File size: 3,092 Bytes
06f2b0d | 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 | import os
import cv2
import torch
import argparse
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from skimage import img_as_ubyte
# from basicsr.models.archs.restormer_arch import Restormer
from xrestormer.archs.xrestormer_arch import XRestormer
import yaml
def load_img(filepath):
with open(filepath, 'rb') as f:
value_buf = f.read()
img_np = np.frombuffer(value_buf, np.uint8)
img = cv2.imdecode(img_np, cv2.IMREAD_COLOR)
img = img.astype(np.float32) / 255.
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# return cv2.cvtColor(cv2.imread(filepath), cv2.COLOR_BGR2RGB)
def save_img(filepath, img):
cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
def load_model(yaml_path, weight_path):
"""加载 Restormer 模型"""
try:
from yaml import CLoader as Loader
except ImportError:
from yaml import Loader
with open(yaml_path, mode='r') as f:
config = yaml.load(f, Loader=Loader)
net_config = config['network_g']
net_type = net_config.pop('type', None)
model = XRestormer(**net_config)
# checkpoint = torch.load(weight_path)
# model.load_network(weight_path)
model.cuda()
model.eval()
return model
def restore_image(model, input_image_path, output_image_path, factor=64):
"""用模型复原单张图片"""
img_t = load_img(input_image_path)
# img = np.float32(load_img(input_image_path)) / 255.0
# img_t = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0).cuda()
# pad到8的倍数
h, w = img_t.shape[2], img_t.shape[3]
H, W = ((h + factor) // factor) * factor, ((w + factor) // factor) * factor
padh, padw = H - h, W - w
if padh != 0 or padw != 0:
img_t = F.pad(img_t, (0, padw, 0, padh), 'reflect')
with torch.no_grad():
restored = model(img_t)
restored = restored[:, :, :h, :w]
restored = torch.clamp(restored, 0, 1).cpu().permute(0, 2, 3, 1).squeeze(0).numpy()
save_img(output_image_path, img_as_ubyte(restored))
print(f"✅ Saved restored image to: {output_image_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Single image restoration using Restormer")
parser.add_argument("--input", required=True, type=str, help="Path to input image")
parser.add_argument("--output", required=True, type=str, help="Path to save output image")
parser.add_argument("--model", required=True, choices=['xrestormer.gaussian_denoise'],
help="Model type to use")
args = parser.parse_args()
# 模型配置路径（根据你本地路径修改）
model_configs = {
"xrestormer.gaussian_denoise": {
"yaml": "/hdd/Restoration/X-Restormer/options/test/002_xrestormer_denoise.yml",
"weights": "/hdd/Restoration/Inference/Restormer/Denoising/pretrained_models/gaussian_color_denoising_sigma15.pth"
},
}
config = model_configs[args.model]
model = load_model(config["yaml"], config["weights"])
restore_image(model, args.input, args.output)
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