61 lines
1.5 KiB
Python
61 lines
1.5 KiB
Python
import base64
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import io
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import numpy as np
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import requests
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import torch
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from PIL import Image
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# Tensor to PIL
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def tensor_to_pil(image):
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return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
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# Convert PIL to Tensor
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def pil_to_tensor(image):
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return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
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def base64_to_image(base64_string):
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# 去除前缀
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base64_list = base64_string.split(",", 1)
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if len(base64_list) == 2:
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prefix, base64_data = base64_list
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else:
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base64_data = base64_list[0]
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# 从base64字符串中解码图像数据
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image_data = base64.b64decode(base64_data)
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# 创建一个内存流对象
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image_stream = io.BytesIO(image_data)
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# 使用PIL的Image模块打开图像数据
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image = Image.open(image_stream)
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return image
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def image_to_base64(pli_image, pnginfo=None):
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# 创建一个BytesIO对象，用于临时存储图像数据
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image_data = io.BytesIO()
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# 将图像保存到BytesIO对象中，格式为PNG
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pli_image.save(image_data, format='PNG', pnginfo=pnginfo)
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# 将BytesIO对象的内容转换为字节串
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image_data_bytes = image_data.getvalue()
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# 将图像数据编码为Base64字符串
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encoded_image = "data:image/png;base64," + base64.b64encode(image_data_bytes).decode('utf-8')
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return encoded_image
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def read_image_from_url(image_url):
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response = requests.get(image_url)
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img = Image.open(io.BytesIO(response.content))
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return img
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