""
+
+    _target: Type = field(default_factory=lambda: VanillaDataManager[HDRInputDataset])
+    """Target class to instantiate."""
+    

nerfstudio/lantern/dataset.py

@@ -0,0 +1,140 @@
+# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+HDR Dataset of .exr format
+"""
+from __future__ import annotations
+
+import os
+from copy import deepcopy
+from pathlib import Path
+from typing import Dict, List
+
+os.environ["OPENCV_IO_ENABLE_OPENEXR"]="1"
+
+import cv2
+import numpy as np
+import numpy.typing as npt
+import torch
+from jaxtyping import Float
+from PIL import Image
+from torch import Tensor
+from torch.utils.data import Dataset
+
+from nerfstudio.cameras.cameras import Cameras
+from nerfstudio.data.dataparsers.base_dataparser import DataparserOutputs
+from nerfstudio.data.datasets.base_dataset import InputDataset
+from nerfstudio.data.utils.data_utils import get_image_mask_tensor_from_path
+
+
+class HDRInputDataset(InputDataset):
+    """Dataset that returns HDR images, OpenEXR.
+
+    Args:
+        dataparser_outputs: description of where and how to read input images.
+        scale_factor: The scaling factor for the dataparser outputs
+    """
+
+    # exclude_batch_keys_from_device: List[str] = ["image", "mask"]
+    # cameras: Cameras
+
+    def __init__(self, dataparser_outputs: DataparserOutputs, scale_factor: float = 1.0):
+        super().__init__(dataparser_outputs, scale_factor)
+        
+    def __len__(self):
+        return len(self._dataparser_outputs.image_filenames)
+
+    def get_numpy_image(self, image_idx: int) -> npt.NDArray[np.uint8]:
+        """Returns the image of shape (H, W, 3 or 4), with np.float32 values.
+
+        Args:
+            image_idx: The image index in the dataset.
+        """
+        image_filename = self._dataparser_outputs.image_filenames[image_idx]
+        hdr_image = cv2.imread(str(image_filename),  cv2.IMREAD_UNCHANGED)
+        if self.scale_factor != 1.0:
+            # Here cv2 img shape is (height, width, BGR)
+            height, width = hdr_image.shape[:2]
+            newsize = (int(width * self.scale_factor), int(height * self.scale_factor))
+            hdr_image = cv2.resize(hdr_image, newsize, interpolation = cv2.INTER_LINEAR)
+        hdr_image = cv2.cvtColor(hdr_image, cv2.COLOR_BGR2RGB)
+        hdr_image = hdr_image.astype("float32")  # shape is (h, w) or (h, w, 3 or 4)
+        if len(hdr_image.shape) == 2:
+            hdr_image = hdr_image[:, :, None].repeat(3, axis=2)
+        assert len(hdr_image.shape) == 3
+        assert hdr_image.dtype == np.float32
+        assert hdr_image.shape[2] in [3, 4], f"Image shape of {hdr_image.shape} is in correct."
+        return hdr_image
+
+    def get_image(self, image_idx: int) -> Float[Tensor, "image_height image_width num_channels"]:
+        """Returns a 3 channel image.
+
+        Args:
+            image_idx: The image index in the dataset.
+        """
+        # Compress HDR using: pixel = log(pixel + 1.)
+            
+        image = torch.from_numpy(self.get_numpy_image(image_idx).astype("float32"))
+        # image = torch.log(1. + image)
+        # image = torch.log(1. + image)
+        # image = torch.log(1. + image)
+        
+        # Compress HDR using u-law: pixel = log(u * pixel + 1.) / log(u + 1.)
+        u = 5000.
+        image = torch.log(1. + u * image) / torch.log(torch.tensor(1.+u))
+        
+        if self._dataparser_outputs.alpha_color is not None and image.shape[-1] == 4:
+            image = image[:, :, :3] * image[:, :, -1:] + self._dataparser_outputs.alpha_color * (1.0 - image[:, :, -1:])
+        return image
+
+    def get_data(self, image_idx: int) -> Dict:
+        """Returns the ImageDataset data as a dictionary.
+
+        Args:
+            image_idx: The image index in the dataset.
+        """
+        image = self.get_image(image_idx)
+        data = {"image_idx": image_idx, "image": image}
+        if self._dataparser_outputs.mask_filenames is not None:
+            mask_filepath = self._dataparser_outputs.mask_filenames[image_idx]
+            data["mask"] = get_image_mask_tensor_from_path(filepath=mask_filepath, scale_factor=self.scale_factor)
+            assert (
+                data["mask"].shape[:2] == data["image"].shape[:2]
+            ), f"Mask and image have different shapes. Got {data['mask'].shape[:2]} and {data['image'].shape[:2]}"
+        metadata = self.get_metadata(data)
+        data.update(metadata)
+        return data
+
+    def get_metadata(self, data: Dict) -> Dict:
+        """Method that can be used to process any additional metadata that may be part of the model inputs.
+
+        Args:
+            image_idx: The image index in the dataset.
+        """
+        del data
+        return {}
+
+    def __getitem__(self, image_idx: int) -> Dict:
+        data = self.get_data(image_idx)
+        return data
+
+    @property
+    def image_filenames(self) -> List[Path]:
+        """
+        Returns image filenames for this dataset.
+        The order of filenames is the same as in the Cameras object for easy mapping.
+        """
+
+        return self._dataparser_outputs.image_filenames

