Using Machine Learning to convert a photo to a Studio Ghibli image

Transforming a real-world photo into a Studio Ghibli-style image involves multiple steps, combining artistic principles with machine learning techniques. Below is a precise step-by-step approach, considering an ML engineer’s perspective.

Step 1: Image Preprocessing

Goal: Prepare the input image for stylization.

1. Resize and Denoise:

   • Resize the image to a resolution suited for neural style transfer (e.g., 512×512 or 1024×1024).

   • Apply Gaussian blur or a bilateral filter to remove high-frequency noise while preserving edges.

   • Example: cv2.GaussianBlur(image, (5, 5), 0)

2. Edge Detection (Contour Extraction):

   • Extract major edges using Canny edge detection or HED (Holistically-Nested Edge Detection).

   • Helps define outlines as seen in Ghibli backgrounds.

   • Example: cv2.Canny(image, threshold1, threshold2)

3. Segmentation (Color Clustering):

   • Use K-means clustering or SLIC (Superpixel segmentation) to separate major color regions.

   • Reduce photorealistic detail while keeping object boundaries clear.

   • Example: sklearn.cluster.KMeans(n_clusters=8).fit(image)

Step 2: Color Mapping to Ghibli Palette

Goal: Adjust colors to match Studio Ghibli’s warm and pastel aesthetics.

1. Extract Target Color Palette:

   • Use existing Ghibli scenes from films like My Neighbor Totoro or Spirited Away as references.

   • Extract dominant colors using K-means clustering or a pre-trained GAN-based color transfer model.

   • Example: dominant_colors = ColorThief(image).get_palette(color_count=10)

2. Apply Color Transfer:

   • Use techniques like Histogram Matching or Neural Color Transfer.

   • Example: Reinhard color transfer (used in style transfer pipelines).

   • skimage.exposure.match_histograms(input_image, reference_image)

Step 3: Linework Enhancement

Goal: Create the signature clean but expressive linework.

1. Convert to Line Art:

   • Use XDoG (eXtended Difference of Gaussians) to generate soft, painterly edges.

   • Example: XDoG = G(image) - α * G(image, σ)

2. Refinement with CNN-based Line Extraction:

   • Pre-trained deep learning models like DeepEdge or AnimeGANv2 can extract high-quality anime-like outlines.

Step 4: Texture and Brush Stroke Simulation

Goal: Add painterly textures characteristic of Ghibli’s hand-painted backgrounds.

1. Apply Soft Shading & Blending:

   • Use Bilateral Filters to smooth colors while retaining structure.

   • Example: cv2.bilateralFilter(image, d, sigmaColor, sigmaSpace)

2. Simulate Brush Strokes:

   • Apply GAN-based texture synthesis (e.g., using Deep Image Prior) to mimic the hand-drawn feel.

   • Example: Train a CycleGAN model with real Ghibli backgrounds as style reference.

Step 5: Final Composition & Post-processing

Goal: Ensure coherence and aesthetic consistency.

1. Depth and Atmospheric Perspective:

   • Apply Depth Estimation models (e.g., MiDaS) to simulate depth-based color fading.

   • Lighten and desaturate distant objects for a Ghibli-style background effect.

2. Hand-Painted Final Touches (AI-Assisted or Manual Refinements):

   • Introduce subtle gradients and color overlays using Neural Style Transfer.

Example ML Pipelines for Automation

• Style Transfer Models: AnimeGANv2, Fast Neural Style Transfer (NST)

• Line Extraction: DeepEdge, HED, XDoG

• Color Matching: Reinhard Transfer, GAN-based color adaptation

• Texture Simulation: CycleGAN for anime-like painterly textures

This workflow balances ML automation with artistic refinements to create a Ghibli-style image from a real-world photo. Let me know if you need a hands-on implementation guide!