"""
Preprocessing module for FetalCLIP.

Supports two pipelines:
1. DICOM (Full): US region extraction, fan isolation, text removal, denoising
2. Image (Basic): Square padding, resize
"""

import cv2
import copy
import numpy as np
from PIL import Image
from typing import Tuple, Dict, List, Optional
from io import BytesIO

# Try importing DICOM-specific libraries
try:
    from pydicom import dcmread
    from pydicom.pixel_data_handlers import convert_color_space
    DICOM_AVAILABLE = True
except ImportError:
    DICOM_AVAILABLE = False

try:
    from skimage.restoration import denoise_nl_means, estimate_sigma
    SKIMAGE_AVAILABLE = True
except ImportError:
    SKIMAGE_AVAILABLE = False

try:
    import albumentations as A
    ALBUMENTATIONS_AVAILABLE = True
except ImportError:
    ALBUMENTATIONS_AVAILABLE = False


# ============================================================================
# CONSTANTS
# ============================================================================

TARGET_SIZE = (512, 512)
INTERPOLATION = cv2.INTER_LANCZOS4

INTENSITY_THRESHOLD = 0
SMALL_VIEW_MARGIN_CROP_Y = 1

YELLOW_BOX_BACKGROUND_PIXEL = np.array([57, 57, 57])
MIN_YELLOW_BOX_RECT_AREA = 2_000

MASK_INPAINTING_DILATE_KERNEL = np.ones((9, 9), np.uint8)
DENOISE_NL_MEANS_PATCH_KW = dict(
    patch_size=7,
    patch_distance=6,
    channel_axis=-1,
)
INPAINT_RADIUS = 5


# ============================================================================
# TEXT DETECTION UTILITIES (from utils_husain.py)
# ============================================================================

def rgb2gray(rgb: np.ndarray) -> np.ndarray:
    """Convert RGB to grayscale while keeping 3 channels."""
    r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    gray = 0.299 * r + 0.5870 * g + 0.1140 * b
    rgb_grey = rgb.copy()
    rgb_grey[:, :, 0] = gray
    rgb_grey[:, :, 1] = gray
    rgb_grey[:, :, 2] = gray
    return rgb_grey


def mask_filter(image: np.ndarray, grey_threshold: int) -> np.ndarray:
    """Create binary mask for pixels above threshold."""
    img = image.copy()
    grey_img = rgb2gray(img)
    convert = np.zeros((img.shape[0], img.shape[1], 3))
    idxs = np.where(
        (grey_img[:, :, 0] > grey_threshold)
        & (grey_img[:, :, 1] > grey_threshold)
        & (grey_img[:, :, 2] > grey_threshold)
    )
    convert[idxs] = [255, 255, 255]
    return np.uint8(convert)


def maximize_contrast(img_grayscale: np.ndarray) -> np.ndarray:
    """Enhance contrast using morphological operations."""
    height, width = img_grayscale.shape
    structuring_element = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    
    img_top_hat = cv2.morphologyEx(img_grayscale, cv2.MORPH_TOPHAT, structuring_element)
    img_black_hat = cv2.morphologyEx(img_grayscale, cv2.MORPH_BLACKHAT, structuring_element)
    
    img_plus_top_hat = cv2.add(img_grayscale, img_top_hat)
    result = cv2.subtract(img_plus_top_hat, img_black_hat)
    
    return result


def detect_white_annotation(img: np.ndarray) -> np.ndarray:
    """Detect white text/annotations."""
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    dif = maximize_contrast(img_gray)
    dif_rgb = cv2.cvtColor(dif, cv2.COLOR_GRAY2BGR)
    masked_img = mask_filter(dif_rgb, 254)
    dilation = cv2.dilate(masked_img, np.ones((3, 3), np.uint8), iterations=1)
    mask = cv2.cvtColor(dilation, cv2.COLOR_BGR2GRAY)
    return mask


def detect_cyan(img: np.ndarray) -> np.ndarray:
    """Detect cyan colored text."""
    image_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    lowers = np.uint8([85, 150, 20])
    uppers = np.uint8([95, 255, 255])
    mask = np.array(cv2.inRange(image_hsv, lowers, uppers))
    return mask


def detect_purple_text(img: np.ndarray) -> np.ndarray:
    """Detect purple colored text."""
    image_hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
    lowers = np.uint8([110, 100, 50])
    uppers = np.uint8([130, 255, 255])
    mask = np.array(cv2.inRange(image_hsv, lowers, uppers))
    return mask


def detect_orange_text(img: np.ndarray) -> np.ndarray:
    """Detect orange colored text."""
    image_hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
    lowers = np.uint8([12, 150, 100])
    uppers = np.uint8([27, 255, 255])
    mask = np.array(cv2.inRange(image_hsv, lowers, uppers))
    return mask