nerfstudio/lantern/field.py

@@ -0,0 +1,71 @@
+"""
+HDR Nerfstudio Field
+
+Currently this subclasses the NerfactoField. Consider subclassing the base Field.
+"""
+
+from typing import Literal, Optional
+
+from torch import Tensor, nn
+
+from nerfstudio.field_components.mlp import MLP
+from nerfstudio.field_components.spatial_distortions import SpatialDistortion
+from nerfstudio.fields.nerfacto_field import \
+    NerfactoField  # for subclassing NerfactoField
+
+
+class LanternNerfactoField(NerfactoField):
+    """Template Field
+
+    Args:
+        aabb: parameters of scene aabb bounds
+        num_images: number of images in the dataset
+    """
+
+    aabb: Tensor
+
+    def __init__(
+        self,
+        aabb: Tensor,
+        num_images: int,
+        num_layers: int = 2,
+        hidden_dim: int = 64,
+        geo_feat_dim: int = 15,
+        num_levels: int = 16,
+        base_res: int = 16,
+        max_res: int = 2048,
+        log2_hashmap_size: int = 19,
+        num_layers_color: int = 3,
+        num_layers_transient: int = 2,
+        features_per_level: int = 2,
+        hidden_dim_color: int = 64,
+        hidden_dim_transient: int = 64,
+        appearance_embedding_dim: int = 32,
+        transient_embedding_dim: int = 16,
+        use_transient_embedding: bool = False,
+        use_semantics: bool = False,
+        num_semantic_classes: int = 100,
+        pass_semantic_gradients: bool = False,
+        use_pred_normals: bool = False,
+        use_average_appearance_embedding: bool = False,
+        spatial_distortion: Optional[SpatialDistortion] = None,
+        implementation: Literal["tcnn", "torch"] = "tcnn",
+    ) -> None:
+        super().__init__(aabb, num_images, num_layers, hidden_dim,
+                         geo_feat_dim, num_levels, base_res, 
+                         max_res, log2_hashmap_size, num_layers_color,
+                         num_layers_transient, features_per_level, hidden_dim_color,
+                         hidden_dim_transient, appearance_embedding_dim, transient_embedding_dim,
+                         use_transient_embedding, use_semantics, num_semantic_classes,
+                         pass_semantic_gradients, use_pred_normals, use_average_appearance_embedding,
+                         spatial_distortion, implementation)
+        # Define the last part MLP for RGB outputs
+        self.mlp_head = MLP(
+            in_dim=self.direction_encoding.get_out_dim() + self.geo_feat_dim + self.appearance_embedding_dim,
+            num_layers=num_layers_color,
+            layer_width=hidden_dim_color,
+            out_dim=3,
+            activation=nn.ReLU(),
+            out_activation=nn.ReLU(),
+            implementation=implementation,
+        )

