SnapOtter/packages/ai/python/restore.py

"""AI photo restoration pipeline.

Multi-step pipeline for restoring old and damaged photos:
1. Scratch & damage detection (morphological analysis)
2. Damage inpainting (LaMa ONNX)
3. Face enhancement (CodeFormer ONNX)
4. Noise reduction (OpenCV NLMeans)
5. Optional B&W colorization (DDColor ONNX)
"""
import sys
import json
import os

try:
    import numpy as np
    import cv2
    from PIL import Image
except ImportError as _e:
    _msg = str(_e)
    _hint = "Fix with: apt-get install -y libgl1" if "libGL" in _msg else "Install opencv-python-headless, numpy, and Pillow."
    print(json.dumps({"error": f"Missing dependency: {_msg}. {_hint}"}))
    sys.exit(1)


def emit_progress(percent, stage):
    """Emit structured progress to stderr for bridge.ts to capture."""
    print(json.dumps({"progress": percent, "stage": stage}), file=sys.stderr, flush=True)


# ── Model paths ───────────────────────────────────────────────────────

_MODELS_BASE = os.environ.get("MODELS_PATH", "/opt/models")

LAMA_MODEL_DIR = os.environ.get("LAMA_MODEL_DIR", os.path.join(_MODELS_BASE, "lama"))
LAMA_MODEL_PATH = os.path.join(LAMA_MODEL_DIR, "lama_fp32.onnx")
LAMA_LOCAL_CACHE = os.path.join(os.path.expanduser("~"), ".cache", "snapotter", "lama")
LAMA_LOCAL_PATH = os.path.join(LAMA_LOCAL_CACHE, "lama_fp32.onnx")

CODEFORMER_MODEL_DIR = os.environ.get("CODEFORMER_MODEL_DIR", os.path.join(_MODELS_BASE, "codeformer"))
CODEFORMER_MODEL_PATH = os.path.join(CODEFORMER_MODEL_DIR, "codeformer.onnx")
CODEFORMER_LOCAL_CACHE = os.path.join(
    os.path.expanduser("~"), ".cache", "snapotter", "codeformer"
)
CODEFORMER_LOCAL_PATH = os.path.join(CODEFORMER_LOCAL_CACHE, "codeformer.onnx")

DDCOLOR_MODEL_PATH = os.environ.get(
    "DDCOLOR_MODEL_PATH", os.path.join(_MODELS_BASE, "ddcolor", "ddcolor.onnx")
)

LAMA_MODEL_SIZE = 512
CODEFORMER_SIZE = 512


# ── Scratch detection ─────────────────────────────────────────────────

def detect_scratches(img_bgr, _sensitivity=None):
    gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
    h, w = gray.shape
    base_dim = min(h, w)

    # Pre-filter compression artifacts before enhancement
    filtered = cv2.bilateralFilter(gray, d=5, sigmaColor=50, sigmaSpace=50)

    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
    enhanced = clahe.apply(filtered)

    # Adaptive kernel sizing based on image dimensions
    if base_dim < 300:
        max_k = max(9, base_dim // 15)
        kernel_sizes = [9, max_k | 1]
    else:
        kernel_sizes = [
            max(9, base_dim // 80),
            max(15, base_dim // 50),
            max(25, base_dim // 30),
        ]

    angles = [0, 22.5, 45, 67.5, 90, 112.5, 135, 157.5]

    # Accumulate morphological responses before thresholding
    response = np.zeros_like(gray, dtype=np.float32)

    for ksize in kernel_sizes:
        ksize = ksize | 1
        for angle in angles:
            kernel = _make_line_kernel_rotated(ksize, angle)
            blackhat = cv2.morphologyEx(enhanced, cv2.MORPH_BLACKHAT, kernel)
            tophat = cv2.morphologyEx(enhanced, cv2.MORPH_TOPHAT, kernel)
            combined = cv2.add(blackhat, tophat)
            response = np.maximum(response, combined.astype(np.float32))

    # Adaptive threshold via Otsu on the response map
    response_u8 = np.clip(response, 0, 255).astype(np.uint8)
    otsu_thresh, mask = cv2.threshold(response_u8, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

    if otsu_thresh < 40:
        return np.zeros_like(gray)

    # When Otsu is borderline, use a high fixed threshold to only catch strong scratches
    if otsu_thresh < 60:
        _, mask = cv2.threshold(response_u8, 100, 255, cv2.THRESH_BINARY)

