{"id":1110412,"date":"2025-01-08T17:19:32","date_gmt":"2025-01-08T09:19:32","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1110412.html"},"modified":"2025-01-08T17:19:34","modified_gmt":"2025-01-08T09:19:34","slug":"python%e5%a6%82%e4%bd%95%e6%8a%8a%e4%b8%80%e4%ba%9b%e5%9b%be%e7%89%87%e9%a3%8e%e6%a0%bc%e5%8c%96","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1110412.html","title":{"rendered":"Python\u5982\u4f55\u628a\u4e00\u4e9b\u56fe\u7247\u98ce\u683c\u5316"},"content":{"rendered":"

\"Python\u5982\u4f55\u628a\u4e00\u4e9b\u56fe\u7247\u98ce\u683c\u5316\"<\/p>\n

\u4e00\u3001Python\u5982\u4f55\u628a\u4e00\u4e9b\u56fe\u7247\u98ce\u683c\u5316<\/p>\n<\/p>\n

Python\u5c06\u56fe\u7247\u98ce\u683c\u5316\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u5e38\u89c1\u7684\u6709\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u3001\u5229\u7528\u56fe\u50cf\u5904\u7406\u5e93\u3001\u901a\u8fc7\u6ee4\u955c\u548c\u7279\u6548\u6765\u5b9e\u73b0\u3002\u5176\u4e2d\uff0c\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u662f\u6700\u5148\u8fdb\u7684\u6280\u672f\uff0c\u6548\u679c\u4e5f\u6700\u4f73\u3002<\/strong> \u4f8b\u5982\uff0c\u5229\u7528\u795e\u7ecf\u98ce\u683c\u8fc1\u79fb\uff08Neural Style Transfer\uff09\u53ef\u4ee5\u5c06\u4e00\u5f20\u56fe\u7247\u7684\u98ce\u683c\u8fc1\u79fb\u5230\u53e6\u4e00\u5f20\u56fe\u7247\u4e0a\u3002\u795e\u7ecf\u98ce\u683c\u8fc1\u79fb\u5229\u7528\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u6765\u63d0\u53d6\u56fe\u50cf\u7684\u5185\u5bb9\u548c\u98ce\u683c\u7279\u5f81\uff0c\u901a\u8fc7\u4f18\u5316\u7b97\u6cd5\u5c06\u8fd9\u4e24\u8005\u7ed3\u5408\u5728\u4e00\u8d77\uff0c\u4ece\u800c\u751f\u6210\u98ce\u683c\u5316\u7684\u56fe\u7247\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u56fe\u7247\u98ce\u683c\u5316\u7684\u591a\u79cd\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e8c\u3001\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b<\/p>\n<\/p>\n

\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8fdb\u884c\u56fe\u7247\u98ce\u683c\u5316\u662f\u5f53\u524d\u6700\u70ed\u95e8\u7684\u65b9\u6cd5\u4e4b\u4e00\uff0c\u5c24\u5176\u662f\u795e\u7ecf\u98ce\u683c\u8fc1\u79fb\uff08Neural Style Transfer\uff09\u3002\u8fd9\u79cd\u65b9\u6cd5\u7684\u6838\u5fc3\u601d\u60f3\u662f\u5c06\u4e00\u5f20\u56fe\u50cf\u7684\u5185\u5bb9\u4e0e\u53e6\u4e00\u5f20\u56fe\u50cf\u7684\u98ce\u683c\u8fdb\u884c\u7ed3\u5408\uff0c\u751f\u6210\u65b0\u7684\u98ce\u683c\u5316\u56fe\u50cf\u3002\u5e38\u7528\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u6709TensorFlow\u548cPyTorch\u3002<\/p>\n<\/p>\n

    \n
  1. TensorFlow\u5b9e\u73b0\u795e\u7ecf\u98ce\u683c\u8fc1\u79fb<\/li>\n<\/ol>\n

    TensorFlow\u662f\u4e00\u4e2a\u6d41\u884c\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684API\u6765\u5b9e\u73b0\u795e\u7ecf\u98ce\u683c\u8fc1\u79fb\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n