def detect_green_text(img: np.ndarray) -> np.ndarray:
    """Detect green colored text."""
    image_hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
    lowers = np.uint8([50, 100, 50])
    uppers = np.uint8([70, 255, 255])
    mask = np.array(cv2.inRange(image_hsv, lowers, uppers))
    return mask


def detect_annotation(img: np.ndarray) -> np.ndarray:
    """Detect all text annotations (white, cyan, orange, purple, green)."""
    d1 = (detect_white_annotation(img) >= 127).astype(np.float32)
    d2 = (detect_cyan(img) >= 127).astype(np.float32)
    d3 = (detect_orange_text(img) >= 127).astype(np.float32)
    d4 = (detect_purple_text(img) >= 127).astype(np.float32)
    d5 = (detect_green_text(img) >= 127).astype(np.float32)

    inpaint_mask = d1 + d2 + d3 + d4 + d5
    inpaint_mask = (inpaint_mask > 0).astype(np.uint8) * 255

    inpaint_mask = maximize_contrast(inpaint_mask)
    blur = cv2.GaussianBlur(inpaint_mask, (5, 5), 0)
    ret3, th3 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    th3 = cv2.bitwise_or(th3, inpaint_mask)
    return th3


# ============================================================================
# DICOM UTILITIES (from utils_adam.py)
# ============================================================================

def remove_text_box(im: np.ndarray, box_background_pixel: np.ndarray, min_rect_area: int = 2000) -> np.ndarray:
    """Remove yellow/gray text boxes from image."""
    binary = np.all(im == box_background_pixel, axis=-1).astype(np.uint8)
    binary = binary * 255
    contours, hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    
    if len(contours) == 0:
        return im

    contour = max(contours, key=cv2.contourArea)
    x, y, w, h = cv2.boundingRect(contour)

    if w * h >= min_rect_area:
        im[y:y+h, x:x+w] = 0

    return im


def pad_to_square(im: np.ndarray) -> np.ndarray:
    """Pad image to square using black padding."""
    if ALBUMENTATIONS_AVAILABLE:
        target_size = max(im.shape[:2])
        return A.PadIfNeeded(min_height=target_size, min_width=target_size,
                             border_mode=0, value=(0, 0, 0))(image=im)["image"]
    else:
        # Fallback without albumentations
        height, width = im.shape[:2]
        max_side = max(height, width)
        
        if len(im.shape) == 3:
            result = np.zeros((max_side, max_side, im.shape[2]), dtype=im.dtype)
        else:
            result = np.zeros((max_side, max_side), dtype=im.dtype)
        
        y_offset = (max_side - height) // 2
        x_offset = (max_side - width) // 2
        result[y_offset:y_offset+height, x_offset:x_offset+width] = im
        
        return result


def get_fan_region(im: np.ndarray, threshold: int = 1) -> np.ndarray:
    """Extract the ultrasound fan/cone region."""
    imgray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    
    ret, thresh = cv2.threshold(imgray, threshold, 255, 0)
    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    
    if len(contours) == 0:
        return im
    
    contour = max(contours, key=cv2.contourArea)
    
    # Create mask
    filled_image = np.zeros_like(im)
    cv2.drawContours(filled_image, [contour], -1, (255, 255, 255), thickness=cv2.FILLED)
    
    # Crop to bounding box
    x, y, w, h = cv2.boundingRect(contour)
    cropped_image = im[y:y+h, x:x+w]
    filled_image = filled_image[y:y+h, x:x+w]
    
    # Apply mask
    masked_image = cv2.bitwise_and(cropped_image, filled_image)
    
    return masked_image


def get_us_region_from_dcm(us, sv_mc_y: int = 1) -> np.ndarray:
    """Extract ultrasound region from DICOM using metadata."""
    # Initialize default coordinates
    x0_f, x1_f, y0_f, y1_f = None, None, None, None
    x0, x1, y0, y1 = 0, us.pixel_array.shape[1], 0, us.pixel_array.shape[0]

