filters.py

import cv2
import numpy as np
from registry import registry


@registry.register("Original")
def original(image):
    return image


@registry.register("Dot Effect", defaults={
    "dot_size": 10,
    "dot_spacing": 2,
    "invert": False,
}, min_vals={
    "dot_size": 1,
    "dot_spacing": 1,
}, max_vals={
    "dot_size": 20,
    "dot_spacing": 10,
}, step_vals={
    "dot_size": 1,
    "dot_spacing": 1,
})
def dot_effect(image, dot_size: int = 10, dot_spacing: int = 2, invert: bool = False):
    """
    ## Convert your image into a dotted pattern.

    **Args:**
    * `image` (numpy.ndarray): Input image (BGR or grayscale)
    * `dot_size` (int): Size of each dot
    * `dot_spacing` (int): Spacing between dots
    * `invert` (bool): Invert the dots

    **Returns:**
    * `numpy.ndarray`: Dotted image
    """
    # Convert to grayscale if image is color
    if len(image.shape) == 3:
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    else:
        gray = image

    # Apply adaptive thresholding to improve contrast
    gray = cv2.adaptiveThreshold(
        gray,
        255,
        cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
        cv2.THRESH_BINARY,
        25,  # Block size
        5    # Constant subtracted from mean
    )

    height, width = gray.shape
    canvas = np.zeros_like(gray) if not invert else np.full_like(gray, 255)

    y_dots = range(0, height, dot_size + dot_spacing)
    x_dots = range(0, width, dot_size + dot_spacing)

    dot_color = 255 if not invert else 0
    for y in y_dots:
        for x in x_dots:
            region = gray[y:min(y+dot_size, height), x:min(x+dot_size, width)]
            if region.size > 0:
                brightness = np.mean(region)

            # Dynamic dot sizing based on brightness
            relative_brightness = brightness / 255.0
            if invert:
                relative_brightness = 1 - relative_brightness

            # Draw circle with size proportional to brightness
            radius = int((dot_size/2) * relative_brightness)
            if radius > 0:
                cv2.circle(canvas,
                           (x + dot_size//2, y + dot_size//2),
                           radius,
                           (dot_color),
                           -1)

    return canvas


@registry.register("Pixelize", defaults={
    "pixel_size": 10,
}, min_vals={
    "pixel_size": 1,
}, max_vals={
    "pixel_size": 50,
}, step_vals={
    "pixel_size": 1,
})
def pixelize(image, pixel_size: int = 10):
    """
    ## Apply a pixelization effect to the image.

    **Args:**
    * `image` (numpy.ndarray): Input image (BGR or grayscale)
    * `pixel_size` (int): Size of each pixel block

    **Returns:**
    * `numpy.ndarray`: Pixelized image
    """
    height, width = image.shape[:2]

    # Resize the image to a smaller size
    small_height = height // pixel_size
    small_width = width // pixel_size
    small_image = cv2.resize(
        image, (small_width, small_height), interpolation=cv2.INTER_LINEAR)

    # Resize back to the original size with nearest neighbor interpolation
    pixelized_image = cv2.resize(
        small_image, (width, height), interpolation=cv2.INTER_NEAREST)

    return pixelized_image


@registry.register("Sketch Effect")
def sketch_effect(image):
    """
    ## Apply a sketch effect to the image.

    **Args:**
    * `image` (numpy.ndarray): Input image (BGR or grayscale)

    **Returns:**
    * `numpy.ndarray`: Sketch effect applied image
    """
    # Convert the image to grayscale
    if len(image.shape) == 3:
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    else:
        gray = image

    # Invert the grayscale image
    inverted_gray = cv2.bitwise_not(gray)

    # Apply Gaussian blur to the inverted image
    blurred = cv2.GaussianBlur(inverted_gray, (21, 21), 0)  # Fixed kernel size

    # Blend the grayscale image with the blurred inverted image
    sketch = cv2.divide(gray, 255 - blurred, scale=256)

    return sketch


@registry.register("Warm", defaults={
    "intensity": 30,
}, min_vals={
    "intensity": 0,
}, max_vals={
    "intensity": 100,
}, step_vals={
    "intensity": 1,
})
def warm_filter(image, intensity: int = 30):
    """
    ## Adds a warm color effect to the image.