    import tensorflow as tf<\/p>\n
    from tensorflow.keras.applications import VGG19<\/p>\n
    from tensorflow.keras.preprocessing.image import load_img, img_to_array<\/p>\n
    from tensorflow.keras.models import Model<\/p>\n
    import numpy as np<\/p>\n
    \u52a0\u8f7d\u5185\u5bb9\u56fe\u50cf\u548c\u98ce\u683c\u56fe\u50cf<\/strong><\/h2>\n
    content_image = load_img('content.jpg')<\/p>\n
    style_image = load_img('style.jpg')<\/p>\n
    \u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u6570\u7ec4<\/strong><\/h2>\n
    content_array = img_to_array(content_image)<\/p>\n
    style_array = img_to_array(style_image)<\/p>\n
    \u9884\u5904\u7406\u56fe\u50cf<\/strong><\/h2>\n
    content_array = tf.keras.applications.vgg19.preprocess_input(content_array)<\/p>\n
    style_array = tf.keras.applications.vgg19.preprocess_input(style_array)<\/p>\n
    \u52a0\u8f7dVGG19\u6a21\u578b<\/strong><\/h2>\n
    vgg = VGG19(include_top=False, weights='imagenet')<\/p>\n
    vgg.trAI<\/a>nable = False<\/p>\n
    \u63d0\u53d6\u5185\u5bb9\u548c\u98ce\u683c\u7279\u5f81<\/strong><\/h2>\n
    content_layer = 'block5_conv2'<\/p>\n
    style_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1']<\/p>\n
    content_model = Model(inputs=vgg.input, outputs=vgg.get_layer(content_layer).output)<\/p>\n
    style_models = [Model(inputs=vgg.input, outputs=vgg.get_layer(layer).output) for layer in style_layers]<\/p>\n
    \u5b9a\u4e49\u635f\u5931\u51fd\u6570<\/strong><\/h2>\n
    def compute_loss(content_output, style_outputs, generated_output):<\/p>\n
        content_loss = tf.reduce_mean((generated_output - content_output)  2)<\/p>\n
        style_loss = 0<\/p>\n
        for style_output, generated_style in zip(style_outputs, generated_output):<\/p>\n
            style_loss += tf.reduce_mean((generated_style - style_output)  2)<\/p>\n
        total_loss = content_loss + style_loss<\/p>\n
        return total_loss<\/p>\n
    \u4f18\u5316\u56fe\u50cf<\/strong><\/h2>\n
    generated_image = tf.Variable(content_array)<\/p>\n
    optimizer = tf.optimizers.Adam(learning_rate=0.02)<\/p>\n
    for step in range(1000):<\/p>\n
        with tf.GradientTape() as tape:<\/p>\n
            generated_output = content_model(generated_image)<\/p>\n
            style_outputs = [style_model(generated_image) for style_model in style_models]<\/p>\n
            loss = compute_loss(content_model(content_array), style_outputs, generated_output)<\/p>\n
        grads = tape.gradient(loss, generated_image)<\/p>\n
        optimizer.apply_gradients([(grads, generated_image)])<\/p>\n
    \u53cd\u9884\u5904\u7406\u56fe\u50cf<\/strong><\/h2>\n
    generated_image = tf.keras.applications.vgg19.deprocess_input(generated_image.numpy())<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. PyTorch\u5b9e\u73b0\u795e\u7ecf\u98ce\u683c\u8fc1\u79fb<\/li>\n<\/ol>\n
      PyTorch\u4e5f\u662f\u4e00\u4e2a\u6d41\u884c\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528PyTorch\u5b9e\u73b0\u795e\u7ecf\u98ce\u683c\u8fc1\u79fb\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
      import torch<\/p>\n
      import torch.nn as nn<\/p>\n
      import torch.optim as optim<\/p>\n
      from torchvision import models, transforms<\/p>\n
      from PIL import Image<\/p>\n
      \u52a0\u8f7d\u5185\u5bb9\u56fe\u50cf\u548c\u98ce\u683c\u56fe\u50cf<\/strong><\/h2>\n
      content_image = Image.open('content.jpg')<\/p>\n
      style_image = Image.open('style.jpg')<\/p>\n
      \u56fe\u50cf\u9884\u5904\u7406<\/strong><\/h2>\n
      transform = transforms.Compose([<\/p>\n
          transforms.Resize((512, 512)),<\/p>\n
          transforms.ToTensor(),<\/p>\n
          transforms.Lambda(lambda x: x.mul(255))<\/p>\n
      ])<\/p>\n
      content_tensor = transform(content_image).unsqueeze(0)<\/p>\n
      style_tensor = transform(style_image).unsqueeze(0)<\/p>\n
      \u52a0\u8f7dVGG19\u6a21\u578b<\/strong><\/h2>\n
      vgg = models.vgg19(pretrained=True).features<\/p>\n
      for param in vgg.parameters():<\/p>\n
          param.requires_grad_(False)<\/p>\n
      \u63d0\u53d6\u5185\u5bb9\u548c\u98ce\u683c\u7279\u5f81<\/strong><\/h2>\n