    # Connected component filtering
    mask = _filter_components(mask, h * w)

    # Connect nearby scratch segments (no opening - it erodes thin scratch lines)
    kernel_close = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))
    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel_close)

    # Coverage cap: if > 15%, keep only strongest detections
    coverage = np.count_nonzero(mask) / (h * w)
    if coverage > 0.15:
        print(f"[restore] Coverage cap triggered: {coverage:.1%} > 15%, keeping strongest detections",
              file=sys.stderr, flush=True)
        masked_response = response_u8.copy()
        masked_response[mask == 0] = 0
        nonzero = masked_response[masked_response > 0]
        if len(nonzero) > 0:
            target_count = int(h * w * 0.15)
            cutoff = np.percentile(nonzero, max(0, 100 * (1 - target_count / len(nonzero))))
            _, mask = cv2.threshold(response_u8, max(cutoff, otsu_thresh), 255, cv2.THRESH_BINARY)
            mask = _filter_components(mask, h * w)

    # Dilate for cleaner inpainting boundaries
    kernel_dilate = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
    mask = cv2.dilate(mask, kernel_dilate, iterations=2)

    return mask


def _make_line_kernel_rotated(size, angle_deg):
    kernel = np.zeros((size, size), np.uint8)
    mid = size // 2
    kernel[mid, :] = 1
    if angle_deg == 0:
        return kernel
    M = cv2.getRotationMatrix2D((float(mid), float(mid)), angle_deg, 1.0)
    rotated = cv2.warpAffine(kernel, M, (size, size),
                              flags=cv2.INTER_NEAREST,
                              borderMode=cv2.BORDER_CONSTANT, borderValue=0)
    if np.count_nonzero(rotated) == 0:
        rotated[mid, mid] = 1
    return rotated


def _filter_components(mask, total_pixels):
    num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(mask, connectivity=8)
    max_area = int(total_pixels * 0.05)
    filtered = np.zeros_like(mask)

    for i in range(1, num_labels):
        area = stats[i, cv2.CC_STAT_AREA]
        if area < 20 or area > max_area:
            continue
        bw = stats[i, cv2.CC_STAT_WIDTH]
        bh = stats[i, cv2.CC_STAT_HEIGHT]
        elongation = max(bw, bh) / max(min(bw, bh), 1)
        if elongation >= 2.5 or area >= 200:
            filtered[labels == i] = 255

    return filtered


# ── LaMa inpainting ──────────────────────────────────────────────────

def _get_lama_path():
    """Resolve LaMa model path, downloading only if allowed."""
    if os.path.exists(LAMA_MODEL_PATH):
        return LAMA_MODEL_PATH
    if os.path.exists(LAMA_LOCAL_PATH):
        return LAMA_LOCAL_PATH
    from offline_guard import ensure_download_allowed
    ensure_download_allowed("LaMa inpainting model (lama_fp32.onnx)")
    os.makedirs(LAMA_LOCAL_CACHE, exist_ok=True)
    import urllib.request
    url = "https://huggingface.co/Carve/LaMa-ONNX/resolve/main/lama_fp32.onnx"
    urllib.request.urlretrieve(url, LAMA_LOCAL_PATH)
    return LAMA_LOCAL_PATH


def inpaint_damage(img_bgr, mask):
    from gpu import safe_onnx_session

    model_path = _get_lama_path()
    session, _device = safe_onnx_session(model_path)

    orig_h, orig_w = img_bgr.shape[:2]
    img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)

    if orig_h <= LAMA_MODEL_SIZE and orig_w <= LAMA_MODEL_SIZE:
        inpainted_rgb = _inpaint_padded(img_rgb, mask, session)
    else:
        inpainted_rgb = _inpaint_tiled(img_rgb, mask, session)

