mirror of
https://github.com/snapotter-hq/SnapOtter.git
synced 2026-08-03 07:46:42 +02:00
feat: kill all silent fallbacks — fail clearly, never degrade silently
Remove 9 silent fallback chains in the Python sidecar: - upscale: RealESRGAN→Lanczos (now errors with install guidance) - upscale: GFPGAN skip (now errors with install guidance) - gpu: GPU→CPU (now reports device in response, never silent) - remove_bg: alpha matting fallback (now errors with retry guidance) - remove_bg: GPU→CPU session (now reports device) - colorize: DDColor→OpenCV (now errors with install guidance) - enhance_faces: CodeFormer→GFPGAN (now errors with install guidance) - ocr: quality cascade (now errors at requested level) - bridge: dispatcher crash retry (now reports retry in stderr) Also: raise red_eye max_faces 10→50, face_landmarks max_num_faces configurable, restore.py min face size 48→24px.
This commit is contained in:
ashim-hq
@@ -50,7 +50,7 @@ def colorize_ddcolor(img_bgr, intensity):
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emit_progress(15, "Loading DDColor model")
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session = safe_onnx_session(DDCOLOR_MODEL_PATH)
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session, _device = safe_onnx_session(DDCOLOR_MODEL_PATH)
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input_name = session.get_inputs()[0].name
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input_shape = session.get_inputs()[0].shape
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# Dynamic dims are strings ('w', 'h'), so default to 512 if not int
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@@ -181,41 +181,39 @@ def main():
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result_bgr = None
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method = "unknown"
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# Try DDColor first
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if model_choice in ("auto", "ddcolor"):
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try:
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if os.path.exists(DDCOLOR_MODEL_PATH):
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result_bgr, method = colorize_ddcolor(img_bgr, intensity)
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elif model_choice == "ddcolor":
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if not os.path.exists(DDCOLOR_MODEL_PATH):
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raise FileNotFoundError(f"DDColor model not found: {DDCOLOR_MODEL_PATH}")
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result_bgr, method = colorize_ddcolor(img_bgr, intensity)
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except Exception as e:
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import traceback
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print(f"[colorize] DDColor failed: {e}", file=sys.stderr, flush=True)
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traceback.print_exc(file=sys.stderr)
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if model_choice == "ddcolor":
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# User explicitly requested ddcolor — fail, don't degrade
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raise
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result_bgr = None
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print(json.dumps({
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"success": False,
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"error": (
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f"DDColor is not available: {e}. "
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"Install the colorize feature or use model=opencv for basic colorization."
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),
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}))
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sys.exit(1)
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# Try OpenCV fallback only in auto mode
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if result_bgr is None and model_choice in ("auto", "opencv"):
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elif model_choice == "opencv":
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try:
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if os.path.exists(OPENCV_PROTO_PATH) and os.path.exists(OPENCV_MODEL_PATH):
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result_bgr, method = colorize_opencv(img_bgr, intensity)
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elif model_choice == "opencv":
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if not (os.path.exists(OPENCV_PROTO_PATH) and os.path.exists(OPENCV_MODEL_PATH)):
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raise FileNotFoundError(f"OpenCV colorize models not found: {OPENCV_PROTO_PATH}")
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result_bgr, method = colorize_opencv(img_bgr, intensity)
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except Exception as e:
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import traceback
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print(f"[colorize] OpenCV fallback failed: {e}", file=sys.stderr, flush=True)
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print(f"[colorize] OpenCV failed: {e}", file=sys.stderr, flush=True)
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traceback.print_exc(file=sys.stderr)
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if model_choice == "opencv":
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raise
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result_bgr = None
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raise
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if result_bgr is None:
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else:
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print(json.dumps({
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"success": False,
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"error": "No colorization model available. Install DDColor or OpenCV models.",
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"error": f"Unknown model '{model_choice}'. Use 'auto', 'ddcolor', or 'opencv'.",
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}))
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sys.exit(1)
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@@ -270,19 +270,18 @@ def main():
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model_used = "codeformer"
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elif model_choice == "auto":
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# Try CodeFormer first, fall back to GFPGAN.
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# Catch broad Exception because codeformer-pip can fail in
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# unexpected ways (AttributeError, TypeError, etc.)
