118 lines
3.8 KiB
Python
118 lines
3.8 KiB
Python
from flask import Flask, render_template, request, redirect, url_for, send_file, jsonify, g
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from mode_style_transfer import StyleTransferModel
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from PIL import Image
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import io
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import torch
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from torchvision.models import vgg19, VGG19_Weights
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import torchvision.transforms as transforms
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import os
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import matplotlib.pyplot as plt
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import base64
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import torch.nn as nn
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app = Flask(__name__)
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# Image transformation
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imsize = 512 if torch.cuda.is_available() else 128
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loader = transforms.Compose([
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transforms.Resize(imsize),
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transforms.ToTensor()
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])
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Global variable to store the output tensor
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output_tensor = None
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def image_loader(image_bytes):
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image = Image.open(io.BytesIO(image_bytes))
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image = loader(image).unsqueeze(0)
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return image.to(device, torch.float)
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def tensor_to_image(tensor):
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image = tensor.clone().detach().squeeze(0)
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image = transforms.ToPILImage()(image)
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return image
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def image_to_base64(image):
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img_io = io.BytesIO()
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image.save(img_io, 'JPEG')
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img_io.seek(0)
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return base64.b64encode(img_io.getvalue()).decode('utf-8')
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@app.route('/', methods=['GET', 'POST'])
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def index():
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if request.method == 'POST':
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global output_tensor
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content_image_file = request.files['content_image']
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style_image_file = request.files['style_image']
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# Load images directly from the uploaded files
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content_image = Image.open(content_image_file)
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style_image = Image.open(style_image_file)
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# Pass the images to the StyleTransferModel
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style_transfer = StyleTransferModel(content_image, style_image)
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output = style_transfer.run_style_transfer()
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output_tensor = output
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# Convert the output tensor to an image
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output_image = tensor_to_image(output)
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# Convert the image to Base64 for JSON response
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image_base64 = image_to_base64(output_image)
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return jsonify({'image': image_base64})
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return render_template('index.html')
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@app.route('/visualize', methods=['GET'])
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def visualize():
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pretrained_model = vgg19(weights=VGG19_Weights.DEFAULT).features.eval().to(device)
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# Extract convolutional layers from VGG19
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conv_layers = []
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for module in pretrained_model.children():
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if isinstance(module, nn.Conv2d):
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conv_layers.append(module)
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# Pass the resulting image through the convolutional layers and capture feature maps
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feature_maps = []
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layer_names = []
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input_image = output_tensor.clone()
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for i, layer in enumerate(conv_layers):
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input_image = layer(input_image)
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feature_maps.append(input_image)
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layer_names.append(f"Layer {i + 1}: {str(layer)}")
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# Process and feature maps
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processed_feature_maps = []
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for feature_map in feature_maps:
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feature_map = feature_map.squeeze(0) # Remove the batch dimension
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mean_feature_map = torch.mean(feature_map, dim=0).cpu().detach().numpy() # Compute mean across channels
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processed_feature_maps.append(mean_feature_map)
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# Plot the feature maps
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fig = plt.figure(figsize=(20, 20))
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for i, fm in enumerate(processed_feature_maps):
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ax = fig.add_subplot(4, 4, i + 1) # Adjust grid size as needed
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ax.imshow(fm, cmap='viridis') # Display feature map as image
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ax.axis("off")
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ax.set_title(layer_names[i], fontsize=8)
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plt.tight_layout()
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# Save the plot to a BytesIO object and encode it as base64
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img_io = io.BytesIO()
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plt.savefig(img_io, format='png')
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img_io.seek(0)
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plt.close(fig)
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plot_base64 = base64.b64encode(img_io.getvalue()).decode('utf-8')
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# Return the image as a base64-encoded string that can be embedded in HTML
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return f'<img src="data:image/png;base64,{plot_base64}" alt="Layer Visualizations"/>'
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#run the app
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if __name__ == '__main__':
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app.run(debug=True)
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