nerfstudio/lantern/model.py

@@ -0,0 +1,190 @@
+"""
+Lantern Model File
+
+Currently this subclasses the Nerfacto model. Consider subclassing from the base Model.
+"""
+from dataclasses import dataclass, field
+from typing import Dict, List, Literal, Tuple, Type
+
+import numpy as np
+import torch
+from torchmetrics.functional import structural_similarity_index_measure
+from torchmetrics.image import PeakSignalNoiseRatio
+from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
+
+from nerfstudio.field_components.spatial_distortions import SceneContraction
+from nerfstudio.fields.density_fields import HashMLPDensityField
+from nerfstudio.lantern.field import LanternNerfactoField
+from nerfstudio.model_components.losses import (
+    MSELoss, distortion_loss, interlevel_loss, orientation_loss,
+    pred_normal_loss, scale_gradients_by_distance_squared)
+from nerfstudio.model_components.ray_samplers import (ProposalNetworkSampler,
+                                                      UniformSampler)
+from nerfstudio.model_components.renderers import (AccumulationRenderer,
+                                                   DepthRenderer,
+                                                   NormalsRenderer,
+                                                   RGBRenderer)
+from nerfstudio.model_components.scene_colliders import NearFarCollider
+from nerfstudio.model_components.shaders import NormalsShader
+from nerfstudio.models.base_model import Model, ModelConfig  # for custom Model
+from nerfstudio.models.nerfacto import (  # for subclassing Nerfacto model
+    NerfactoModel, NerfactoModelConfig)
+from nerfstudio.utils import colormaps
+
+
+@dataclass
+class LanternModelConfig(NerfactoModelConfig):
+    """Template Model Configuration.
+
+    Add your custom model config parameters here.
+    """
+
+    _target: Type = field(default_factory=lambda: LanternModel)
+
+
+class LanternModel(NerfactoModel):
+    """Template Model."""
+
+    config: LanternModelConfig
+
+    def populate_modules(self):
+        super().populate_modules()
+
+        if self.config.disable_scene_contraction:
+            scene_contraction = None
+        else:
+            scene_contraction = SceneContraction(order=float("inf"))
+
+        # Fields
+        self.field = LanternNerfactoField(
+            self.scene_box.aabb,
+            hidden_dim=self.config.hidden_dim,
+            num_levels=self.config.num_levels,
+            max_res=self.config.max_res,
+            base_res=self.config.base_res,
+            features_per_level=self.config.features_per_level,
+            log2_hashmap_size=self.config.log2_hashmap_size,
+            hidden_dim_color=self.config.hidden_dim_color,
+            hidden_dim_transient=self.config.hidden_dim_transient,
+            spatial_distortion=scene_contraction,
+            num_images=self.num_train_data,
+            use_pred_normals=self.config.predict_normals,
+            use_average_appearance_embedding=self.config.use_average_appearance_embedding,
+            appearance_embedding_dim=self.config.appearance_embed_dim,
+            implementation=self.config.implementation,
+        )
+
+        self.density_fns = []
+        num_prop_nets = self.config.num_proposal_iterations
+        # Build the proposal network(s)
+        self.proposal_networks = torch.nn.ModuleList()
+        if self.config.use_same_proposal_network:
+            assert len(self.config.proposal_net_args_list) == 1, "Only one proposal network is allowed."
+            prop_net_args = self.config.proposal_net_args_list[0]
+            network = HashMLPDensityField(
+                self.scene_box.aabb,
+                spatial_distortion=scene_contraction,
+                **prop_net_args,
+                implementation=self.config.implementation,
+            )
+            self.proposal_networks.append(network)
+            self.density_fns.extend([network.density_fn for _ in range(num_prop_nets)])
+        else:
+            for i in range(num_prop_nets):
+                prop_net_args = self.config.proposal_net_args_list[min(i, len(self.config.proposal_net_args_list) - 1)]
+                network = HashMLPDensityField(
+                    self.scene_box.aabb,
+                    spatial_distortion=scene_contraction,
