from __future__ import annotations

import math

import cv2
import numpy as np
from PIL import Image

from .image_preprocess import canonical_square, decode_rgb


FACTORS = ("line", "color", "texture", "geometry")
INTERVENTION_VERSION = "lens-safe-v4"
TRAIN_FAMILIES = {
    "line": ("dilate_erode", "blur_sharpen", "darkness_contrast"),
    "color": ("palette_remap", "split_tone", "tone_curve"),
    "texture": ("smooth_detail", "frequency", "grain_noise"),
    "geometry": ("crop_zoom_translate", "perspective", "lens_warp"),
}
VALIDATION_FAMILIES = {
    "line": "edge_overlay",
    "color": "channel_mixer",
    "texture": "median_speckle",
    "geometry": "shear",
}
LEVELS = {"weak": 0.50, "medium": 0.80, "strong": 1.0}


def _uint8(array: np.ndarray) -> np.ndarray:
    return np.clip(array, 0, 255).astype(np.uint8)


def _edges(array: np.ndarray) -> np.ndarray:
    gray = cv2.cvtColor(array, cv2.COLOR_RGB2GRAY)
    return cv2.Canny(gray, 60, 150).astype(np.float32) / 255.0


def _pixel_transform(
    image: Image.Image, factor: str, family: str, signed: float, seed: int
) -> Image.Image:
    magnitude = abs(signed)
    direction = 1 if signed >= 0 else -1
    array = np.asarray(image.convert("RGB"), dtype=np.uint8)

    if factor == "line":
        if family in {"dilate_erode", "edge_overlay"}:
            edge = _edges(array)
            kernel_size = 3 if magnitude <= 0.5 else 5 if magnitude < 1.0 else 7
            kernel = np.ones((kernel_size, kernel_size), np.uint8)
            edge = cv2.dilate(edge, kernel)
            if direction > 0:
                alpha = (0.72 if family == "edge_overlay" else 0.64) * magnitude
                result = array.astype(np.float32) * (1.0 - alpha * edge[..., None])
            else:
                smooth = cv2.bilateralFilter(array, 11, 80, 9).astype(np.float32)
                weight = np.clip(0.90 * magnitude * edge, 0.0, 0.90)[..., None]
                result = array * (1.0 - weight) + smooth * weight
        elif family == "blur_sharpen":
            blurred = cv2.GaussianBlur(array, (0, 0), 1.2 + 2.4 * magnitude)
            result = blurred if direction < 0 else cv2.addWeighted(
                array, 1.0 + 1.45 * magnitude, blurred, -1.45 * magnitude, 0
            )
        elif family == "darkness_contrast":
            edge = cv2.GaussianBlur(_edges(array), (0, 0), 1.0)
            if direction > 0:
                result = array.astype(np.float32) * (1.0 - 0.70 * magnitude * edge[..., None])
            else:
                smooth = cv2.GaussianBlur(array, (0, 0), 2.6)
                weight = np.clip(0.90 * magnitude * edge, 0.0, 0.90)[..., None]
                result = array * (1.0 - weight) + smooth * weight
        else:
            raise ValueError(f"unknown line family: {family}")
        return Image.fromarray(_uint8(result))

