from IPython.display import Image
Image(r'C:\thisAKcode.github.io\images\truchet1.png', width = 200)

import requests
from PIL import Image
from io import BytesIO
import numpy as np
import matplotlib.pyplot as plt
from skimage.color import rgb2gray
from skimage.filters import threshold_multiotsu


def posterize(img, classes=3):
    gray = rgb2gray(img) if img.ndim == 3 else img
    thresholds = threshold_multiotsu(gray, classes=classes)
    levels = np.digitize(gray, bins=thresholds)
    return (levels * (255 // (classes - 1))).astype(np.uint8)

def show(img, title, cmap=None, colorbar=False):
    plt.figure(figsize=(8, 8))
    im = plt.imshow(img, cmap=cmap)
    plt.title(title); plt.axis('off')
    if colorbar: plt.colorbar(im, fraction=0.03)
    plt.show()

def show_posterize(imgs: list, titles=None, classes=3):
    """imgs: list of numpy arrays (RGB or gray)"""
    n = len(imgs)
    fig, axes = plt.subplots(n, 2, figsize=(8, 4 * n))
    if n == 1:
        axes = [axes]  # normalize shape

    for i, img in enumerate(imgs):
        result = posterize(img, classes)
        axes[i][0].imshow(img, cmap="gray" if img.ndim == 2 else None)
        axes[i][0].set_title(titles[i] if titles else f"Original {i+1}")
        axes[i][1].imshow(result, cmap="gray", vmin=0, vmax=255)
        axes[i][1].set_title(f"Posterized ({classes} levels)")
        for ax in axes[i]:
            ax.axis("off")

    plt.tight_layout()
    plt.show()
davids_head_url = r"https://upload.wikimedia.org/wikipedia/commons/thumb/8/86/%27David%27_by_Michelangelo_Fir_JBU013.jpg/250px-%27David%27_by_Michelangelo_Fir_JBU013.jpg?utm_source=commons.wikimedia.org&utm_campaign=index&utm_content=thumbnail" # Using a more direct URL

# Define headers to mimic a browser request
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
}

# Download the image with headers
response = requests.get(davids_head_url, headers=headers)
response.raise_for_status() # Check for HTTP errors

img_data = Image.open(BytesIO(response.content))

# Convert to numpy array
davids_head_img = np.array(img_data.convert('L')) # Convert to grayscale for posterize function

show_posterize([davids_head_img], titles=['David\'s Head'], classes=3)



# add code to show processed image here:

from skimage.filters.rank import mean_bilateral  # or scipy
from skimage.exposure import equalize_adapthist

def posterize_photo(img, classes=3, highlight_thresh=0.93):
    gray = rgb2gray(img) if img.ndim == 3 else img.astype(float) / 255

    # 1. Edge-preserving smooth — kills noise, keeps contours sharp
    # Optimized: Reduced sigma_spatial for faster computation
    from skimage.restoration import denoise_bilateral
    smooth = denoise_bilateral(gray, sigma_color=0.01, sigma_spatial=1) # Smaller sigma_spatial

    # 2. CLAHE — lifts midtone separation locally
    # Optimized: Added kernel_size for faster computation
    enhanced = equalize_adapthist(smooth, clip_limit=0.02, kernel_size=(128, 128)) # Smaller kernel_size (default is (8,8) or larger depending on image size)

    # 3. Isolate specular highlights BEFORE Otsu sees them
    highlight_mask = enhanced > highlight_thresh

    # 4. Run Otsu only on non-highlight pixels
    non_highlight = enhanced[~highlight_mask]
    thresholds = threshold_multiotsu(non_highlight, classes=classes - 1)

    # 5. Assemble result
    result = np.digitize(enhanced, bins=thresholds)  # 0, 1, 2
    result[highlight_mask] = classes - 1             # force highlights → white

    return (result * (255 // (classes - 1))).astype(np.uint8)
_tweaked = posterize_photo(davids_head_img, classes=3, highlight_thresh=0.77)


show(_tweaked, 'David\'s Head - Tweaked', cmap="gray", colorbar=False)


from skimage.exposure import adjust_gamma, rescale_intensity
from skimage.filters import gaussian

def posterize_photo2(img, classes=3, gamma=0.8, blur=1.2):
    gray = rgb2gray(img) if img.ndim == 3 else img

    # stretch full tonal range first
    stretched = rescale_intensity(gray)

    # gamma < 1 lifts shadows → better midtone separation
    adjusted = adjust_gamma(stretched, gamma=gamma)

    # cheap noise removal, preserves edges better than bilateral at this sigma
    smoothed = gaussian(adjusted, sigma=blur)

    thresholds = threshold_multiotsu(smoothed, classes=classes)
    result = np.digitize(smoothed, bins=thresholds)
    return (result * (255 // (classes - 1))).astype(np.uint8)
_tweaked = posterize_photo2(davids_head_img, classes=3, gamma=1.2, blur=1.5)


show(_tweaked, 'David\'s Head - Tweaked', cmap="gray", colorbar=False)
_tweaked = posterize_photo2(davids_head_img, classes=3, gamma=0.6, blur=1.5)

from skimage.filters import threshold_multiotsu
from skimage.color import rgb2gray
import numpy as np
import matplotlib.pyplot as plt # Import matplotlib

def stencil_posterize(img, classes=3):
    """Hyper-minimal stencil: light grey → dark grey, 3 classes only"""
    # Normalize to 0-1
    gray = rgb2gray(img) if img.ndim == 3 else img.astype(float) / 255.0

    # Find optimal thresholds
    thresholds = threshold_multiotsu(gray, classes=classes)

    # Digitize to classes [0, 1, 2]
    posterized = np.digitize(gray, bins=thresholds)

    # Map to custom range: light grey (200) → dark grey (50), skip pure white/black
    # Level 0 → 200 (light)
    # Level 1 → 125 (mid)
    # Level 2 → 50  (dark)
    levels = np.array([60, 140, 220], dtype=np.uint8)

    return levels[posterized]

# Test
preresult = posterize(davids_head_img, 3)

result = stencil_posterize(_tweaked)
plt.imshow(result, cmap='gray') # Use plt.imshow
plt.show()