downscale cape
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import argparse
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import os
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import tqdm
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import cv2
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import scipy.interpolate as spi
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import numpy as np
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import plotly.graph_objects as go
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import plotly.express as ex
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import torch
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from torchvision.transforms import Resize
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from torchvision.transforms.functional import InterpolationMode
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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def get_img_idxs():
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img_idxs = dict()
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img_coords = dict()
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uv_masks = dict()
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uv_mask = np.load('custom_data/garment_uv_mask_full.npy')
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coords = np.transpose(np.stack(np.nonzero((1 - uv_mask))), (1,0))
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img_idx = np.transpose(np.stack(np.nonzero((uv_mask))), (1,0))
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img_idxs[512] = img_idx
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img_coords[512] = coords
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uv_masks[512] = uv_mask.astype(np.int32)
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for res in [32, 64, 128, 256]:
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down = resizers_nearest[res](torch.from_numpy(uv_mask[None,:,:]).to(device)).cpu().numpy()
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down = np.squeeze(down.astype(bool))
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coords = np.transpose(np.stack(np.nonzero((1 - down))), (1,0))
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img_idx = np.transpose(np.stack(np.nonzero((down))), (1,0))
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img_idxs[res] = img_idx
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img_coords[res] = coords
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uv_masks[res] = down.astype(np.int32)
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uv_masks_body = dict()
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uv_mask_body = np.load('custom_utils/mask_256x512.npy')
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uv_mask_body = cv2.dilate(uv_mask_body.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2)
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uv_masks_body[512] = uv_mask_body
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for res in [32, 64, 128, 256]:
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down = resizers_nearest[res](torch.from_numpy(uv_mask_body[None,:,:]).to(device)).cpu().numpy()
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down = np.squeeze(down.astype(bool))
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uv_masks_body[res] = down
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return img_idxs, img_coords, uv_masks, uv_masks_body
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def fill(img, mask):
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coords = img_coords[img.shape[0]]
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img_idx = img_idxs[img.shape[0]]
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# coords = np.transpose(np.stack(np.nonzero((1 - uv_masks[img.shape[0]]))), (1,0))
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# img_idx = np.transpose(np.stack(np.nonzero((uv_masks[img.shape[0]]))), (1,0))
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img_data = img[img_idx[:,0], img_idx[:,1], :]
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interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data)
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img_2 = interpolator(coords)
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res = np.zeros((512,256,3), dtype=np.float32)
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res[coords[:,0], coords[:,1], :] = img_2
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res[img_idx[:,0], img_idx[:,1], :] = img_data
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return res
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def fill_label(img, mask):
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coords = img_coords[img.shape[0]]
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img_idx = img_idxs[img.shape[0]]
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img_data = img[img_idx[:,0], img_idx[:,1]]
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interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data)
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img_2 = interpolator(coords)
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res = np.zeros((512,256), dtype=np.float32)
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res[coords[:,0], coords[:,1]] = img_2
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res[img_idx[:,0], img_idx[:,1]] = img_data
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return res
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def generate_downscaled_uv_map(folder, id, size):
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img = np.load(os.path.join(folder, 'uv_mask', id + '.npz')); img = img['uv_mask'].astype(np.float32)
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down = tf.image.resize(img[:,:,tf.newaxis], size=(size, int(size/2)), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
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down = np.squeeze(down.numpy().astype(bool))
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np.savez_compressed(os.path.join(folder, 'uv_mask' + '_' + str(size), id + '.npz'), uv_mask = down)
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class UVMaskStore():
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def __init__(self, folder, size):
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self.size = size
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def __getitem__(self, id) -> dict:
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return uv_masks[self.size]
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def downscale_uv_maps_posed_body(folder, type, sizes):
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for size in sizes:
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os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True)
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frames = os.listdir(os.path.join(folder, type))[int(args.start):int(args.end)]
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for frame in tqdm.tqdm(frames):
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img = np.load(os.path.join(folder, type, frame)); img = img['uv_posed_body_no_orient']
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img = fill(img, uv_masks_body[512])
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for size in sizes:
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mask = uv_masks_body[size]
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down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy()
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down = down * mask[None,:,:]
