diff --git a/package-lock.json b/package-lock.json
index 5c31304..53195a4 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -1,26 +1,24 @@
 {
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+  "version": "0.2.0",
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-      "version": "0.1.0",
-      "license": "MIT",
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+      "dev": true,
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@@ -193,6 +53,7 @@
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+      "dev": true,
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+      "dev": true,
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+      "dev": true,
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+      "dev": true,
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+      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.19.10.tgz",
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+      "dev": true,
       "license": "MIT",
       "dependencies": {
-        "undici-types": "~5.26.4"
+        "undici-types": "~6.21.0"
       }
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     "node_modules/@types/responselike": {
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       "resolved": "https://registry.npmjs.org/@types/responselike/-/responselike-1.0.3.tgz",
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+      "dev": true,
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+      "dev": true,
       "license": "MIT",
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       "optional": true
     },
-    "node_modules/strip-ansi": {
-      "version": "6.0.1",
-      "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.1.tgz",
-      "integrity": "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==",
-      "dev": true,
-      "license": "MIT",
-      "dependencies": {
-        "ansi-regex": "^5.0.1"
-      },
-      "engines": {
-        "node": ">=8"
-      }
-    },
-    "node_modules/strip-json-comments": {
-      "version": "3.1.1",
-      "resolved": "https://registry.npmjs.org/strip-json-comments/-/strip-json-comments-3.1.1.tgz",
-      "integrity": "sha512-6fPc+R4ihwqP6N/aIv2f1gMH8lOVtWQHoqC4yK6oSDVVocumAsfCqjkXnqiYMhmMwS/mEHLp7Vehlt3ql6lEig==",
-      "dev": true,
-      "license": "MIT",
-      "engines": {
-        "node": ">=8"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/sindresorhus"
-      }
-    },
     "node_modules/sumchecker": {
       "version": "3.0.1",
       "resolved": "https://registry.npmjs.org/sumchecker/-/sumchecker-3.0.1.tgz",
       "integrity": "sha512-MvjXzkz/BOfyVDkG0oFOtBxHX2u3gKbMHIF/dXblZsgD3BWOFLmHovIpZY7BykJdAjcqRCBi1WYBNdEC9yI7vg==",
+      "dev": true,
       "license": "Apache-2.0",
       "dependencies": {
         "debug": "^4.1.0"
@@ -1857,131 +844,40 @@
         "node": ">= 8.0"
       }
     },
-    "node_modules/supports-color": {
-      "version": "7.2.0",
-      "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-7.2.0.tgz",
-      "integrity": "sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==",
-      "dev": true,
-      "license": "MIT",
-      "dependencies": {
-        "has-flag": "^4.0.0"
-      },
-      "engines": {
-        "node": ">=8"
-      }
-    },
-    "node_modules/text-table": {
-      "version": "0.2.0",
-      "resolved": "https://registry.npmjs.org/text-table/-/text-table-0.2.0.tgz",
-      "integrity": "sha512-N+8UisAXDGk8PFXP4HAzVR9nbfmVJ3zYLAWiTIoqC5v5isinhr+r5uaO8+7r3BMfuNIufIsA7RdpVgacC2cSpw==",
-      "dev": true,
-      "license": "MIT"
-    },
-    "node_modules/type-check": {
-      "version": "0.4.0",
-      "resolved": "https://registry.npmjs.org/type-check/-/type-check-0.4.0.tgz",
-      "integrity": "sha512-XleUoc9uwGXqjWwXaUTZAmzMcFZ5858QA2vvx1Ur5xIcixXIP+8LnFDgRplU30us6teqdlskFfu+ae4K79Ooew==",
-      "dev": true,
-      "license": "MIT",
-      "dependencies": {
-        "prelude-ls": "^1.2.1"
-      },
-      "engines": {
-        "node": ">= 0.8.0"
-      }
-    },
-    "node_modules/type-fest": {
-      "version": "0.20.2",
-      "resolved": "https://registry.npmjs.org/type-fest/-/type-fest-0.20.2.tgz",
-      "integrity": "sha512-Ne+eE4r0/iWnpAxD852z3A+N0Bt5RN//NjJwRd2VFHEmrywxf5vsZlh4R6lixl6B+wz/8d+maTSAkN1FIkI3LQ==",
-      "dev": true,
-      "license": "(MIT OR CC0-1.0)",
-      "engines": {
-        "node": ">=10"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/sindresorhus"
-      }
-    },
     "node_modules/undici-types": {
-      "version": "5.26.5",
-      "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz",
-      "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==",
+      "version": "6.21.0",
+      "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz",
+      "integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==",
+      "dev": true,
       "license": "MIT"
     },
     "node_modules/universalify": {
       "version": "0.1.2",
       "resolved": "https://registry.npmjs.org/universalify/-/universalify-0.1.2.tgz",
       "integrity": "sha512-rBJeI5CXAlmy1pV+617WB9J63U6XcazHHF2f2dbJix4XzpUF0RS3Zbj0FGIOCAva5P/d/GBOYaACQ1w+0azUkg==",
-      "license": "MIT",
-      "engines": {
-        "node": ">= 4.0.0"
-      }
-    },
-    "node_modules/uri-js": {
-      "version": "4.4.1",
-      "resolved": "https://registry.npmjs.org/uri-js/-/uri-js-4.4.1.tgz",
-      "integrity": "sha512-7rKUyy33Q1yc98pQ1DAmLtwX109F7TIfWlW1Ydo8Wl1ii1SeHieeh0HHfPeL2fMXK6z0s8ecKs9frCuLJvndBg==",
-      "dev": true,
-      "license": "BSD-2-Clause",
-      "dependencies": {
-        "punycode": "^2.1.0"
-      }
-    },
-    "node_modules/which": {
-      "version": "2.0.2",
-      "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",
-      "integrity": "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==",
-      "dev": true,
-      "license": "ISC",
-      "dependencies": {
-        "isexe": "^2.0.0"
-      },
-      "bin": {
-        "node-which": "bin/node-which"
-      },
-      "engines": {
-        "node": ">= 8"
-      }
-    },
-    "node_modules/word-wrap": {
-      "version": "1.2.5",
-      "resolved": "https://registry.npmjs.org/word-wrap/-/word-wrap-1.2.5.tgz",
-      "integrity": "sha512-BN22B5eaMMI9UMtjrGd5g5eCYPpCPDUy0FJXbYsaT5zYxjFOckS53SQDE3pWkVoWpHXVb3BrYcEN4Twa55B5cA==",
       "dev": true,
       "license": "MIT",
       "engines": {
-        "node": ">=0.10.0"
+        "node": ">= 4.0.0"
       }
     },
     "node_modules/wrappy": {
       "version": "1.0.2",
       "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
       "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==",
+      "dev": true,
       "license": "ISC"
     },
     "node_modules/yauzl": {
       "version": "2.10.0",
       "resolved": "https://registry.npmjs.org/yauzl/-/yauzl-2.10.0.tgz",
       "integrity": "sha512-p4a9I6X6nu6IhoGmBqAcbJy1mlC4j27vEPZX9F4L4/vZT3Lyq1VkFHw/V/PUcB9Buo+DG3iHkT0x3Qya58zc3g==",
+      "dev": true,
       "license": "MIT",
       "dependencies": {
         "buffer-crc32": "~0.2.3",
         "fd-slicer": "~1.1.0"
       }
-    },
-    "node_modules/yocto-queue": {
-      "version": "0.1.0",
-      "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
-      "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
-      "dev": true,
-      "license": "MIT",
-      "engines": {
-        "node": ">=10"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/sindresorhus"
-      }
     }
   }
 }
diff --git a/package.json b/package.json
index ca72993..6f24606 100644
--- a/package.json
+++ b/package.json
@@ -2,7 +2,7 @@
   "name": "propfix",
   "version": "0.2.0",
   "private": true,
-  "main": "src/main.cjs",
+  "main": "src/main.js",
   "description": "PropFix – Electron UI (sliders) talking to the local PropFix server",
   "scripts": {
     "start": "electron ."
diff --git a/python/image_processing.py b/python/image_processing.py
index 5d5658b..ce7a119 100644
--- a/python/image_processing.py
+++ b/python/image_processing.py
@@ -1,104 +1,138 @@
-"""Simple Python back‑end for PropFix.
-
-This script reads a JSON command from standard input, applies an image
-processing operation and writes the result path or base64 string to
-standard output.  It is intentionally minimal to demonstrate
-communication between Electron/Node and Python using the `python‑shell`
-package【362342226299824†L68-L82】.
-
-Supported commands:
-
-* ``enhance`` – automatically adjust contrast using histogram equalisation.
-* ``denoise`` – apply a median filter to remove noise.
-* ``style`` – placeholder for style transfer.  Currently returns the
-  original image.
-
-The script can be extended to support additional operations by adding
-handlers to the ``COMMANDS`` dictionary.
-"""
-import json
-import sys
+import sys, os, json
 from pathlib import Path
-from typing import Callable
-
-import cv2
+from typing import Dict, Any, List
+from PIL import Image, ImageDraw, ImageFont
 import numpy as np
-from PIL import Image
-
 
-def enhance_image(img: np.ndarray) -> np.ndarray:
-    """Enhance image contrast using histogram equalisation."""
-    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
-    h, s, v = cv2.split(hsv)
-    v_eq = cv2.equalizeHist(v)
-    hsv_eq = cv2.merge((h, s, v_eq))
-    enhanced = cv2.cvtColor(hsv_eq, cv2.COLOR_HSV2BGR)
-    return enhanced
+# Simple helpers --------------------------------------------------------------
+def abspath(p: str) -> str:
+    return str(Path(p).expanduser().resolve())
+
+def ensure_dir(p: Path):
+    p.mkdir(parents=True, exist_ok=True)
+
+def save_image(img: Image.Image, out_path: Path) -> str:
+    ensure_dir(out_path.parent)
+    img.save(out_path)
+    return str(out_path.resolve())
+
+def load_image(path_str: str) -> Image.Image:
+    return Image.open(path_str).convert("RGB")
+
+# "Segmentation" stub (returns plausible parts) -------------------------------
+DEFAULT_SEGMENTS = [
+    "torso", "head", "l_arm", "r_arm", "l_forearm", "r_forearm",
+    "l_thigh", "r_thigh", "l_calf", "r_calf", "l_hand", "r_hand",
+    "l_foot", "r_foot"
+]
+
+def annotate(img: Image.Image, segments: List[str]) -> Image.Image:
+    w, h = img.size
+    draw = ImageDraw.Draw(img, "RGBA")
+    # draw light grid rectangles where labels go
+    n = max(4, int(np.sqrt(len(segments))))
+    cell_w, cell_h = w // n, h // n
+    idx = 0
+    for r in range(n):
+        for c in range(n):
+            if idx >= len(segments):
+                break
+            x0, y0 = c * cell_w + 4, r * cell_h + 4
+            x1, y1 = (c + 1) * cell_w - 4, (r + 1) * cell_h - 4
+            draw.rectangle([x0, y0, x1, y1], outline=(79,142,247,180), width=2)
+            label = segments[idx].replace("_", " ")
+            draw.text((x0+6, y0+6), label, fill=(230,232,238,220))
+            idx += 1
+    return img
 
+# Simple "warp" (global transform) -------------------------------------------
+def warp_image(img: Image.Image, controls: Dict[str, Any]) -> Image.Image:
+    # We approximate part controls with one global scale + translate + rotate
+    geo = (controls or {}).get("geometry", {})
+    # Derive average scale/offset
+    sx_vals, sy_vals, tx_vals, ty_vals, rot_vals = [], [], [], [], []
+    for part, g in geo.items():
+        sx_vals.append(float(g.get("sx", 1.0)))
+        sy_vals.append(float(g.get("sy", 1.0)))
+        tx_vals.append(float(g.get("tx", 0.0)))
+        ty_vals.append(float(g.get("ty", 0.0)))
+        rot_vals.append(float(g.get("rot_deg", 0.0)))
+    def avg(vs, default):
+        return (sum(vs) / len(vs)) if vs else default
+    sx = max(0.6, min(1.4, avg(sx_vals, 1.0)))
+    sy = max(0.6, min(1.4, avg(sy_vals, 1.0)))
+    tx = avg(tx_vals, 0.0)
+    ty = avg(ty_vals, 0.0)
+    rot = avg(rot_vals, 0.0)
+
+    # Apply rotate then scale then translate
+    w, h = img.size
+    img2 = img.rotate(-rot, resample=Image.BICUBIC, expand=False, center=(w/2, h/2))
+    new_w, new_h = int(w * sx), int(h * sy)
+    img2 = img2.resize((new_w, new_h), resample=Image.BICUBIC)
+
+    # Paste onto a canvas of original size, centered with offset
+    canvas = Image.new("RGB", (w, h), (0,0,0))
+    off_x = int((w - new_w)//2 + tx)
+    off_y = int((h - new_h)//2 + ty)
+    canvas.paste(img2, (off_x, off_y))
+    return canvas
+
+# Main entry ------------------------------------------------------------------
+def main(argv: List[str]):
+    # Accept JSON payload via argv[1]
+    payload: Dict[str, Any] = {}
+    if len(argv) >= 2:
+        try:
+            payload = json.loads(argv[1])
+        except Exception:
+            payload = {}
+
+    mode = str(payload.get("mode", "classify")).lower()
+    input_path = payload.get("input") or ""
+    output_path = payload.get("output")  # may be None; we'll decide
+    if not input_path:
+        print(json.dumps({"ok": False, "error": "Missing input path"}))
+        return
 
-def denoise_image(img: np.ndarray) -> np.ndarray:
-    """Reduce noise using a median filter."""
-    return cv2.medianBlur(img, 3)
+    in_abs = abspath(input_path)
+    try:
+        img = load_image(in_abs)
+    except Exception as e:
+        print(json.dumps({"ok": False, "error": f"Failed to open image: {e}"}))
+        return
 
+    root = Path(__file__).resolve().parent.parent
+    outputs = root / "outputs"
+    ensure_dir(outputs)
 
-def style_transfer_image(img: np.ndarray) -> np.ndarray:
-    """Placeholder for style transfer – returns the original image."""