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try:
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fidelity_weight = 1.0 - strength
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enhanced = enhance_with_codeformer(img_array, fidelity_weight)
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model_used = "codeformer"
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except Exception as e:
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import traceback
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print(f"[enhance-faces] CodeFormer failed, falling back to GFPGAN: {e}", file=sys.stderr, flush=True)
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print(f"[enhance-faces] CodeFormer failed: {e}", file=sys.stderr, flush=True)
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traceback.print_exc(file=sys.stderr)
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enhanced = enhance_with_gfpgan(img_array, only_center_face)
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model_used = "gfpgan"
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raise RuntimeError(
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f"CodeFormer is not available: {e}. "
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"Install the face-enhance feature or use model=gfpgan."
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) from e
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finally:
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# Restore stdout after ALL AI processing
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@@ -54,14 +54,14 @@ def extract_key_points(lms):
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# ── Old API: mp.solutions (mediapipe < 0.10.30) ───────────────────
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def detect_with_solutions(img_array):
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def detect_with_solutions(img_array, max_faces=1):
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"""Use the legacy mp.solutions.face_mesh API."""
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import mediapipe as mp
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mp_face_mesh = mp.solutions.face_mesh
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face_mesh = mp_face_mesh.FaceMesh(
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static_image_mode=True,
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max_num_faces=1,
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max_num_faces=max_faces,
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refine_landmarks=True,
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min_detection_confidence=0.5,
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)
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@@ -99,7 +99,7 @@ def ensure_model():
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return MODEL_PATH
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def detect_with_tasks(img_path):
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def detect_with_tasks(img_path, max_faces=1):
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"""Use the new mp.tasks.vision.FaceLandmarker API."""
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import mediapipe as mp
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@@ -108,7 +108,7 @@ def detect_with_tasks(img_path):
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options = mp.tasks.vision.FaceLandmarkerOptions(
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base_options=mp.tasks.BaseOptions(model_asset_path=model_path),
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running_mode=mp.tasks.vision.RunningMode.IMAGE,
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num_faces=1,
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num_faces=max_faces,
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min_face_detection_confidence=0.5,
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output_face_blendshapes=False,
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output_facial_transformation_matrixes=False,
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@@ -133,6 +133,8 @@ def main():
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output_path = sys.argv[2] # unused but kept for bridge.ts compatibility
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settings = json.loads(sys.argv[3]) if len(sys.argv) > 3 else {}
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max_faces = settings.get("max_num_faces", 1)
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try:
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emit_progress(10, "Loading image")
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from PIL import Image
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@@ -146,16 +148,14 @@ def main():
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emit_progress(20, "Initializing face mesh")
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# Try the legacy solutions API first (Docker / older mediapipe),
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# fall back to the tasks API (newer mediapipe versions).
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landmarks_list = None
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try:
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img_array = np.array(img)
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emit_progress(30, "Detecting face landmarks")
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landmarks_list = detect_with_solutions(img_array)
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landmarks_list = detect_with_solutions(img_array, max_faces)
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except AttributeError:
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emit_progress(30, "Detecting face landmarks")
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landmarks_list = detect_with_tasks(input_path)
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landmarks_list = detect_with_tasks(input_path, max_faces)
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if landmarks_list is None:
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print(json.dumps({
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@@ -1,10 +1,16 @@
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"""Runtime GPU/CUDA detection utility."""
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import functools
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import json
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import os
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import subprocess
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import sys
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def emit_info(msg):
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"""Emit an informational JSON message to stderr for the bridge to capture."""
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print(json.dumps({"info": msg}), file=sys.stderr, flush=True)
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@functools.lru_cache(maxsize=1)
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def gpu_available():
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"""Return True if a usable CUDA GPU is present at runtime."""
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@@ -51,24 +57,34 @@ def gpu_available():
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def onnx_providers():
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"""Return ONNX Runtime execution providers in priority order."""
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"""Return (providers, device) tuple.
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providers: ONNX Runtime execution providers in priority order.
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device: "cuda" or "cpu" — reflects which hardware will actually be used.
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"""
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if gpu_available():
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return ["CUDAExecutionProvider", "CPUExecutionProvider"]
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return ["CPUExecutionProvider"]
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return (["CUDAExecutionProvider", "CPUExecutionProvider"], "cuda")
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emit_info("No GPU detected, processing on CPU")
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return (["CPUExecutionProvider"], "cpu")
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def safe_onnx_session(model_path, providers=None):
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"""Create an ONNX Runtime InferenceSession with graceful CUDA EP fallback."""
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"""Create an ONNX Runtime InferenceSession with graceful CUDA EP fallback.
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Returns (session, device) where device is "cuda" or "cpu".