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np.savez_compressed(os.path.join(folder, type + '_' + str(size), frame), uv_posed_body_no_orient = down)
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def downscale_uv_normals(folder, type, sizes):
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for size in sizes:
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os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True)
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frames = os.listdir(os.path.join(folder, type))[int(args.start):int(args.end)]
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for frame in tqdm.tqdm(frames):
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img = np.load(os.path.join(folder, type, frame)); img = img['uv_normals']
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img = fill(img, uv_masks_body[512])
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for size in sizes:
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mask = uv_masks_body[size]
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down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy()
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down = down * mask[None,:,:]
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np.savez_compressed(os.path.join(folder, type + '_' + str(size), frame), uv_normals = down)
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def downscale_uv_maps_unposed(folder, type, sizes):
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for size in sizes:
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os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True)
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frames = os.listdir(os.path.join(folder, type))[int(args.start):int(args.end)]
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for frame in tqdm.tqdm(frames):
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img = np.load(os.path.join(folder, type, frame)); img = img['uv_unposed_no_shape']
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mask_full = uv_mask_store[512][None]
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img = fill(img, mask_full)
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for size in sizes:
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mask = uv_mask_store[size][None]
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down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy()
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down = down * mask[None,:,:]
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np.savez_compressed(os.path.join(folder, type + '_' + str(size), frame), uv_unposed_no_shape = down)
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def downscale_uv_maps_static(folder, type, sizes):
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for size in sizes:
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os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True)
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frames = os.listdir(os.path.join(folder, type))[int(args.start):int(args.end)]
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for frame in tqdm.tqdm(frames):
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img = np.load(os.path.join(folder, type, frame)); img = img['uv_static']
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mask_full = uv_mask_store[512][None]
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img = fill(img, mask_full)
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for size in sizes:
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down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy()
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mask = uv_mask_store[size][None]
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down = down * mask[None,:,:]
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np.savez_compressed(os.path.join(folder, type + '_' + str(size), frame), uv_static = down)
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def downscale_uv_maps_mask(folder, type, size):
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os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True)
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frames = os.listdir(os.path.join(folder, type))[int(args.start):int(args.end)]
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for frame in tqdm.tqdm(frames):
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img = np.load(os.path.join(folder, type, frame)); img = img['uv_mask'].astype(np.float32)
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down = tf.image.resize(img[:,:,tf.newaxis], size=(size, int(size/2)), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
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down = np.squeeze(down.numpy().astype(bool))
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np.savez_compressed(os.path.join(folder, type + '_' + str(size), frame), uv_mask = down)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("-f", "--folder", help = "end of range", default='/data-local/127_2/cape_processed/train/male')
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parser.add_argument("-s", "--start", help = "start of range", default=0)
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parser.add_argument("-e", "--end", help = "end of range", default=100000)
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parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
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parser.add_argument("-d", "--debug", help = "end of range", default=False, action='store_true')
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args = parser.parse_args()
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device = 'cuda'
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sizes = [32, 64, 128, 256]
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folder = args.folder
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resizers = { res: Resize(size=(res, int(res/2)), interpolation=InterpolationMode.BICUBIC).to(device) for res in sizes }
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resizers_nearest = { res: Resize(size=(res, int(res/2)), interpolation=InterpolationMode.NEAREST_EXACT).to(device) for res in sizes }
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img_idxs, img_coords, uv_masks, uv_masks_body = get_img_idxs()
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uv_mask_store = { size: UVMaskStore(folder, size=size) for size in sizes }
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uv_mask_store[512] = UVMaskStore(folder, size=512)
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# downscale_uv_maps_mask(folder=folder, type='uv_mask', size=32)
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# downscale_uv_maps_mask(folder=folder, type='uv_mask', size=64)
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# downscale_uv_maps_mask(folder=folder, type='uv_mask', size=128)
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# downscale_uv_maps_mask(folder=folder, type='uv_mask', size=256)
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# downscale_uv_maps_static(folder=folder, type='uv_static', sizes=sizes)
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downscale_uv_maps_unposed(folder=folder, type='uv_unposed_no_shape', sizes=sizes)
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downscale_uv_normals(folder=folder, type='uv_normals', sizes=sizes)
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downscale_uv_maps_posed_body(folder=folder, type='uv_posed_body_no_orient', sizes=sizes)
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print()
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