    # Feathered composite: only replace masked areas
    mask_float = mask.astype(np.float32) / 255.0
    feather_r = max(5, min(orig_w, orig_h) // 100)
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (feather_r, feather_r))
    dilated = cv2.dilate(mask_float, kernel, iterations=1)
    blur_size = feather_r * 2 + 1
    alpha = cv2.GaussianBlur(dilated, (blur_size, blur_size), 0)
    alpha = np.clip(alpha * 1.2, 0.0, 1.0)[:, :, np.newaxis]

    composited = (img_rgb.astype(np.float32) * (1.0 - alpha) +
                  inpainted_rgb.astype(np.float32) * alpha)
    composited = np.clip(composited, 0, 255).astype(np.uint8)

    return cv2.cvtColor(composited, cv2.COLOR_RGB2BGR)


def _lama_single(session, tile_rgb, tile_mask):
    img_input = tile_rgb.astype(np.float32) / 255.0
    img_input = np.transpose(img_input, (2, 0, 1))[np.newaxis, ...]
    mask_binary = (tile_mask > 127).astype(np.float32)
    mask_input = mask_binary[np.newaxis, np.newaxis, ...]
    outputs = session.run(None, {"image": img_input, "mask": mask_input})
    result = outputs[0][0]
    result = np.transpose(result, (1, 2, 0))
    return np.clip(result, 0, 255).astype(np.uint8)


def _inpaint_padded(img_rgb, mask, session):
    h, w = img_rgb.shape[:2]
    sz = LAMA_MODEL_SIZE
    pad_bottom = sz - h
    pad_right = sz - w
    padded_img = cv2.copyMakeBorder(img_rgb, 0, pad_bottom, 0, pad_right,
                                     cv2.BORDER_REFLECT_101)
    padded_mask = cv2.copyMakeBorder(mask, 0, pad_bottom, 0, pad_right,
                                      cv2.BORDER_CONSTANT, value=0)
    result = _lama_single(session, padded_img, padded_mask)
    return result[:h, :w]


def _make_cosine_window(size):
    x = np.linspace(0, np.pi, size)
    w1d = (1 - np.cos(x)) / 2
    return np.outer(w1d, w1d).astype(np.float32)


def _inpaint_tiled(img_rgb, mask, session):
    h, w = img_rgb.shape[:2]
    sz = LAMA_MODEL_SIZE
    stride = 384
    window = _make_cosine_window(sz)

    result_sum = np.zeros((h, w, 3), dtype=np.float64)
    weight_sum = np.zeros((h, w), dtype=np.float64)

    y_starts = list(range(0, max(h - sz, 0) + 1, stride))
    if len(y_starts) == 0 or y_starts[-1] + sz < h:
        y_starts.append(max(0, h - sz))

    x_starts = list(range(0, max(w - sz, 0) + 1, stride))
    if len(x_starts) == 0 or x_starts[-1] + sz < w:
        x_starts.append(max(0, w - sz))

    for y in y_starts:
        for x in x_starts:
            y2 = y + sz
            x2 = x + sz

            # Pad if tile extends beyond image
            if y2 > h or x2 > w:
                tile_img = cv2.copyMakeBorder(
                    img_rgb[y:min(y2, h), x:min(x2, w)],
                    0, max(0, y2 - h), 0, max(0, x2 - w),
                    cv2.BORDER_REFLECT_101)
                tile_mask = cv2.copyMakeBorder(
                    mask[y:min(y2, h), x:min(x2, w)],
                    0, max(0, y2 - h), 0, max(0, x2 - w),
                    cv2.BORDER_CONSTANT, value=0)
            else:
                tile_img = img_rgb[y:y2, x:x2]
                tile_mask = mask[y:y2, x:x2]

            if np.count_nonzero(tile_mask) == 0:
                tile_result = tile_img.astype(np.float64)
            else:
                tile_result = _lama_single(session, tile_img, tile_mask).astype(np.float64)

            # Clip to actual image bounds
            ey = min(y2, h) - y
            ex = min(x2, w) - x
            win = window[:ey, :ex]

            result_sum[y:y+ey, x:x+ex] += tile_result[:ey, :ex] * win[:, :, np.newaxis]
            weight_sum[y:y+ey, x:x+ex] += win

    weight_sum = np.maximum(weight_sum, 1e-8)
    result = result_sum / weight_sum[:, :, np.newaxis]
    return np.clip(result, 0, 255).astype(np.uint8)


# ── CodeFormer face enhancement ──────────────────────────────────────

def _get_codeformer_path():
    """Resolve CodeFormer ONNX model path, downloading only if allowed."""
    if os.path.exists(CODEFORMER_MODEL_PATH):
        return CODEFORMER_MODEL_PATH
    if os.path.exists(CODEFORMER_LOCAL_PATH):
        return CODEFORMER_LOCAL_PATH

    from offline_guard import ensure_download_allowed
    ensure_download_allowed("CodeFormer model (codeformer.onnx)")
    os.makedirs(CODEFORMER_LOCAL_CACHE, exist_ok=True)
    emit_progress(35, "Downloading CodeFormer model")
    from huggingface_hub import hf_hub_download
    hf_hub_download(
        repo_id="facefusion/models-3.0.0",
        filename="codeformer.onnx",
        local_dir=CODEFORMER_LOCAL_CACHE,
    )
    return CODEFORMER_LOCAL_PATH