    **Args:**
    * `image` (numpy.ndarray): Input image (BGR)
    * `intensity` (int): Intensity of the warm effect (0-100)

    **Returns:**
    * `numpy.ndarray`: Image with warm color effect
    """
    # Convert intensity to actual adjustment values
    intensity_scale = intensity / 100.0
    
    # Split the image into BGR channels
    b, g, r = cv2.split(image.astype(np.float32))
    
    # Increase red, slightly increase green, decrease blue
    r = np.clip(r * (1 + 0.5 * intensity_scale), 0, 255)
    g = np.clip(g * (1 + 0.1 * intensity_scale), 0, 255)
    b = np.clip(b * (1 - 0.1 * intensity_scale), 0, 255)
    
    return cv2.merge([b, g, r]).astype(np.uint8)


@registry.register("Cool", defaults={
    "intensity": 30,
}, min_vals={
    "intensity": 0,
}, max_vals={
    "intensity": 100,
}, step_vals={
    "intensity": 1,
})
def cool_filter(image, intensity: int = 30):
    """
    ## Adds a cool color effect to the image.

    **Args:**
    * `image` (numpy.ndarray): Input image (BGR)
    * `intensity` (int): Intensity of the cool effect (0-100)

    **Returns:**
    * `numpy.ndarray`: Image with cool color effect
    """
    # Convert intensity to actual adjustment values
    intensity_scale = intensity / 100.0
    
    # Split the image into BGR channels
    b, g, r = cv2.split(image.astype(np.float32))
    
    # Increase blue, slightly increase green, decrease red
    b = np.clip(b * (1 + 0.5 * intensity_scale), 0, 255)
    g = np.clip(g * (1 + 0.1 * intensity_scale), 0, 255)
    r = np.clip(r * (1 - 0.1 * intensity_scale), 0, 255)
    
    return cv2.merge([b, g, r]).astype(np.uint8)


@registry.register("Saturation", defaults={
    "factor": 50,
}, min_vals={
    "factor": 0,
}, max_vals={
    "factor": 100,
}, step_vals={
    "factor": 1,
})
def adjust_saturation(image, factor: int = 50):
    """
    ## Adjusts the saturation of the image.

    **Args:**
    * `image` (numpy.ndarray): Input image (BGR)
    * `factor` (int): Saturation factor (0-100, 50 is normal)

    **Returns:**
    * `numpy.ndarray`: Image with adjusted saturation
    """
    # Convert factor to multiplication value (0.0 to 2.0)
    factor = (factor / 50.0)
    
    # Convert to HSV
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV).astype(np.float32)
    
    # Adjust saturation
    hsv[:, :, 1] = np.clip(hsv[:, :, 1] * factor, 0, 255)
    
    # Convert back to BGR
    return cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2BGR)


@registry.register("Vintage", defaults={
    "intensity": 50,
}, min_vals={
    "intensity": 0,
}, max_vals={
    "intensity": 100,
}, step_vals={
    "intensity": 1,
})
def vintage_filter(image, intensity: int = 50):
    """
    ## Adds a vintage/retro effect to the image.

    **Args:**
    * `image` (numpy.ndarray): Input image (BGR)
    * `intensity` (int): Intensity of the vintage effect (0-100)

    **Returns:**
    * `numpy.ndarray`: Image with vintage effect
    """
    intensity_scale = intensity / 100.0
    
    # Split channels
    b, g, r = cv2.split(image.astype(np.float32))
    
    # Adjust colors for vintage look
    r = np.clip(r * (1 + 0.3 * intensity_scale), 0, 255)
    g = np.clip(g * (1 - 0.1 * intensity_scale), 0, 255)
    b = np.clip(b * (1 - 0.2 * intensity_scale), 0, 255)
    
