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README.md

@@ -1,13 +1,45 @@
 ---
-title: Neural Style Tf Deepdreaming
-emoji: 🐨
+title: neural-style-tf-deepdreaming
+emoji: 🖼️➕🖼️🤖
 colorFrom: blue
-colorTo: purple
+colorTo: magenta
 sdk: gradio
-sdk_version: 4.37.2
+sdk_version: "4.12.0"
 app_file: app.py
 pinned: false
-license: ecl-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Combining Images with Deep Dreaming and Style Transfer
+
+This project is an experimental application v1.0 that combines two images using Deep Dreaming and Style Transfer techniques to create unique and visually appealing results.
+
+## Project Overview
+
+This application allows users to upload two images and adjust parameters to combine them using Deep Dreaming (a technique that amplifies patterns in images using a convolutional neural network) and Style Transfer (which applies the style of one image to another using a neural network). The interface provides sliders to adjust the weight of each image, the density of the style, and the sharpness of the content image.
+
+## Technical Details
+
+The project utilizes the following technologies:
+- **Neural Style Transfer**: Implemented using TensorFlow and a pre-trained model from TensorFlow Hub.
+- **Deep Dreaming**: Utilizes a pre-trained VGG19 model from PyTorch to extract and amplify image features.
+- **Gradio**: Provides an interactive web interface for users to upload images and adjust parameters.
+
+## License
+
+ecl
+
+## Developer Information
+
+Developed by Ramon Mayor Martins, Ph.D. (2024)
+- Email: [email protected]
+- Homepage: https://rmayormartins.github.io/
+- Twitter: @rmayormartins
+- GitHub: https://github.com/rmayormartins
+
+## Acknowledgements
+
+Special thanks to Instituto Federal de Santa Catarina (Federal Institute of Santa Catarina) IFSC-São José-Brazil.
+
+## Contact
+
+For any queries or suggestions, please contact the developer using the information provided above.

app.py

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+import torch
+import torch.nn as nn
+import torchvision.models as models
+import torchvision.transforms as transforms
+from PIL import Image
+import numpy as np
+import gradio as gr
+import tensorflow as tf
+import tensorflow_hub as hub
+import cv2
+
+# Conf
+IMAGE_SIZE = (256, 256)
+style_transfer_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
+
+#  (VGG19)
+vgg = models.vgg19(weights=models.VGG19_Weights.IMAGENET1K_V1).features
+vgg.eval()
+
+# 
+def load_image_pytorch(image, transform=None):
+    if transform:
+        image = transform(image).unsqueeze(0)
+    return image
+
+# 
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# Deep Dreaming
+def deep_dream(model, image, iterations, lr):
+    for i in range(iterations):
+        image.requires_grad = True
+        features = model(image)
+        loss = features.norm()
+        loss.backward()
+        with torch.no_grad():
+            image += lr * image.grad
+            image = image.clamp(0, 1)
+        image.grad = None
+    return image
+
+# 
+def load_image_tensorflow(image):
+    image = cv2.resize(image, IMAGE_SIZE, interpolation=cv2.INTER_AREA)
+    image = image.astype(np.float32)[np.newaxis, ...] / 255.
+    if image.shape[-1] == 4:
+        image = image[..., :3]
+    return image
+
+# 
+def apply_sharpness(image, intensity):
+    kernel = np.array([[0, -intensity, 0],
+                       [-intensity, 1 + 4 * intensity, -intensity],
+                       [0, -intensity, 0]])
+    sharp_image = cv2.filter2D(image, -1, kernel)
+    return np.clip(sharp_image, 0, 255)
+
+# 
+def interpolate_images(baseline, target, alpha):
+    return baseline + alpha * (target - baseline)
+
+# + Deep Dreaming e Transferência de Estilo
+def combine_images(image1, image2, weight1, style_density, content_sharpness):
+    # Carregar e pré-processar as imagens
+    image1_pil = Image.fromarray(image1)
+    image2_pil = Image.fromarray(image2)
+    
+    image1_pytorch = load_image_pytorch(image1_pil, transform)
+    image2_pytorch = load_image_pytorch(image2_pil, transform)
+    
+    # 
+    weight2 = 1 - weight1
+    mixed_image_pytorch = weight1 * image1_pytorch + weight2 * image2_pytorch
+    
+    # Deep Dreaming
+    mixed_image_pytorch = deep_dream(vgg, mixed_image_pytorch, iterations=20, lr=0.01)
+    
+    # 
+    mixed_image_pytorch = mixed_image_pytorch.squeeze(0).permute(1, 2, 0).detach().numpy()
+    mixed_image_pytorch = (mixed_image_pytorch * 255).astype(np.uint8)
+    
+    # 
+    content_image = load_image_tensorflow(mixed_image_pytorch)
+    style_image = load_image_tensorflow(image2)
+    
+    # 
+    content_image_sharp = apply_sharpness(content_image[0], intensity=content_sharpness)
+    content_image_sharp = content_image_sharp[np.newaxis, ...]
+    
+    # Transferência de Estilo
+    stylized_image = style_transfer_model(tf.constant(content_image_sharp), tf.constant(style_image))[0]
+    
+    # 
+    stylized_image = interpolate_images(baseline=content_image[0], target=stylized_image.numpy(), alpha=style_density)
+    stylized_image = np.array(stylized_image * 255, np.uint8)
+    stylized_image = np.squeeze(stylized_image)
+    
+    return stylized_image
+
+# 
+example1 = np.array(Image.open("example1.jpg"))
+example2 = np.array(Image.open("example2.jpg"))
+
+# Gradio
+interface = gr.Interface(
+    fn=combine_images,
+    inputs=[
+        gr.Image(type="numpy", label="Imagem 1"),  
+        gr.Image(type="numpy", label="Imagem 2"),    
+        gr.Slider(minimum=0, maximum=1, value=0.5, label="Peso da Imagem 1"),
+        gr.Slider(minimum=0, maximum=1, value=0.5, label="Densidade do Estilo"),
+        gr.Slider(minimum=0, maximum=10, value=1, label="Nitidez do Conteúdo")
+    ],
+    outputs="image",
+    title="Combinação de Imagens com Deep Dreaming e Transferência de Estilo",
+    description="Ajuste os pesos e a densidade do estilo para combinar e estilizar as imagens.",
+    examples=[
+        ["example1.jpg", "example2.jpg", 0.5, 0.5, 1]
+    ]
+)
+
+interface.launch()

requirements.txt

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+torch==2.0.1
+torchvision==0.15.2
+Pillow==9.5.0
+gradio
+tensorflow==2.12.0
+tensorflow_hub==0.13.0
+opencv-python==4.7.0.72