import torch, torchvision import py3d_tools as p3d 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 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: # 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. 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)