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"""
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import onnxruntime as ort
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device = "cpu"
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if providers is None:
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providers = onnx_providers()
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providers, device = onnx_providers()
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try:
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return ort.InferenceSession(model_path, providers=providers)
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session = ort.InferenceSession(model_path, providers=providers)
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return session, device
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except Exception as e:
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if "CUDAExecutionProvider" in providers:
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print(f"[gpu] CUDA EP init failed ({e}), falling back to CPU",
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file=sys.stderr, flush=True)
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return ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
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emit_info(f"CUDA init failed ({e}), falling back to CPU")
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session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
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return session, "cpu"
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raise
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@@ -111,7 +111,7 @@ def main():
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model_path = _get_model_path()
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from gpu import safe_onnx_session
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session = safe_onnx_session(model_path)
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session, _device = safe_onnx_session(model_path)
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emit_progress(20, "Loading images")
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img = Image.open(input_path).convert("RGB")
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+28
-36
@@ -245,19 +245,23 @@ def main():
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text = run_paddleocr_v5(input_path, language)
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engine_used = "paddleocr-v5"
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except ImportError as e:
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print(json.dumps({"success": False, "error": f"PaddleOCR is not installed: {e}"}))
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print(json.dumps({
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"success": False,
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"error": (
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f"PaddleOCR is not installed: {e}. "
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"Install the OCR feature or use quality=fast for Tesseract."
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),
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}))
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sys.exit(1)
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except Exception as e:
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print(json.dumps({
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"warning": f"PaddleOCR PP-OCRv5 failed ({type(e).__name__}: {e}), falling back to Tesseract"
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}), file=sys.stderr, flush=True)
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emit_progress(25, "PaddleOCR failed, falling back to Tesseract")
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try:
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text = run_tesseract(input_path, language, is_auto=was_auto)
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engine_used = "tesseract (fallback from balanced)"
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except FileNotFoundError:
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print(json.dumps({"success": False, "error": "OCR engines unavailable: PaddleOCR failed and Tesseract is not installed"}))
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sys.exit(1)
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"success": False,
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"error": (
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f"PaddleOCR PP-OCRv5 failed: {type(e).__name__}: {e}. "
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"Install the OCR feature or use quality=fast for Tesseract."
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),
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}))
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sys.exit(1)
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elif quality == "best":
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try:
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@@ -265,34 +269,22 @@ def main():
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engine_used = "paddleocr-vl"
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except ImportError as e:
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print(json.dumps({
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"warning": f"PaddleOCR-VL not available ({e}), trying PP-OCRv5"
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}), file=sys.stderr, flush=True)
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emit_progress(20, "VL model unavailable, trying PP-OCRv5")
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try:
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text = run_paddleocr_v5(input_path, language)
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engine_used = "paddleocr-v5 (fallback from best)"
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except Exception as e2:
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print(json.dumps({
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"warning": f"PP-OCRv5 also failed ({type(e2).__name__}: {e2}), falling back to Tesseract"
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}), file=sys.stderr, flush=True)
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emit_progress(25, "PP-OCRv5 failed, falling back to Tesseract")
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text = run_tesseract(input_path, language, is_auto=was_auto)
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engine_used = "tesseract (fallback from best)"
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"success": False,
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"error": (
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f"PaddleOCR-VL is not available: {e}. "
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"Install the OCR feature or use quality=balanced for PP-OCRv5."
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),
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}))
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sys.exit(1)
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except Exception as e:
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print(json.dumps({
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"warning": f"PaddleOCR-VL failed ({type(e).__name__}: {e}), trying PP-OCRv5"
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}), file=sys.stderr, flush=True)
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emit_progress(20, "VL model failed, trying PP-OCRv5")
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try:
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text = run_paddleocr_v5(input_path, language)
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engine_used = "paddleocr-v5 (fallback from best)"
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except Exception as e2:
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print(json.dumps({
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"warning": f"PP-OCRv5 also failed ({type(e2).__name__}: {e2}), falling back to Tesseract"
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}), file=sys.stderr, flush=True)
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emit_progress(25, "PP-OCRv5 failed, falling back to Tesseract")
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text = run_tesseract(input_path, language, is_auto=was_auto)
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engine_used = "tesseract (fallback from best)"
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"success": False,
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"error": (
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f"PaddleOCR-VL failed: {type(e).__name__}: {e}. "
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"Install the OCR feature or use quality=balanced for PP-OCRv5."