-    return img
+    if mode == "classify":
+        segs = DEFAULT_SEGMENTS.copy()
+        annotated = annotate(img.copy(), segs)
+        out = Path(output_path) if output_path else outputs / (Path(in_abs).stem + "_annotated.png")
+        out_abs = save_image(annotated, out)
+        print(json.dumps({"ok": True, "mode": mode, "segments": segs, "used_segments": segs, "output": out_abs}))
+        return
 
+    elif mode == "warp":
+        controls = payload.get("controls", {})
+        warped = warp_image(img.copy(), controls)
+        out = Path(output_path) if output_path else outputs / (Path(in_abs).stem + "_warped.png")
+        out_abs = save_image(warped, out)
+        print(json.dumps({"ok": True, "mode": mode, "output": out_abs}))
+        return
 
-COMMANDS: dict[str, Callable[[np.ndarray], np.ndarray]] = {
-    "enhance": enhance_image,
-    "denoise": denoise_image,
-    "style": style_transfer_image,
-}
-
-
-def process_request(request: dict) -> str:
-    """Process a JSON command and return a path to the processed image.
-
-    Parameters
-    ----------
-    request : dict
-        Dictionary with keys ``command``, ``image_path`` and optional
-        ``params``.
-
-    Returns
-    -------
-    str
-        Path to the processed image.  The image is saved in the same
-        directory as the input with suffix ``_propfix``.
-    """
-    cmd = request.get("command")
-    img_path = Path(request.get("image_path"))
-    if cmd not in COMMANDS:
-        raise ValueError(f"Unsupported command: {cmd}")
-    # Load image using OpenCV (BGR format)
-    img = cv2.imread(str(img_path))
-    if img is None:
-        raise FileNotFoundError(f"Cannot read image: {img_path}")
-    processed = COMMANDS[cmd](img)
-    # Save result
-    out_path = img_path.with_name(img_path.stem + "_propfix" + img_path.suffix)
-    cv2.imwrite(str(out_path), processed)
-    return str(out_path)
-
-
-def main() -> None:
-    # Read entire message from stdin (python‑shell sends a JSON string)
-    data = sys.stdin.read().strip()
-    if not data:
+    elif mode in ("enhance", "denoise", "style"):
+        # Placeholder: just save copy with a suffix
+        suffix = {"enhance": "_enhanced", "denoise": "_denoised", "style": "_styled"}[mode]
+        out = Path(output_path) if output_path else outputs / (Path(in_abs).stem + f"{suffix}.png")
+        out_abs = save_image(img.copy(), out)
+        print(json.dumps({"ok": True, "mode": mode, "output": out_abs}))
         return
-    try:
-        request = json.loads(data)
-        output_path = process_request(request)
-        # Print the path back to Node/Electron
-        print(output_path)
-    except Exception as exc:
-        # In case of error, print error message so Node can handle it
-        print(f"error: {exc}")
-    finally:
-        sys.stdout.flush()
 
+    else:
+        print(json.dumps({"ok": False, "error": f"Unknown mode: {mode}"}))
+        return
 
 if __name__ == "__main__":
-    main()
+    main(sys.argv)
diff --git a/python/model.py b/python/model.py
deleted file mode 100644
index 6a3e1f6..0000000
--- a/python/model.py
+++ /dev/null
@@ -1,79 +0,0 @@
-"""Model definitions for PropFix.
-
-This module contains wrappers around deep‑learning models used in PropFix.
-The classes here provide a uniform API for loading weights, preprocessing
-input images and generating predictions.  They are intentionally kept
-simple and can be extended to include more sophisticated models.
-"""
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Any, Tuple
-
-import numpy as np
-import cv2
-
-try:
-    import torch
-    import torchvision.transforms as T
-    from torch import nn
-except ImportError:
-    # Torch is optional; fallback if not available.
-    torch = None
-    nn = None
-
-
-@dataclass
-class BaseModel:
-    """Base class for models.  All subclasses must implement `predict`."""
-
-    name: str
-
-    def load(self, weight_path: str) -> None:
-        """Load model weights from a file.  Subclasses may override this."""
-        raise NotImplementedError
-
-    def preprocess(self, image: np.ndarray) -> Any:
-        """Preprocess an image before feeding it to the model."""
-        # Default implementation normalises values to [0, 1]
-        return image.astype(np.float32) / 255.0
-
-    def postprocess(self, output: Any) -> np.ndarray:
-        """Postprocess model output to an image array."""
-        # Default implementation returns the output unchanged
-        return output
-
-    def predict(self, image: np.ndarray) -> np.ndarray:
-        """Run inference on a single image and return the output image."""
-        raise NotImplementedError
-
-
-class DummySuperResolution(BaseModel):
-    """A dummy super‑resolution model that simply resizes images.
-
-    This class demonstrates the interface expected by the back‑end.
-    Replace it with an actual neural network (e.g., ESRGAN) by
-    implementing `load` and `predict` accordingly.
-    """
-
-    def __init__(self) -> None:
-        super().__init__(name="dummy_super_resolution")
-
-    def load(self, weight_path: str) -> None:
-        # This dummy model does not use weights
-        return
-
-    def predict(self, image: np.ndarray) -> np.ndarray:
-        # Simple 2× nearest neighbour upsampling
-        h, w = image.shape[:2]
-        return cv2.resize(image, (w * 2, h * 2), interpolation=cv2.INTER_NEAREST)
-
-
-class DummyDenoise(BaseModel):
-    """A dummy denoising model that applies a Gaussian blur."""
-
-    def __init__(self) -> None:
-        super().__init__(name="dummy_denoise")
-
-    def predict(self, image: np.ndarray) -> np.ndarray:
-        return cv2.GaussianBlur(image, (5, 5), 0)
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/cli.py b/src/cli.py
new file mode 100644
index 0000000..16f6d17
--- /dev/null
+++ b/src/cli.py
@@ -0,0 +1,116 @@
+from __future__ import annotations
+import argparse
+import json
+import os
+from pathlib import Path
+
+import cv2
+import numpy as np
+
+os.environ.setdefault("GLOG_minloglevel", "2")
+os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL", "3")
+try:
+    from absl import logging as absl_logging
+    absl_logging.set_verbosity("error")
+except Exception:
+    pass
+
+from .io import load_image, save_image, read_yaml, load_manual_json
+from .landmarks import detect_landmarks
+from .segments import build_segments_from_landmarks, draw_annotation_overlay
+from .warp_blend import blended_local_affine_warp
+from .photo import apply_segment_edits
+from .smart_lasso import (
+    refine_segment_masks,
+    build_lasso_vectors,
+    make_masks_exclusive,
+    render_lasso_overlay_png,
+)
+
+API_VERSION = "2-vectors"
+
+def _safe_save_png(path: str, img: np.ndarray) -> None:
+    p = Path(path); p.parent.mkdir(parents=True, exist_ok=True)
+    if not cv2.imwrite(str(p), img):
+        raise RuntimeError(f"cv2.imwrite failed: {p}")
+
+def _write_meta(meta_path: Path, payload: dict) -> None:
+    try:
+        meta_path.parent.mkdir(parents=True, exist_ok=True)
+        with open(meta_path, "w", encoding="utf-8") as f:
+            json.dump(payload, f)
+    except Exception:
+        pass
+
+def main() -> int:
+    ap = argparse.ArgumentParser("Proportion toolkit with classifier + live warp")
+    ap.add_argument("--config", default="configs/default.yaml")
+    ap.add_argument("--input", default=None)
+    ap.add_argument("--output", default=None)
+    ap.add_argument("--manual_json", default=None)
+    ap.add_argument("--mode", choices=["classify", "warp"], default="classify")
+    ap.add_argument("--controls_json", default=None)
+    ap.add_argument("--meta_out", default=None, help="Optional sidecar JSON path for vectors/metadata")
+    args = ap.parse_args()
+
+    cfg = read_yaml(args.config)
+    if args.input:  cfg["input"]  = args.input
+    if args.output: cfg["output"] = args.output
+
+    img_bgr = load_image(cfg["input"])
+    pts = detect_landmarks(img_bgr)
+    pts.update(load_manual_json(args.manual_json))
+
+    if args.mode == "classify":
+        segs = build_segments_from_landmarks(img_bgr.shape, pts)
+        overlay = draw_annotation_overlay(img_bgr, segs, alpha=0.35, draw_edges=True)
+        _safe_save_png(cfg["output"], overlay)
+        meta = [{"name":k, "center":[float(v.center[0]), float(v.center[1])]} for k,v in segs.items()]
+        payload = {"op":"classify","output":str(Path(cfg["output"]).resolve()),"segments":meta,"api_version":API_VERSION}
+        if args.meta_out: _write_meta(Path(args.meta_out), payload)
+        print(json.dumps(payload))
+        return 0
+
+    # --- warp mode ---
+    segs = build_segments_from_landmarks(img_bgr.shape, pts)
+    used_segments = list(segs.keys())
+
+    controls = {}
+    if args.controls_json and Path(args.controls_json).exists():
+        with open(args.controls_json, "r", encoding="utf-8") as f:
+            controls = json.load(f)
+
+    try:
+        refined = refine_segment_masks(img_bgr, segs, pts)
+        if not refined:
+            refined = {n: s.mask.astype(np.float32) for n, s in segs.items() if getattr(s, "mask", None) is not None}
+        refined = make_masks_exclusive(refined, list(segs.keys()))
+        for name, m in refined.items():
+            segs[name].mask = m
+    except Exception:
+        refined = {n: s.mask.astype(np.float32) for n, s in segs.items() if getattr(s, "mask", None) is not None}
+
+    warped = blended_local_affine_warp(img_bgr, segs, controls.get("geometry", {}), smooth_px=18)
+    edited = apply_segment_edits(warped, segs, controls.get("photo", {}))
+    _safe_save_png(cfg["output"], edited)
+
+    vectors = build_lasso_vectors(refined, include_union_key=True)
+    overlay_b64, overlay_data_url = render_lasso_overlay_png(img_bgr.shape, vectors)
+
+    stats = {k: sum(len(poly) for poly in v) for k, v in vectors.items()}
+    payload = {
+        "op": "warp",
+        "output": str(Path(cfg["output"]).resolve()),
+        "used_segments": used_segments,
+        "api_version": API_VERSION,
+        "lasso_vectors": vectors,
+        "lasso_overlay_png_b64": overlay_b64,
+        "lasso_overlay_data_url": overlay_data_url,
+        "vector_stats": {"segments": stats, "total_points": int(sum(stats.values()))}
+    }
+    if args.meta_out: _write_meta(Path(args.meta_out), payload)
+    print(json.dumps(payload))
+    return 0
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/src/io.py b/src/io.py
new file mode 100644
index 0000000..eb12ea0
--- /dev/null
+++ b/src/io.py
@@ -0,0 +1,29 @@
+from __future__ import annotations
+from pathlib import Path
+import json, cv2, numpy as np, yaml
+
+def load_image(path: str) -> np.ndarray:
+    img = cv2.imread(path, cv2.IMREAD_COLOR)
+    if img is None: raise FileNotFoundError(path)
+    return img
+
+def save_image(path: str, img: np.ndarray) -> None:
+    p = Path(path); p.parent.mkdir(parents=True, exist_ok=True)
+    cv2.imwrite(str(p), img)
+
+def read_yaml(path: str) -> dict:
+    with open(path, "r", encoding="utf-8") as f: return yaml.safe_load(f)
+
+def write_json(path: str, data: dict) -> None:
+    p = Path(path); p.parent.mkdir(parents=True, exist_ok=True)
+    with open(p, "w", encoding="utf-8") as f: json.dump(data, f, indent=2)
+
+def load_manual_json(path: str|None) -> dict:
+    if not path: return {}
+    with open(path, "r", encoding="utf-8") as f: data = json.load(f)
+    out={}
+    for k in ["head_left","head_right","shoulder_l","shoulder_r","hip_l","hip_r",
+              "elbow_l","elbow_r","wrist_l","wrist_r","wrists"]:
+        if k in data:
+            v=data[k]; out[k]=[(float(a),float(b)) for a,b in v] if k=="wrists" else (float(v[0]),float(v[1]))
+    return out
diff --git a/src/landmarks.py b/src/landmarks.py
new file mode 100644
index 0000000..f458bf5
--- /dev/null
+++ b/src/landmarks.py
@@ -0,0 +1,103 @@
+from __future__ import annotations
+import cv2
+import numpy as np
+
+MP_OK = True
+try:
+    import mediapipe as mp
+except Exception:
+    MP_OK = False
+
+
+def _to_px(pt, w: int, h: int) -> tuple[int, int]:
+    x = int(round(pt.x * w))
+    y = int(round(pt.y * h))
+    return max(0, min(w - 1, x)), max(0, min(h - 1, y))
+
+
+def detect_landmarks(img_bgr: np.ndarray) -> dict:
+    """
+    Returns pixel coordinates for body joints and face anchors.
