diff --git a/data/downscale_cape.py b/data/downscale_cape.py new file mode 100644 index 0000000..87cac14 --- /dev/null +++ b/data/downscale_cape.py @@ -0,0 +1,207 @@ +import argparse +import os +import tqdm +import cv2 +import scipy.interpolate as spi +import numpy as np +import plotly.graph_objects as go +import plotly.express as ex +import torch +from torchvision.transforms import Resize +from torchvision.transforms.functional import InterpolationMode + +os.environ["CUDA_VISIBLE_DEVICES"] = "0" + + +def get_img_idxs(): + img_idxs = dict() + img_coords = dict() + uv_masks = dict() + uv_mask = np.load('custom_data/garment_uv_mask_full.npy') + coords = np.transpose(np.stack(np.nonzero((1 - uv_mask))), (1,0)) + img_idx = np.transpose(np.stack(np.nonzero((uv_mask))), (1,0)) + img_idxs[512] = img_idx + img_coords[512] = coords + uv_masks[512] = uv_mask.astype(np.int32) + for res in [32, 64, 128, 256]: + down = resizers_nearest[res](torch.from_numpy(uv_mask[None,:,:]).to(device)).cpu().numpy() + down = np.squeeze(down.astype(bool)) + coords = np.transpose(np.stack(np.nonzero((1 - down))), (1,0)) + img_idx = np.transpose(np.stack(np.nonzero((down))), (1,0)) + img_idxs[res] = img_idx + img_coords[res] = coords + uv_masks[res] = down.astype(np.int32) + + + uv_masks_body = dict() + uv_mask_body = np.load('custom_utils/mask_256x512.npy') + 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) + uv_masks_body[512] = uv_mask_body + for res in [32, 64, 128, 256]: + down = resizers_nearest[res](torch.from_numpy(uv_mask_body[None,:,:]).to(device)).cpu().numpy() + down = np.squeeze(down.astype(bool)) + uv_masks_body[res] = down + + return img_idxs, img_coords, uv_masks, uv_masks_body + + +def fill(img, mask): + coords = img_coords[img.shape[0]] + img_idx = img_idxs[img.shape[0]] + # coords = np.transpose(np.stack(np.nonzero((1 - uv_masks[img.shape[0]]))), (1,0)) + # img_idx = np.transpose(np.stack(np.nonzero((uv_masks[img.shape[0]]))), (1,0)) + img_data = img[img_idx[:,0], img_idx[:,1], :] + + interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data) + img_2 = interpolator(coords) + res = np.zeros((512,256,3), dtype=np.float32) + res[coords[:,0], coords[:,1], :] = img_2 + res[img_idx[:,0], img_idx[:,1], :] = img_data + + return res + + +def fill_label(img, mask): + coords = img_coords[img.shape[0]] + img_idx = img_idxs[img.shape[0]] + img_data = img[img_idx[:,0], img_idx[:,1]] + + interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data) + img_2 = interpolator(coords) + res = np.zeros((512,256), dtype=np.float32) + res[coords[:,0], coords[:,1]] = img_2 + res[img_idx[:,0], img_idx[:,1]] = img_data + + return res + + +def generate_downscaled_uv_map(folder, id, size): + img = np.load(os.path.join(folder, 'uv_mask', id + '.npz')); img = img['uv_mask'].astype(np.float32) + down = tf.image.resize(img[:,:,tf.newaxis], size=(size, int(size/2)), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR) + down = np.squeeze(down.numpy().astype(bool)) + np.savez_compressed(os.path.join(folder, 'uv_mask' + '_' + str(size), id + '.npz'), uv_mask = down) + + +class UVMaskStore(): + def __init__(self, folder, size): + self.size = size + + def __getitem__(self, id) -> dict: + return uv_masks[self.size] + + +def downscale_uv_maps_posed_body(folder, type, sizes): + for size in sizes: + os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True) + + frames = os.listdir(os.path.join(folder, type))[int(args.start):int(args.end)] + + for frame in tqdm.tqdm(frames): + img = np.load(os.path.join(folder, type, frame)); img = img['uv_posed_body_no_orient'] + img = fill(img, uv_masks_body[512]) + for size in sizes: + mask = uv_masks_body[size] + down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy() + down = down * mask[None,:,:] + np.savez_compressed(os.path.join(folder, type + '_' + str(size), frame), uv_posed_body_no_orient = down) + + +def downscale_uv_normals(folder, type, sizes): + for size in sizes: + os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True) + + frames = os.listdir(os.path.join(folder, type))[int(args.start):int(args.end)] + + for frame in tqdm.tqdm(frames): + img = np.load(os.path.join(folder, type, frame)); img = img['uv_normals'] + img = fill(img, uv_masks_body[512]) + for size in sizes: + mask = uv_masks_body[size] + down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy() + down = down * mask[None,:,:] + np.savez_compressed(os.path.join(folder, type + '_' + str(size), frame), uv_normals = down) + + + +def downscale_uv_maps_unposed(folder, type, sizes): + for size in sizes: + os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True) + + frames = os.listdir(os.path.join(folder, type))[int(args.start):int(args.end)] + + for frame in tqdm.tqdm(frames): + img = np.load(os.path.join(folder, type, frame)); img = img['uv_unposed_no_shape'] + mask_full = uv_mask_store[512][None] + img = fill(img, mask_full) + for size in sizes: + mask = uv_mask_store[size][None] + down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy() + down = down * mask[None,:,:] + np.savez_compressed(os.path.join(folder, type + '_' + str(size), frame), uv_unposed_no_shape = down) + + +def downscale_uv_maps_static(folder, type, sizes): + for size in sizes: + os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True) + + frames = os.listdir(os.path.join(folder, type))[int(args.start):int(args.end)] + + for frame in tqdm.tqdm(frames): + img = np.load(os.path.join(folder, type, frame)); img = img['uv_static'] + mask_full = uv_mask_store[512][None] + img = fill(img, mask_full) + + for size in sizes: + down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy() + mask = uv_mask_store[size][None] + down = down * mask[None,:,:] + np.savez_compressed(os.path.join(folder, type + '_' + str(size), frame), uv_static = down) + + +def downscale_uv_maps_mask(folder, type, size): + os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True) + + frames = os.listdir(os.path.join(folder, type))[int(args.start):int(args.end)] + + for frame in tqdm.tqdm(frames): + img = np.load(os.path.join(folder, type, frame)); img = img['uv_mask'].astype(np.float32) + down = tf.image.resize(img[:,:,tf.newaxis], size=(size, int(size/2)), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR) + down = np.squeeze(down.numpy().astype(bool)) + np.savez_compressed(os.path.join(folder, type + '_' + str(size), frame), uv_mask = down) + + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("-f", "--folder", help = "end of range", default='/data-local/127_2/cape_processed/train/male') + parser.add_argument("-s", "--start", help = "start of range", default=0) + parser.add_argument("-e", "--end", help = "end of range", default=100000) + parser.add_argument("-n", "--name", help = "end of range", default='lhunor') + parser.add_argument("-d", "--debug", help = "end of range", default=False, action='store_true') + args = parser.parse_args() + + device = 'cuda' + + sizes = [32, 64, 128, 256] + folder = args.folder + + resizers = { res: Resize(size=(res, int(res/2)), interpolation=InterpolationMode.BICUBIC).to(device) for res in sizes } + resizers_nearest = { res: Resize(size=(res, int(res/2)), interpolation=InterpolationMode.NEAREST_EXACT).to(device) for res in sizes } + + img_idxs, img_coords, uv_masks, uv_masks_body = get_img_idxs() + + uv_mask_store = { size: UVMaskStore(folder, size=size) for size in sizes } + uv_mask_store[512] = UVMaskStore(folder, size=512) + + # downscale_uv_maps_mask(folder=folder, type='uv_mask', size=32) + # downscale_uv_maps_mask(folder=folder, type='uv_mask', size=64) + # downscale_uv_maps_mask(folder=folder, type='uv_mask', size=128) + # downscale_uv_maps_mask(folder=folder, type='uv_mask', size=256) + # downscale_uv_maps_static(folder=folder, type='uv_static', sizes=sizes) + + downscale_uv_maps_unposed(folder=folder, type='uv_unposed_no_shape', sizes=sizes) + downscale_uv_normals(folder=folder, type='uv_normals', sizes=sizes) + downscale_uv_maps_posed_body(folder=folder, type='uv_posed_body_no_orient', sizes=sizes) + + + print() \ No newline at end of file