    # Create sepia-like effect
    result = cv2.merge([b, g, r]).astype(np.uint8)
    
    # Add slight blur for softness
    if intensity > 0:
        blur_amount = int(3 * intensity_scale) * 2 + 1
        result = cv2.GaussianBlur(result, (blur_amount, blur_amount), 0)
    
    return result


@registry.register("Vignette", defaults={
    "intensity": 50,
}, min_vals={
    "intensity": 0,
}, max_vals={
    "intensity": 100,
}, step_vals={
    "intensity": 1,
})
def vignette_effect(image, intensity: int = 50):
    """
    ## Adds a vignette effect (darker corners) to the image.

    **Args:**
    * `image` (numpy.ndarray): Input image (BGR)
    * `intensity` (int): Intensity of the vignette (0-100)

    **Returns:**
    * `numpy.ndarray`: Image with vignette effect
    """
    height, width = image.shape[:2]
    
    # Create a vignette mask
    X_resultant = np.abs(np.linspace(-1, 1, width)[None, :])
    Y_resultant = np.abs(np.linspace(-1, 1, height)[:, None])
    mask = np.sqrt(X_resultant**2 + Y_resultant**2)
    mask = 1 - np.clip(mask, 0, 1)
    
    # Adjust mask based on intensity
    mask = (mask - mask.min()) / (mask.max() - mask.min())
    mask = mask ** (1 + intensity/50)
    
    # Apply mask to image
    mask = mask[:, :, None]
    result = image.astype(np.float32) * mask
    
    return np.clip(result, 0, 255).astype(np.uint8)


@registry.register("HDR Effect", defaults={
    "strength": 50,
}, min_vals={
    "strength": 0,
}, max_vals={
    "strength": 100,
}, step_vals={
    "strength": 1,
})
def hdr_effect(image, strength: int = 50):
    """
    ## Applies an HDR-like effect to enhance image details.

    **Args:**
    * `image` (numpy.ndarray): Input image (BGR)
    * `strength` (int): Strength of the HDR effect (0-100)

    **Returns:**
    * `numpy.ndarray`: Image with HDR-like effect
    """
    strength_scale = strength / 100.0
    
    # Convert to LAB color space
    lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB).astype(np.float32)
    
    # Split channels
    l, a, b = cv2.split(lab)
    
    # Apply CLAHE to L channel
    clahe = cv2.createCLAHE(clipLimit=3.0 * strength_scale, tileGridSize=(8, 8))
    l = clahe.apply(l.astype(np.uint8)).astype(np.float32)
    
    # Enhance local contrast
    if strength > 0:
        blur = cv2.GaussianBlur(l, (0, 0), 3)
        detail = cv2.addWeighted(l, 1 + strength_scale, blur, -strength_scale, 0)
        l = cv2.addWeighted(l, 1 - strength_scale/2, detail, strength_scale/2, 0)
    
    # Merge channels and convert back
    enhanced_lab = cv2.merge([l, a, b])
    result = cv2.cvtColor(enhanced_lab.astype(np.uint8), cv2.COLOR_LAB2BGR)
    
    return result


@registry.register("Gaussian Blur", defaults={
    "kernel_size": 5,
}, min_vals={
    "kernel_size": 1,
}, max_vals={
    "kernel_size": 31,
}, step_vals={
    "kernel_size": 2,
})
def gaussian_blur(image, kernel_size: int = 5):
    """
    ## Apply Gaussian blur effect to the image.

    **Args:**
    * `image` (numpy.ndarray): Input image (BGR)
    * `kernel_size` (int): Size of the Gaussian kernel (must be odd)

    **Returns:**
    * `numpy.ndarray`: Blurred image
    """
    # Ensure kernel size is odd
    if kernel_size % 2 == 0:
        kernel_size += 1
    
    return cv2.GaussianBlur(image, (kernel_size, kernel_size), 0)


@registry.register("Sharpen", defaults={
    "amount": 50,
}, min_vals={
    "amount": 0,
}, max_vals={
    "amount": 100,
}, step_vals={
    "amount": 1,
})
def sharpen(image, amount: int = 50):
    """
    ## Sharpen the image.