    if factor == "color":
        unit = array.astype(np.float32) / 255.0
        if family == "palette_remap":
            hsv = cv2.cvtColor(array, cv2.COLOR_RGB2HSV).astype(np.float32)
            hsv[..., 0] = np.mod(hsv[..., 0] + direction * 30.0 * magnitude, 180.0)
            saturation = 1.0 + 0.95 * magnitude if direction > 0 else 1.0 - 0.70 * magnitude
            hsv[..., 1] *= saturation
            hsv[..., 2] = 255.0 * np.power(
                hsv[..., 2] / 255.0, math.exp(-direction * 0.38 * magnitude)
            )
            result = cv2.cvtColor(_uint8(hsv), cv2.COLOR_HSV2RGB).astype(np.float32)
            balance = direction * 0.16 * magnitude
            result[..., 0] *= 1.0 + balance
            result[..., 2] *= 1.0 - balance
        elif family == "split_tone":
            luminance = np.sum(unit * np.array([0.213, 0.715, 0.072], np.float32), axis=2)
            shadows = np.power(1.0 - luminance, 1.4)[..., None]
            highlights = np.power(luminance, 1.4)[..., None]
            cool_shadow = np.array([-0.16, 0.02, 0.28], np.float32)
            warm_highlight = np.array([0.30, 0.12, -0.14], np.float32)
            result = unit + direction * magnitude * (
                shadows * cool_shadow + highlights * warm_highlight
            )
            result = 0.5 + (result - 0.5) * (1.0 + 0.38 * magnitude)
            result = _uint8(result * 255.0)
            hsv = cv2.cvtColor(result, cv2.COLOR_RGB2HSV).astype(np.float32)
            hsv[..., 0] = np.mod(hsv[..., 0] + direction * 12.0 * magnitude, 180.0)
            hsv[..., 1] *= 1.0 + 0.40 * magnitude
            result = cv2.cvtColor(_uint8(hsv), cv2.COLOR_HSV2RGB)
        elif family == "tone_curve":
            channel_gamma = np.exp(
                direction * magnitude * np.array([-0.62, -0.18, 0.48], np.float32)
            )
            result = np.power(np.clip(unit, 0.0, 1.0), channel_gamma)
            contrast = 1.0 + direction * 0.48 * magnitude
            result = 0.5 + (result - 0.5) * contrast
            luminance = np.sum(result * np.array([0.213, 0.715, 0.072], np.float32), axis=2, keepdims=True)
            result = luminance + (result - luminance) * (1.0 + 0.55 * magnitude)
            result *= 1.0 + direction * 0.10 * magnitude
            result = result * 255.0
        elif family == "channel_mixer":
            delta = np.array(
                [[0.22, 0.12, -0.20], [-0.12, 0.20, 0.08], [0.08, -0.20, 0.24]],
                np.float32,
            )
            matrix = np.eye(3, dtype=np.float32) + direction * magnitude * delta
            result = unit @ matrix.T
            result = np.power(
                np.clip(result, 0.0, 1.0), math.exp(-direction * 0.45 * magnitude)
            )
            result = 255.0 * result
        else:
            raise ValueError(f"unknown color family: {family}")
        return Image.fromarray(_uint8(result))

    if factor == "texture":
        result = array.astype(np.float32)
        if family == "smooth_detail":
            smooth = cv2.bilateralFilter(array, 13, 70 + 55 * magnitude, 11)
            if direction < 0:
                result = cv2.addWeighted(array, 1.0 - 0.85 * magnitude, smooth, 0.85 * magnitude, 0)
            else:
                result = array + (array.astype(np.float32) - smooth) * 1.35 * magnitude
        elif family == "frequency":
            low = cv2.GaussianBlur(array, (0, 0), 1.5 + 1.5 * magnitude)
            high = array.astype(np.float32) - low.astype(np.float32)
            result = array + direction * high * 1.55 * magnitude
        elif family in {"grain_noise", "median_speckle"}:
            if direction < 0:
                kernel = 5 if magnitude >= 0.8 else 3
                median = cv2.medianBlur(array, kernel)
                result = cv2.addWeighted(array, 1.0 - 0.85 * magnitude, median, 0.85 * magnitude, 0)
            else:
                rng = np.random.default_rng(seed)
                noise = rng.normal(0, 26.0 * magnitude, array.shape[:2]).astype(np.float32)
                noise = cv2.GaussianBlur(noise, (0, 0), 0.35)[..., None]
                result = array.astype(np.float32) + noise
        else:
            raise ValueError(f"unknown texture family: {family}")
        return Image.fromarray(_uint8(result))

    raise ValueError(f"pixel transform does not support factor: {factor}")


def _apply_homography(
    image: Image.Image, matrix: np.ndarray
) -> tuple[Image.Image, callable]:
    array = np.asarray(image)
    height, width = array.shape[:2]
    warped = cv2.warpPerspective(
        array, matrix, (width, height), flags=cv2.INTER_LANCZOS4, borderMode=cv2.BORDER_REFLECT_101
    )

    def map_points(points: np.ndarray) -> np.ndarray:
        return cv2.perspectiveTransform(points.astype(np.float32)[None], matrix)[0]

    return Image.fromarray(warped), map_points


def _geometry_transform(
    image: Image.Image, family: str, signed: float, seed: int
) -> tuple[Image.Image, callable]:
    magnitude = abs(signed)
    direction = 1 if signed >= 0 else -1
    width, height = image.size
    center = np.array([width / 2, height / 2], dtype=np.float32)

    if family == "crop_zoom_translate":
        rng = np.random.default_rng(seed)
        scale = 1.0 + direction * 0.20 * magnitude
        shift = np.array(
            [direction * 0.10 * width, (1 if rng.integers(2) else -1) * 0.07 * height],
            dtype=np.float32,
        ) * magnitude
        matrix = np.array(
            [[scale, 0, center[0] * (1 - scale) + shift[0]], [0, scale, center[1] * (1 - scale) + shift[1]], [0, 0, 1]],
            dtype=np.float32,
        )
        return _apply_homography(image, matrix)