      def get_features(image, model):<\/p>\n
          layers = {<\/p>\n
              '0': 'conv1_1',<\/p>\n
              '5': 'conv2_1',<\/p>\n
              '10': 'conv3_1',<\/p>\n
              '19': 'conv4_1',<\/p>\n
              '21': 'conv4_2',  # content layer<\/p>\n
              '28': 'conv5_1'<\/p>\n
          }<\/p>\n
          features = {}<\/p>\n
          x = image<\/p>\n
          for name, layer in model._modules.items():<\/p>\n
              x = layer(x)<\/p>\n
              if name in layers:<\/p>\n
                  features[layers[name]] = x<\/p>\n
          return features<\/p>\n
      content_features = get_features(content_tensor, vgg)<\/p>\n
      style_features = get_features(style_tensor, vgg)<\/p>\n
      \u8ba1\u7b97\u98ce\u683c\u77e9\u9635<\/strong><\/h2>\n
      def gram_matrix(tensor):<\/p>\n
          _, d, h, w = tensor.size()<\/p>\n
          tensor = tensor.view(d, h * w)<\/p>\n
          gram = torch.mm(tensor, tensor.t())<\/p>\n
          return gram<\/p>\n
      style_grams = {layer: gram_matrix(style_features[layer]) for layer in style_features}<\/p>\n
      \u5b9a\u4e49\u635f\u5931\u51fd\u6570<\/strong><\/h2>\n
      def compute_loss(content_features, style_grams, target_features):<\/p>\n
          content_loss = torch.mean((target_features['conv4_2'] - content_features['conv4_2'])  2)<\/p>\n
          style_loss = 0<\/p>\n
          for layer in style_grams:<\/p>\n
              target_feature = target_features[layer]<\/p>\n
              target_gram = gram_matrix(target_feature)<\/p>\n
              style_gram = style_grams[layer]<\/p>\n
              layer_loss = torch.mean((target_gram - style_gram)  2)<\/p>\n
              style_loss += layer_loss<\/p>\n
          total_loss = content_loss + style_loss<\/p>\n
          return total_loss<\/p>\n
      \u4f18\u5316\u56fe\u50cf<\/strong><\/h2>\n
      target = content_tensor.clone().requires_grad_(True)<\/p>\n
      optimizer = optim.Adam([target], lr=0.003)<\/p>\n
      for step in range(2000):<\/p>\n
          target_features = get_features(target, vgg)<\/p>\n
          loss = compute_loss(content_features, style_grams, target_features)<\/p>\n
          optimizer.zero_grad()<\/p>\n
          loss.backward()<\/p>\n
          optimizer.step()<\/p>\n
      \u8f6c\u6362\u56fe\u50cf\u683c\u5f0f<\/strong><\/h2>\n
      target = target.squeeze().detach().cpu().numpy().transpose(1, 2, 0)<\/p>\n
      target = np.clip(target, 0, 255).astype('uint8')<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e09\u3001\u56fe\u50cf\u5904\u7406\u5e93<\/p>\n<\/p>\n
      \u9664\u4e86\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8fdb\u884c\u56fe\u7247\u98ce\u683c\u5316\uff0cPython\u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u5982PIL\uff08Pillow\uff09\u3001OpenCV\u7b49\u3002\u8fd9\u4e9b\u5e93\u53ef\u4ee5\u5b9e\u73b0\u57fa\u672c\u7684\u56fe\u50cf\u5904\u7406\u64cd\u4f5c\uff0c\u5982\u6ee4\u955c\u3001\u7279\u6548\u7b49\uff0c\u4ece\u800c\u8fbe\u5230\u98ce\u683c\u5316\u7684\u6548\u679c\u3002<\/p>\n<\/p>\n
        \n
      1. PIL\uff08Pillow\uff09<\/li>\n<\/ol>\n
        Pillow\u662fPython Imaging Library\u7684\u5206\u652f\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528Pillow\u5b9e\u73b0\u56fe\u7247\u98ce\u683c\u5316\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
        from PIL import Image, ImageFilter, ImageEnhance<\/p>\n
        \u52a0\u8f7d\u56fe\u50cf<\/strong><\/h2>\n
        image = Image.open('image.jpg')<\/p>\n
        \u5e94\u7528\u6ee4\u955c<\/strong><\/h2>\n
        image = image.filter(ImageFilter.DETAIL)<\/p>\n
        \u589e\u5f3a\u5bf9\u6bd4\u5ea6<\/strong><\/h2>\n
        enhancer = ImageEnhance.Contrast(image)<\/p>\n
        image = enhancer.enhance(2.0)<\/p>\n
        \u4fdd\u5b58\u98ce\u683c\u5316\u56fe\u50cf<\/strong><\/h2>\n
        image.save('stylized_image.jpg')<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. OpenCV<\/li>\n<\/ol>\n
          OpenCV\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u56fe\u50cf\u5904\u7406\u51fd\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528OpenCV\u5b9e\u73b0\u56fe\u7247\u98ce\u683c\u5316\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