+                    **prop_net_args,
+                    implementation=self.config.implementation,
+                )
+                self.proposal_networks.append(network)
+            self.density_fns.extend([network.density_fn for network in self.proposal_networks])
+
+        # Samplers
+        def update_schedule(step):
+            return np.clip(
+                np.interp(step, [0, self.config.proposal_warmup], [0, self.config.proposal_update_every]),
+                1,
+                self.config.proposal_update_every,
+            )
+
+        # Change proposal network initial sampler if uniform
+        initial_sampler = None  # None is for piecewise as default (see ProposalNetworkSampler)
+        if self.config.proposal_initial_sampler == "uniform":
+            initial_sampler = UniformSampler(single_jitter=self.config.use_single_jitter)
+
+        self.proposal_sampler = ProposalNetworkSampler(
+            num_nerf_samples_per_ray=self.config.num_nerf_samples_per_ray,
+            num_proposal_samples_per_ray=self.config.num_proposal_samples_per_ray,
+            num_proposal_network_iterations=self.config.num_proposal_iterations,
+            single_jitter=self.config.use_single_jitter,
+            update_sched=update_schedule,
+            initial_sampler=initial_sampler,
+        )
+
+        # Collider
+        self.collider = NearFarCollider(near_plane=self.config.near_plane, far_plane=self.config.far_plane)
+
+        # renderers
+        self.renderer_rgb = RGBRenderer(background_color=self.config.background_color)
+        self.renderer_accumulation = AccumulationRenderer()
+        self.renderer_depth = DepthRenderer()
+        self.renderer_normals = NormalsRenderer()
+
+        # shaders
+        self.normals_shader = NormalsShader()
+
+        # TODO losses
+        self.rgb_loss = MSELoss()
+
+        # metrics
+        self.psnr = PeakSignalNoiseRatio(data_range=1.0)
+        self.ssim = structural_similarity_index_measure
+        self.lpips = LearnedPerceptualImagePatchSimilarity(normalize=True)
+    
+    def get_image_metrics_and_images(
+        self, outputs: Dict[str, torch.Tensor], batch: Dict[str, torch.Tensor]
+        ) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]:
+        gt_rgb = batch["image"].to(self.device)
+        predicted_rgb = outputs["rgb"]  # Blended with background (black if random background)
+        gt_rgb = self.renderer_rgb.blend_background(gt_rgb)
+        acc = colormaps.apply_colormap(outputs["accumulation"])
+        depth = colormaps.apply_depth_colormap(
+            outputs["depth"],
+            accumulation=outputs["accumulation"],
+        )
+
+        combined_rgb = torch.cat([gt_rgb, predicted_rgb], dim=1)
+        combined_acc = torch.cat([acc], dim=1)
+        combined_depth = torch.cat([depth], dim=1)
+
+        # Switch images from [H, W, C] to [1, C, H, W] for metrics computations
+        gt_rgb_tonemapped = torch.clamp(colormaps.apply_colormap(gt_rgb), 0, 1)
+        predicted_rgb_tonemapped = torch.clamp(colormaps.apply_colormap(predicted_rgb), 0, 1)
+        
+        gt_rgb_tonemapped = torch.moveaxis(gt_rgb_tonemapped, -1, 0)[None, ...]
+        predicted_rgb_tonemapped = torch.moveaxis(predicted_rgb_tonemapped, -1, 0)[None, ...]
+        gt_rgb = torch.moveaxis(gt_rgb, -1, 0)[None, ...]
+        predicted_rgb = torch.moveaxis(predicted_rgb, -1, 0)[None, ...]
+
+        psnr = self.psnr(gt_rgb, predicted_rgb)
+        ssim = self.ssim(gt_rgb, predicted_rgb)
+        # tonemapping before computing lpips
+        lpips = self.lpips(gt_rgb_tonemapped, predicted_rgb_tonemapped)
+
+        # all of these metrics will be logged as scalars
+        metrics_dict = {"psnr": float(psnr.item()), "ssim": float(ssim)}  # type: ignore
+        metrics_dict["lpips"] = float(lpips)
+
+        images_dict = {"img": combined_rgb, "accumulation": combined_acc, "depth": combined_depth}
+
+        for i in range(self.config.num_proposal_iterations):
+            key = f"prop_depth_{i}"
+            prop_depth_i = colormaps.apply_depth_colormap(
+                outputs[key],
+                accumulation=outputs["accumulation"],
+            )
+            images_dict[key] = prop_depth_i
+
+        return metrics_dict, images_dict

