import torch import torchvision from . import py3d_tools as p3d import midas_utils from PIL import Image import numpy as np import sys, math import cv2 import pandas as pd import gc import math import lpips from PIL import Image, ImageOps import requests import torch from torch import nn from torch.nn import functional as F import torchvision import torchvision.transforms as T import torchvision.transforms.functional as TF from tqdm import tqdm from resize_right import resize from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults import numpy as np from numpy import asarray MAX_ADABINS_AREA = 500000 MIN_ADABINS_AREA = 448*448 @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,spherical=False): 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: 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() # AdaBins """ predictions using nyu dataset """ print("Running AdaBins depth estimation implementation...") infer_helper = InferenceHelper(dataset='nyu', device=device) 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. elif image_pil_area < MIN_ADABINS_AREA: scale = math.sqrt(MIN_ADABINS_AREA) / math.sqrt(image_pil_area) depth_input = img_pil.resize((int(w*scale), int(h*scale)), Image.BICUBIC) else: depth_input = img_pil try: _, adabins_depth = infer_helper.predict_pil(depth_input) if image_pil_area != MAX_ADABINS_AREA: adabins_depth = torchvision.transforms.functional.resize(torch.from_numpy(adabins_depth), image_tensor.shape[-2:], interpolation=torchvision.transforms.functional.InterpolationMode.BICUBIC).squeeze().to(device) else: adabins_depth = torch.from_numpy(adabins_depth).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 = p3d.FoVPerspectiveCameras(near, far, pixel_aspect, fov=fov_deg, degrees=True, device=device) persp_cam_new = p3d.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) if spherical: spherical_grid = get_spherical_projection(h, w, torch.tensor([0,0], device=device), -0.4,device=device)#align_corners=False stage_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=True) new_image = torch.nn.functional.grid_sample(stage_image, spherical_grid,align_corners=True) #, mode=sampling_mode, padding_mode=padding_mode, align_corners=False) else: 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 def get_spherical_projection(H, W, center, magnitude,device): xx, yy = torch.linspace(-1, 1, W,dtype=torch.float32,device=device), torch.linspace(-1, 1, H,dtype=torch.float32,device=device) gridy, gridx = torch.meshgrid(yy, xx) grid = torch.stack([gridx, gridy], dim=-1) d = center - grid d_sum = torch.sqrt((d**2).sum(axis=-1)) grid += d * d_sum.unsqueeze(-1) * magnitude return grid.unsqueeze(0)