# ── Model path for new mp.tasks API ─────────────────────────────────

_FACE_DETECT_MODEL_URL = "https://storage.googleapis.com/mediapipe-models/face_detector/blaze_face_short_range/float16/latest/blaze_face_short_range.tflite"
_FACE_DETECT_DOCKER_PATH = os.path.join(_MODELS_BASE, "mediapipe", "blaze_face_short_range.tflite")
_FACE_DETECT_LOCAL_DIR = os.path.join(os.path.dirname(__file__), "..", "..", "..", ".models")
_FACE_DETECT_LOCAL_PATH = os.path.join(_FACE_DETECT_LOCAL_DIR, "blaze_face_short_range.tflite")


def _ensure_face_detect_model():
    """Resolve face detector model. Docker path first, then local dev."""
    if os.path.exists(_FACE_DETECT_DOCKER_PATH):
        return _FACE_DETECT_DOCKER_PATH
    if os.path.exists(_FACE_DETECT_LOCAL_PATH):
        return _FACE_DETECT_LOCAL_PATH
    from offline_guard import ensure_download_allowed
    ensure_download_allowed("Face detection model (blaze_face_short_range.tflite)")
    os.makedirs(_FACE_DETECT_LOCAL_DIR, exist_ok=True)
    import urllib.request
    emit_progress(15, "Downloading face detection model")
    urllib.request.urlretrieve(_FACE_DETECT_MODEL_URL, _FACE_DETECT_LOCAL_PATH)
    return _FACE_DETECT_LOCAL_PATH


def enhance_faces(img_bgr, fidelity=0.7):
    """Enhance faces in the image using CodeFormer ONNX.

    1. Detect faces with MediaPipe
    2. Crop each face with generous padding
    3. Run CodeFormer ONNX inference
    4. Paste enhanced face back with feathered blending

    Args:
        img_bgr: Input BGR image.
        fidelity: 0.0 = aggressive enhancement, 1.0 = faithful to original.

    Returns:
        Tuple of (enhanced BGR image, number of faces found).
    """
    import mediapipe as mp
    from gpu import safe_onnx_session

    # Detect faces — downscale large images for reliable MediaPipe detection
    img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
    ih, iw = img_bgr.shape[:2]

    max_dim = 1920
    longest = max(ih, iw)
    if longest > max_dim:
        det_scale = max_dim / longest
        det_rgb = cv2.resize(
            img_rgb,
            (int(iw * det_scale), int(ih * det_scale)),
            interpolation=cv2.INTER_AREA,
        )
        inv_scale = 1.0 / det_scale
    else:
        det_rgb = img_rgb
        inv_scale = 1.0

    try:
        mp_face = mp.solutions.face_detection
        all_detections = []
        for model_sel in [0, 1]:
            detector = mp_face.FaceDetection(
                model_selection=model_sel, min_detection_confidence=0.4
            )
            results = detector.process(det_rgb)
            detector.close()
            for detection in (results.detections or []):
                all_detections.append(detection)

        if not all_detections:
            return img_bgr, 0

        dh, dw = det_rgb.shape[:2]
        face_boxes = []
        for detection in all_detections:
            bbox = detection.location_data.relative_bounding_box
            face_boxes.append({
                "x": int(bbox.xmin * dw * inv_scale),
                "y": int(bbox.ymin * dh * inv_scale),
                "w": int(bbox.width * dw * inv_scale),
                "h": int(bbox.height * dh * inv_scale),
            })

    except AttributeError:
        model_path = _ensure_face_detect_model()
        options = mp.tasks.vision.FaceDetectorOptions(
            base_options=mp.tasks.BaseOptions(model_asset_path=model_path),
            running_mode=mp.tasks.vision.RunningMode.IMAGE,
            min_detection_confidence=0.4,
        )
        fd = mp.tasks.vision.FaceDetector.create_from_options(options)
        mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=det_rgb)
        result = fd.detect(mp_image)
        fd.close()

        if not result.detections:
            return img_bgr, 0

        face_boxes = []
        for detection in result.detections:
            bbox = detection.bounding_box
            face_boxes.append({
                "x": int(bbox.origin_x * inv_scale),
                "y": int(bbox.origin_y * inv_scale),
                "w": int(bbox.width * inv_scale),
                "h": int(bbox.height * inv_scale),
            })