    # Check for ultrasound regions metadata
    if hasattr(us, 'SequenceOfUltrasoundRegions') and len(us.SequenceOfUltrasoundRegions) > 0:
        regions = us.SequenceOfUltrasoundRegions
        
        if len(regions) == 2 and int(regions[0].RegionDataType) == 1 and int(regions[1].RegionDataType) == 1:
            # Image with small view (picture-in-picture)
            x0_f = np.min([regions[0].RegionLocationMinX0, regions[1].RegionLocationMinX0])
            x1_f = np.max([regions[0].RegionLocationMinX0, regions[1].RegionLocationMinX0])
            y0_f = np.max([regions[0].RegionLocationMinY0, regions[1].RegionLocationMinY0])
            y1_f = np.max([regions[0].RegionLocationMaxY1, regions[1].RegionLocationMaxY1])
            
            x0 = min(regions[0].RegionLocationMinX0, regions[1].RegionLocationMinX0)
            x1 = max(regions[0].RegionLocationMaxX1, regions[1].RegionLocationMaxX1)
            y0 = min(regions[0].RegionLocationMinY0, regions[1].RegionLocationMinY0)
            y1 = max(regions[0].RegionLocationMaxY1, regions[1].RegionLocationMaxY1)

        elif len(regions) >= 1 and int(regions[0].RegionDataType) == 1:
            x0 = regions[0].RegionLocationMinX0
            x1 = regions[0].RegionLocationMaxX1
            y0 = regions[0].RegionLocationMinY0
            y1 = regions[0].RegionLocationMaxY1

    ds = copy.deepcopy(us.pixel_array)
    
    # Handle color space conversion
    if hasattr(us, 'PhotometricInterpretation'):
        if 'ybr_full' in us.PhotometricInterpretation.lower():
            ds = convert_color_space(ds, "YBR_FULL", "RGB", per_frame=True)

    # Remove small view if present
    if x0_f is not None:
        ds[y0_f - sv_mc_y:y1_f, x0_f:x1_f, :] = 0

    # Crop to ultrasound region
    ds = ds[y0:y1, x0:x1, :]

    return ds


# ============================================================================
# MAIN PREPROCESSING FUNCTIONS
# ============================================================================

def preprocess_dicom(file_bytes: bytes) -> Tuple[Image.Image, Dict]:
    """
    Full DICOM preprocessing pipeline.
    
    Steps:
    1. Parse DICOM file
    2. Extract ultrasound region from metadata
    3. Remove yellow text boxes
    4. Extract fan/cone region
    5. Detect text annotations
    6. Inpaint to remove text
    7. Denoise using non-local means
    8. Pad to square
    9. Resize to target size
    
    Returns:
        Tuple of (PIL Image, metadata dict)
    """
    if not DICOM_AVAILABLE:
        raise RuntimeError("pydicom not installed. Install with: pip install pydicom")
    
    # Parse DICOM
    us = dcmread(BytesIO(file_bytes))
    
    # Extract ultrasound region
    ds = get_us_region_from_dcm(us, sv_mc_y=SMALL_VIEW_MARGIN_CROP_Y)
    
    # Remove text box
    img = remove_text_box(ds.copy(), box_background_pixel=YELLOW_BOX_BACKGROUND_PIXEL, 
                          min_rect_area=MIN_YELLOW_BOX_RECT_AREA)
    
    # Extract fan region
    fan = get_fan_region(img, threshold=INTENSITY_THRESHOLD)
    
    # Detect annotations
    image_grey = fan.copy()
    mask_inpaint = detect_annotation(fan)
    mask_inpaint = cv2.dilate(mask_inpaint, MASK_INPAINTING_DILATE_KERNEL)
    
    # Inpaint to remove text
    dst = cv2.inpaint(image_grey, mask_inpaint, INPAINT_RADIUS, cv2.INPAINT_TELEA)
    dst = dst / np.max(dst) if np.max(dst) > 0 else dst
    
    # Denoise
    if SKIMAGE_AVAILABLE:
        sigma = estimate_sigma(dst, channel_axis=-1, average_sigmas=True)
        median = denoise_nl_means(dst, h=0.8 * sigma, fast_mode=True, **DENOISE_NL_MEANS_PATCH_KW)
        median = np.clip(median * 255, 0, 255).astype(np.uint8)
    else:
        median = np.clip(dst * 255, 0, 255).astype(np.uint8)
    
    # Pad to square (model will resize to 224×224)
    img = pad_to_square(median)
    padded_size = max(img.shape[:2])
    
    # Extract metadata
    try:
        rows = getattr(us, 'Rows', fan.shape[0])
        columns = getattr(us, 'Columns', fan.shape[1])
        
        if hasattr(us, 'PixelSpacing') and us.PixelSpacing is not None:
            orig_pixel_spacing = [float(sp) for sp in us.PixelSpacing]
        else:
            orig_pixel_spacing = [1.0, 1.0]
    except Exception:
        rows = fan.shape[0]
        columns = fan.shape[1]
        orig_pixel_spacing = [1.0, 1.0]
    
    metadata = {
        'original_size': (rows, columns),
        'original_pixel_spacing': orig_pixel_spacing,
        'fan_size': (fan.shape[0], fan.shape[1]),
        'padded_size': padded_size,
        'pixel_spacing': orig_pixel_spacing[0],  # Original spacing, model.py adjusts for resize
        'processed_size': (padded_size, padded_size),
    }
    
