Create app.py

app.py

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+import argparse
+import os
+import yaml
+import torch
+import torch.backends.cudnn as cudnn
+import numpy as np
+from data_web import web_input
+from models import DenoisingDiffusion, DiffusiveRestoration
+import utils
+import PIL.Image as Image
+from torchvision.utils import make_grid
+import gradio as gr
+import tempfile
+
+# tempfile.tempdir = "/home/dachuang/gradio/tmp/"
+
+# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+title_markdown = ("""
+欢迎来到甲骨文文字演变模拟器
+你只需要输入一张甲骨文图片，就可以看到它在不同随机种子下演变到汉字的结果。
+""")
+
+
+def config_get():
+    parser = argparse.ArgumentParser()
+    # 参数配置文件路径
+    parser.add_argument("--config", default='config_web.yml', type=str, required=False, help="Path to the config file")
+    args = parser.parse_args()
+
+    with open(os.path.join(args.config), "r") as f:
+        config = yaml.safe_load(f)
+    new_config = dict2namespace(config)
+
+    return new_config
+
+
+def dict2namespace(config):
+    namespace = argparse.Namespace()
+    for key, value in config.items():
+        if isinstance(value, dict):
+            new_value = dict2namespace(value)
+        else:
+            new_value = value
+        setattr(namespace, key, new_value)
+    return namespace
+
+
+config = config_get()
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+print("=> using device: {}".format(device))
+config.device = device
+# torch.backends.cudnn.benchmark = True
+print("=> creating diffusion model")
+diffusion = DenoisingDiffusion(config, test=True)
+model = DiffusiveRestoration(diffusion, config)
+
+
+# 加载数据
+# seed = 61
+# torch.manual_seed(seed)
+# np.random.seed(seed)
+# if torch.cuda.is_available():
+#     torch.cuda.manual_seed_all(seed)
+# image_path = "/home/dachuang/hsguan/JGWNET/壴30.png"
+# img = Image.open(image_path).convert('RGB')
+# image_web = np.array(img)
+# image = web_input(image_web)
+
+# output_image = model.web_restore(image, r=config.data.grid_r)
+# utils.logging.save_image(output_image, 'web/0.png')
+# grid = make_grid(output_image)
+# ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
+
+
+# im = Image.fromarray(ndarr)
+# im.save('web_output.png', format=None)
+
+def sepia(image_web, seed):
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+    image = web_input(image_web)
+    output_image = model.web_restore(image, r=config.data.grid_r)
+    grid = make_grid(output_image)
+    ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
+    # torch.cuda.empty_cache()
+    return ndarr
+
+
+demo = gr.Interface(sepia,
+                    inputs=[gr.Image(label="输入甲骨文图片", height=600, width=600), gr.Number(label="随机种子")],
+                    outputs=gr.Image(label="输出汉字图片", height=600, width=600),
+                    title=title_markdown)
+demo.queue().launch(
+    server_name="127.0.0.1",
+    server_port=7681,
+    share=True
+)