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| import cv2 | |
| import numpy as np | |
| import paddle | |
| import paddle.nn.functional as F | |
| from Generater import Generater | |
| import gradio as gr | |
| generator = Generater() | |
| # # oslist =os.listdir("纹理") | |
| # # print(oslist) | |
| G_path ='Gmodel_state33003.pdparams' | |
| layer_state_dictg = paddle.load(G_path) | |
| generator.set_state_dict(layer_state_dictg)#导入训练好的参数文件 | |
| def style_transfer(content_img,style_img): | |
| g_input = content_img.astype('float32') / 127.5 - 1 # 归一化 | |
| g_input = g_input[np.newaxis, ...].transpose(0, 3, 1, 2) # NHWC -> NCHW | |
| g_input = paddle.to_tensor(g_input) # numpy -> tensor | |
| h,w = g_input.shape[-2:] | |
| p = max([h,w]) | |
| g_input = F.interpolate(g_input,scale_factor=(512/p)) | |
| g_input_s = style_img.astype('float32') / 127.5 - 1 # 归一化 | |
| g_input_s = g_input_s[np.newaxis, ...].transpose(0, 3, 1, 2) # NHWC -> NCHW | |
| g_input_s = paddle.to_tensor(g_input_s) # numpy -> tensor | |
| h,w = g_input_s.shape[-2:] | |
| p = max([h,w]) | |
| g_input_s = F.interpolate(g_input_s,scale_factor=(512/p)) | |
| i = paddle.to_tensor([1.]) | |
| g_output = generator(g_input,g_input_s,i) | |
| g_output = g_output.detach().numpy() # tensor -> numpy | |
| g_output = g_output.transpose(0, 2, 3, 1)[0] # NCHW -> NHWC | |
| g_output = (g_output+1) *127.5 # 反归一化 | |
| g_output = g_output.astype(np.uint8) | |
| output = g_output | |
| # cv2.imwrite(os.path.join("./test", str(i.numpy()[0])+'qt.png'), g_output)#保存图片到本地 | |
| return output | |
| interface = gr.Interface(fn=style_transfer, inputs=["image","image"], outputs="image") | |
| interface.launch(share=True) | |