295 lines
15 KiB
Python
295 lines
15 KiB
Python
import os
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import sys
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os.environ["CUDA_VISIBLE_DEVICES"] = '0'
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import math
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import time
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import torch
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import kornia
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import pytorch3d
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import torchvision as tv
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import numpy as np
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import scipy.io as sio
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import plotly.express as ex
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from torch.utils.data import Dataset
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
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from data.render import Render, DepthRenderer
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from superSMPL.IO import readPC2Frame, writePC2
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from utils.data_utils import Frame, read_frames_file, mask_faces, get_normals_from_depth, get_camera
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from cloth3d.read import DataReader
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from cloth3d.util import quads2tris, zRotMatrix
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class Dataset(Dataset):
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def __init__(self, folder, folder_rgb, frames_file='frames_all.txt', device='cuda', use_cache=True, random_view=True):
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self.folder = folder
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self.folder_rgb = folder_rgb
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self.device = device
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self.random_view = random_view
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self.transforms = tv.transforms.ToTensor()
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self.frames = read_frames_file(os.path.join(folder, frames_file))
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self.render = DepthRenderer()
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self.deodr_render : Render = Render(max_depth=10)
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self.body_faces = np.load('custom_data/smpl_faces.npy')
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self.reader = DataReader(folder, 'superSMPL')
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self.cache = dict() if use_cache else None
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def __len__(self):
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return len(self.frames)
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# TODO: make it work with two part outfits
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def __getitem__(self, idx):
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frame : Frame = self.frames[idx]
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if self.cache is not None:
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if frame in self.cache.keys():
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return self.cache[frame]
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path = os.path.join(self.folder, frame.id)
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info = self.reader.read_info(frame.id)
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zrot = info['zrot'] # if not self.random_view else (((np.random.random(1) * 2) -1) * np.pi)[0]
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trans = info['trans'][:,frame.frame_nr].astype(np.float32)
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# mesh_posed = readPC2Frame(os.path.join(path, frame.garment + '_registered.pc16'), frame.frame_nr, True)
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mesh_posed = self.reader.read_garment_vertices(frame.id, frame.garment, frame.frame_nr, trans, zrot)
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mesh_posed = torch.tensor(mesh_posed, device=self.device)
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mesh_posed_no_trans = self.reader.read_garment_vertices(frame.id, frame.garment, frame.frame_nr, None, zrot)
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mesh_posed_no_trans = torch.tensor(mesh_posed_no_trans, device=self.device)
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mesh_mask = np.load(os.path.join(path, frame.garment + '_mask.npy'))
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# TODO: what's the difference between masking and reading .obj file. is the obj file the not registered version?
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faces = mask_faces(self.body_faces, mesh_mask)
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# faces = quads2tris(self.reader.read_garment_topology(frame.id, frame.garment))
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# body = readPC2Frame(os.path.join(path, 'Body_posed.pc16'), frame.frame_nr, True)
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body, body_faces, root_joint, joints, joints_no_trans = self.reader.read_human(frame.id, frame.frame_nr, zrot, use_trans=True)
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body_no_trans, _, _, _, _ = self.reader.read_human(frame.id, frame.frame_nr, zrot, use_trans=False)
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body = torch.tensor(body.astype(np.float32), device=self.device)
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body_no_trans = torch.tensor(body_no_trans.astype(np.float32), device=self.device)
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body_faces = quads2tris(body_faces)
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if self.random_view:
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x_angle = (torch.rand(1, device=self.device) * 2 * torch.pi - torch.pi) / 10
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y_angle = (torch.rand(1, device=self.device) * 2 * torch.pi - torch.pi) / 10
