Spaces:
Sleeping
Sleeping
File size: 5,653 Bytes
6071021 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | import argparse
import os
import cv2
import numpy as np
import torch
import sys
import utils.imgops as ops
import utils.architecture.architecture as arch
parser = argparse.ArgumentParser()
parser.add_argument('--input', default='input', help='Input folder')
parser.add_argument('--output', default='output', help='Output folder')
parser.add_argument('--reverse', help='Reverse Order', action="store_true")
parser.add_argument('--tile_size', default=512,
help='Tile size for splitting', type=int)
parser.add_argument('--seamless', action='store_true',
help='Seamless upscaling')
parser.add_argument('--mirror', action='store_true',
help='Mirrored seamless upscaling')
parser.add_argument('--replicate', action='store_true',
help='Replicate edge pixels for padding')
parser.add_argument('--cpu', action='store_true',
help='Use CPU instead of CUDA')
parser.add_argument('--ishiiruka', action='store_true',
help='Save textures in the format used in Ishiiruka Dolphin material map texture packs')
parser.add_argument('--ishiiruka_texture_encoder', action='store_true',
help='Save textures in the format used by Ishiiruka Dolphin\'s Texture Encoder tool')
args = parser.parse_args()
if not os.path.exists(args.input):
print('Error: Folder [{:s}] does not exist.'.format(args.input))
sys.exit(1)
elif os.path.isfile(args.input):
print('Error: Folder [{:s}] is a file.'.format(args.input))
sys.exit(1)
elif os.path.isfile(args.output):
print('Error: Folder [{:s}] is a file.'.format(args.output))
sys.exit(1)
elif not os.path.exists(args.output):
os.mkdir(args.output)
device = torch.device('cpu' if args.cpu else 'cuda')
input_folder = os.path.normpath(args.input)
output_folder = os.path.normpath(args.output)
NORMAL_MAP_MODEL = 'utils/models/1x_NormalMapGenerator-CX-Lite_200000_G.pth'
OTHER_MAP_MODEL = 'utils/models/1x_FrankenMapGenerator-CX-Lite_215000_G.pth'
def process(img, model):
img = img * 1. / np.iinfo(img.dtype).max
img = img[:, :, [2, 1, 0]]
img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
img_LR = img.unsqueeze(0)
img_LR = img_LR.to(device)
output = model(img_LR).data.squeeze(
0).float().cpu().clamp_(0, 1).numpy()
output = output[[2, 1, 0], :, :]
output = np.transpose(output, (1, 2, 0))
output = (output * 255.).round()
return output
def load_model(model_path):
global device
state_dict = torch.load(model_path)
model = arch.RRDB_Net(3, 3, 32, 12, gc=32, upscale=1, norm_type=None, act_type='leakyrelu',
mode='CNA', res_scale=1, upsample_mode='upconv')
model.load_state_dict(state_dict, strict=True)
del state_dict
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
return model.to(device)
images=[]
for root, _, files in os.walk(input_folder):
for file in sorted(files, reverse=args.reverse):
if file.split('.')[-1].lower() in ['png', 'jpg', 'jpeg', 'gif', 'bmp', 'tiff', 'tga']:
images.append(os.path.join(root, file))
models = [
# NORMAL MAP
load_model(NORMAL_MAP_MODEL),
# ROUGHNESS/DISPLACEMENT MAPS
load_model(OTHER_MAP_MODEL)
]
for idx, path in enumerate(images, 1):
base = os.path.splitext(os.path.relpath(path, input_folder))[0]
output_dir = os.path.dirname(os.path.join(output_folder, base))
os.makedirs(output_dir, exist_ok=True)
print(idx, base)
# read image
try:
img = cv2.imread(path, cv2.cv2.IMREAD_COLOR)
except:
img = cv2.imread(path, cv2.IMREAD_COLOR)
# Seamless modes
if args.seamless:
img = cv2.copyMakeBorder(img, 16, 16, 16, 16, cv2.BORDER_WRAP)
elif args.mirror:
img = cv2.copyMakeBorder(img, 16, 16, 16, 16, cv2.BORDER_REFLECT_101)
elif args.replicate:
img = cv2.copyMakeBorder(img, 16, 16, 16, 16, cv2.BORDER_REPLICATE)
img_height, img_width = img.shape[:2]
# Whether or not to perform the split/merge action
do_split = img_height > args.tile_size or img_width > args.tile_size
if do_split:
rlts = ops.esrgan_launcher_split_merge(img, process, models, scale_factor=1, tile_size=args.tile_size)
else:
rlts = [process(img, model) for model in models]
if args.seamless or args.mirror or args.replicate:
rlts = [ops.crop_seamless(rlt) for rlt in rlts]
normal_map = rlts[0]
roughness = rlts[1][:, :, 1]
displacement = rlts[1][:, :, 0]
if args.ishiiruka_texture_encoder:
r = 255 - roughness
g = normal_map[:, :, 1]
b = displacement
a = normal_map[:, :, 2]
output = cv2.merge((b, g, r, a))
cv2.imwrite(os.path.join(output_folder, '{:s}.mat.png'.format(base)), output)
else:
normal_name = '{:s}.nrm.png'.format(base) if args.ishiiruka else '{:s}_Normal.png'.format(base)
cv2.imwrite(os.path.join(output_folder, normal_name), normal_map)
rough_name = '{:s}.spec.png'.format(base) if args.ishiiruka else '{:s}_Roughness.png'.format(base)
rough_img = 255 - roughness if args.ishiiruka else roughness
cv2.imwrite(os.path.join(output_folder, rough_name), rough_img)
displ_name = '{:s}.bump.png'.format(base) if args.ishiiruka else '{:s}_Displacement.png'.format(base)
cv2.imwrite(os.path.join(output_folder, displ_name), displacement)
|