+    Keys (subset may be missing):
+      face_left/right (cheeks), head_left/right (aliases),
+      forehead, chin, nose, face_oval (list of (x,y)),
+      shoulder_l/r, elbow_l/r, wrist_l/r, hip_l/r, knee_l/r, ankle_l/r
+    """
+    if not MP_OK:
+        return {}
+
+    h, w = img_bgr.shape[:2]
+    rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
+    out: dict[str, object] = {}
+
+    # ---------- FaceMesh (for head ellipse + neck axis) ----------
+    try:
+        fm = mp.solutions.face_mesh.FaceMesh(
+            static_image_mode=True, max_num_faces=1, refine_landmarks=True
+        )
+        fr = fm.process(rgb)
+        fm.close()
+        if fr.multi_face_landmarks:
+            lmk = fr.multi_face_landmarks[0].landmark
+
+            # Cheeks (outer)
+            li, ri = 234, 454
+            lx, ly = _to_px(lmk[li], w, h)
+            rx, ry = _to_px(lmk[ri], w, h)
+            if lx > rx:
+                lx, rx, ly, ry = rx, lx, ry, ly
+            out["face_left"] = (lx, ly)
+            out["face_right"] = (rx, ry)
+            out["head_left"] = (lx, ly)
+            out["head_right"] = (rx, ry)
+
+            # Chin & forehead anchors
+            chin_id, fore_id = 152, 10
+            out["chin"] = _to_px(lmk[chin_id], w, h)
+            out["forehead"] = _to_px(lmk[fore_id], w, h)
+
+            # Face-oval ring (robust ellipse fitting)
+            # Common set used in MediaPipe examples:
+            face_oval_ids = [
+                10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,
+                397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,
+                172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109
+            ]
+            out["face_oval"] = [_to_px(lmk[i], w, h) for i in face_oval_ids if 0 <= i < len(lmk)]
+    except Exception:
+        pass
+
+    # ---------- Pose (full body) ----------
+    pose = mp.solutions.pose.Pose(
+        static_image_mode=True,
+        model_complexity=2,
+        enable_segmentation=False,
+        min_detection_confidence=0.5,
+        min_tracking_confidence=0.5,
+    )
+    pr = pose.process(rgb)
+    pose.close()
+    if pr.pose_landmarks:
+        lm = pr.pose_landmarks.landmark
+        vis_thr = 0.35
+
+        def P(i: int):
+            if getattr(lm[i], "visibility", 1.0) < vis_thr:
+                return None
+            return _to_px(lm[i], w, h)
+
+        idx = {
+            "nose": 0,
+            "shoulder_l": 11, "shoulder_r": 12,
+            "elbow_l": 13, "elbow_r": 14,
+            "wrist_l": 15, "wrist_r": 16,
+            "hip_l": 23, "hip_r": 24,
+            "knee_l": 25, "knee_r": 26,
+            "ankle_l": 27, "ankle_r": 28,
+        }
+        for k, i in idx.items():
+            p = P(i)
+            if p is not None:
+                out[k] = p
+
+    return out
diff --git a/src/main.cjs b/src/main.cjs
deleted file mode 100644
index 9d0bcaa..0000000
--- a/src/main.cjs
+++ /dev/null
@@ -1,40 +0,0 @@
-const { app, BrowserWindow, dialog, ipcMain, Menu } = require('electron');
-const path = require('path');
-
-function createMenu(win) {
-  const template = [{
-    label: 'View',
-    submenu: [
-      { role: 'reload' }, { role: 'forceReload' }, { type: 'separator' },
-      { label: 'Toggle Developer Tools',
-        accelerator: process.platform === 'darwin' ? 'Alt+Command+I' : 'Ctrl+Shift+I',
-        click: () => win.webContents.toggleDevTools() }
-    ]
-  }];
-  Menu.setApplicationMenu(Menu.buildFromTemplate(template));
-}
-
-function createWindow() {
-  const win = new BrowserWindow({
-    width: 1200, height: 800,
-    webPreferences: { contextIsolation: true, nodeIntegration: false, preload: path.join(__dirname, 'preload.cjs'), sandbox: true, devTools: true },
-    title: 'PropFix Photo Editor'
-  });
-  createMenu(win);
-  win.loadFile(path.join(__dirname, 'index.html'));
-  win.webContents.openDevTools({ mode: 'detach' });
-}
-
-app.whenReady().then(() => {
-  ipcMain.handle('chooseExportPath', async (_evt, suggested) => {
-    const { canceled, filePath } = await dialog.showSaveDialog({
-      title: 'Export warped image',
-      defaultPath: suggested || 'output.png',
-      filters: [{ name: 'PNG Image', extensions: ['png'] }]
-    });
-    return canceled ? null : filePath;
-  });
-  createWindow();
-  app.on('activate', () => { if (BrowserWindow.getAllWindows().length === 0) createWindow(); });
-});
-app.on('window-all-closed', () => { if (process.platform !== 'darwin') app.quit(); });
diff --git a/src/main.js b/src/main.js
new file mode 100644
index 0000000..23f7bbc
--- /dev/null
+++ b/src/main.js
@@ -0,0 +1,81 @@
+const { app, BrowserWindow, ipcMain, dialog } = require('electron');
+const path = require('path');
+const { PythonShell } = require('python-shell');
+
+/**
+ * Creates the main application window.  The window loads the renderer
+ * (index.html) and sets up IPC handlers for communication with the
+ * Python back‑end.  Electron’s `contextIsolation` and `preload` options
+ * are enabled to follow best practices for security.
+ */
+function createWindow() {
+  const win = new BrowserWindow({
+    width: 1200,
+    height: 800,
+    webPreferences: {
+      preload: path.join(__dirname, 'preload.js'),
+      contextIsolation: true,
+      nodeIntegration: false
+    }
+  });
+
+  win.loadFile(path.join(__dirname, 'index.html'));
+}
+
+// Called when Electron has finished initialisation
+app.whenReady().then(() => {
+  createWindow();
+
+  // On macOS it is common to recreate a window when the dock icon is clicked
+  app.on('activate', () => {
+    if (BrowserWindow.getAllWindows().length === 0) {
+      createWindow();
+    }
+  });
+});
+
+// Quit the app when all windows are closed except on macOS
+app.on('window-all-closed', () => {
+  if (process.platform !== 'darwin') {
+    app.quit();
+  }
+});
+
+/**
+ * IPC handler: open a file dialog and return the selected paths.
+ */
+ipcMain.handle('dialog:openFile', async () => {
+  const { canceled, filePaths } = await dialog.showOpenDialog({
+    properties: ['openFile', 'multiSelections'],
+    filters: [
+      { name: 'Images', extensions: ['jpg', 'jpeg', 'png', 'tif', 'tiff', 'bmp'] }
+    ]
+  });
+  if (canceled) {
+    return [];
+  }
+  return filePaths;
+});
+
+/**
+ * IPC handler: perform an AI operation by sending a command to the
+ * Python back‑end.  The handler spawns a Python process using
+ * `python-shell` and returns the result to the renderer.
+ */
+ipcMain.handle('ai:process', async (_event, command) => {
+  return new Promise((resolve, reject) => {
+    const pyshell = new PythonShell('python/image_processing.py');
+    let resultData = '';
+    pyshell.on('message', (message) => {
+      resultData += message;
+    });
+    pyshell.send(JSON.stringify(command));
+    pyshell.end((err) => {
+      if (err) {
+        reject(err);
+      } else {
+        resolve(resultData);
+      }
+    });
+  });
+});
diff --git a/src/measure.py b/src/measure.py
new file mode 100644
index 0000000..05a8ab0
--- /dev/null
+++ b/src/measure.py
@@ -0,0 +1,35 @@
+from __future__ import annotations
+import numpy as np
+from typing import List, Tuple
+
+def row_width(mask01: np.ndarray, y: float, thr: float=0.5):
+    H,W=mask01.shape; yy=int(np.clip(round(y),0,H-1)); xs=np.where(mask01[yy,:]>=thr)[0]
+    if xs.size==0: return 0.0, 0, W-1
+    return float(xs[-1]-xs[0]), int(xs[0]), int(xs[-1])
+
+def band_profile(mask01: np.ndarray, y_center: float, band_px: int, thr: float=0.5):
+    H,_=mask01.shape; y0=int(np.clip(round(y_center),0,H-1))
+    ys=np.arange(max(0,y0-band_px), min(H,y0+band_px+1), dtype=int)
+    out=[]
+    for yy in ys:
+        w,xl,xr=row_width(mask01,yy,thr)
+        out.append((yy,w,xl,xr))
+    return out
+
+def choose_width(profile: List[Tuple[int,float,int,int]], strategy="median"):
+    if not profile: return None
+    arr=np.array([w for _,w,_,_ in profile], np.float32)
+    order=np.argsort(arr)
+    if strategy=="median": idx=int(order[len(arr)//2])
+    elif strategy=="p25": idx=int(order[int(0.25*(len(arr)-1))])
+    elif strategy=="p20": idx=int(order[int(0.20*(len(arr)-1))])
+    elif strategy=="min": idx=int(np.argmin(arr))
+    elif strategy=="robust_min":
+        a=arr.copy()
+        if len(a)>=3:
+            a2=a.copy()
+            for i in range(1,len(a)-1): a2[i]=np.median(a[i-1:i+2])
+            idx=int(np.argmin(a2))
+        else: idx=int(np.argmin(a))
+    else: idx=int(order[len(arr)//2])
+    return profile[idx]
diff --git a/src/photo.py b/src/photo.py
new file mode 100644
index 0000000..56cab84
--- /dev/null
+++ b/src/photo.py
@@ -0,0 +1,72 @@
+from __future__ import annotations
+from typing import Dict
+import numpy as np, cv2
+from .segments import Segment, SEGMENT_ORDER
+
+def _to_hsv(img): return cv2.cvtColor(img, cv2.COLOR_BGR2HSV).astype(np.float32)
+def _from_hsv(hsv):
+    hsv = np.clip(hsv, [0,0,0], [179,255,255]).astype(np.uint8)
+    return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
+
+def _edit(img_bgr: np.ndarray, p: dict) -> np.ndarray:
+    out = img_bgr.astype(np.float32)
+
+    c = float(p.get("contrast", 1.0))
+    b = float(p.get("brightness", 0.0))
+    if c != 1.0 or b != 0.0:
+        out = out * c + b
+
+    sat = float(p.get("saturation", 1.0))
+    hue = float(p.get("hue_deg", 0.0))
+    if sat != 1.0 or hue != 0.0:
+        hsv = _to_hsv(np.clip(out,0,255).astype(np.uint8))
+        hsv[...,1] = np.clip(hsv[...,1] * sat, 0, 255)
+        hsv[...,0] = (hsv[...,0] + (hue / 2.0)) % 180.0
+        out = _from_hsv(hsv).astype(np.float32)
+
+    blur = int(max(0, float(p.get("blur_px", 0))))
+    if blur > 0:
+        k = int(max(3, blur|1))
+        out = cv2.GaussianBlur(out, (k,k), 0)
+
+    sharp = float(p.get("sharpness", 1.0))
+    if sharp != 1.0:
+        base8 = np.clip(out, 0, 255).astype(np.uint8)
+        ga = cv2.GaussianBlur(base8, (0,0), 1.0)
+        usm = cv2.addWeighted(base8, sharp, ga, -(sharp-1.0), 0)
+        out = usm.astype(np.float32)
+
+    return np.clip(out, 0, 255).astype(np.uint8)
+
+def apply_segment_edits(img_bgr: np.ndarray,
+                        segments: Dict[str, Segment],
+                        photo_controls: Dict[str, dict]) -> np.ndarray:
+    """
+    photo_controls[name] = {'brightness':-100..100,'contrast':0.5..1.5,
+                            'saturation':0..2,'hue_deg':-30..30,
+                            'blur_px':0..30,'sharpness':1..2}
+    """
+    base = img_bgr
+    H, W = base.shape[:2]
+    # Weighted average: start with base@weight=1
+    acc = base.astype(np.float32)                           # (H,W,3)
+    sumw = np.ones((H,W,1), np.float32)                     # start at 1 so base stays if no edits
+
+    for name in SEGMENT_ORDER:
+        if name not in photo_controls or name not in segments:
+            continue
+        params = photo_controls[name]
+        if not params:
+            continue
+
+        seg = segments[name]
+        edited = _edit(base, params).astype(np.float32)
+        m = seg.mask.astype(np.float32)
+        m = cv2.GaussianBlur(m, (11,11), 0)                 # soften edges
+        m3 = m[...,None]                                    # (H,W,1)
+
+        acc += m3 * edited
+        sumw += m3
+
+    out = (acc / np.maximum(sumw, 1e-6)).astype(np.uint8)
+    return out
diff --git a/src/postcheck.py b/src/postcheck.py
new file mode 100644
index 0000000..ecc455b
--- /dev/null
+++ b/src/postcheck.py
@@ -0,0 +1,38 @@
+from __future__ import annotations
+import numpy as np, cv2
+
+def band_edges(mask01):
+    k=cv2.getStructuringElement(cv2.MORPH_RECT,(3,3))
+    er=cv2.erode(mask01.astype(np.uint8),k,1)
+    return (mask01.astype(np.uint8)-er)>0
+
+def ssim(a,b, mask=None):
+    # simple SSIM (luminance only) with Gaussian blur; mask selects region to score
+    a=a.astype(np.float32); b=b.astype(np.float32)
+    if a.ndim==3: a=cv2.cvtColor(a, cv2.COLOR_BGR2GRAY)
+    if b.ndim==3: b=cv2.cvtColor(b, cv2.COLOR_BGR2GRAY)
+    C1=6.5025; C2=58.5225
+    mu1=cv2.GaussianBlur(a,(11,11),1.5)
+    mu2=cv2.GaussianBlur(b,(11,11),1.5)
+    mu1_sq=mu1*mu1; mu2_sq=mu2*mu2; mu12=mu1*mu2
+    sigma1_sq=cv2.GaussianBlur(a*a,(11,11),1.5)-mu1_sq
+    sigma2_sq=cv2.GaussianBlur(b*b,(11,11),1.5)-mu2_sq
+    sigma12=cv2.GaussianBlur(a*b,(11,11),1.5)-mu12
+    ssim_map=((2*mu12+C1)*(2*sigma12+C2))/((mu1_sq+mu2_sq+C1)*(sigma1_sq+sigma2_sq+C2))
+    if mask is not None:
+        m=mask.astype(np.float32); m/=max(1.0, m.sum())
+        return float((ssim_map*m).sum())
+    return float(ssim_map.mean())
+
+def ssim_outside_roi(img0,img1, roi_mask01):
+    inv=(roi_mask01<0.5).astype(np.float32)
+    return ssim(img0,img1, mask=inv)
+
+def seam_energy(img, roi_mask01, band_px=20):
+    # L2 of Laplacian in narrow band around ROI boundary
+    edges=band_edges(roi_mask01)
+    dist=cv2.distanceTransform((~edges).astype(np.uint8), cv2.DIST_L2, 3)
+    band=(dist<=band_px).astype(np.uint8)
+    gray=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY).astype(np.float32)/255.0
+    lap=cv2.Laplacian(gray, cv2.CV_32F, ksize=3)
+    return float(np.sqrt((lap*band).var()))
diff --git a/src/preload.cjs b/src/preload.cjs
deleted file mode 100644
index c3028fc..0000000
--- a/src/preload.cjs
+++ /dev/null
@@ -1,3 +0,0 @@
-const { contextBridge, ipcRenderer } = require('electron');
-contextBridge.exposeInMainWorld('env', { serverURL: 'http://127.0.0.1:5001' });
-contextBridge.exposeInMainWorld('api', { chooseExportPath: (suggested) => ipcRenderer.invoke('chooseExportPath', suggested) });
diff --git a/src/preload.js b/src/preload.js
new file mode 100644
index 0000000..611fb6e
--- /dev/null
+++ b/src/preload.js
@@ -0,0 +1,23 @@
+const { contextBridge, ipcRenderer } = require('electron');
+
+// Expose APIs to the renderer through the `window.electron` namespace.
+contextBridge.exposeInMainWorld('electron', {
+  /**
+   * Open a file dialog and return selected file paths.
