Upload app.py

app.py

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+'''
+Author: Egrt
+Date: 2022-01-04 21:46:25
+LastEditors: Egrt
+LastEditTime: 2022-01-07 19:49:19
+FilePath: \License-super-resolution-master\app.py
+'''
+from Utilities.io import DataLoader
+from Models.RRDBNet import RRDBNet
+import numpy as np
+import gradio as gr
+import cv2
+import os
+loader = DataLoader()
+# --------加载模型---------- #
+MODEL_PATH = 'Pretrained/rrdb'
+model = RRDBNet(blockNum=10)
+model.load_weights(MODEL_PATH)
+
+# --------模型推理---------- #
+def inference(file):
+    # 将np转Tensor
+    input_image= loader.input_image(file.name)
+    # 维度扩张
+    input_image= np.expand_dims(input_image, axis=0)
+    yPred      = model.predict(input_image)
+    yPred      = np.squeeze(np.clip(yPred, a_min=0, a_max=1))
+    return yPred
+
+# --------网页信息---------- #  
+title = "车牌超分辨率"
+description = "基于生成对抗网络的车牌超分辨率，可从24×12像素的超低分辨率车牌图片恢复到正常可视状态@西南科技大学智能控制与图像处理研究室"
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2108.10257' target='_blank'>SwinIR: Image Restoration Using Swin Transformer</a> | <a href='https://github.com/JingyunLiang/SwinIR' target='_blank'>Github Repo</a></p>"
+example_img_dir  = 'Samples'
+example_img_name = os.listdir(example_img_dir)
+examples=[[os.path.join(example_img_dir, image_path)] for image_path in example_img_name if image_path.endswith('.jpg')]
+gr.Interface(
+    inference, 
+    [gr.inputs.Image(type="file", label="Input")], 
+    gr.outputs.Image(type="numpy", label="Output"),
+    title=title,
+    description=description,
+    article=article,
+    enable_queue=True,
+    examples=examples
+    ).launch(debug=True)