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z_angle = (torch.rand(1, device=self.device) * 2 * torch.pi - torch.pi)
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view = pytorch3d.transforms.axis_angle_to_matrix(torch.concat((x_angle, y_angle, z_angle)))
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zRot = torch.tensor(zRotMatrix(-zrot), device=self.device)
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mesh_posed = torch.matmul(zRot, mesh_posed.T).T
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mesh_posed = mesh_posed - torch.tensor(trans, device=self.device)
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body = torch.matmul(zRot, body.T).T
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body = body - torch.tensor(trans, device=self.device)
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transform = pytorch3d.transforms.Rotate(view)
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mesh_posed = transform.transform_points(mesh_posed)
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# mesh_posed_no_trans = transform.transform_points(mesh_posed_no_trans)
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body = transform.transform_points(body)
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# body_no_trans = transform.transform_points(body_no_trans)
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zRot = torch.tensor(zRotMatrix(zrot), device=self.device)
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mesh_posed = mesh_posed + torch.tensor(trans, device=self.device)
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mesh_posed = torch.matmul(zRot, mesh_posed.T).T
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body = body + torch.tensor(trans, device=self.device)
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body = torch.matmul(zRot, body.T).T
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else:
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view = torch.eye(3, device=self.device)
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combined = torch.concat((body, mesh_posed[mesh_mask, :]))
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combined_no_trans = torch.concat((body_no_trans, mesh_posed_no_trans[mesh_mask, :]))
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combined_faces = torch.Tensor(np.concatenate((body_faces, faces + body.shape[0]))).to(self.device)
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start = time.perf_counter()
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# depth_image = self.render.render(mesh_posed[mask, :], faces, info['camLoc'][0])[:,:,0]
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# depth_image = self.render.render(combined, combined_faces, info['camLoc'][0])[:,:,0]
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deodr_depth = self.deodr_render.render(cam=info['camLoc'].astype(np.float32), mesh=combined.cpu().numpy(), faces=combined_faces.cpu().numpy().astype(np.int32))
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# deodr_depth = self.deodr_render.render(cam=info['camLoc'].astype(np.float32), mesh=combined, faces=combined_faces.cpu().numpy().astype(np.int32))
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print(f'deodr time: {time.perf_counter() - start}')
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start = time.perf_counter()
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depth_image, depth_mask, v2i, K, camera, pix_to_face = self.render(combined, combined_faces, info['camLoc'])
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cam_no_trans = info['camLoc'].copy()
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cam_no_trans[1:] = 0
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depth_image_no_trans, depth_mask_no_trans, _, _, camera_no_trans, _ = self.render(combined_no_trans, combined_faces, cam_no_trans)
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# camera_no_trans, _ = get_camera(cam_no_trans, [480, 640], self.device)
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print(f'pytorch3d time: {time.perf_counter() - start}')
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start = time.perf_counter()
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# TODO: is it sure I need this K, and not some transformed one?
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normal_image = get_normals_from_depth(depth_image[None,None,:,:], depth_mask[None,:,:], K)
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normal_image = torch.permute(normal_image[0], [2,0,1])
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print(f'normals time: {time.perf_counter() - start}')
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depth_mask_body = (pix_to_face <= body_faces.shape[0]) * depth_mask
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depth_mask_garment = (pix_to_face > body_faces.shape[0]) * depth_mask
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top_left = root_joint.astype(np.float32)
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top_left[:,1] -= 1.25; top_left[:,2] += 1.25
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bottom_right = root_joint.astype(np.float32)
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bottom_right[:,1] += 1.25; bottom_right[:,2] -= 1.25
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corners = torch.tensor(np.concatenate((top_left, bottom_right), axis=0), device=self.device)
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corners_pixel = camera.transform_points_screen(corners)[:,:2].round().to(torch.int64)
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cropper = lambda x: tv.transforms.functional.crop(x, corners_pixel[0,1], corners_pixel[0,0], corners_pixel[1,1] - corners_pixel[0,1], corners_pixel[1,0] - corners_pixel[0,0])
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resizer = tv.transforms.Resize((256,256), interpolation=tv.transforms.functional.InterpolationMode.NEAREST)
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depth_mask_garment = resizer(cropper(depth_mask_garment)[None,None,:,:])[0,0,:,:]
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depth_mask = resizer(cropper(depth_mask)[None,None,:,:])[0,0,:,:]
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scaled = resizer(cropper(depth_image)[None,None,:,:])[0,0,:,:] * depth_mask
