"""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 torch_gpu_available device = "cuda" if torch_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()