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),
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}))
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sys.exit(1)
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else:
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print(json.dumps({"success": False, "error": f"Unknown quality: {quality}"}))
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@@ -32,7 +32,7 @@ def _ensure_face_mesh_model():
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return _LOCAL_MODEL_PATH
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def _mesh_with_solutions(img_array, max_faces=10, min_confidence=0.5):
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def _mesh_with_solutions(img_array, max_faces=50, min_confidence=0.5):
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"""FaceMesh using legacy mp.solutions API (mediapipe < 0.10.30).
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Returns list of landmark lists. Each landmark has .x, .y attributes.
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@@ -54,7 +54,7 @@ def _mesh_with_solutions(img_array, max_faces=10, min_confidence=0.5):
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return [face.landmark for face in results.multi_face_landmarks]
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def _mesh_with_tasks(img_array, max_faces=10, min_confidence=0.5):
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def _mesh_with_tasks(img_array, max_faces=50, min_confidence=0.5):
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"""FaceMesh using new mp.tasks API (mediapipe >= 0.10.30).
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Returns list of landmark lists. Each landmark has .x, .y attributes.
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@@ -79,7 +79,7 @@ def _mesh_with_tasks(img_array, max_faces=10, min_confidence=0.5):
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return result.face_landmarks
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def _detect_face_mesh(img_array, max_faces=10, min_confidence=0.5):
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def _detect_face_mesh(img_array, max_faces=50, min_confidence=0.5):
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"""Detect face mesh, trying legacy API first then falling back to tasks API."""
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try:
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return _mesh_with_solutions(img_array, max_faces, min_confidence)
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@@ -76,14 +76,15 @@ def main():
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emit_progress(10, "Loading model")
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providers = onnx_providers()
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providers, device = onnx_providers()
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try:
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session = new_session(model, providers=providers)
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except Exception as e:
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if "CUDAExecutionProvider" in providers:
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print(f"[remove-bg] GPU session failed ({e}), falling back to CPU",
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file=sys.stderr, flush=True)
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from gpu import emit_info
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emit_info(f"GPU session failed ({e}), falling back to CPU")
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session = new_session(model, providers=["CPUExecutionProvider"])
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device = "cpu"
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else:
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raise
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@@ -92,7 +93,6 @@ def main():
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with open(input_path, "rb") as f:
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input_data = f.read()
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# Try with alpha matting for better edges, fall back without
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emit_progress(30, "Analyzing image")
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try:
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output_data = remove(
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@@ -103,8 +103,9 @@ def main():
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alpha_matting_background_threshold=10,
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)
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except Exception as e:
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print(f"[remove-bg] Alpha matting failed ({e}), using standard removal", file=sys.stderr, flush=True)
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output_data = remove(input_data, session=session)
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raise RuntimeError(
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f"Alpha matting failed: {e}. Try again without alpha matting or with a different model."
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) from e
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emit_progress(80, "Background removed")
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@@ -115,7 +116,7 @@ def main():
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with open(output_path, "wb") as f:
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f.write(output_data)
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result = json.dumps({"success": True, "model": model})
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result = json.dumps({"success": True, "model": model, "device": device})