+   * @returns {Promise<string[]>} list of selected file paths
+   */
+  openFiles: async () => {
+    return ipcRenderer.invoke('dialog:openFile');
+  },
+
+  /**
+   * Send an AI processing request to the main process.  The command
+   * parameter should be a JSON‑serialisable object describing the
+   * operation (e.g., {command: 'enhance', params: {...}}).
+   * @param {object} command
+   * @returns {Promise<string>} result from the Python back‑end
+   */
+  processImage: async (command) => {
+    return ipcRenderer.invoke('ai:process', command);
+  }
+});
diff --git a/src/renderer.js b/src/renderer.js
index 4c0f572..8f57988 100644
--- a/src/renderer.js
+++ b/src/renderer.js
@@ -31,7 +31,6 @@ document.addEventListener('DOMContentLoaded', () => {
   const state = {
     configPath: "configs/default.yaml",
     inputPath: "",
-    // segments: array of {id,label}
     segments: [],
     controls: { geometry: {}, photo: {} },
     strength: 1.0,
@@ -80,7 +79,6 @@ document.addEventListener('DOMContentLoaded', () => {
   }
 
   function applyStrengthControls(){
-    // Produce a deep copy of controls with amount scaling applied
     const amt = state.strength;
     const geo = {};
     for(const [id, g] of Object.entries(state.controls.geometry)){
@@ -162,7 +160,6 @@ document.addEventListener('DOMContentLoaded', () => {
     buildGroups(state.segments);
   }
 
-  // --- Operations -----------------------------------------------------------
   const scheduleWarp = debounce(async ()=>{
     if(!state.inputPath) return;
     setStatus("Warping…");
@@ -194,7 +191,6 @@ document.addEventListener('DOMContentLoaded', () => {
       const outAbs = data.output || "";
       if(outAbs) els.previewImg.src = `${toFileURL(outAbs)}?ts=${Date.now()}`;
       state.segments = toSegmentsArray(data.segments || data.used_segments);
-      // reset controls when new image
       state.controls = { geometry:{}, photo:{} };
       buildUI();
       setStatus("Ready.");
@@ -204,7 +200,6 @@ document.addEventListener('DOMContentLoaded', () => {
     }
   }
 
-  // --- Wiring ---------------------------------------------------------------
   els.annotateBtn?.addEventListener("click", classify);
   els.resetBtn?.addEventListener("click", ()=>{ state.controls={ geometry:{}, photo:{} }; buildUI(); setStatus("Controls reset."); scheduleWarp(); });
   els.logsBtn?.addEventListener("click", ()=>{ els.logsPane.classList.toggle("hidden"); });
@@ -221,7 +216,6 @@ document.addEventListener('DOMContentLoaded', () => {
   els.fillBtn?.addEventListener("click", ()=> setZoom("fill"));
   els.oneBtn?.addEventListener("click", ()=> setZoom("one"));
 
-  // Initialize
   setZoom("fit");
   if(els.inputPath && els.inputPath.value.trim()){ state.inputPath=els.inputPath.value.trim(); els.previewImg.src=toFileURL(state.inputPath); }
   buildUI();
diff --git a/src/seg.py b/src/seg.py
new file mode 100644
index 0000000..5f5737d
--- /dev/null
+++ b/src/seg.py
@@ -0,0 +1,165 @@
+from __future__ import annotations
+import cv2, numpy as np
+
+MP_OK = True
+try:
+    import mediapipe as mp
+except Exception:
+    MP_OK = False
+
+
+def _mask_selfie(img_bgr):
+    seg = mp.solutions.selfie_segmentation.SelfieSegmentation(model_selection=1)
+    rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
+    res = seg.process(rgb)
+    seg.close()
+    if res.segmentation_mask is None:
+        m = np.ones(img_bgr.shape[:2], np.float32)
+    else:
+        m = res.segmentation_mask.astype(np.float32)
+        m = cv2.GaussianBlur(m, (0, 0), 3.0)
+    return np.clip(m, 0.0, 1.0)
+
+
+def _mask_grabcut(img_bgr, rect):
+    h, w = img_bgr.shape[:2]
+    x0, y0, x1, y1 = rect
+    x0 = max(0, min(w - 2, int(x0)))
+    x1 = max(x0 + 1, min(w - 1, int(x1)))
+    y0 = max(0, min(h - 2, int(y0)))
+    y1 = max(y0 + 1, min(h - 1, int(y1)))
+    rect = (x0, y0, x1 - x0, y1 - y0)
+    bgd = np.zeros((1, 65), np.float64)
+    fgd = np.zeros((1, 65), np.float64)
+    mask = np.zeros((h, w), np.uint8)
+    cv2.grabCut(img_bgr, mask, rect, bgd, fgd, 5, cv2.GC_INIT_WITH_RECT)
+    m = np.where((mask == cv2.GC_FGD) | (mask == cv2.GC_PR_FGD), 1.0, 0.0).astype(np.float32)
+    return np.clip(cv2.GaussianBlur(m, (0, 0), 1.5), 0.0, 1.0)
+
+
+def torso_rect_from_pts(pts):
+    sl, sr = pts["shoulder_l"], pts["shoulder_r"]
+    hl, hr = pts["hip_l"], pts["hip_r"]
+    shoulder_y = 0.5 * (sl[1] + sr[1])
+    hip_y = 0.5 * (hl[1] + hr[1])
+    span = abs(hip_y - shoulder_y)
+    x_left = min(sl[0], hl[0]) - 0.8 * abs(sr[0] - sl[0])
+    x_right = max(sr[0], hr[0]) + 0.8 * abs(sr[0] - sl[0])
+    y_top = min(sl[1], sr[1]) - 0.8 * span
+    y_bot = max(hl[1], hr[1]) + 0.8 * span
+    return (x_left, y_top, x_right, y_bot)
+
+
+def build_person_mask(img_bgr, pts, backend="auto"):
+    rect = torso_rect_from_pts(pts)
+    if backend in ("auto", "mp") and MP_OK:
+        m = _mask_selfie(img_bgr)
+        h, w = m.shape
+        x0, y0, x1, y1 = int(max(0, rect[0])), int(max(0, rect[1])), int(min(w - 1, rect[2])), int(min(h - 1, rect[3]))
+        roi = m[max(0, y0) : min(h, y1), max(0, x0) : min(w, x1)]
+        if roi.size > 0 and float(np.mean(roi)) > 0.05:
+            return m
+    return _mask_grabcut(img_bgr, rect)
+
+
+def _draw_capsule(mask, a, b, r):
+    ax, ay = int(a[0]), int(a[1])
+    bx, by = int(b[0]), int(b[1])
+    cv2.circle(mask, (ax, ay), r, 0.0, -1, cv2.LINE_AA)
+    cv2.circle(mask, (bx, by), r, 0.0, -1, cv2.LINE_AA)
+    cv2.line(mask, (ax, ay), (bx, by), 0.0, thickness=r * 2, lineType=cv2.LINE_AA)
+
+
+def suppress_arms_hands(mask01, pts, frac_radius=0.12):
+    m = mask01.copy()
+    sl, sr = pts["shoulder_l"], pts["shoulder_r"]
+    sh_span = max(10.0, float(sr[0] - sl[0]))
+    r = int(frac_radius * sh_span)
+    if "elbow_l" in pts and "wrist_l" in pts:
+        _draw_capsule(m, pts["elbow_l"], pts["wrist_l"], r)
+    if "elbow_r" in pts and "wrist_r" in pts:
+        _draw_capsule(m, pts["elbow_r"], pts["wrist_r"], r)
+    for c in pts.get("wrists", []):
+        cv2.circle(m, (int(c[0]), int(c[1])), max(4, r // 2 if r > 0 else 6), 0.0, -1, cv2.LINE_AA)
+    return m
+
+
+def torso_only_mask(
+    person01,
+    pts,
+    tighten: float = 0.88,
+    vtop_frac: float = 0.10,
+    vbot_frac: float = 0.08,
+    feather_px: int = 16,
+):
+    """
+    Build a torso-only mask with strict vertical gating and soft top/bottom feathering.
+
+    vtop_frac: start *below* shoulder row (fraction of shoulder–hip span)
+    vbot_frac: end   *above* hip row      (fraction of shoulder–hip span)
+    tighten:   lateral clamp vs shoulder half-width (0..1)
+    feather_px: vertical cosine feather (pixels) to avoid hard seams
+    """
+    H, W = person01.shape
+    sl, sr = pts["shoulder_l"], pts["shoulder_r"]
+    hl, hr = pts["hip_l"], pts["hip_r"]
+    shoulder_y = 0.5 * (sl[1] + sr[1])
+    hip_y = 0.5 * (hl[1] + hr[1])
+    span = abs(hip_y - shoulder_y)
+
+    # STRICT vertical band: below shoulders and above hips
+    top = int(round(shoulder_y + vtop_frac * span))
+    bot = int(round(hip_y - vbot_frac * span))
+    top = max(0, min(H - 2, top))
+    bot = max(top + 1, min(H - 1, bot))
+
+    # Lateral clamp around torso center
+    midx = 0.5 * (sl[0] + sr[0] + hl[0] + hr[0]) * 0.5
+    half = 0.5 * (sr[0] - sl[0])
+    gate_half = tighten * max(half, 10.0)
+
+    m = np.zeros_like(person01, np.float32)
+    m[top : bot + 1, :] = person01[top : bot + 1, :]
+
+    xs = np.arange(W)[None, :].astype(np.float32)
+    lat_gate = ((xs >= (midx - gate_half)) & (xs <= (midx + gate_half))).astype(np.float32)
+    m *= lat_gate
+
+    # Vertical cosine feather to avoid seams at top/bottom
+    if feather_px > 0:
+        v = np.zeros((H, 1), np.float32)
+        v[top : bot + 1, 0] = 1.0
+        tband = min(feather_px, bot - top + 1)
+        if tband > 0:
+            tt = np.linspace(0, np.pi, tband, dtype=np.float32)
+            v[top : top + tband, 0] = 0.5 * (1 - np.cos(tt))
+            v[bot - tband + 1 : bot + 1, 0] = 0.5 * (1 - np.cos(tt[::-1]))
+        m *= v
+
+    # Clean small specks
+    k = max(1, int(round(0.01 * W)))
+    ker = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * k + 1, 2 * k + 1))
+    m = cv2.morphologyEx(m, cv2.MORPH_OPEN, ker, 1)
+    m = cv2.morphologyEx(m, cv2.MORPH_CLOSE, ker, 1)
+
+    return np.clip(m, 0.0, 1.0)
+
+
+def shoulder_dilate(mask01, pts, band_frac=0.06, px=None):
+    H, W = mask01.shape
+    sl, sr = pts["shoulder_l"], pts["shoulder_r"]
+    hl, hr = pts["hip_l"], pts["hip_r"]
+    shoulder_y = int(round(0.5 * (sl[1] + sr[1])))
+    hip_y = int(round(0.5 * (hl[1] + hr[1])))
+    span = abs(hip_y - shoulder_y)
+    band = max(1, int(round(band_frac * span)))
+    top = max(0, shoulder_y - band)
+    bot = min(H - 1, shoulder_y + band)
+    if px is None:
+        px = max(2, int(round(0.02 * W)))
+    patch = mask01[top : bot + 1, :]
+    ker = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * px + 1, 2 * px + 1))
+    patch = cv2.dilate(patch, ker, 1)
+    out = mask01.copy()
+    out[top : bot + 1, :] = patch
+    return out
diff --git a/src/segments.py b/src/segments.py
new file mode 100644
index 0000000..9e1966a
--- /dev/null
+++ b/src/segments.py
@@ -0,0 +1,293 @@
+from __future__ import annotations
+from dataclasses import dataclass
+from typing import Dict, Tuple
+import numpy as np
+import cv2
+
+@dataclass
+class Segment:
+    name: str
+    mask: np.ndarray  # float32 [0,1], HxW
+    center: Tuple[float, float]
+    ex: Tuple[float, float]  # local width axis (unit)
+    ey: Tuple[float, float]  # local length axis (unit)
+    color: Tuple[int, int, int]  # BGR
+
+
+SEGMENT_ORDER = [
+    "head", "neck", "torso",
+    "upper_arm_l", "upper_arm_r",
+    "forearm_l", "forearm_r",
+    "hand_l", "hand_r",
+    "thigh_l", "thigh_r",
+    "shin_l", "shin_r",
+    "foot_l", "foot_r",
+]
+
+_PALETTE = {
+    "head": (40, 160, 255),
+    "neck": (80, 80, 255),
+    "torso": (0, 200, 80),
+    "upper_arm_l": (220, 160, 60),
+    "upper_arm_r": (60, 180, 220),
+    "forearm_l": (220, 80, 80),
+    "forearm_r": (80, 120, 220),
+    "hand_l": (160, 60, 200),
+    "hand_r": (60, 60, 180),
+    "thigh_l": (160, 220, 60),
+    "thigh_r": (60, 220, 160),
+    "shin_l": (180, 80, 160),
+    "shin_r": (120, 60, 160),
+    "foot_l": (80, 180, 120),
+    "foot_r": (60, 160, 120),
+}
+
+# ----------------- helpers -----------------
+def _unit(v: np.ndarray) -> np.ndarray:
+    n = float(np.hypot(v[0], v[1])) + 1e-9
+    return v / n
+
+
+def _perp(v: np.ndarray) -> np.ndarray:
+    return np.array([-v[1], v[0]], dtype=np.float32)
+
+
+def _soften(mask: np.ndarray, ks: int) -> np.ndarray:
+    ks = int(max(3, ks | 1))
+    m = cv2.GaussianBlur(mask, (ks, ks), 0)
+    return np.clip(m, 0.0, 1.0).astype(np.float32)
+
+
+def _fill_poly(shape, pts: np.ndarray, blur_px=11) -> np.ndarray:
+    H, W = shape[:2]
+    m = np.zeros((H, W), np.float32)
+    cv2.fillConvexPoly(m, pts.astype(np.int32), 1.0)
+    return _soften(m, blur_px)
+
+
+def _fill_circle(shape, c, r, blur_px=11) -> np.ndarray:
+    H, W = shape[:2]
+    m = np.zeros((H, W), np.float32)
+    cv2.circle(m, (int(c[0]), int(c[1])), int(max(1.0, r)), 1.0, -1, cv2.LINE_AA)
+    return _soften(m, blur_px)
+
+
+def _bone(a, b) -> tuple[np.ndarray, float, np.ndarray, np.ndarray]:
+    a = np.array(a, np.float32)
+    b = np.array(b, np.float32)
+    v = b - a
+    L = float(np.hypot(v[0], v[1])) + 1e-6
+    ey = _unit(v)  # along-bone (length)
+    ex = _perp(ey)  # across-bone (width)
+    return a, L, ex, ey
+
+
+def _quad_along(a, b, half_w: float) -> np.ndarray:
+    a = np.array(a, np.float32)
+    b = np.array(b, np.float32)
+    _, _, ex, ey = _bone(a, b)
+    p0 = a + ex * half_w
+    p1 = b + ex * half_w
+    p2 = b - ex * half_w
+    p3 = a - ex * half_w
+    return np.stack([p0, p1, p2, p3], axis=0)
+
+
+def _rect_centered(c, ex, ey, w, h) -> np.ndarray:
+    """Rotated rectangle polygon centered at c with axes ex(width)/ey(length)."""
