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# Adapted from Marigold :https://github.com/prs-eth/Marigold
from typing import Any, Dict, Union
import torch
from torch.utils.data import DataLoader, TensorDataset
import numpy as np
from tqdm.auto import tqdm
from PIL import Image
from diffusers import (
DiffusionPipeline,
DDIMScheduler,
AutoencoderKL,
)
from models.unet_2d_condition import UNet2DConditionModel
from diffusers.utils import BaseOutput
from transformers import CLIPTextModel, CLIPTokenizer
from utils.image_util import resize_max_res,chw2hwc,colorize_depth_maps
from utils.depth_ensemble import ensemble_depths
from utils.normal_ensemble import ensemble_normals
from utils.batch_size import find_batch_size
import cv2
class DepthNormalPipelineOutput(BaseOutput):
"""
Output class for GeoWizard monocular depth & normal prediction pipeline.
Args:
depth_np (`np.ndarray`):
Predicted depth map, with depth values in the range of [0, 1].
depth_colored (`PIL.Image.Image`):
Colorized depth map, with the shape of [3, H, W] and values in [0, 1].
normal_np (`np.ndarray`):
Predicted normal map, with depth values in the range of [0, 1].
normal_colored (`PIL.Image.Image`):
Colorized normal map, with the shape of [3, H, W] and values in [0, 1].
uncertainty (`None` or `np.ndarray`):
Uncalibrated uncertainty(MAD, median absolute deviation) coming from ensembling.
"""
depth_np: np.ndarray
depth_colored: Image.Image
normal_np: np.ndarray
normal_colored: Image.Image
uncertainty: Union[None, np.ndarray]
class DepthNormalEstimationPipeline(DiffusionPipeline):
# two hyper-parameters
latent_scale_factor = 0.18215
def __init__(self,
unet:UNet2DConditionModel,
vae:AutoencoderKL,
scheduler:DDIMScheduler,
text_encoder:CLIPTextModel,
tokenizer:CLIPTokenizer,
):
super().__init__()
self.register_modules(
unet=unet,
vae=vae,
scheduler=scheduler,
text_encoder=text_encoder,
tokenizer=tokenizer,
)
self.empty_text_embed = None
@torch.no_grad()
def __call__(self,
input_image:Image,
denoising_steps: int = 10,
ensemble_size: int = 10,
processing_res: int = 768,
match_input_res:bool =True,
batch_size:int = 0,
domain: str = "indoor",
color_map: str="Spectral",
show_progress_bar:bool = True,
ensemble_kwargs: Dict = None,
) -> DepthNormalPipelineOutput:
# inherit from thea Diffusion Pipeline
device = self.device
input_size = input_image.size
# adjust the input resolution.
if not match_input_res:
assert (
processing_res is not None
)," Value Error: `resize_output_back` is only valid with "
assert processing_res >=0
assert denoising_steps >=1
assert ensemble_size >=1
# --------------- Image Processing ------------------------
# Resize image
if processing_res >0:
input_image = resize_max_res(
input_image, max_edge_resolution=processing_res
)
# Convert the image to RGB, to 1. reomve the alpha channel.
input_image = input_image.convert("RGB")
image = np.array(input_image)
# Normalize RGB Values.
rgb = np.transpose(image,(2,0,1))
rgb_norm = rgb / 255.0 * 2.0 - 1.0 # [0, 255] -> [-1, 1]
rgb_norm = torch.from_numpy(rgb_norm).to(self.dtype)
rgb_norm = rgb_norm.to(device)
assert rgb_norm.min() >= -1.0 and rgb_norm.max() <= 1.0
# ----------------- predicting depth -----------------
duplicated_rgb = torch.stack([rgb_norm] * ensemble_size)
single_rgb_dataset = TensorDataset(duplicated_rgb)
# find the batch size
if batch_size>0:
_bs = batch_size
else:
_bs = 1
single_rgb_loader = DataLoader(single_rgb_dataset, batch_size=_bs, shuffle=False)
# predicted the depth
depth_pred_ls = []
normal_pred_ls = []
if show_progress_bar:
iterable_bar = tqdm(
single_rgb_loader, desc=" " * 2 + "Inference batches", leave=False
)
else:
iterable_bar = single_rgb_loader
for batch in iterable_bar:
(batched_image, )= batch # here the image is still around 0-1
depth_pred_raw, normal_pred_raw = self.single_infer(
input_rgb=batched_image,
num_inference_steps=denoising_steps,
domain=domain,
show_pbar=show_progress_bar,
)
depth_pred_ls.append(depth_pred_raw.detach().clone())
normal_pred_ls.append(normal_pred_raw.detach().clone())
depth_preds = torch.concat(depth_pred_ls, axis=0).squeeze() #(10,224,768)
normal_preds = torch.concat(normal_pred_ls, axis=0).squeeze()
torch.cuda.empty_cache() # clear vram cache for ensembling
# ----------------- Test-time ensembling -----------------
if ensemble_size > 1:
depth_pred, pred_uncert = ensemble_depths(
depth_preds, **(ensemble_kwargs or {})
)
normal_pred = ensemble_normals(normal_preds)
else:
depth_pred = depth_preds
normal_pred = normal_preds
pred_uncert = None
# ----------------- Post processing -----------------
# Scale prediction to [0, 1]
min_d = torch.min(depth_pred)
max_d = torch.max(depth_pred)
depth_pred = (depth_pred - min_d) / (max_d - min_d)