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except ImportError as e:
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print(f"[remove-bg] Import failed: {e}", file=sys.stderr, flush=True)
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@@ -155,7 +155,7 @@ def inpaint_damage(img_bgr, mask):
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from gpu import safe_onnx_session
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model_path = _get_lama_path()
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session = safe_onnx_session(model_path)
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session, _device = safe_onnx_session(model_path)
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orig_h, orig_w = img_bgr.shape[:2]
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img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
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@@ -317,7 +317,7 @@ def enhance_faces(img_bgr, fidelity=0.7):
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# Load CodeFormer model
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model_path = _get_codeformer_path()
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session = safe_onnx_session(model_path)
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session, _device = safe_onnx_session(model_path)
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input_names = [inp.name for inp in session.get_inputs()]
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result = img_bgr.copy()
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@@ -329,8 +329,7 @@ def enhance_faces(img_bgr, fidelity=0.7):
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w = face_box["w"]
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h = face_box["h"]
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# Skip very small faces (under 48px) - enhancement won't help
|
||||
if w < 48 or h < 48:
|
||||
if w < 24 or h < 24:
|
||||
continue
|
||||
|
||||
# Expand bounding box by ~80% for hair, forehead, chin
|
||||
@@ -473,7 +472,7 @@ def colorize_bw(img_bgr, intensity=0.85):
|
||||
if not os.path.exists(DDCOLOR_MODEL_PATH):
|
||||
return img_bgr, False
|
||||
|
||||
session = safe_onnx_session(DDCOLOR_MODEL_PATH)
|
||||
session, _device = safe_onnx_session(DDCOLOR_MODEL_PATH)
|
||||
input_name = session.get_inputs()[0].name
|
||||
input_shape = session.get_inputs()[0].shape
|
||||
model_size = (
|
||||
@@ -545,6 +544,9 @@ def main():
|
||||
scratch_sensitivity = "medium"
|
||||
|
||||
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:
|
||||
@@ -633,6 +635,7 @@ def main():
|
||||
"facesEnhanced": faces_found,
|
||||
"isGrayscale": bw_detected,
|
||||
"colorized": colorized,
|
||||
"device": device,
|
||||
"output_path": output_path,
|
||||
}))
|
||||
|
||||
|
||||
@@ -126,32 +126,30 @@ def main():
|
||||
result = Image.fromarray(output_array)
|
||||
method = "realesrgan"
|
||||
|
||||
# Face enhancement with GFPGAN
|
||||
if face_enhance:
|
||||
emit_progress(82, "Enhancing faces")
|
||||
try:
|
||||
from gfpgan import GFPGANer
|
||||
from gfpgan import GFPGANer
|
||||
|
||||
if os.path.exists(GFPGAN_MODEL_PATH):
|
||||
face_enhancer = GFPGANer(
|
||||
model_path=GFPGAN_MODEL_PATH,
|
||||
upscale=scale,
|
||||
arch="clean",
|
||||
channel_multiplier=2,
|
||||
bg_upsampler=upsampler,
|
||||
)
|
||||
_, _, face_output = face_enhancer.enhance(
|
||||
img_array,
|
||||
has_aligned=False,
|
||||
only_center_face=False,
|
||||
paste_back=True,
|
||||
)
|
||||
result = Image.fromarray(face_output)
|
||||
emit_progress(88, "Face enhancement complete")
|
||||
else:
|
||||
emit_progress(88, "Face model not found, skipping")
|
||||
except (ImportError, RuntimeError, OSError):
|
||||
emit_progress(88, "Face enhancement unavailable, skipping")
|
||||
if not os.path.exists(GFPGAN_MODEL_PATH):
|
||||
raise FileNotFoundError(
|
||||
f"GFPGAN model not found at {GFPGAN_MODEL_PATH}. "
|
||||
"Install the upscale-enhance feature or disable faceEnhance."
|
||||
)
|
||||
face_enhancer = GFPGANer(
|
||||
model_path=GFPGAN_MODEL_PATH,
|
||||
upscale=scale,
|
||||
arch="clean",
|
||||
channel_multiplier=2,
|
||||
bg_upsampler=upsampler,
|
||||
)
|
||||
_, _, face_output = face_enhancer.enhance(
|
||||
img_array,
|
||||
has_aligned=False,
|
||||
only_center_face=False,
|
||||
paste_back=True,
|
||||
)
|
||||
result = Image.fromarray(face_output)
|
||||
emit_progress(88, "Face enhancement complete")
|
||||
|
||||
finally:
|
||||
# Restore stdout after ALL AI processing
|
||||
@@ -164,19 +162,23 @@ def main():
|
||||
import traceback
|
||||
print(f"[upscale] Real-ESRGAN failed: {e}", file=sys.stderr, flush=True)
|
||||
traceback.print_exc(file=sys.stderr)
|
||||
if model_choice == "realesrgan":
|
||||
# User explicitly requested realesrgan — fail, don't degrade
|
||||
raise RuntimeError(f"Real-ESRGAN unavailable: {e}") from e
|
||||
result = None
|
||||
print(json.dumps({
|
||||
"success": False,
|
||||
"error": (
|
||||
f"Real-ESRGAN is not available: {e}. "
|
||||
"Install the upscale-enhance feature or use model=lanczos for basic upscaling."
|
||||
),
|
||||
}))
|
||||
sys.exit(1)
|
||||
|
||||
# Lanczos path: used when explicitly requested or as auto fallback
|
||||
if result is None:
|
||||
if model_choice not in ("auto", "lanczos"):
|
||||
raise RuntimeError(f"Requested model '{model_choice}' is not available")
|
||||
if result is None and model_choice == "lanczos":
|
||||
emit_progress(50, "Upscaling with Lanczos")
|
||||
result = img.resize(new_size, Image.LANCZOS)
|
||||
method = "lanczos"
|
||||
|
||||
if result is None:
|
||||
raise RuntimeError(f"Requested model '{model_choice}' is not available")
|
||||
|
||||
# Denoise
|
||||
if denoise_strength > 0:
|
||||
emit_progress(90, "Reducing noise")
|
||||
|
||||
Reference in New Issue
Block a user