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normal_image = resizer(cropper(normal_image)[None,:,:,:])[0,:,:,:] * depth_mask[None,:,:]
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root_dist = np.sqrt(np.sum((root_joint[0] - info['camLoc']) ** 2))
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scaled = root_dist - scaled
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# root_joint_pixel = camera.transform_points_screen(torch.tensor(root_joint.astype(np.float32), device=combined.get_device()))[0,:2].round()
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# joints_pixel = camera.transform_points_screen(torch.tensor(joints.astype(np.float32), device=combined.get_device()))[0,:,:2]
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# joints_no_trans_pixel = camera_no_trans.transform_points_screen(torch.tensor(joints_no_trans.astype(np.float32), device=combined.get_device()))[0,:,:2]
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# ratio = ((joints_pixel[1,:] - joints_pixel[2,:]) ** 2).sum().sqrt() / ((joints_no_trans_pixel[1,:] - joints_no_trans_pixel[2,:]) ** 2).sum().sqrt()
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# center_offset = torch.tensor([240, 320], device=root_joint_pixel.get_device()) - root_joint_pixel
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# center_offset_ratio = center_offset / torch.tensor([480, 640], device=root_joint_pixel.get_device())
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# bbox = tv.ops.masks_to_boxes(depth_mask[None,:,:])[0,:]
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# width = bbox[2] - bbox[0]
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# height = bbox[3] - bbox[1]
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# width = width + (width % 2)
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# height = height + (height % 2)
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# dims = torch.tensor([width, height], device=width.get_device())
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# # crop_size = torch.max(dims)
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# # top = (root_joint_pixel[1] - height / 2).round().to(torch.int64)
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# # left = (root_joint_pixel[0] - width / 2).round().to(torch.int64)
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# cropped = tv.transforms.functional.crop(depth_image, bbox[1].to(torch.int64), bbox[0].to(torch.int64), height.to(torch.int64), width.to(torch.int64))
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# new_root_joint_pixel = root_joint_pixel - bbox[:2]
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# offset = dims / 2 - new_root_joint_pixel
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# bbox[:2] = bbox[:2] - offset
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# dims = dims + torch.abs(offset)
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# # cropped_2 = tv.transforms.functional.crop(depth_image, bbox[1].to(torch.int64), bbox[0].to(torch.int64), dims[1].to(torch.int64), dims[0].to(torch.int64))
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# left = torch.tensor([0], device=bbox.get_device())
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# right = torch.tensor([0], device=bbox.get_device())
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# top = torch.tensor([0], device=bbox.get_device())
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# bottom = torch.tensor([0], device=bbox.get_device())
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# if offset[0] > 0:
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# left = 2 * offset[0]
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# else:
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# right = -1 * 2 * offset[0]
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# if offset[1] > 0:
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# top = 2 * offset[1]
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# else:
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# bottom = -1 * 2 * offset[1]
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# # centered = tv.transforms.functional.crop(cropped, new_top.to(torch.int64), new_left.to(torch.int64), new_height.to(torch.int64), new_width.to(torch.int64))
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# centered = torch.nn.functional.pad(cropped, (left.to(torch.int64), right.to(torch.int64), top.to(torch.int64), bottom.to(torch.int64)), value=-1)
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# orig = depth_image[root_joint_pixel.to(torch.int64)[1], root_joint_pixel.to(torch.int64)[0]].cpu().numpy()
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# new = centered[(centered.shape[0] // 2), (centered.shape[1] // 2)].cpu().numpy()
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# assert new == orig, 'Center pad did not work'
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# scaled = tv.transforms.Resize((centered.shape[1] / ratio).round().int().item(), interpolation=tv.transforms.functional.InterpolationMode.NEAREST)(centered[None,None,:,:])[0,0,:,:]
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# left, right = math.floor((depth_image.shape[1] - scaled.shape[1]) / 2), round((depth_image.shape[1] - scaled.shape[1]) / 2)
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# top, bottom = math.floor((depth_image.shape[0] - scaled.shape[0]) / 2), round((depth_image.shape[0] - scaled.shape[0]) / 2)
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# upscaled = torch.nn.functional.pad(scaled, (left, right, top, bottom), value=-1)
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# Verifying
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# pc = kornia.geometry.depth.depth_to_3d(depth_image[None,None,:,:], K, normalize_points=True)
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# pc = pc[0,:,depth_mask].cpu().numpy()
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# all_faces = combined_faces
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# visible_faces_idx = pix_to_face * depth_mask
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# visible_faces_idx = visible_faces_idx[torch.nonzero(visible_faces_idx, as_tuple=True)]
