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disco_xform_utils.py
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disco_xform_utils.py
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import torch, torchvision
import pytorch3d.renderer.cameras as p3dCam
import midas_utils
from PIL import Image
import numpy as np
import sys, math
try:
from infer import InferenceHelper
except:
print("disco_xform_utils.py failed to import InferenceHelper. Please ensure that AdaBins directory is in the path (i.e. via sys.path.append('./AdaBins') or other means).")
sys.exit()
MAX_ADABINS_AREA = 500000
@torch.no_grad()
def transform_image_3d(img_filepath, midas_model, midas_transform, device, rot_mat=torch.eye(3).unsqueeze(0), translate=(0.,0.,-0.04), near=2000, far=20000, fov_deg=60, padding_mode='border', sampling_mode='bicubic', midas_weight = 0.3):
img_pil = Image.open(open(img_filepath, 'rb')).convert('RGB')
w, h = img_pil.size
image_tensor = torchvision.transforms.functional.to_tensor(img_pil).to(device)
use_adabins = midas_weight < 1.0
if use_adabins:
# AdaBins
"""
predictions using nyu dataset
"""
print("Running AdaBins depth estimation implementation...")
infer_helper = InferenceHelper(dataset='nyu')
image_pil_area = w*h
if image_pil_area > MAX_ADABINS_AREA:
scale = math.sqrt(MAX_ADABINS_AREA) / math.sqrt(image_pil_area)
depth_input = img_pil.resize((int(w*scale), int(h*scale)), Image.LANCZOS) # LANCZOS is supposed to be good for downsampling.
else:
depth_input = img_pil
try:
_, adabins_depth = infer_helper.predict_pil(depth_input)
adabins_depth = torchvision.transforms.functional.resize(torch.from_numpy(adabins_depth), image_tensor.shape[-2:], interpolation=torchvision.transforms.functional.InterpolationMode.BICUBIC).squeeze().to(device)
adabins_depth_np = adabins_depth.cpu().numpy()
except:
pass
torch.cuda.empty_cache()
# MiDaS
img_midas = midas_utils.read_image(img_filepath)
img_midas_input = midas_transform({"image": img_midas})["image"]
midas_optimize = True
# MiDaS depth estimation implementation
print("Running MiDaS depth estimation implementation...")
sample = torch.from_numpy(img_midas_input).float().to(device).unsqueeze(0)
if midas_optimize==True and device == torch.device("cuda"):
sample = sample.to(memory_format=torch.channels_last)
sample = sample.half()
prediction_torch = midas_model.forward(sample)
prediction_torch = torch.nn.functional.interpolate(
prediction_torch.unsqueeze(1),
size=img_midas.shape[:2],
mode="bicubic",
align_corners=False,
).squeeze()
prediction_np = prediction_torch.clone().cpu().numpy()
print("Finished depth estimation.")
torch.cuda.empty_cache()
# MiDaS makes the near values greater, and the far values lesser. Let's reverse that and try to align with AdaBins a bit better.
prediction_np = np.subtract(50.0, prediction_np)
prediction_np = prediction_np / 19.0
if use_adabins:
adabins_weight = 1.0 - midas_weight
depth_map = prediction_np*midas_weight + adabins_depth_np*adabins_weight
else:
depth_map = prediction_np
depth_map = np.expand_dims(depth_map, axis=0)
depth_tensor = torch.from_numpy(depth_map).squeeze().to(device)
pixel_aspect = 1.0 # really.. the aspect of an individual pixel! (so usually 1.0)
persp_cam_old = p3dCam.FoVPerspectiveCameras(near, far, pixel_aspect, fov=fov_deg, degrees=True, device=device)
persp_cam_new = p3dCam.FoVPerspectiveCameras(near, far, pixel_aspect, fov=fov_deg, degrees=True, R=rot_mat, T=torch.tensor([translate]), device=device)
# range of [-1,1] is important to torch grid_sample's padding handling
y,x = torch.meshgrid(torch.linspace(-1.,1.,h,dtype=torch.float32,device=device),torch.linspace(-1.,1.,w,dtype=torch.float32,device=device))
z = torch.as_tensor(depth_tensor, dtype=torch.float32, device=device)
xyz_old_world = torch.stack((x.flatten(), y.flatten(), z.flatten()), dim=1)
# Transform the points using pytorch3d. With current functionality, this is overkill and prevents it from working on Windows.
# If you want it to run on Windows (without pytorch3d), then the transforms (and/or perspective if that's separate) can be done pretty easily without it.
xyz_old_cam_xy = persp_cam_old.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]
xyz_new_cam_xy = persp_cam_new.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]
offset_xy = xyz_new_cam_xy - xyz_old_cam_xy
# affine_grid theta param expects a batch of 2D mats. Each is 2x3 to do rotation+translation.
identity_2d_batch = torch.tensor([[1.,0.,0.],[0.,1.,0.]], device=device).unsqueeze(0)
# coords_2d will have shape (N,H,W,2).. which is also what grid_sample needs.
coords_2d = torch.nn.functional.affine_grid(identity_2d_batch, [1,1,h,w], align_corners=False)
offset_coords_2d = coords_2d - torch.reshape(offset_xy, (h,w,2)).unsqueeze(0)
new_image = torch.nn.functional.grid_sample(image_tensor.add(1/512 - 0.0001).unsqueeze(0), offset_coords_2d, mode=sampling_mode, padding_mode=padding_mode, align_corners=False)
img_pil = torchvision.transforms.ToPILImage()(new_image.squeeze().clamp(0,1.))
torch.cuda.empty_cache()
return img_pil