    # Load CodeFormer model
    model_path = _get_codeformer_path()
    session, _device = safe_onnx_session(model_path)
    input_names = [inp.name for inp in session.get_inputs()]

    result = img_bgr.copy()
    faces_enhanced = 0

    for i, face_box in enumerate(face_boxes):
        x = face_box["x"]
        y = face_box["y"]
        w = face_box["w"]
        h = face_box["h"]

        if w < 48 or h < 48:
            continue

        # Clamp fidelity for small faces to prevent over-smoothing
        face_fidelity = fidelity
        if max(w, h) < 120:
            face_fidelity = max(fidelity, 0.85)

        # Expand bounding box by ~80% for hair, forehead, chin
        pad_x = int(w * 0.8)
        pad_y = int(h * 0.8)
        x1 = max(0, x - pad_x)
        y1 = max(0, y - pad_y)
        x2 = min(iw, x + w + pad_x)
        y2 = min(ih, y + h + pad_y)

        # Crop face region
        face_crop = img_bgr[y1:y2, x1:x2].copy()
        crop_h, crop_w = face_crop.shape[:2]

        # Resize to 512x512 for CodeFormer
        face_resized = cv2.resize(face_crop, (CODEFORMER_SIZE, CODEFORMER_SIZE),
                                  interpolation=cv2.INTER_LANCZOS4)

        # Preprocess: BGR -> RGB, normalize to [-1, 1]
        face_rgb = cv2.cvtColor(face_resized, cv2.COLOR_BGR2RGB)
        face_input = face_rgb.astype(np.float32) / 255.0
        face_input = (face_input - 0.5) / 0.5
        face_input = np.transpose(face_input, (2, 0, 1))
        face_input = np.expand_dims(face_input, 0)  # (1, 3, 512, 512)

        # Build model inputs
        model_inputs = {}
        for name in input_names:
            if name == "input":
                model_inputs[name] = face_input.astype(np.float32)
            elif name == "weight":
                model_inputs[name] = np.array([face_fidelity]).astype(np.float64)

        # Run inference
        try:
            output = session.run(None, model_inputs)[0][0]  # (3, 512, 512)
        except Exception as e:
            print(f"[restore] CodeFormer inference failed for face {i}: {e}", file=sys.stderr, flush=True)
            continue

        # Postprocess: [-1, 1] -> [0, 255], RGB -> BGR
        output = np.clip(output, -1, 1)
        output = (output + 1) / 2
        output = np.transpose(output, (1, 2, 0))  # (512, 512, 3)
        output = (output * 255.0).clip(0, 255).astype(np.uint8)
        output_bgr = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)

        # Resize back to original crop size
        enhanced_crop = cv2.resize(output_bgr, (crop_w, crop_h),
                                   interpolation=cv2.INTER_LANCZOS4)

        # Create feathered elliptical mask for smooth blending
        blend_mask = np.zeros((crop_h, crop_w), dtype=np.float32)
        center = (crop_w // 2, crop_h // 2)
        axes = (int(crop_w * 0.42), int(crop_h * 0.45))
        cv2.ellipse(blend_mask, center, axes, 0, 0, 360, 1.0, -1)

        # Feather the mask edges
        blur_r = max(5, min(crop_w, crop_h) // 8) | 1
        blend_mask = cv2.GaussianBlur(blend_mask, (blur_r, blur_r), 0)
        blend_mask = blend_mask[:, :, np.newaxis]

        # Blend enhanced face into result
        face_region = result[y1:y2, x1:x2].astype(np.float32)
        blended = face_region * (1.0 - blend_mask) + enhanced_crop.astype(np.float32) * blend_mask
        result[y1:y2, x1:x2] = np.clip(blended, 0, 255).astype(np.uint8)
        faces_enhanced += 1

    return result, faces_enhanced


# ── Denoising ─────────────────────────────────────────────────────────

def denoise_image(img_bgr, strength=40):
    """Apply noise reduction using Non-Local Means in LAB color space.

    Processes luminance and chrominance channels independently
    for better color preservation.

    Args:
        img_bgr: Input BGR image.
        strength: 0-100, higher = more aggressive denoising.

