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96 lines
2.8 KiB
Python
96 lines
2.8 KiB
Python
import os
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import torch
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import numpy as np
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import imageio
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import json
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import torch.nn.functional as F
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import cv2
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trans_t = lambda t : torch.Tensor([
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[1,0,0,0],
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[0,1,0,0],
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[0,0,1,t],
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[0,0,0,1]]).float()
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rot_phi = lambda phi : torch.Tensor([
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[1,0,0,0],
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[0,np.cos(phi),-np.sin(phi),0],
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[0,np.sin(phi), np.cos(phi),0],
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[0,0,0,1]]).float()
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rot_theta = lambda th : torch.Tensor([
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[np.cos(th),0,-np.sin(th),0],
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[0,1,0,0],
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[np.sin(th),0, np.cos(th),0],
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[0,0,0,1]]).float()
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def pose_spherical(theta, phi, radius):
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c2w = trans_t(radius)
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c2w = rot_phi(phi/180.*np.pi) @ c2w
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c2w = rot_theta(theta/180.*np.pi) @ c2w
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c2w = torch.Tensor(np.array([[-1,0,0,0],[0,0,1,0],[0,1,0,0],[0,0,0,1]])) @ c2w
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return c2w
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def load_blender_data(basedir, half_res=False, testskip=1):
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splits = ['train', 'val', 'test']
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metas = {}
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for s in splits:
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with open(os.path.join(basedir, 'transforms_{}.json'.format(s)), 'r') as fp:
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metas[s] = json.load(fp)
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all_imgs = []
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all_poses = []
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counts = [0]
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for s in splits:
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meta = metas[s]
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imgs = []
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poses = []
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if s=='train' or testskip==0:
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skip = 1
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else:
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skip = testskip
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for frame in meta['frames'][::skip]:
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fname = os.path.join(basedir, frame['file_path'] + '.png')
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imgs.append(imageio.imread(fname))
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poses.append(np.array(frame['transform_matrix']))
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imgs = (np.array(imgs) / 255.).astype(np.float32) # keep all 4 channels (RGBA)
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poses = np.array(poses).astype(np.float32)
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counts.append(counts[-1] + imgs.shape[0])
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all_imgs.append(imgs)
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all_poses.append(poses)
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i_split = [np.arange(counts[i], counts[i+1]) for i in range(3)]
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imgs = np.concatenate(all_imgs, 0)
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poses = np.concatenate(all_poses, 0)
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H, W = imgs[0].shape[:2]
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camera_angle_x = float(meta['camera_angle_x'])
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focal = .5 * W / np.tan(.5 * camera_angle_x)
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render_poses = torch.stack([pose_spherical(angle, -30.0, 4.0) for angle in np.linspace(-180,180,40+1)[:-1]], 0)
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if half_res:
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H = H//2
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W = W//2
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focal = focal/2.
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imgs_half_res = np.zeros((imgs.shape[0], H, W, 4))
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for i, img in enumerate(imgs):
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# According to the api defined in the link below, the dimension
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# should be represented as (W, H).
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# https://www.tutorialkart.com/opencv/python/opencv-python-resize-image/
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imgs_half_res[i] = cv2.resize(img, (W, H), interpolation=cv2.INTER_AREA)
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# imgs_half_res[i] = cv2.resize(img, (H, W), interpolation=cv2.INTER_AREA)
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imgs = imgs_half_res
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# imgs = tf.image.resize_area(imgs, [400, 400]).numpy()
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return imgs, poses, render_poses, [H, W, focal], i_split
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