+    ex = _unit(np.asarray(ex, np.float32))
+    ey = _unit(np.asarray(ey, np.float32))
+    c = np.asarray(c, np.float32)
+    dx = ex * (0.5 * w)
+    dy = ey * (0.5 * h)
+    p0 = c - dx - dy
+    p1 = c + dx - dy
+    p2 = c + dx + dy
+    p3 = c - dx + dy
+    return np.stack([p0, p1, p2, p3], axis=0)
+
+
+def _centroid(mask: np.ndarray) -> tuple[float, float]:
+    ys, xs = np.nonzero(mask > 1e-3)
+    if xs.size == 0:
+        H, W = mask.shape
+        return W * 0.5, H * 0.5
+    return float(xs.mean()), float(ys.mean())
+
+
+# ----------------- segment construction -----------------
+def build_segments_from_landmarks(shape, pts: Dict[str, tuple]) -> Dict[str, Segment]:
+    H, W = shape[:2]
+    segs: Dict[str, Segment] = {}
+
+    need = ("shoulder_l", "shoulder_r", "hip_l", "hip_r")
+    if not all(k in pts for k in need):
+        return segs
+
+    sl = np.array(pts["shoulder_l"], np.float32)
+    sr = np.array(pts["shoulder_r"], np.float32)
+    hl = np.array(pts["hip_l"], np.float32)
+    hr = np.array(pts["hip_r"], np.float32)
+
+    shoulder_span = float(np.hypot(*(sr - sl))) + 1e-6
+    pelvis_span = float(np.hypot(*(hr - hl))) + 1e-6
+    span = max(shoulder_span, pelvis_span)
+    shoulder_y = float(0.5 * (sl[1] + sr[1]))
+    mid_s = 0.5 * (sl + sr)
+
+    # ---- Torso ----
+    torso_poly = np.stack([sl, sr, hr, hl], axis=0)
+    torso_m = _fill_poly((H, W, 3), torso_poly, blur_px=17)
+    cx, cy = _centroid(torso_m)
+    segs["torso"] = Segment("torso", torso_m, (cx, cy), (1.0, 0.0), (0.0, 1.0), _PALETTE["torso"])
+
+    # ===================== HEAD (cheek-centered; aligned to forehead→chin) =====================
+    head_w = None
+    head_h = None
+    head_center = None
+    ex_face = None
+    ey_face = None
+
+    if all(k in pts for k in ("forehead", "chin", "face_left", "face_right")):
+        F = np.array(pts["forehead"], np.float32)
+        C = np.array(pts["chin"], np.float32)
+        L = np.array(pts["face_left"], np.float32)
+        R = np.array(pts["face_right"], np.float32)
+
+        v = C - F
+        ey_face = _unit(v)  # downwards axis along face height
+        ex_face = _perp(ey_face)  # left–right across face
+        # ensure ex points from left cheek to right cheek
+        if np.dot(R - L, ex_face) < 0:
+            ex_face = -ex_face
+
+        head_h = float(np.linalg.norm(v))
+        head_w = float(abs(np.dot(R - L, ex_face)))
+
+        # base center along forehead→chin, then recenter along ex to cheek midpoint
+        center = F + 0.46 * v  # 0.46 positions center slightly below mid-face
+        cheek_mid = 0.5 * (L + R)
+        # replace the ex-component with that of the cheek midpoint (horizontal centering)
+        ex_delta = np.dot(cheek_mid - center, ex_face)
+        head_center = center + ex_face * ex_delta
+
+        # ellipse radii (tightened to avoid hair/ears)
+        rx = 0.95 * (0.5 * head_w)  # semi-axis along ex
+        ry = 0.88 * (0.5 * head_h)  # semi-axis along ey
+
+        # draw ellipse
+        ang = float(np.degrees(np.arctan2(ex_face[1], ex_face[0])))
+        mask = np.zeros((H, W), np.uint8)
+        cv2.ellipse(
+            mask,
+            (int(head_center[0]), int(head_center[1])),
+            (int(max(1.0, rx)), int(max(1.0, ry))),
+            ang, 0, 360, 255, -1, cv2.LINE_AA
+        )
+        head_m = _soften(mask.astype(np.float32) / 255.0, 15)
+        hcx, hcy = _centroid(head_m)
+        segs["head"] = Segment("head", head_m, (hcx, hcy), (1.0, 0.0), (0.0, 1.0), _PALETTE["head"])
+    else:
+        # Fallback: circle above shoulders
+        head_center = np.array([float(mid_s[0]), float(mid_s[1] - 0.55 * span)], np.float32)
+        r = 0.30 * span
+        ex_face = np.array([1.0, 0.0], np.float32)
+        ey_face = np.array([0.0, 1.0], np.float32)
+        head_w = head_h = 2.0 * r
+        head_m = _fill_circle((H, W, 3), head_center, r, blur_px=15)
+        hcx, hcy = _centroid(head_m)
+        segs["head"] = Segment("head", head_m, (hcx, hcy), (1.0, 0.0), (0.0, 1.0), _PALETTE["head"])
+
+    # ===================== NECK (Fixed alignment) =====================
+    chin_pt = np.array(pts.get("chin", head_center + ey_face * (0.5 * head_h)), np.float32)
+    s_mid = 0.5 * (sl + sr)  # Shoulder midpoint
+
+    # Calculate vector from chin to shoulders and neck dimensions
+    v = s_mid - chin_pt
+    neck_h_val = max(0.01, np.dot(v, ey_face))  # Height along face axis
+    v_ex_val = np.dot(v, ex_face)  # Lateral offset along face axis
+
+    # Position neck center to connect head and torso
+    neck_center = chin_pt + (0.5 * neck_h_val) * ey_face + v_ex_val * ex_face
+    neck_w_val = 0.45 * shoulder_span  # Width proportional to shoulder span
+
+    # Create neck polygon and mask
+    neck_poly = _rect_centered(neck_center, ex_face, ey_face, neck_w_val, neck_h_val)
+    neck_m = _fill_poly((H, W, 3), neck_poly, blur_px=13)
+    ncx, ncy = _centroid(neck_m)
+    segs["neck"] = Segment("neck", neck_m, (ncx, ncy), tuple(ex_face.tolist()), tuple(ey_face.tolist()), _PALETTE["neck"])
+
+    # ---- Arms ----
+    def _maybe(name_a, name_b, half_scale, color_key, seg_name):
+        if name_a in pts and name_b in pts:
+            a, b = pts[name_a], pts[name_b]
+            _, L, exL, eyL = _bone(a, b)
+            m = _fill_poly((H, W, 3), _quad_along(a, b, float(half_scale * L)), 11)
+            cx_, cy_ = _centroid(m)
+            segs[seg_name] = Segment(seg_name, m, (cx_, cy_), tuple(exL), tuple(eyL), _PALETTE[color_key])
+
+    _maybe("shoulder_l", "elbow_l", 0.14, "upper_arm_l", "upper_arm_l")
+    _maybe("shoulder_r", "elbow_r", 0.14, "upper_arm_r", "upper_arm_r")
+    _maybe("elbow_l", "wrist_l", 0.12, "forearm_l", "forearm_l")
+    _maybe("elbow_r", "wrist_r", 0.12, "forearm_r", "forearm_r")
+
+    if "wrist_l" in pts:
+        hm = _fill_circle((H, W, 3), pts["wrist_l"], 0.10 * span, 9)
+        segs["hand_l"] = Segment("hand_l", hm, _centroid(hm), (1.0, 0.0), (0.0, 1.0), _PALETTE["hand_l"])
+    if "wrist_r" in pts:
+        hm = _fill_circle((H, W, 3), pts["wrist_r"], 0.10 * span, 9)
+        segs["hand_r"] = Segment("hand_r", hm, _centroid(hm), (1.0, 0.0), (0.0, 1.0), _PALETTE["hand_r"])
+
+    # ---- Legs ----
+    _maybe("hip_l", "knee_l", 0.18, "thigh_l", "thigh_l")
+    _maybe("hip_r", "knee_r", 0.18, "thigh_r", "thigh_r")
+    _maybe("knee_l", "ankle_l", 0.14, "shin_l", "shin_l")
+    _maybe("knee_r", "ankle_r", 0.14, "shin_r", "shin_r")
+
+    if "ankle_l" in pts:
+        fm = _fill_circle((H, W, 3), pts["ankle_l"], 0.12 * span, 9)
+        segs["foot_l"] = Segment("foot_l", fm, _centroid(fm), (1.0, 0.0), (0.0, 1.0), _PALETTE["foot_l"])
+    if "ankle_r" in pts:
+        fm = _fill_circle((H, W, 3), pts["ankle_r"], 0.12 * span, 9)
+        segs["foot_r"] = Segment("foot_r", fm, _centroid(fm), (1.0, 0.0), (0.0, 1.0), _PALETTE["foot_r"])
+
+    return segs
+
+
+# ----------------- visualization -----------------
+def draw_annotation_overlay(
+        img_bgr: np.ndarray,
+        segments: Dict[str, Segment],
+        alpha: float = 0.35,
+        draw_edges: bool = True,
+        draw_keypoints: Dict[str, tuple] | None = None,
+) -> np.ndarray:
+    """Color overlay per segment with optional edges and keypoints."""