    # Convert to PIL
    if len(img.shape) == 2:
        img_rgb = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
    else:
        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    
    img_pil = Image.fromarray(img_rgb)
    
    steps_applied = [
        "dicom_parsing",
        "us_region_extraction",
        "text_box_removal",
        "fan_extraction",
        "annotation_detection",
        "inpainting",
        "denoising" if SKIMAGE_AVAILABLE else "normalization",
        "square_padding",
    ]
    
    return img_pil, {
        "type": "dicom",
        "pipeline": "full",
        "steps_applied": steps_applied,
        "metadata": metadata
    }


def preprocess_image(image: Image.Image) -> Tuple[Image.Image, Dict]:
    """
    Basic image preprocessing pipeline.
    
    Steps:
    1. Convert to RGB if needed
    2. Pad to square
    3. (Model will resize to 224)
    
    Returns:
        Tuple of (PIL Image, preprocessing info dict)
    """
    # Convert to RGB
    if image.mode not in ('RGB', 'L'):
        image = image.convert('RGB')
    
    width, height = image.size
    max_side = max(width, height)
    
    # Create square image with black padding
    padding_color = (0, 0, 0) if image.mode == "RGB" else 0
    new_image = Image.new(image.mode, (max_side, max_side), padding_color)
    
    # Center the original
    padding_left = (max_side - width) // 2
    padding_top = (max_side - height) // 2
    new_image.paste(image, (padding_left, padding_top))
    
    # Ensure RGB
    if new_image.mode == 'L':
        new_image = new_image.convert('RGB')
    
    steps_applied = [
        "rgb_conversion",
        "square_padding",
    ]
    
    return new_image, {
        "type": "image",
        "pipeline": "basic",
        "steps_applied": steps_applied,
        "metadata": {
            "original_size": (height, width),
            "processed_size": (max_side, max_side),
        }
    }


def is_dicom_file(file_bytes: bytes, filename: str) -> bool:
    """Check if file is a DICOM file."""
    # Check by extension
    lower_name = filename.lower()
    if lower_name.endswith('.dcm') or lower_name.endswith('.dicom'):
        return True
    
    # Check DICOM magic number (DICM at offset 128)
    if len(file_bytes) > 132:
        if file_bytes[128:132] == b'DICM':
            return True
    
    return False


def image_to_base64(image: Image.Image) -> str:
    """Convert PIL Image to base64 string."""
    buffered = BytesIO()
    image.save(buffered, format="PNG")
    import base64
    return base64.b64encode(buffered.getvalue()).decode('utf-8')


def get_dicom_preview(file_bytes: bytes) -> str:
    """Extract raw image from DICOM for preview (no preprocessing)."""
    if not DICOM_AVAILABLE:
        raise RuntimeError("pydicom not installed")
    
    us = dcmread(BytesIO(file_bytes))
    ds = us.pixel_array
    
    # Handle color space
    if hasattr(us, 'PhotometricInterpretation'):
        if 'ybr_full' in us.PhotometricInterpretation.lower():
            ds = convert_color_space(ds, "YBR_FULL", "RGB", per_frame=True)
    
    # Handle video (take first frame)
    if len(ds.shape) == 4:
        ds = ds[0]
    
    # Normalize to 0-255
    if ds.max() > 255:
        ds = ((ds - ds.min()) / (ds.max() - ds.min()) * 255).astype(np.uint8)
    
    # Convert to RGB if grayscale
    if len(ds.shape) == 2:
        ds = cv2.cvtColor(ds, cv2.COLOR_GRAY2RGB)
    elif ds.shape[2] == 3:
        ds = cv2.cvtColor(ds, cv2.COLOR_BGR2RGB)
    
    img_pil = Image.fromarray(ds)
    return image_to_base64(img_pil)


def preprocess_file(file_bytes: bytes, filename: str) -> Tuple[Image.Image, Dict]:
    """
    Automatically detect file type and apply appropriate preprocessing.
    
    Returns:
        Tuple of (PIL Image, preprocessing info dict with base64 image)
    """
    if is_dicom_file(file_bytes, filename):
        processed_image, info = preprocess_dicom(file_bytes)
        # Add base64 encoded image for frontend display
        info["processed_image_base64"] = image_to_base64(processed_image)
        return processed_image, info
    else:
        # Regular image
        image = Image.open(BytesIO(file_bytes))
        processed_image, info = preprocess_image(image)
        info["processed_image_base64"] = image_to_base64(processed_image)
        return processed_image, info