    Returns:
        Denoised BGR image.
    """
    if strength <= 0:
        return img_bgr

    # Map 0-100 to NLMeans filter strength
    h = 3 + (strength / 100) * 12  # 3-15

    lab = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LAB)
    l_ch, a_ch, b_ch = cv2.split(lab)

    # Denoise luminance channel
    l_ch = cv2.fastNlMeansDenoising(l_ch, None, h, 7, 21)

    # Lightly denoise color channels to remove chroma noise
    color_h = h * 0.5
    if color_h > 1:
        a_ch = cv2.fastNlMeansDenoising(a_ch, None, color_h, 7, 21)
        b_ch = cv2.fastNlMeansDenoising(b_ch, None, color_h, 7, 21)

    result = cv2.merge([l_ch, a_ch, b_ch])
    return cv2.cvtColor(result, cv2.COLOR_LAB2BGR)


# ── B&W detection ────────────────────────────────────────────────────

def is_grayscale(img_bgr):
    """Detect if an image is grayscale/B&W.

    Checks if color channels are nearly identical by measuring
    the standard deviation of channel differences.
    """
    if len(img_bgr.shape) == 2:
        return True
    if img_bgr.shape[2] == 1:
        return True

    b, g, r = cv2.split(img_bgr)
    diff_rg = np.abs(r.astype(np.float32) - g.astype(np.float32)).mean()
    diff_rb = np.abs(r.astype(np.float32) - b.astype(np.float32)).mean()
    diff_gb = np.abs(g.astype(np.float32) - b.astype(np.float32)).mean()
    avg_diff = (diff_rg + diff_rb + diff_gb) / 3

    return bool(avg_diff < 5.0)


# ── DDColor colorization ─────────────────────────────────────────────

def colorize_bw(img_bgr, intensity=0.85):
    """Colorize a B&W image using DDColor ONNX.

    Reuses the DDColor model that the colorize tool already downloads.
    """
    from gpu import safe_onnx_session

    if not os.path.exists(DDCOLOR_MODEL_PATH):
        raise FileNotFoundError(
            f"DDColor model not found at {DDCOLOR_MODEL_PATH}. "
            "Install the 'object-eraser-colorize' bundle to enable colorization."
        )

    session, _device = safe_onnx_session(DDCOLOR_MODEL_PATH)
    input_name = session.get_inputs()[0].name
    input_shape = session.get_inputs()[0].shape
    model_size = (
        input_shape[2]
        if len(input_shape) == 4 and isinstance(input_shape[2], int)
        else 512
    )

    orig_h, orig_w = img_bgr.shape[:2]
    img_lab = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LAB)
    orig_l = img_lab[:, :, 0].astype(np.float32)

    # Prepare input
    img_resized = cv2.resize(img_bgr, (model_size, model_size))
    img_float = img_resized.astype(np.float32) / 255.0
    img_nchw = np.transpose(img_float, (2, 0, 1))[np.newaxis, ...]

    output = session.run(None, {input_name: img_nchw})[0]
    ab_pred = output[0]  # (2, model_size, model_size)

    # Resize ab channels back to original
    ab_resized = np.zeros((2, orig_h, orig_w), dtype=np.float32)
    for i in range(2):
        ab_resized[i] = cv2.resize(ab_pred[i], (orig_w, orig_h))

    ab_a = np.clip(ab_resized[0], -128, 127)
    ab_b = np.clip(ab_resized[1], -128, 127)

    # Apply intensity blending
    if intensity < 1.0:
        orig_a = img_lab[:, :, 1].astype(np.float32) - 128.0
        orig_b = img_lab[:, :, 2].astype(np.float32) - 128.0
        ab_a = orig_a * (1 - intensity) + ab_a * intensity
        ab_b = orig_b * (1 - intensity) + ab_b * intensity

    result_lab = np.zeros((orig_h, orig_w, 3), dtype=np.uint8)
    result_lab[:, :, 0] = np.clip(orig_l, 0, 255).astype(np.uint8)
    result_lab[:, :, 1] = np.clip(ab_a + 128.0, 0, 255).astype(np.uint8)
    result_lab[:, :, 2] = np.clip(ab_b + 128.0, 0, 255).astype(np.uint8)