+    H, W = img_bgr.shape[:2]
+    color_img = np.zeros_like(img_bgr, np.float32)
+
+    for name in SEGMENT_ORDER:
+        seg = segments.get(name)
+        if seg is None:
+            continue
+        c = np.array(seg.color, np.float32)[None, None, :]
+        m = seg.mask[..., None].astype(np.float32)
+        color_img += m * c
+
+    color_img = np.clip(color_img, 0, 255).astype(np.uint8)
+    blended = cv2.addWeighted(img_bgr, 1.0, color_img, alpha, 0)
+
+    if draw_edges:
+        edge = np.zeros((H, W), np.uint8)
+        for seg in segments.values():
+            e = cv2.Canny((seg.mask * 255).astype(np.uint8), 40, 120)
+            edge = np.maximum(edge, e)
+        blended[edge > 0] = (0.6 * blended[edge > 0] + 0.4 * np.array([0, 255, 255])).astype(np.uint8)
+
+    if draw_keypoints:
+        for k, (x, y) in draw_keypoints.items():
+            cv2.circle(blended, (int(x), int(y)), 3, (0, 0, 255), -1, cv2.LINE_AA)
+            cv2.putText(blended, k, (int(x) + 4, int(y) - 4),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 255, 255), 1, cv2.LINE_AA)
+
+    return blended
diff --git a/src/smart_lasso.py b/src/smart_lasso.py
new file mode 100644
index 0000000..7ec3eee
--- /dev/null
+++ b/src/smart_lasso.py
@@ -0,0 +1,383 @@
+from __future__ import annotations
+from typing import Dict, Tuple, Iterable, List
+import numpy as np
+import cv2
+import base64
+
+# Project types
+try:
+    from .segments import Segment, SEGMENT_ORDER
+except Exception:
+    Segment = object  # type: ignore
+    SEGMENT_ORDER = []
+
+try:
+    from .seg import build_person_mask
+except Exception:
+    # Safety fallback
+    def build_person_mask(img_bgr, pts):  # type: ignore
+        h, w = img_bgr.shape[:2]
+        return np.ones((h, w), np.float32)
+
+GC_BGD, GC_FGD, GC_PR_BGD, GC_PR_FGD = 0, 1, 2, 3
+
+HEAD_AUX_NAMES: Tuple[str, ...] = (
+    "hair", "headband", "ribbon", "ribbon_tail", "bandana", "band", "head_accessory"
+)
+
+# -------------------------- utilities --------------------------
+
+def _bbox_from_mask(mask01: np.ndarray, thr: float = 0.20) -> Tuple[int, int, int, int] | None:
+    ys, xs = np.where(mask01 > thr)
+    if xs.size == 0:
+        return None
+    return int(xs.min()), int(ys.min()), int(xs.max()), int(ys.max())
+
+def _pad_rect(x0: int, y0: int, x1: int, y1: int, pad: int, W: int, H: int) -> Tuple[int, int, int, int]:
+    return max(0, x0 - pad), max(0, y0 - pad), min(W - 1, x1 + pad), min(H - 1, y1 + pad)
+
+def _soft(mask01: np.ndarray, k: int) -> np.ndarray:
+    k = int(max(3, (k | 1)))
+    return cv2.GaussianBlur(mask01.astype(np.float32), (k, k), 0)
+
+def _morph_kernel(r: int) -> np.ndarray:
+    r = max(1, int(r))
+    return cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * r + 1, 2 * r + 1))
+
+def _largest_components_touching_or_red(
+    refined01: np.ndarray,
+    prior_bin: np.ndarray,
+    red_keep: np.ndarray | None,
+    keep_k: int,
+    dilate_r: int,
+) -> np.ndarray:
+    if refined01.max() <= 0:
+        return refined01
+    prior_dil = cv2.dilate(prior_bin.astype(np.uint8), _morph_kernel(max(1, dilate_r)), 1)
+    rbin = (refined01 > 0.5).astype(np.uint8)
+    num, labels = cv2.connectedComponents(rbin, connectivity=8)
+    if num <= 1:
+        return refined01
+    areas = []
+    for i in range(1, num):
+        comp = (labels == i)
+        touch = (comp & (prior_dil > 0)).any()
+        redok = False
+        if (not touch) and red_keep is not None:
+            redok = (comp & (red_keep > 0)).any()
+        if not (touch or redok):
+            continue
+        areas.append((int(comp.sum()), i))
+    if not areas:
+        return np.zeros_like(refined01, dtype=np.float32)
+    areas.sort(reverse=True)
+    keep_ids = {i for _, i in areas[:keep_k]}
+    kept = np.zeros_like(rbin)
+    for i in keep_ids:
+        kept |= (labels == i)
+    return refined01 * kept.astype(np.float32)
+
+def _distance_gate(refined01: np.ndarray, prior_bin: np.ndarray, tau_px: float) -> np.ndarray:
+    if refined01.max() <= 0:
+        return refined01
+    support = (prior_bin > 0).astype(np.uint8)
+    if support.max() == 0:
+        return refined01
+    inv = (1 - support).astype(np.uint8)
+    dist = cv2.distanceTransform(inv, cv2.DIST_L2, 3)
+    if tau_px <= 1:
+        return refined01
+    gate = np.exp(-(dist / float(tau_px)) ** 2)
+    return refined01 * gate.astype(np.float32)
+
+# --------------------- color accessory detection ---------------------
+
+def _red_mask(img_bgr: np.ndarray) -> np.ndarray:
+    hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)
+    h, s, v = cv2.split(hsv)
+    red = ((h < 14) | (h > 170)) & (s > 70) & (v > 40)
+    red = red.astype(np.uint8) * 255
+    red = cv2.medianBlur(red, 5)
+    red = cv2.morphologyEx(red, cv2.MORPH_OPEN, _morph_kernel(2))
+    red = cv2.dilate(red, _morph_kernel(1), 1)
+    return (red > 0).astype(np.uint8)
+
+def _detect_red_accessories_near_head(img_bgr: np.ndarray, head_bb: Tuple[int, int, int, int]) -> np.ndarray:
+    H, W = img_bgr.shape[:2]
+    x0, y0, x1, y1 = head_bb
+    cw = max(8, x1 - x0 + 1)
+    cx = (x0 + x1) // 2
+    red = _red_mask(img_bgr)
+    num, labels, stats, cents = cv2.connectedComponentsWithStats(red, 8)
+    if num <= 1:
+        return np.zeros((H, W), np.float32)
+    band_top = y0 - int(0.8 * (y1 - y0))
+    band_bot = y1 + int(0.8 * (y1 - y0))
+    out = np.zeros_like(red, np.uint8)
+    for i in range(1, num):
+        area = stats[i, cv2.CC_STAT_AREA]
+        rcx, rcy = cents[i]
+        if area < 120: continue
+        if rcy < band_top or rcy > band_bot: continue
+        if abs(rcx - cx) > 2.0 * cw: continue
+        out[labels == i] = 1
+    return _soft(out.astype(np.float32), 7)
+
+# --------------------- seeding & refinement ---------------------
+
+def _to_gc_roi(H: int, W: int, roi: Tuple[int, int, int, int]) -> np.ndarray:
+    x0, y0, x1, y1 = roi
+    gc = np.full((H, W), GC_BGD, np.uint8)
+    gc[y0:y1 + 1, x0:x1 + 1] = GC_PR_BGD
+    return gc
+
+def _seed_confidence(
+    name: str,
+    seg01_for_seed: np.ndarray,
+    person01: np.ndarray,
+    img_bgr: np.ndarray,
+    roi: Tuple[int, int, int, int],
+) -> np.ndarray:
+    H, W = seg01_for_seed.shape
+    x0, y0, x1, y1 = roi
+    seg_bin = (seg01_for_seed > 0.4).astype(np.uint8)
+    roi_w, roi_h = (x1 - x0 + 1), (y1 - y0 + 1)
+    base_r = max(1, int(round(0.06 * min(roi_w, roi_h))))
+    ker_in = _morph_kernel(base_r)
+    ker_near = _morph_kernel(max(1, int(1.8 * base_r)))
+    sure_fg = cv2.erode(seg_bin, ker_in, 1)
+    near_field = cv2.dilate(seg_bin, ker_near, 1)
+    sp = person01[y0:y1 + 1, x0:x1 + 1]
+    nb_person = (person01 < 0.25).astype(np.uint8)
+    headish = name in ("head", "hair", "ribbon", "headband", "ribbon_tail", "bandana")
+    gc = _to_gc_roi(H, W, roi)
+    view = gc[y0:y1 + 1, x0:x1 + 1]
+    sf = sure_fg[y0:y1 + 1, x0:x1 + 1]
+    view[sf > 0] = GC_FGD
+    near_big = (near_field[y0:y1 + 1, x0:x1 + 1] > 0)
+    nb = nb_person[y0:y1 + 1, x0:x1 + 1]
+    far_outside = (nb > 0) & (~near_big)
+    if not headish:
+        view[far_outside] = GC_BGD
+    else:
+        view[far_outside] = np.where(view[far_outside] == GC_FGD, GC_FGD, GC_PR_BGD)
+    pr_fg = ((sp > 0.25) | near_big) & (sf == 0)
+    view[pr_fg] = np.where(view[pr_fg] == GC_FGD, GC_FGD, GC_PR_FGD)
+    gray = cv2.cvtColor(img_bgr[y0:y1 + 1, x0:x1 + 1, :], cv2.COLOR_BGR2GRAY)
+    edges = cv2.Canny(gray, 40, 120)
+    edge_buf = cv2.dilate(edges, _morph_kernel(max(1, base_r // 2)))
+    view[(edge_buf > 0) & (sf == 0)] = np.where(
+        view[(edge_buf > 0) & (sf == 0)] == GC_FGD, GC_FGD, GC_PR_FGD
+    )
+    return gc
+
+def _grabcut_refine(img_bgr: np.ndarray, gc_mask: np.ndarray, roi: Tuple[int, int, int, int]) -> np.ndarray:
+    x0, y0, x1, y1 = roi
+    roi_img = img_bgr[y0:y1 + 1, x0:x1 + 1, :]
+    roi_gc = gc_mask[y0:y1 + 1, x0:x1 + 1].copy()
+    bgd = np.zeros((1, 65), np.float64)
+    fgd = np.zeros((1, 65), np.float64)
+    cv2.grabCut(roi_img, roi_gc, None, bgd, fgd, 3, cv2.GC_INIT_WITH_MASK)
+    m_roi = ((roi_gc == GC_FGD) | (roi_gc == GC_PR_FGD)).astype(np.float32)
+    m_full = np.zeros(img_bgr.shape[:2], np.float32)
+    m_full[y0:y1 + 1, x0:x1 + 1] = m_roi
+    feather = max(5, int(round(0.02 * (max(1, (x1 - x0) + (y1 - y0))))))
+    m_full = _soft(m_full, feather)
+    return np.clip(m_full, 0.0, 1.0)
+
+# --------------------- refinement driver ---------------------
+
+def refine_segment_masks(
+    img_bgr: np.ndarray,
+    segments: Dict[str, Segment],
+    pts: Dict[str, Tuple[float, float]] | None = None,
+) -> Dict[str, np.ndarray]:
+    H, W = img_bgr.shape[:2]
+    out: Dict[str, np.ndarray] = {}
+    try:
+        person01 = build_person_mask(img_bgr, pts or {})
+    except Exception:
+        person01 = np.ones((H, W), np.float32)
+
+    raw: Dict[str, np.ndarray] = {}
+    for name in (SEGMENT_ORDER or list(segments.keys())):
+        seg = segments.get(name)
+        if seg is not None and getattr(seg, "mask", None) is not None:
+            raw[name] = seg.mask.astype(np.float32)
+
+    neck_prior = raw.get("neck", None)
+
+    for name in (SEGMENT_ORDER or list(segments.keys())):
+        seg = segments.get(name)
+        if seg is None or getattr(seg, "mask", None) is None:
+            continue
+        try:
+            seg01_prior = raw[name]
+            headish = name in ("head", "hair", "ribbon", "headband", "ribbon_tail", "bandana")
+            red_keep = None
+            if name == "head":
+                extras = [raw[n] for n in HEAD_AUX_NAMES if n in raw]
+                seed = seg01_prior
+                if extras:
+                    seed = np.maximum(seed, np.max(np.stack(extras, axis=0), axis=0))
+                bb = _bbox_from_mask(seg01_prior, thr=0.20)
+                if bb is not None:
+                    red_keep = _detect_red_accessories_near_head(img_bgr, bb)
+                    if red_keep is not None:
+                        seed = np.maximum(seed, red_keep)
+                seg01_for_seed = seed
+            else:
+                seg01_for_seed = seg01_prior
+
+            bb = _bbox_from_mask(seg01_for_seed, thr=0.20)
+            if bb is None:
+                out[name] = seg01_prior
+                continue
+            pad_scale = 0.12 if headish else 0.06
+            pad = max(6, int(round(pad_scale * min(W, H))))
+            x0, y0, x1, y1 = _pad_rect(*bb, pad, W, H)
+            gc_mask = _seed_confidence(name, seg01_for_seed, person01, img_bgr, (x0, y0, x1, y1))
+            refined = _grabcut_refine(img_bgr, gc_mask, (x0, y0, x1, y1))
+            refined = _largest_components_touching_or_red(
+                refined, (seg01_for_seed > 0.4),
+                red_keep if name == "head" else None,
+                keep_k=5, dilate_r=max(2, pad // 3)
+            )
+            tau = max(5, pad * (0.6 if headish else 0.9))
+            refined = _distance_gate(refined, (seg01_for_seed > 0.4), tau_px=tau)
+            if name == "head" and neck_prior is not None:
+                neck_dil = cv2.dilate((neck_prior > 0.4).astype(np.uint8), _morph_kernel(max(2, pad // 3)))
+                refined *= (1.0 - neck_dil.astype(np.float32))
+            if headish:
+                refined *= np.clip(person01 + 0.20, 0.0, 1.0)
+                refined = np.maximum(refined, _soft(seg01_prior, 5))
+            else:
+                refined *= np.clip(person01 * 1.25, 0.0, 1.0)
+                refined = np.maximum(refined, _soft(seg01_prior, 5))
+            out[name] = np.clip(refined, 0.0, 1.0).astype(np.float32)
+        except Exception:
+            out[name] = seg.mask.astype(np.float32)
+    return out
+
+# --------------------- exclusivity ---------------------
+
+def make_masks_exclusive(refined: Dict[str, np.ndarray], priority: Iterable[str]) -> Dict[str, np.ndarray]:
+    names = [n for n in priority if n in refined] or list(refined.keys())
+    H, W = next(iter(refined.values())).shape
+    assigned = np.zeros((H, W), np.float32)
+    out: Dict[str, np.ndarray] = {}
+    for n in names:
+        m = np.clip(refined[n], 0.0, 1.0)
+        m = m * (1.0 - assigned)
+        if m.max() > 0:
+            m = _soft(m, 3)
+        out[n] = np.clip(m, 0.0, 1.0)
+        assigned = np.maximum(assigned, out[n])
+    for n, m in refined.items():
+        if n in out:
+            continue
+        mm = np.clip(m, 0.0, 1.0) * (1.0 - assigned)
+        out[n] = mm
+        assigned = np.maximum(assigned, mm)
+    return out
+
+# --------------------- vectors & overlay ---------------------
+
+def _contours_from_mask(mask01: np.ndarray, min_area: float = 64.0, approx_eps: float = 2.0) -> List[List[Tuple[int,int]]]:
+    binm = (mask01 > 0.5).astype(np.uint8) * 255
+    if binm.max() == 0:
+        return []
+    cnts, _ = cv2.findContours(binm, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    polys: List[List[Tuple[int,int]]] = []
+    for c in cnts:
+        area = float(cv2.contourArea(c))
+        if area < min_area:
+            continue
+        peri = float(cv2.arcLength(c, True))
+        eps = max(approx_eps, 0.01 * peri)
+        approx = cv2.approxPolyDP(c, eps, True)
+        pts = [(int(p[0][0]), int(p[0][1])) for p in approx]
+        polys.append(pts)
+    return polys
+
+def build_lasso_vectors(refined: Dict[str, np.ndarray], include_union_key: bool = True) -> Dict[str, List[List[Tuple[int,int]]]]:
+    out: Dict[str, List[List[Tuple[int,int]]]] = {}
+    union = None
+    for name, m in refined.items():
+        out[name] = _contours_from_mask(m, min_area=64.0, approx_eps=2.0)
+        union = m if union is None else np.maximum(union, m)
+    if include_union_key and union is not None:
+        out["person"] = _contours_from_mask(union, min_area=128.0, approx_eps=2.0)
+    return out
+
+_PALETTE_HEX = {
+    "head":"#3f80ff", "neck":"#ffd85a", "torso":"#ff50ff",
+    "upper_arm_l":"#50c850", "upper_arm_r":"#50c8c8",
+    "forearm_l":"#54b854", "forearm_r":"#54b8c8",
+    "hand_l":"#3cb37c",  "hand_r":"#3cb3c3",
+    "thigh_l":"#b478ff", "thigh_r":"#ff7878",
+    "shin_l":"#a46cff",  "shin_r":"#ff6c6c",
+    "foot_l":"#9670ff",  "foot_r":"#ff7070",
+    "hair":"#aaaaaa", "headband":"#4040ff", "ribbon":"#4040ff", "ribbon_tail":"#4040ff",
+    "person":"#ffffff"
+}
+
+def _hex_to_bgr(hex_str: str) -> Tuple[int,int,int]:
+    h = hex_str.lstrip("#")
+    if len(h) == 3:
+        r = int(h[0]*2,16); g = int(h[1]*2,16); b = int(h[2]*2,16)
+    else:
+        r = int(h[0:2],16); g = int(h[2:4],16); b = int(h[4:6],16)
+    return (b,g,r)
+
+def _draw_dashed_polyline(img_bgra: np.ndarray, pts: List[Tuple[int,int]], color_bgra: Tuple[int,int,int,int], dash_px: int = 8, gap_px: int = 8, thickness: int = 2):
+    def _lerp(p0, p1, t):
+        return (p0[0] + (p1[0]-p0[0])*t, p0[1] + (p1[1]-p0[1])*t)
+    n = len(pts)
+    if n < 2: return
+    segs = []
+    for i in range(n):
+        p0 = pts[i]; p1 = pts[(i+1)%n]
+        dx = p1[0]-p0[0]; dy = p1[1]-p0[1]
+        L = (dx*dx + dy*dy) ** 0.5
+        if L < 1: continue
+        t = 0.0; on = True
+        while t < 1.0:
+            step = (dash_px if on else gap_px) / L
+            t2 = min(1.0, t + step)
+            if on:
+                a = _lerp(p0,p1,t); b = _lerp(p0,p1,t2)
+                segs.append(((int(round(a[0])),int(round(a[1]))),(int(round(b[0])),int(round(b[1])))))
+            on = not on; t = t2
+    b,g,r,a = color_bgra
+    for a0,a1 in segs:
+        cv2.line(img_bgra, a0, a1, (b,g,r,a), thickness, lineType=cv2.LINE_AA)
+
+def render_lasso_overlay_png(image_shape: Tuple[int,int, int], vectors: Dict[str, List[List[Tuple[int,int]]]]) -> Tuple[str, str]:
+    H, W = image_shape[:2]
+    overlay = np.zeros((H, W, 4), np.uint8)  # BGRA
+
+    fill_alpha = 46  # ~0.18 * 255
+    line_alpha = 255
+    thickness = 2
+
+    for name, polys in vectors.items():
+        if not isinstance(polys, list) or name == "person":
+            continue
+        color_hex = _PALETTE_HEX.get(name, "#b0b0b0")
+        bgr = _hex_to_bgr(color_hex)
+
+        for poly in polys:
+            if len(poly) < 3: continue
+            pts = np.array(poly, np.int32).reshape((-1,1,2))
+            cv2.fillPoly(overlay, [pts], (bgr[0], bgr[1], bgr[2], fill_alpha))
+
+        for poly in polys:
+            if len(poly) < 2: continue
+            _draw_dashed_polyline(overlay, poly, (255,255,255,line_alpha), dash_px=8, gap_px=8, thickness=thickness)
+
+    ok, buf = cv2.imencode(".png", overlay)
+    if not ok:
+        return "", ""
+    b64 = base64.b64encode(buf.tobytes()).decode("ascii")
+    return b64, f"data:image/png;base64,{b64}"
diff --git a/src/solver.py b/src/solver.py
new file mode 100644
index 0000000..5a1ef95
--- /dev/null
+++ b/src/solver.py
@@ -0,0 +1,18 @@
+from __future__ import annotations
+import math
+EPS=1e-6
+
+def compute_scales(head_w, sh_w, hp_w, sh_per_head, hp_per_sh, policy):
+    tgt_sh=sh_per_head*head_w
+    tgt_hp=hp_per_sh*tgt_sh
+    s_sh=float(tgt_sh/max(EPS,sh_w)); s_hp=float(tgt_hp/max(EPS,hp_w))
+    if policy=="verdict": s_sh=max(1.0,s_sh); s_hp=min(1.0,s_hp)
+    return tgt_sh, tgt_hp, s_sh, s_hp
+
+def per_pass_factors(s_total, shrink_floor=0.85, widen_ceiling=1.10):
+    if s_total>=1.0:
+        n=max(1,int(math.ceil(math.log(s_total+1e-9)/math.log(widen_ceiling+1e-9))))
+        return n, s_total**(1.0/n)
+    else:
+        n=max(1,int(math.ceil(math.log(max(s_total,1e-9))/math.log(max(shrink_floor,1e-9)))))
+        return n, s_total**(1.0/n)
diff --git a/src/styles.css b/src/styles.css
index 8fc9090..1b48377 100644
--- a/src/styles.css
+++ b/src/styles.css
@@ -75,4 +75,4 @@ details { border:1px solid #15213e; border-radius:10px; margin-bottom:10px; back
 .status { padding:8px 12px; border-top:1px solid var(--border); color:#9fb2d0; font-size:12px; }
 
 /* Logs */
-#logsPane textarea { width:100%; height:180px; background:#0b1222; color:var(--fg); border:1px solid var(--border); border-radius:10px; padding:8px; }
+#logsPane textarea { width:100%; height:180px; background:#0b1222; color:#fff; border:1px solid var(--border); border-radius:10px; padding:8px; }
diff --git a/src/warp_blend.py b/src/warp_blend.py
new file mode 100644
index 0000000..9f91ff9
--- /dev/null
+++ b/src/warp_blend.py
@@ -0,0 +1,409 @@
+# src/warp_blend.py
+from __future__ import annotations
+from typing import Dict, List, Tuple
+import numpy as np
+import cv2
+
+# If segments module is available, import metadata/types; otherwise keep loose typing.