          import cv2<\/p>\n
          import numpy as np<\/p>\n
          \u52a0\u8f7d\u56fe\u50cf<\/strong><\/h2>\n
          image = cv2.imread('image.jpg')<\/p>\n
          \u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
          gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n
          \u5e94\u7528\u9ad8\u65af\u6a21\u7cca<\/strong><\/h2>\n
          blurred = cv2.GaussianBlur(gray, (21, 21), 0)<\/p>\n
          \u68c0\u6d4b\u8fb9\u7f18<\/strong><\/h2>\n
          edges = cv2.Canny(blurred, 50, 150)<\/p>\n
          \u53cd\u8f6c\u989c\u8272<\/strong><\/h2>\n
          edges = cv2.bitwise_not(edges)<\/p>\n
          \u5408\u5e76\u8fb9\u7f18\u548c\u539f\u59cb\u56fe\u50cf<\/strong><\/h2>\n
          stylized_image = cv2.bitwise_and(image, image, mask=edges)<\/p>\n
          \u4fdd\u5b58\u98ce\u683c\u5316\u56fe\u50cf<\/strong><\/h2>\n
          cv2.imwrite('stylized_image.jpg', stylized_image)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u56db\u3001\u6ee4\u955c\u548c\u7279\u6548<\/p>\n<\/p>\n
          \u9664\u4e86\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u548c\u56fe\u50cf\u5904\u7406\u5e93\uff0cPython\u8fd8\u53ef\u4ee5\u901a\u8fc7\u5e94\u7528\u6ee4\u955c\u548c\u7279\u6548\u6765\u5b9e\u73b0\u56fe\u7247\u98ce\u683c\u5316\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u6ee4\u955c\u548c\u7279\u6548\uff1a<\/p>\n<\/p>\n
            \n
          1. \u6000\u65e7\u6ee4\u955c<\/li>\n<\/ol>\n
            \u6000\u65e7\u6ee4\u955c\u53ef\u4ee5\u8ba9\u56fe\u50cf\u770b\u8d77\u6765\u50cf\u65e7\u7167\u7247\u4e00\u6837\uff0c\u589e\u52a0\u590d\u53e4\u611f\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b9e\u73b0\u6000\u65e7\u6ee4\u955c\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
            import cv2<\/p>\n
            import numpy as np<\/p>\n
            \u52a0\u8f7d\u56fe\u50cf<\/strong><\/h2>\n
            image = cv2.imread('image.jpg')<\/p>\n
            \u5b9a\u4e49\u6000\u65e7\u6ee4\u955c<\/strong><\/h2>\n
            filter = np.array([[0.272, 0.534, 0.131],<\/p>\n
                               [0.349, 0.686, 0.168],<\/p>\n
                               [0.393, 0.769, 0.189]])<\/p>\n
            \u5e94\u7528\u6ee4\u955c<\/strong><\/h2>\n
            stylized_image = cv2.transform(image, filter)<\/p>\n
            \u4fdd\u5b58\u98ce\u683c\u5316\u56fe\u50cf<\/strong><\/h2>\n
            cv2.imwrite('stylized_image.jpg', stylized_image)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u5361\u901a\u7279\u6548<\/li>\n<\/ol>\n
              \u5361\u901a\u7279\u6548\u53ef\u4ee5\u8ba9\u56fe\u50cf\u770b\u8d77\u6765\u50cf\u5361\u901a\u753b\u4e00\u6837\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b9e\u73b0\u5361\u901a\u7279\u6548\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
              import cv2<\/p>\n
              import numpy as np<\/p>\n
              \u52a0\u8f7d\u56fe\u50cf<\/strong><\/h2>\n
              image = cv2.imread('image.jpg')<\/p>\n
              \u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
              gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n
              \u5e94\u7528\u9ad8\u65af\u6a21\u7cca<\/strong><\/h2>\n
              blurred = cv2.GaussianBlur(gray, (5, 5), 0)<\/p>\n
              \u68c0\u6d4b\u8fb9\u7f18<\/strong><\/h2>\n
              edges = cv2.Canny(blurred, 100, 200)<\/p>\n
              \u53cd\u8f6c\u989c\u8272<\/strong><\/h2>\n
              edges = cv2.bitwise_not(edges)<\/p>\n
              \u5e94\u7528\u9ad8\u65af\u6a21\u7cca<\/strong><\/h2>\n
              color = cv2.bilateralFilter(image, 9, 300, 300)<\/p>\n
              \u5408\u5e76\u8fb9\u7f18\u548c\u989c\u8272\u56fe\u50cf<\/strong><\/h2>\n
              stylized_image = cv2.bitwise_and(color, color, mask=edges)<\/p>\n
              \u4fdd\u5b58\u98ce\u683c\u5316\u56fe\u50cf<\/strong><\/h2>\n
              cv2.imwrite('stylized_image.jpg', stylized_image)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u4e94\u3001\u603b\u7ed3<\/p>\n<\/p>\n
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