121 lines
3.5 KiB
Python
121 lines
3.5 KiB
Python
import os
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import sys
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import kornia
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import torch
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import numpy as np
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import scipy.io as sio
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from math import cos, sin
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from pytorch3d.utils import cameras_from_opencv_projection
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
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# from superSMPL.IO import readPC2Frame, readOBJ
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class Frame:
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def __init__(self, id, frame_nr, garment):
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self.id = id
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self.frame_nr = frame_nr
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self.garment = garment
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def __hash__(self):
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return hash((self.id, self.frame_nr))
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def __eq__(self, other):
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return (self.id, self.frame_nr) == (other.id, other.frame_nr)
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def mask_faces(F, mask):
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F_mask = np.take(mask, F.reshape(-1), axis=0)
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F_mask = F_mask.reshape((-1, 3)).sum(axis=1) == 3
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V_ind = np.zeros(mask.shape, dtype=np.int32)
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V_ind[mask] = np.arange(mask.sum())
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F_new = np.take(V_ind, F[F_mask, :].reshape(-1), axis=0).reshape((-1, 3))
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return F_new
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def read_frames_file(file):
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frames = []
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with open(file, 'r') as f:
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lines = f.readlines()
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for line in lines:
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id, frame_nr, garment = line.split()
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frames.append(Frame(id, int(frame_nr), garment))
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return frames
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def get_normals_from_depth(depth_image, depth_mask, camera_intrinsic):
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depth_dilated = kornia.morphology.dilation(depth_image, torch.ones((3,3), device=depth_image.get_device()), engine='convolution')
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depth_dilated = kornia.morphology.dilation(depth_dilated, torch.ones((3,3), device=depth_image.get_device()), engine='convolution')
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depth_dilated = torch.permute(depth_dilated, [0,2,3,1])
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depth_image = torch.permute(depth_image, [0,2,3,1])
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depth_dilated[depth_mask,:] = depth_image[depth_mask,:]
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depth_dilated = torch.permute(depth_dilated, [0,3,1,2])
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normals = kornia.geometry.depth.depth_to_normals(depth_dilated, camera_intrinsic, normalize_points=True)
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normals = torch.permute(normals, [0,2,3,1]) * depth_mask[:,:,:,None]
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return normals
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def center_crop_resize(img, new_center, size):
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pass
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""" CAMERA """
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def intrinsic(RES_X=640, RES_Y=480):
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f_mm = 50 # blender default
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sensor_w_mm = 36 # blender default
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sensor_h_mm = sensor_w_mm * RES_Y / RES_X
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fx_px = f_mm * RES_X / sensor_w_mm
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fy_px = f_mm * RES_Y / sensor_h_mm
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u = RES_X / 2
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v = RES_Y / 2
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return np.array([[fx_px, 0, u],
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[0, fy_px, v],
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[0, 0, 1]], np.float32)
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def extrinsic(camLoc):
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R_w2bc = np.array([[0, 1, 0],
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[0, 0, 1],
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[1, 0, 0]], np.float32)
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T_w2bc = -1 * R_w2bc.dot(camLoc)
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R_bc2cv = np.array([[1, 0, 0],
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[0, -1, 0],
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[0, 0, -1]], np.float32)
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R_w2cv = R_bc2cv.dot(R_w2bc)
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T_w2cv = R_bc2cv.dot(T_w2bc)
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return np.concatenate((R_w2cv, T_w2cv[:,None]), axis=1)
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def proj(camLoc):
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return intrinsic().dot(extrinsic(camLoc))
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def get_camera(cam, img_size, device):
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size = np.array(img_size, dtype=np.float32)
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ext = torch.tensor(extrinsic(cam).astype(np.float32), device=device)
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K = torch.tensor(intrinsic()[None, :, :], device=device)
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cameras = cameras_from_opencv_projection(R=ext[None, :, :3], tvec=ext[None, :, 3], camera_matrix=K, image_size=torch.tensor(size[None, :], device=device))
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return cameras, K
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def filter_info(info, frame_nr):
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frame_specific = set(['poses', 'trans'])
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general = set(['shape', 'camLoc', 'zrot', 'gender'])
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out = dict()
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for key in info.keys():
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if key in frame_specific:
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out[key] = info[key][:, frame_nr]
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elif key in general:
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out[key] = info[key]
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return out |