+try:
+    from .segments import Segment, SEGMENT_ORDER
+except Exception:
+    Segment = object  # type: ignore
+    SEGMENT_ORDER: List[str] = []
+
+EPS = 1e-6
+
+
+# ------------------------- utilities -------------------------
+
+def _ensure_f32_mask(m: np.ndarray | None) -> np.ndarray | None:
+    """Return a float32 mask in [0,1] or None."""
+    if m is None:
+        return None
+    m = m.astype(np.float32, copy=False)
+    if m.max() > 1.001:
+        m *= (1.0 / 255.0)
+    return np.clip(m, 0.0, 1.0)
+
+
+def _build_support_and_norm(ws: np.ndarray,
+                            support_thr: float = 0.03,
+                            soften_hi: float = 0.12) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """
+    Normalize N masks so they sum to 1 where total weight > support_thr.
+    Returns:
+      ws_norm: (N,H,W) normalized weights (zeros outside support)
+      support_mask: (H,W) bool where sum(ws) > support_thr
+      influence: (H,W) float in [0,1], soft ramp from support_thr..soften_hi
+    """
+    s = ws.sum(axis=0)  # (H,W)
+    support = s > support_thr
+    out = np.zeros_like(ws, dtype=np.float32)
+    if np.any(support):
+        out[:, support] = ws[:, support] / s[None, support]
+    influence = np.clip((s - support_thr) / max(soften_hi - support_thr, 1e-6), 0.0, 1.0)
+    return out, support, influence
+
+
+def _R(theta_rad: float) -> np.ndarray:
+    c, s = np.cos(theta_rad), np.sin(theta_rad)
+    return np.array([[c, -s], [s,  c]], np.float32)
+
+
+def _affine_from_axes(ex_vec, ey_vec, sx, sy, theta_deg):
+    """
+    Forward local->global linear part T: scale by (sx,sy) along local axes,
+    then rotate by theta_deg; expressed in global coordinates.
+    """
+    ex = np.array(ex_vec, np.float32)
+    ey = np.array(ey_vec, np.float32)
+    exu = ex / (np.linalg.norm(ex) + EPS)
+    eyu = ey / (np.linalg.norm(ey) + EPS)
+    Rw = np.stack([exu, eyu], axis=1).astype(np.float32)  # columns are local axes in global coords
+    S  = np.diag([sx, sy]).astype(np.float32)
+    Rl = _R(np.deg2rad(theta_deg)).astype(np.float32)
+    try:
+        Rw_inv = np.linalg.inv(Rw)
+    except np.linalg.LinAlgError:
+        Rw_inv = np.linalg.pinv(Rw)
+    T = Rw @ (Rl @ S) @ Rw_inv
+    return T
+
+
+def _synthesize_gaussian_mask(H, W, center_xy, ex_vec, ey_vec, k_scale=0.8) -> np.ndarray:
+    """Fallback soft mask aligned to local axes."""
+    cx, cy = float(center_xy[0]), float(center_xy[1])
+    ex = np.array(ex_vec, np.float32)
+    ey = np.array(ey_vec, np.float32)
+
+    sigma_floor = max(0.06 * min(H, W), 30.0)
+    sigx = max(sigma_floor, float(np.linalg.norm(ex)) * k_scale)
+    sigy = max(sigma_floor, float(np.linalg.norm(ey)) * k_scale)
+
+    yy, xx = np.meshgrid(
+        np.arange(H, dtype=np.float32),
+        np.arange(W, dtype=np.float32),
+        indexing="ij"
+    )
+    Xc = xx - cx
+    Yc = yy - cy
+
+    exu = ex / (np.linalg.norm(ex) + EPS)
+    eyu = ey / (np.linalg.norm(ey) + EPS)
+    u = Xc * exu[0] + Yc * exu[1]
+    v = Xc * eyu[0] + Yc * eyu[1]
+
+    m = np.exp(-0.5 * ((u / sigx) ** 2 + (v / sigy) ** 2)).astype(np.float32)
+    m -= m.min()
+    mx = m.max()
+    if mx > EPS:
+        m /= mx
+    return m
+
+
+def _min_singular_value_2x2(a11, a12, a21, a22):
+    """sigma_min of 2x2 field A via sqrt(min eig of A^T A)."""
+    s11 = a11 * a11 + a21 * a21
+    s12 = a11 * a12 + a21 * a22
+    s22 = a12 * a12 + a22 * a22
+    tr = s11 + s22
+    det = s11 * s22 - s12 * s12
+    disc = np.maximum(tr * tr - 4.0 * det, 0.0)
+    lam_min = 0.5 * (tr - np.sqrt(disc))
+    lam_min = np.maximum(lam_min, EPS)
+    return np.sqrt(lam_min)
+
+
+def _build_mipmap(img_u8: np.ndarray) -> List[np.ndarray]:
+    """Gaussian pyramid [L0(full), L1(1/2), ...] until min(H,W)<16. Input must be uint8."""
+    levels = [img_u8]
+    H, W = img_u8.shape[:2]
+    while min(H, W) >= 16:
+        levels.append(cv2.pyrDown(levels[-1]))
+        H, W = levels[-1].shape[:2]
+    return levels
+
+
+# ------------------------- main warp -------------------------
+
+def blended_local_affine_warp(img_bgr: np.ndarray,
+                              segments: Dict[str, "Segment"],
+                              geometry: Dict[str, Dict[str, float]],
+                              smooth_px: int = 18) -> np.ndarray:
+    """
+    Backward (dest->source) warp with:
+      • Per-part anisotropic linear maps blended by normalized supports.
+      • Mild Jacobian tempering to avoid foldovers.
+      • Per-pixel mip LOD sampling.
+      • Vacated-only inpainting + premultiplied sprite compositing.
+    """
+    H, W = img_bgr.shape[:2]
+
+    # Destination pixel grid
+    xx, yy = np.meshgrid(
+        np.arange(W, dtype=np.float32),
+        np.arange(H, dtype=np.float32),
+        indexing="xy"
+    )
+
+    # Segment order
+    seg_keys = list(segments.keys())
+    if SEGMENT_ORDER:
+        ordered = [n for n in SEGMENT_ORDER if n in segments]
+        names = ordered if ordered else seg_keys
+    else:
+        names = seg_keys
+    if not names:
+        return img_bgr.copy()
+
+    # Prepare masks (raw or synthesized)
+    raw_masks: List[np.ndarray | None] = []
+    total_sum = 0.0
+    for n in names:
+        m = _ensure_f32_mask(getattr(segments[n], "mask", None))
+        raw_masks.append(m)
+        if m is not None:
+            total_sum += float(m.sum())
+    synth_all = (total_sum < 0.005 * H * W)
+
+    masks:   List[np.ndarray] = []
+    centers: List[np.ndarray] = []
+
+    # inverse linear parts (for backward mapping)
+    B00: List[float] = []
+    B01: List[float] = []
+    B10: List[float] = []
+    B11: List[float] = []
+    t0:  List[float] = []
+    t1:  List[float] = []
+
+    for i, name in enumerate(names):
+        seg = segments[name]
+        g = geometry.get(name, {}) if geometry is not None else {}
+
+        # conservative clamps
+        sx  = float(np.clip(g.get("sx", 1.0), 0.5, 1.6))
+        sy  = float(np.clip(g.get("sy", 1.0), 0.5, 1.6))
+        rot = float(np.clip(g.get("rot_deg", 0.0), -35.0, 35.0))
+        tx  = float(g.get("tx", 0.0))
+        ty  = float(g.get("ty", 0.0))
+
+        ex = getattr(seg, "ex", (1.0, 0.0))
+        ey = getattr(seg, "ey", (0.0, 1.0))
+
+        # forward then invert for backward mapping
+        T = _affine_from_axes(ex, ey, sx, sy, rot)
+        try:
+            Binv = np.linalg.inv(T).astype(np.float32)
+        except np.linalg.LinAlgError:
+            Binv = np.linalg.pinv(T).astype(np.float32)
+        B00.append(float(Binv[0, 0])); B01.append(float(Binv[0, 1]))
+        B10.append(float(Binv[1, 0])); B11.append(float(Binv[1, 1]))
+
+        # local-axis translation expressed in global coords
+        t = (np.array(ex, np.float32).reshape(2) * tx +
+             np.array(ey, np.float32).reshape(2) * ty)
+        t0.append(float(t[0]))
+        t1.append(float(t[1]))
+
+        centers.append(np.array([float(seg.center[0]), float(seg.center[1])],
+                                np.float32))
+
+        # mask (raw or synthesized) + feather
+        m = raw_masks[i]
+        weak = (m is None) or (float(m.sum()) < 0.005 * H * W)
+        if synth_all or weak:
+            m = _synthesize_gaussian_mask(H, W, seg.center, ex, ey, k_scale=0.65)
+        if smooth_px and smooth_px > 0:
+            k = int(max(3, (int(smooth_px) | 1)))
+            try:
+                m = cv2.GaussianBlur(m, (k, k), 0)
+            except Exception:
+                m = cv2.GaussianBlur(m, (0, 0), 1.0)
+        masks.append(np.clip(m.astype(np.float32), 0.0, 1.0))
+
+    # Normalize only inside compact support; get soft influence ramp
+    ws_raw = np.stack(masks, axis=0)                       # (N,H,W)
+    ws, support_mask, influence = _build_support_and_norm(ws_raw,
+                                                          support_thr=0.03,
+                                                          soften_hi=0.12)
+    if not np.any(support_mask):
+        return img_bgr.copy()
+
+    # Approximate blended inverse Jacobian inside support
+    Bxx = np.zeros((H, W), np.float32)
+    Bxy = np.zeros((H, W), np.float32)
+    Byx = np.zeros((H, W), np.float32)
+    Byy = np.zeros((H, W), np.float32)
+    for i in range(len(names)):
+        w = ws[i]
+        Bxx += w * B00[i]
+        Bxy += w * B01[i]
+        Byx += w * B10[i]
+        Byy += w * B11[i]
+
+    sigma_min = _min_singular_value_2x2(Bxx, Bxy, Byx, Byy)
+
+    # Mild tempering only where we have support and compression is high
+    s_hi, s_lo = 0.70, 0.55
+    temper = np.ones_like(sigma_min, dtype=np.float32)
+    temper[~support_mask] = 1.0
+    mask_lo = support_mask & (sigma_min < s_lo)
+    mask_hi = support_mask & (sigma_min >= s_lo) & (sigma_min < s_hi)
+    temper[mask_hi] = ((sigma_min[mask_hi] - s_lo) / (s_hi - s_lo))**2
+    temper[mask_lo] = 0.0
+    # ease with influence ramp
+    temper = 1.0 - influence * (1.0 - temper)
+
+    # -------- Blended backward displacement field (correct translation term) -----
+    delta_x = np.zeros((H, W), np.float32)
+    delta_y = np.zeros((H, W), np.float32)
+
+    for i, _name in enumerate(names):
+        cx, cy = centers[i]
+        rel_x = xx - cx
+        rel_y = yy - cy
+
+        # Correct formula: d = (B - I)*(y - c) - t
+        bx = B00[i] * rel_x + B01[i] * rel_y
+        by = B10[i] * rel_x + B11[i] * rel_y
+        dx = (bx - rel_x) - t0[i]
+        dy = (by - rel_y) - t1[i]
+
+        w = ws[i]
+        delta_x += w * dx
+        delta_y += w * dy
+
+    # Apply tempering only inside influenced regions
+    delta_x *= temper
+    delta_y *= temper
+    delta_x[~support_mask] = 0.0
+    delta_y[~support_mask] = 0.0
+
+    # Final backward sampling grid
+    map_x = xx + delta_x
+    map_y = yy + delta_y
+    np.clip(map_x, 0.0, float(W - 1), out=map_x)
+    np.clip(map_y, 0.0, float(H - 1), out=map_y)
+
+    # ======================= Vacated-only compositing =======================
+
+    # 1) Full person mask as union of part masks
+    person_mask = np.zeros((H, W), np.float32)
+    for m in masks:
+        person_mask = np.maximum(person_mask, m)
+    person_mask = np.clip(person_mask, 0.0, 1.0)
+
+    # 2) Destination coverage of the warped person (same backward map)
+    warped_person = cv2.remap(
+        person_mask, map_x, map_y,
+        interpolation=cv2.INTER_LINEAR,
+        borderMode=cv2.BORDER_CONSTANT, borderValue=0.0
+    )
+    warped_person = np.clip(warped_person, 0.0, 1.0)
+
+    # 3) Vacated background = covered before, not covered after
+    vacated = np.clip(person_mask - warped_person, 0.0, 1.0)
+
+    # Clean the inpaint mask (remove specks; avoid over-inpainting)
+    ker3 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
+    ker5 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    vacated = cv2.morphologyEx(vacated, cv2.MORPH_OPEN, ker3, iterations=1)
+    vacated = cv2.morphologyEx(vacated, cv2.MORPH_DILATE, ker3, iterations=1)
+    vacated_u8 = np.clip(vacated * 255.0, 0, 255).astype(np.uint8)
+
+    # 4) Inpaint ONLY the vacated background on a copy of the original image
+    try:
+        dest_bg = cv2.inpaint(
+            img_bgr, (vacated_u8 > 0).astype(np.uint8) * 255,
+            3, cv2.INPAINT_TELEA
+        )
+    except Exception:
+        dest_bg = img_bgr.copy()
+
+    # 5) Sprite = premultiplied person layer (RGB * alpha). Regions outside person are zero.