nerfstudio/process_data/aligned_pano2plane.py

@@ -17,16 +17,16 @@
 import json
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Optional, Literal, Tuple
+from typing import Literal, Optional, Tuple
 
 import numpy as np
 
 from nerfstudio.process_data import equirect_utils, process_data_utils
-from nerfstudio.process_data.base_converter_to_nerfstudio_dataset import (
-    BaseConverterToNerfstudioDataset,
-)
-from nerfstudio.utils.rich_utils import CONSOLE
+from nerfstudio.process_data.base_converter_to_nerfstudio_dataset import \
+    BaseConverterToNerfstudioDataset
 from nerfstudio.utils import io
+from nerfstudio.utils.rich_utils import CONSOLE
+
 
 @dataclass
 class ProcessAlignedPano(BaseConverterToNerfstudioDataset):
@@ -69,11 +69,10 @@ class ProcessAlignedPano(BaseConverterToNerfstudioDataset):
         summary_log = []
         pers_size = equirect_utils.compute_resolution_from_equirect(self.data, self.images_per_equirect)
         CONSOLE.log(f"Generating {self.images_per_equirect} {pers_size} sized images per equirectangular image")
-        # self.data = equirect_utils.generate_planar_projections_from_equirectangular(
-        #     self.metadata, self.data, pers_size, self.images_per_equirect, crop_factor=self.crop_factor
-        # )
+        self.data = equirect_utils.generate_planar_projections_from_equirectangular_GT(
+            self.metadata, self.data, pers_size, self.images_per_equirect, crop_factor=self.crop_factor, clip_output = False
+        )
         self.camera_type = "perspective"
-        self.data = self.data / "planar_projections"
         metadata_dict = io.load_from_json(self.data / "transforms.json")
         # Copy images to output directory
         cropped_images_filename = []

nerfstudio/process_data/equirect_utils.py

@@ -15,32 +15,27 @@
 """Helper utils for processing equirectangular data."""
 
 import os
+
 os.environ["OPENCV_IO_ENABLE_OPENEXR"]="1"
 
 import json
+import math
 import sys
 from pathlib import Path
 from typing import List, Tuple
-import math
 
 import cv2
 import numpy as np
-from numpy.linalg import inv
-
-from scipy.spatial.transform import Rotation
 import torch
-from equilib import Equi2Pers
-from rich.progress import (
-    BarColumn,
-    Progress,
-    TaskProgressColumn,
-    TextColumn,
-    TimeRemainingColumn,
-)
+from numpy.linalg import inv
+from rich.progress import (BarColumn, Progress, TaskProgressColumn, TextColumn,
+                           TimeRemainingColumn)
+from scipy.spatial.transform import Rotation
 
-from nerfstudio.utils.rich_utils import CONSOLE, ItersPerSecColumn
+from equilib import Equi2Pers
 from nerfstudio.process_data.process_data_utils import CAMERA_MODELS
 from nerfstudio.utils import io
+from nerfstudio.utils.rich_utils import CONSOLE, ItersPerSecColumn
 
 
 def _crop_bottom(bound_arr: list, fov: int, crop_factor: float) -> List[float]:
@@ -119,8 +114,11 @@ def generate_planar_projections_from_equirectangular(
     image_dir: Path,
     planar_image_size: Tuple[int, int],
     samples_per_im: int,
+<<<<<<< HEAD
     mask_dir: Path,
     hdr_dir: Path,
+=======
+>>>>>>> origin/jmchen
     crop_factor: Tuple[float, float, float, float] = (0.0, 0.0, 0.0, 0.0),
     is_HDR: bool = False,
     HDR_planar_image_size: Tuple[int, int] = (None, None),
@@ -184,6 +182,7 @@ def generate_planar_projections_from_equirectangular(
     frame_dir = image_dir
     output_dir = image_dir / "planar_projections"
     output_dir.mkdir(exist_ok=True)
+<<<<<<< HEAD
     
     output_mask_dir = None
     if mask_dir is not None:
@@ -197,6 +196,8 @@ def generate_planar_projections_from_equirectangular(
     else:
         equi2pers_hdr = equi2pers
     
+=======
+>>>>>>> origin/jmchen
     num_ims = len(os.listdir(frame_dir))
     progress = Progress(
         TextColumn("[bold blue]Generating Planar Images", justify="right"),
@@ -208,6 +209,7 @@ def generate_planar_projections_from_equirectangular(
 
     with progress:
         for i in progress.track(os.listdir(frame_dir), description="", total=num_ims):
+<<<<<<< HEAD
             if i.lower().endswith((".jpg", ".png", ".jpeg", ".exr")):
                 if i.lower().endswith((".exr")):
                     im = np.array(cv2.imread(os.path.join(frame_dir, i), cv2.IMREAD_UNCHANGED)).astype("float32")
@@ -241,10 +243,17 @@ def generate_planar_projections_from_equirectangular(
                     hdr = torch.tensor(hdr, dtype=torch.float32, device=device)
                     hdr = torch.permute(hdr, (2, 0, 1))
 