# Convert to numpy
depth_pred = depth_pred.cpu().numpy().astype(np.float32)
normal_pred = normal_pred.cpu().numpy().astype(np.float32)
# Resize back to original resolution
if match_input_res:
pred_img = Image.fromarray(depth_pred)
pred_img = pred_img.resize(input_size)
depth_pred = np.asarray(pred_img)
normal_pred = cv2.resize(chw2hwc(normal_pred), input_size, interpolation = cv2.INTER_NEAREST)
# Clip output range: current size is the original size
depth_pred = depth_pred.clip(0, 1)
normal_pred = normal_pred.clip(-1, 1)
# Colorize
depth_colored = colorize_depth_maps(
depth_pred, 0, 1, cmap=color_map
).squeeze() # [3, H, W], value in (0, 1)
depth_colored = (depth_colored * 255).astype(np.uint8)
depth_colored_hwc = chw2hwc(depth_colored)
depth_colored_img = Image.fromarray(depth_colored_hwc)
normal_colored = ((normal_pred + 1)/2 * 255).astype(np.uint8)
normal_colored_img = Image.fromarray(normal_colored)
return DepthNormalPipelineOutput(
depth_np = depth_pred,
depth_colored = depth_colored_img,
normal_np = normal_pred,
normal_colored = normal_colored_img,
uncertainty=pred_uncert,
)
def __encode_text(self, prompt):
text_inputs = self.tokenizer(
prompt,
padding="do_not_pad",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids.to(self.text_encoder.device) #[1,2]
# print(text_input_ids.shape)
text_embed = self.text_encoder(text_input_ids)[0].to(self.dtype) #[1,2,1024]
return text_embed
@torch.no_grad()
def single_infer(self,input_rgb:torch.Tensor,
num_inference_steps:int,
domain:str,
show_pbar:bool,):
device = input_rgb.device
# Set timesteps: inherit from the diffuison pipeline
self.scheduler.set_timesteps(num_inference_steps, device=device) # here the numbers of the steps is only 10.
timesteps = self.scheduler.timesteps # [T]
# encode image
rgb_latent = self.encode_RGB(input_rgb)
# Initial geometric maps (Guassian noise)
geo_latent = torch.randn(rgb_latent.shape, device=device, dtype=self.dtype).repeat(2,1,1,1)
rgb_latent = rgb_latent.repeat(2,1,1,1)
# hybrid switcher
geo_class = torch.tensor([[0., 1.], [1, 0]], device=device, dtype=self.dtype)
geo_embedding = torch.cat([torch.sin(geo_class), torch.cos(geo_class)], dim=-1)
if domain == "indoor":
batch_text_embeds = self.__encode_text('indoor geometry').repeat((rgb_latent.shape[0],1,1))
elif domain == "outdoor":
batch_text_embeds = self.__encode_text('outdoor geometry').repeat((rgb_latent.shape[0],1,1))
elif domain == "object":
batch_text_embeds = self.__encode_text('object geometry').repeat((rgb_latent.shape[0],1,1))
class_embedding = geo_embedding
# Denoising loop
if show_pbar:
iterable = tqdm(
enumerate(timesteps),
total=len(timesteps),
leave=False,
desc=" " * 4 + "Diffusion denoising",
)
else:
iterable = enumerate(timesteps)
for i, t in iterable:
unet_input = torch.cat([rgb_latent, geo_latent], dim=1)
# predict the noise residual
noise_pred = self.unet(
unet_input, t.repeat(2), encoder_hidden_states=batch_text_embeds, class_labels=class_embedding
).sample # [B, 4, h, w]
# compute the previous noisy sample x_t -> x_t-1
geo_latent = self.scheduler.step(noise_pred, t, geo_latent).prev_sample
geo_latent = geo_latent
torch.cuda.empty_cache()
depth = self.decode_depth(geo_latent[0][None])
depth = torch.clip(depth, -1.0, 1.0)
depth = (depth + 1.0) / 2.0
normal = self.decode_normal(geo_latent[1][None])
normal /= (torch.norm(normal, p=2, dim=1, keepdim=True)+1e-5)
normal *= -1.
return depth, normal
def encode_RGB(self, rgb_in: torch.Tensor) -> torch.Tensor:
"""
Encode RGB image into latent.
Args:
rgb_in (`torch.Tensor`):
Input RGB image to be encoded.
Returns:
`torch.Tensor`: Image latent.
"""
# encode
h = self.vae.encoder(rgb_in)
moments = self.vae.quant_conv(h)
mean, logvar = torch.chunk(moments, 2, dim=1)
# scale latent
rgb_latent = mean * self.latent_scale_factor
return rgb_latent
def decode_depth(self, depth_latent: torch.Tensor) -> torch.Tensor:
"""
Decode depth latent into depth map.
Args:
depth_latent (`torch.Tensor`):
Depth latent to be decoded.
Returns:
`torch.Tensor`: Decoded depth map.
"""
# scale latent
depth_latent = depth_latent / self.latent_scale_factor
# decode
z = self.vae.post_quant_conv(depth_latent)
stacked = self.vae.decoder(z)
# mean of output channels
depth_mean = stacked.mean(dim=1, keepdim=True)
return depth_mean
def decode_normal(self, normal_latent: torch.Tensor) -> torch.Tensor:
"""
Decode normal latent into normal map.
Args:
normal_latent (`torch.Tensor`):
Depth latent to be decoded.
Returns:
`torch.Tensor`: Decoded normal map.
"""
# scale latent
normal_latent = normal_latent / self.latent_scale_factor
# decode
z = self.vae.post_quant_conv(normal_latent)
normal = self.vae.decoder(z)
return normal
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