+    alpha = person_mask[..., None].astype(np.float32)  # (H,W,1)
+    sprite_premul = img_bgr.astype(np.float32) * alpha
+
+    # 6) Build pyramids for per-pixel LOD sampling (uint8)
+    mip_rgb = _build_mipmap(np.clip(sprite_premul, 0, 255).astype(np.uint8))
+    mip_a   = _build_mipmap((alpha * 255.0).astype(np.uint8))
+    Lr = len(mip_rgb) - 1
+
+    # LOD map: anti-shrink blur limited to support
+    sigma_c = np.clip(sigma_min, 1e-6, 8.0)
+    lod_raw = np.maximum(0.0, np.log2(1.0 / np.minimum(1.0, sigma_c)))
+    lambda_map = influence * lod_raw
+    lambda_map[~support_mask] = 0.0
+
+    i0 = np.floor(lambda_map).astype(np.int32)
+    i1 = np.minimum(i0 + 1, Lr).astype(np.int32)
+    t  = (lambda_map - i0).astype(np.float32)
+
+    warped_premul = np.zeros((H, W, 3), np.float32)
+    warped_alpha  = np.zeros((H, W, 1), np.float32)
+
+    # 7) LOD-aware backward sampling with constant-zero borders (safe for premultiplied)
+    for lvl in range(Lr + 1):
+        scale = float(1 << lvl)
+
+        # RGB (premultiplied)
+        img_lvl = mip_rgb[lvl].astype(np.float32)
+        Hl, Wl = img_lvl.shape[:2]
+        mx = map_x / scale
+        my = map_y / scale
+        np.clip(mx, 0.0, float(Wl - 1), out=mx)
+        np.clip(my, 0.0, float(Hl - 1), out=my)
+        samp_rgb = cv2.remap(
+            img_lvl, mx, my,
+            interpolation=(cv2.INTER_LINEAR if lvl else cv2.INTER_LANCZOS4),
+            borderMode=cv2.BORDER_CONSTANT, borderValue=0.0
+        )
+
+        # Alpha
+        a_lvl = mip_a[lvl].astype(np.float32)
+        Hl, Wl = a_lvl.shape[:2]
+        ma = map_x / scale
+        na = map_y / scale
+        np.clip(ma, 0.0, float(Wl - 1), out=ma)
+        np.clip(na, 0.0, float(Hl - 1), out=na)
+        samp_a = cv2.remap(
+            a_lvl, ma, na,
+            interpolation=cv2.INTER_LINEAR,
+            borderMode=cv2.BORDER_CONSTANT, borderValue=0.0
+        )
+
+        w = ((i0 == lvl).astype(np.float32) * (1.0 - t) +
+             (i1 == lvl).astype(np.float32) * t)[..., None]
+        warped_premul += samp_rgb * w
+        warped_alpha  += (samp_a[..., None]) * w
+
+    # 8) Un-premultiply safely; clamp alpha to avoid halos
+    warped_alpha = np.clip(warped_alpha / 255.0, 0.0, 1.0).astype(np.float32)
+    denom = np.maximum(warped_alpha, 0.03)  # alpha floor
+    warped_rgb = np.clip(warped_premul / denom, 0.0, 255.0).astype(np.uint8)
+
+    # 9) Tight paste mask to avoid cloning low-alpha fringes
+    warped_mask_u8 = np.clip(warped_alpha[..., 0] * 255.0, 0, 255).astype(np.uint8)
+    warped_mask_u8 = cv2.morphologyEx(warped_mask_u8, cv2.MORPH_ERODE, ker3, iterations=1)
+    warped_mask_u8 = cv2.morphologyEx(warped_mask_u8, cv2.MORPH_DILATE, ker5, iterations=1)
+
+    # 10) Clone center from mask moments; fallback to center
+    M = cv2.moments(warped_mask_u8, binaryImage=True)
+    if M["m00"] > 1.0:
+        cx = int(M["m10"] / M["m00"])
+        cy = int(M["m01"] / M["m00"])
+    else:
+        cx, cy = W // 2, H // 2
+
+    # 11) Final composite
+    try:
+        out = cv2.seamlessClone(
+            warped_rgb, dest_bg, warped_mask_u8, (cx, cy), cv2.NORMAL_CLONE
+        )
+    except Exception:
+        m3 = (warped_mask_u8.astype(np.float32) / 255.0)[..., None]
+        out = (warped_rgb.astype(np.float32) * m3 +
+               dest_bg.astype(np.float32) * (1.0 - m3)).astype(np.uint8)
+
+    return out
diff --git a/src/warp_rowuniform.py b/src/warp_rowuniform.py
new file mode 100644
index 0000000..3a1b4af
--- /dev/null
+++ b/src/warp_rowuniform.py
@@ -0,0 +1,139 @@
+# src/warp_rowuniform.py
+from __future__ import annotations
+import numpy as np
+import cv2
+
+EPS = 1e-6
+
+
+def vertical_scales(pts, s_sh, s_hp, H, sigma_frac=0.30):
+    sl, sr = pts["shoulder_l"], pts["shoulder_r"]
+    hl, hr = pts["hip_l"], pts["hip_r"]
+
+    shoulder_y = 0.5 * (sl[1] + sr[1])
+    hip_y = 0.5 * (hl[1] + hr[1])
+
+    ys = np.arange(H, dtype=np.float32)
+    span = max(20.0, abs(hip_y - shoulder_y))
+    sigma = max(1.0, sigma_frac * span)
+
+    wS = np.exp(-0.5 * ((ys - shoulder_y) / sigma) ** 2)
+    wH = np.exp(-0.5 * ((ys - hip_y) / sigma) ** 2)
+
+    wsum = np.maximum(1.0, wS + wH)
+    wS /= wsum
+    wH /= wsum
+
+    s_y = 1.0 + wS * (s_sh - 1.0) + wH * (s_hp - 1.0)
+    return s_y.astype(np.float32), float(shoulder_y), float(hip_y)
+
+
+def _vertical_feather_from_mask(mask01: np.ndarray, feather_px: int = 24) -> np.ndarray:
+    """Cosine feather 0→1→0 across the vertical extent of the soft mask."""
+    H, W = mask01.shape
+    fg_rows = np.where(np.max(mask01, axis=1) > 1e-3)[0]
+    v = np.zeros((H, 1), np.float32)
+    if fg_rows.size == 0:
+        return v
+    y0, y1 = int(fg_rows[0]), int(fg_rows[-1])
+    v[y0:y1 + 1, 0] = 1.0
+
+    tband = min(feather_px, max(1, (y1 - y0 + 1) // 4))
+    if tband > 0:
+        tt = np.linspace(0, np.pi, tband, dtype=np.float32)
+        v[y0:y0 + tband, 0] = 0.5 * (1.0 - np.cos(tt))
+        v[y1 - tband + 1:y1 + 1, 0] = 0.5 * (1.0 - np.cos(tt[::-1]))
+    return v
+
+
+def _lateral_from_distance(mask01: np.ndarray) -> np.ndarray:
+    """Raised-cosine from edge (0) to row-center (1) using distance transform."""
+    inside = (mask01 > 1e-3).astype(np.uint8)
+    if inside.max() == 0:
+        return np.zeros_like(mask01, np.float32)
+
+    dist = cv2.distanceTransform(inside, cv2.DIST_L2, 3)
+    H, W = inside.shape
+    w = np.zeros((H, W), np.float32)
+    for y in range(H):
+        row = dist[y, :]
+        m = row.max()
+        if m > 0:
+            t = np.clip(row / m, 0.0, 1.0)
+            w[y, :] = 0.5 * (1.0 - np.cos(np.pi * t))
+    return w
+
+
+def influence_from_mask(torso01: np.ndarray,
+                        v_feather_px: int = 24,
+                        blur_ksize: int = 15) -> np.ndarray:
+    """
+    Build a smooth 2D influence map in [0,1] from a soft torso mask:
+      - lateral falloff via distance transform (continuous in x and y)
+      - vertical cosine feather at mask top/bottom
+      - overall Gaussian blur to remove micro-steps
+    """
+    lat = _lateral_from_distance(torso01)
+    vgate = _vertical_feather_from_mask(torso01, feather_px=v_feather_px)
+    w = lat * vgate
+
+    k = max(3, blur_ksize | 1)  # odd
+    w = cv2.GaussianBlur(w, (k, k), 0)
+    return np.clip(w, 0.0, 1.0).astype(np.float32)
+
+
+def build_maps(shape, pts, torso01, s_sh, s_hp,
+               sigma_frac=0.30, s_floor=0.85,
+               v_feather_px: int = 24, blur_ksize: int = 15):
+    """
+    Row-uniform warp:
+      x_in = x_mid + (x_out - x_mid) / s_eff(y,x)
+      s_eff(y,x) = 1 + influence(y,x) * (s_row(y) - 1)
+    """
+    H, W = shape[:2]
+
+    # per-row target scales
+    s_y, sy, hy = vertical_scales(pts, s_sh, s_hp, H, sigma_frac)
+    s_row = np.maximum(s_y[:, None], s_floor)
+
+    # torso influence (soft 2D)
+    influence = influence_from_mask(torso01, v_feather_px=v_feather_px, blur_ksize=blur_ksize)
+
+    # midline x(y)
+    sl, sr = pts["shoulder_l"], pts["shoulder_r"]
+    hl, hr = pts["hip_l"], pts["hip_r"]
+    mid_sx = 0.5 * (sl[0] + sr[0])
+    mid_hx = 0.5 * (hl[0] + hr[0])
+
+    ys = np.arange(H, dtype=np.float32)
+    if abs(hy - sy) < 1.0:
+        x_mid_row = np.full((H,), float(mid_sx), np.float32)
+    else:
+        t = (ys - sy) / (hy - sy)
+        x_mid_row = ((1.0 - t) * mid_sx + t * mid_hx).astype(np.float32)
+    x_mid = x_mid_row[:, None]
+
+    # effective scale field
+    s_eff = 1.0 + influence * (s_row - 1.0)
+    s_eff = cv2.GaussianBlur(s_eff, (1, 7), 0)  # vertical smoothing
+
+    X, Y = np.meshgrid(np.arange(W, dtype=np.float32), np.arange(H, dtype=np.float32))
+    map_x = x_mid + (X - x_mid) / np.maximum(s_eff, EPS)
+    map_y = Y
+    return map_x.astype(np.float32), map_y.astype(np.float32), x_mid_row, s_y, influence
+
+
+def remap_img(img, map_x, map_y):
+    # Lanczos minimizes ringing/striping on fabrics vs bicubic
+    return cv2.remap(img, map_x, map_y, interpolation=cv2.INTER_LANCZOS4,
+                     borderMode=cv2.BORDER_REFLECT101)
+
+
+def remap_mask(mask01, map_x, map_y):
+    # Keep soft float mask across passes (no threshold)
+    return cv2.remap(mask01, map_x, map_y, interpolation=cv2.INTER_LINEAR,
+                     borderMode=cv2.BORDER_CONSTANT, borderValue=0).astype(np.float32)
+
+
+def pad_for_warp(img, pad_px):
+    return cv2.copyMakeBorder(img, 0, 0, pad_px, pad_px, cv2.BORDER_REFLECT101)