+=======
+            if i.lower().endswith((".jpg", ".png", ".jpeg")):
+                im = np.array(cv2.imread(os.path.join(frame_dir, i)))
+                im = torch.tensor(im, dtype=torch.float32, device=device)
+                im = torch.permute(im, (2, 0, 1)) / 255.0
+>>>>>>> origin/jmchen
                 count = 0
                 for u_deg, v_deg in yaw_pitch_pairs:
                     v_rad = torch.pi * v_deg / 180.0
                     u_rad = torch.pi * u_deg / 180.0
+<<<<<<< HEAD
                     pers_image = equi2pers(im, rots={"roll": 0, "pitch": v_rad, "yaw": u_rad})
                     if mask_dir is not None:
                         pers_mask = equi2pers_hdr(mask, rots={"roll": 0, "pitch": v_rad, "yaw": u_rad}) * 255.0
@@ -278,12 +287,24 @@ def generate_planar_projections_from_equirectangular(
     return output_dir, output_mask_dir, output_hdr_dir
 
 def generate_planar_projections_from_equirectangular_2(
+=======
+                    pers_image = equi2pers(im, rots={"roll": 0, "pitch": v_rad, "yaw": u_rad}) * 255.0
+                    assert isinstance(pers_image, torch.Tensor)
+                    pers_image = (pers_image.permute(1, 2, 0)).type(torch.uint8).to("cpu").numpy()
+                    cv2.imwrite(f"{output_dir}/{i[:-4]}_{count}.png", pers_image)
+                    count += 1
+
+    return output_dir
+
+def generate_planar_projections_from_equirectangular_GT(
+>>>>>>> origin/jmchen
     metadata_path: Path,
     image_dir: Path,
     planar_image_size: Tuple[int, int],
     samples_per_im: int,
     mask_dir: Path = None,
     crop_factor: Tuple[float, float, float, float] = (0.0, 0.0, 0.0, 0.0),
+    clip_output: bool = False,
 ) -> Path:
     """Given camera pose, generate planar projections from an equirectangular image.
        And output corresponding camera pose.
@@ -381,7 +402,7 @@ def generate_planar_projections_from_equirectangular_2(
                 for u_deg, v_deg in yaw_pitch_pairs:
                     v_rad = torch.pi * v_deg / 180.0
                     u_rad = torch.pi * u_deg / 180.0
-                    pers_image = equi2pers(im, rots={"roll": 0, "pitch": v_rad, "yaw": u_rad})
+                    pers_image = equi2pers(im, rots={"roll": 0, "pitch": v_rad, "yaw": u_rad}, clip_output=clip_output)
                     if mask_dir is not None:
                         pers_mask = equi2pers(mask, rots={"roll": 0, "pitch": v_rad, "yaw": u_rad}) * 255.0
                     # transform matrix for blender: object.matrix_world 

nerfstudio/utils/colormaps.py

@@ -43,8 +43,11 @@ class ColormapOptions:
     """ Whether to invert the output colormap """
     exposure_scale: float = 0
     """Scale the brightness prior to tonemapping by 2 ^ Exposure"""
+<<<<<<< HEAD
 
 
+=======
+>>>>>>> origin/jmchen
 
 def apply_colormap(
     image: Float[Tensor, "*bs channels"],
@@ -67,6 +70,7 @@ def apply_colormap(
 
     # default for rgb images
     if image.shape[-1] == 3:
+<<<<<<< HEAD
         # print('colormap_options.exposure_scale', colormap_options.exposure_scale)
         # if(colormap_options.exposure_scale != 0):
         #     img_gamma_22 = image ** (2.2) - 1.
@@ -76,6 +80,24 @@ def apply_colormap(
         # else:
         #     return image
         return image
+=======
+        if(colormap_options.exposure_scale != 0 or image.max() > 1.):
+            # Log
+            img_gamma_22 = torch.exp(image) - 1.
+            
+            # u-law
+            u = 5000.
+            img_gamma_22 = torch.exp(image * torch.log(torch.tensor(u+1.))) - 1.
+            img_gamma_22 /= u
+            
+            # img_gamma_22 = torch.exp(img_gamma_22) - 1.
+            # img_gamma_22 = torch.exp(img_gamma_22) - 1.
+            img_gamma_22 *= 2.**(colormap_options.exposure_scale)
+            img_gamma_22 = torch.pow(img_gamma_22, 1./2.2)
+            return torch.clamp(img_gamma_22, 0., 1.)
+        else:
+            return image
+>>>>>>> origin/jmchen
 
     # rendering depth outputs
     if image.shape[-1] == 1 and torch.is_floating_point(image):