    return cv2.cvtColor(result_lab, cv2.COLOR_LAB2BGR), True


# ── Main pipeline ─────────────────────────────────────────────────────

def main():
    input_path = sys.argv[1]
    output_path = sys.argv[2]
    settings = json.loads(sys.argv[3]) if len(sys.argv) > 3 else {}

    scratch_removal = settings.get("scratchRemoval", True)
    face_enhancement = settings.get("faceEnhancement", True)
    fidelity = float(settings.get("fidelity", 0.7))
    do_denoise = settings.get("denoise", True)
    denoise_strength = float(settings.get("denoiseStrength", 25))
    do_colorize = settings.get("colorize", False)
    colorize_strength = float(settings.get("colorizeStrength", 85)) / 100.0

    try:
        from gpu import gpu_available
        device = "cuda" if gpu_available() else "cpu"

        emit_progress(5, "Opening image")
        img_bgr = cv2.imread(input_path, cv2.IMREAD_COLOR)
        if img_bgr is None:
            pil_img = Image.open(input_path).convert("RGB")
            img_bgr = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)

        orig_h, orig_w = img_bgr.shape[:2]
        result = img_bgr.copy()
        steps_applied = []

        emit_progress(8, "Analyzing photo")
        bw_detected = is_grayscale(img_bgr)
        scratch_coverage = 0.0

        if scratch_removal:
            emit_progress(10, "Detecting damage")
            scratch_mask = detect_scratches(result)
            scratch_pixels = np.count_nonzero(scratch_mask)
            total_pixels = scratch_mask.shape[0] * scratch_mask.shape[1]
            scratch_coverage = float(scratch_pixels / total_pixels)

            if scratch_coverage > 0.001:
                emit_progress(15, f"Repairing damage ({scratch_coverage:.1%} affected)")
                result = inpaint_damage(result, scratch_mask)
                steps_applied.append("scratch_removal")
                emit_progress(30, "Damage repaired")
            else:
                emit_progress(15, "No significant damage detected")
        else:
            emit_progress(15, "Scratch removal disabled")

        faces_found = 0
        if face_enhancement:
            emit_progress(35, "Detecting faces")
            try:
                result, faces_found = enhance_faces(result, fidelity)
                if faces_found > 0:
                    steps_applied.append("face_enhancement")
                    emit_progress(65, f"Enhanced {faces_found} face{'s' if faces_found != 1 else ''}")
                else:
                    emit_progress(65, "No faces detected")
            except Exception as e:
                emit_progress(65, f"Face enhancement skipped: {str(e)[:40]}")
        else:
            emit_progress(65, "Face enhancement disabled")

        if do_denoise and denoise_strength > 0:
            emit_progress(70, "Reducing noise")
            result = denoise_image(result, denoise_strength)
            steps_applied.append("denoise")
            emit_progress(80, "Noise reduced")
        else:
            emit_progress(80, "Denoising disabled")

        colorized = False
        if do_colorize and bw_detected:
            total_pixels = orig_h * orig_w
            has_gpu = device == "cuda"
            max_pixels = 8_000_000 if has_gpu else 2_000_000

            if total_pixels > max_pixels and not has_gpu:
                mp = total_pixels / 1_000_000
                emit_progress(92, f"Colorization skipped: image too large for CPU ({mp:.1f}MP, max 2MP)")
            elif not os.path.exists(DDCOLOR_MODEL_PATH):
                emit_progress(92, "Colorization skipped: DDColor model not installed")
            else:
                emit_progress(82, "Colorizing B&W photo")
                try:
                    result, colorized = colorize_bw(result, intensity=colorize_strength)
                    if colorized:
                        steps_applied.append("colorize")
                        emit_progress(92, "Colorization complete")
                    else:
                        emit_progress(92, "Colorization model not available")
                except Exception as e:
                    emit_progress(92, f"Colorization skipped: {str(e)[:40]}")
        else:
            emit_progress(92, "Colorization skipped")

        emit_progress(95, "Saving result")
        cv2.imwrite(output_path, result)

        print(json.dumps({
            "success": True,
            "width": orig_w,
            "height": orig_h,
            "steps": steps_applied,
            "scratchCoverage": round(scratch_coverage * 100, 2),
            "facesEnhanced": faces_found,
            "isGrayscale": bw_detected,
            "colorized": colorized,
            "device": device,
            "output_path": output_path,
        }))

    except Exception as e:
        print(json.dumps({"success": False, "error": str(e)}))
        sys.exit(1)


if __name__ == "__main__":
    main()