    **Args:**
    * `image` (numpy.ndarray): Input image (BGR)
    * `amount` (int): Sharpening intensity (0-100)

    **Returns:**
    * `numpy.ndarray`: Sharpened image
    """
    amount = amount / 100.0
    
    # Create the sharpening kernel
    kernel = np.array([[-1,-1,-1],
                      [-1, 9,-1],
                      [-1,-1,-1]])
    
    # Apply the kernel
    sharpened = cv2.filter2D(image, -1, kernel)
    
    # Blend with original image based on amount
    return cv2.addWeighted(image, 1 - amount, sharpened, amount, 0)


@registry.register("Emboss", defaults={
    "strength": 50,
    "direction": 0,
}, min_vals={
    "strength": 0,
    "direction": 0,
}, max_vals={
    "strength": 100,
    "direction": 7,
}, step_vals={
    "strength": 1,
    "direction": 1,
})
def emboss(image, strength: int = 50, direction: int = 0):
    """
    ## Apply emboss effect to create a 3D look.

    **Args:**
    * `image` (numpy.ndarray): Input image (BGR)
    * `strength` (int): Emboss strength (0-100)
    * `direction` (int): Direction of emboss effect (0-7)

    **Returns:**
    * `numpy.ndarray`: Embossed image
    """
    strength = strength / 100.0 * 2.0  # Scale to 0-2 range
    
    # Define kernels for different directions
    kernels = [
        np.array([[-1,-1, 0],
                 [-1, 1, 1],
                 [ 0, 1, 1]]),  # 0 - top left to bottom right
        np.array([[-1, 0, 1],
                 [-1, 1, 1],
                 [-1, 0, 1]]),  # 1 - left to right
        np.array([[ 0, 1, 1],
                 [-1, 1, 1],
                 [-1,-1, 0]]),  # 2 - bottom left to top right
        np.array([[ 1, 1, 1],
                 [ 0, 1, 0],
                 [-1,-1,-1]]),  # 3 - bottom to top
        np.array([[ 1, 1, 0],
                 [ 1, 1,-1],
                 [ 0,-1,-1]]),  # 4 - bottom right to top left
        np.array([[ 1, 0,-1],
                 [ 1, 1,-1],
                 [ 1, 0,-1]]),  # 5 - right to left
        np.array([[ 0,-1,-1],
                 [ 1, 1,-1],
                 [ 1, 1, 0]]),  # 6 - top right to bottom left
        np.array([[-1,-1,-1],
                 [ 0, 1, 0],
                 [ 1, 1, 1]])   # 7 - top to bottom
    ]
    
    # Apply the kernel
    kernel = kernels[direction % 8]
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    embossed = cv2.filter2D(gray, -1, kernel * strength)
    
    # Normalize to ensure good contrast
    embossed = cv2.normalize(embossed, None, 0, 255, cv2.NORM_MINMAX)
    
    # Convert back to BGR
    return cv2.cvtColor(embossed.astype(np.uint8), cv2.COLOR_GRAY2BGR)


@registry.register("Oil Painting", defaults={
    "size": 5,
    "dynRatio": 1,
}, min_vals={
    "size": 1,
    "dynRatio": 1,
}, max_vals={
    "size": 15,
    "dynRatio": 7,
}, step_vals={
    "size": 2,
    "dynRatio": 1,
})
def oil_painting(image, size: int = 5, dynRatio: int = 1):
    """
    ## Apply oil painting effect to the image.

    **Args:**
    * `image` (numpy.ndarray): Input image (BGR)
    * `size` (int): Size of the neighborhood considered
    * `dynRatio` (int): Dynamic ratio affecting the intensity binning

    **Returns:**
    * `numpy.ndarray`: Image with oil painting effect
    """
    return cv2.xphoto.oilPainting(image, size, dynRatio)