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# visible_vertices = torch.unique(torch.flatten(all_faces[visible_faces_idx, :])).to(torch.long)
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# visible_vertices_mask = (v2i[visible_vertices, 1] < depth_image.shape[0]) * (v2i[visible_vertices, 0] < depth_image.shape[1])
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# visible_vertices = visible_vertices[visible_vertices_mask]
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# visible_pixels_mask = depth_image[v2i[visible_vertices, 1], v2i[visible_vertices, 0]] != -1
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# visible_vertices = visible_vertices[visible_pixels_mask]
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# x = combined[visible_vertices, :]
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# y = np.array(info['camLoc'], dtype=np.float32)
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# dist = torch.sqrt(torch.sum((x - torch.tensor(y[None,:], device=x.get_device())) ** 2, axis=-1))
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# dist_img = depth_image[v2i[visible_vertices, 1], v2i[visible_vertices, 0]]
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# diff = torch.mean(torch.abs(dist - dist_img))
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# Plotting
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# combined = combined.cpu().numpy()
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# depth_image = depth_image.cpu().numpy()
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# dist_img = depth_image[v2i[idx, 1], v2i[idx, 0]]
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# depth_image[v2i[visible_vertices, 1], v2i[visible_vertices, 0]] = 10
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# diff = np.abs(dist - dist_img)
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# import plotly.express as ex
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# import plotly.graph_objects as go
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# ex.imshow(deodr_depth[:,:,0]).show()
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# ex.imshow(depth_image.cpu().numpy()).show()
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# ex.imshow(normal_image[0].cpu().numpy()).show()
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# trans = np.array(info['trans'], dtype=np.float32)[:, self.frames[idx].frame_nr]
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# root = info['camLoc'].copy()
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# root[1:] = 0
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# go.Figure(data = [go.Scatter3d(x=combined[:, 0], y=combined[:, 1], z=combined[:, 2], marker=dict(size=3, line=dict(width=0)), mode='markers'),
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# go.Scatter3d(x=[combined[idx, 0]], y=[combined[idx, 1]], z=[combined[idx, 2]], marker=dict(size=10, line=dict(width=0)), mode='markers'),
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# go.Scatter3d(x=[root[0]], y=[root[1]], z=[root[2]], marker=dict(size=10, line=dict(width=0)), mode='markers'),
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# ]).show()
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# go.Figure(data = [go.Scatter3d(x=pc[0,:], y=pc[1,:], z=pc[2,:], marker=dict(size=3, line=dict(width=0)), mode='markers'),
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# ]).show()
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# v2i_coverage = np.zeros((480, 640), dtype=np.float32)
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# v2i_coverage[v2i[:,0], v2i[:, 1]] = 1
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# ex.imshow(v2i_coverage).show()
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rgb_image = tv.io.read_image(os.path.join(self.folder_rgb, frame.id, f'{frame.frame_nr:04d}.png'), mode=tv.io.ImageReadMode.RGB).to(torch.float32).to(self.device)
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rgb_image = resizer(cropper(rgb_image)[None,:,:,:])[0,:,:,:] * depth_mask[None,:,:]
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# view = torch.tensor(np.concatenate((np.array([zrot], dtype=np.float32), info['camLoc'].astype(np.float32), trans.astype(np.float32)), axis=0), device=self.device)
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# image = self.transforms(image)
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data = { 'mesh_posed': mesh_posed,
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'depth_image': scaled,
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# 'depth_image': upscaled,
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'mesh_mask': mesh_mask,
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'depth_mask': depth_mask,
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'normal_image': normal_image,
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'depth_mask_garment': depth_mask_garment,
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# 'depth_mask_body': depth_mask_body,
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'rgb_image': rgb_image,
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'view': view,
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'info': info,
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}
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self.cache[frame] = data
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return self.cache[frame]
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if __name__ == '__main__':
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from torch.utils.data import DataLoader
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# dataset = Dataset('/home/workspace/local-data/train')
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dataset = Dataset('dataset/CLOTH3D/train', 'dataset/CLOTH3D2RGB/train', 'frames_single.txt', random_view=False)
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train_dataloader = DataLoader(dataset, batch_size=1, drop_last=True, shuffle=False)
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for batch in train_dataloader:
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batch
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pass
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pass
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