    if family in {"perspective", "shear"}:
        source = np.array([[0, 0], [width - 1, 0], [width - 1, height - 1], [0, height - 1]], np.float32)
        if family == "perspective":
            delta = direction * 0.15 * width * magnitude
            target = source + np.array([[delta, 0], [-delta, delta * 0.35], [delta, 0], [-delta, -delta * 0.35]], np.float32)
        else:
            delta = direction * 0.18 * width * magnitude
            target = source + np.array([[delta, 0], [delta, 0], [-delta, 0], [-delta, 0]], np.float32)
        return _apply_homography(image, cv2.getPerspectiveTransform(source, target))

    if family == "lens_warp":
        # Keep radial deformation perceptible without creating a bubble silhouette.
        radial_strength = float(
            np.interp(magnitude, [0.5, 0.8, 1.0], [0.035, 0.055, 0.075])
        )
        k = direction * radial_strength
        yy, xx = np.indices((height, width), dtype=np.float32)
        xd = (xx - center[0]) / (width / 2)
        yd = (yy - center[1]) / (height / 2)
        xs, ys = xd.copy(), yd.copy()
        for _ in range(4):
            radius2 = xs * xs + ys * ys
            factor = 1.0 + k * radius2
            xs, ys = xd / factor, yd / factor
        map_x = xs * (width / 2) + center[0]
        map_y = ys * (height / 2) + center[1]
        warped = cv2.remap(
            np.asarray(image), map_x, map_y, cv2.INTER_LANCZOS4, borderMode=cv2.BORDER_REFLECT_101
        )

        def map_points(points: np.ndarray) -> np.ndarray:
            normalized = (points - center) / np.array([width / 2, height / 2])
            radius2 = np.square(normalized).sum(axis=1, keepdims=True)
            return center + normalized * (1.0 + k * radius2) * np.array([width / 2, height / 2])

        return Image.fromarray(warped), map_points

    raise ValueError(f"unknown geometry family: {family}")


def _face_box_in_square(metadata: dict, crop_box: tuple[int, int, int, int], size: int) -> np.ndarray | None:
    box = (metadata.get("face_detection") or {}).get("primary_box")
    if box is None:
        return None
    left, top, right, bottom = crop_box
    scale_x = size / (right - left)
    scale_y = size / (bottom - top)
    x0, y0, x1, y1 = map(float, box)
    return np.array(
        [[(x0 - left) * scale_x, (y0 - top) * scale_y], [(x1 - left) * scale_x, (y0 - top) * scale_y], [(x1 - left) * scale_x, (y1 - top) * scale_y], [(x0 - left) * scale_x, (y1 - top) * scale_y]],
        dtype=np.float32,
    )


def _crop_face(image: Image.Image, points: np.ndarray, size: int, padding: float = 0.25) -> Image.Image:
    x0, y0 = points.min(axis=0)
    x1, y1 = points.max(axis=0)
    if x1 <= 0 or y1 <= 0 or x0 >= image.width or y0 >= image.height:
        raise ValueError("transformed face left the image")
    side = max(x1 - x0, y1 - y0) * (1 + 2 * padding)
    if side < 8:
        raise ValueError("transformed face is too small")
    cx, cy = (x0 + x1) / 2, (y0 + y1) / 2
    box = (max(0, cx - side / 2), max(0, cy - side / 2), min(image.width, cx + side / 2), min(image.height, cy + side / 2))
    crop = image.crop(tuple(map(int, map(round, box))))
    if min(crop.size) < 4:
        raise ValueError("invalid transformed face crop")
    return crop.resize((size, size), Image.Resampling.LANCZOS)


def apply_intervention(
    full_bytes: bytes,
    face_bytes: bytes | None,
    metadata: dict,
    spec: dict,
    *,
    size: int = 512,
) -> tuple[Image.Image, Image.Image | None]:
    full, crop_box = canonical_square(decode_rgb(full_bytes), size)
    signed = float(spec["signed_intensity"])
    seed = int(spec["operation_seed"])
    factor = str(spec["factor"])
    family = str(spec["family"])

    if factor == "geometry":
        transformed, map_points = _geometry_transform(full, family, signed, seed)
        points = _face_box_in_square(metadata, crop_box, size)
        face = None
        if points is not None:
            try:
                face = _crop_face(transformed, map_points(points), size)
            except ValueError:
                face = None
        if face is None and face_bytes is not None:
            face_source, _ = canonical_square(decode_rgb(face_bytes), size)
            face, _ = _geometry_transform(face_source, family, signed, seed ^ 0x5A17)
        return transformed, face

    transformed = _pixel_transform(full, factor, family, signed, seed)
    face = None
    if face_bytes is not None:
        face, _ = canonical_square(decode_rgb(face_bytes), size)
        face = _pixel_transform(face, factor, family, signed, seed ^ 0x5A17)
    return transformed, face