import sys
sys.path.insert(0, "services/recolor")
import numpy as np
from PIL import Image
import cv2

from fabric_recolor.segmentation.birefnet import BirefnetMatteSegmenter
from fabric_recolor.segmentation.prompts import NEGATIVE_PROMPTS, CAVITY_PROMPTS
from fabric_recolor.masks import (alpha_hysteresis, clean_binary, largest_component,
    fill_small_holes, _silhouette_bands, cavity_anchors)
from fabric_recolor.labcolor import rgb_to_lab

src = "data/media/originals/fd/fd12e584a23312c7c5edda1015fe9715d3f5fd19a2ef3d375d635cefab91f54b.jpg"
img = Image.open(src).convert("RGB")
W0,H0 = img.width, img.height
scale = min(1.0, 1600/max(H0,W0))
seg = img.resize((round(W0*scale), round(H0*scale)), Image.BILINEAR)
arr = np.asarray(seg)
sb = BirefnetMatteSegmenter(); sb._load()
matte = alpha_hysteresis(sb._matte(seg), ramp_px=max(4,round(8*scale)))
mask = clean_binary((matte>=0.5).astype(np.uint8),0.002)
mask = largest_component(mask); mask = fill_small_holes(mask,0.01)
iso = Image.fromarray(arr * mask[...,None].astype(np.uint8))
raw = sb._neg_segmenter().negatives_instances(iso, NEGATIVE_PROMPTS)
cav_ids = {i for i,p in enumerate(NEGATIVE_PROMPTS) if p in CAVITY_PROMPTS}
lab = rgb_to_lab(arr)
med = np.median(lab[mask>0].reshape(-1,3), axis=0)
band,_ = _silhouette_bands(mask,6)
obj = int(mask.sum())
for m,idx in raw:
    comp = (m>0)&(mask>0); a=int(comp.sum())
    if a<16: continue
    bf = float((comp&(band>0)).sum())/a
    de = float(np.linalg.norm(lab[comp].mean(0)-med))
    cav = idx in cav_ids
    verdict=[]
    if cav and a<=0.5*obj and bf<0.005: verdict.append("CAVITY_CUT")
    if de>12: verdict.append("DE_CUT")
    if bf>=0.5: verdict.append("APPENDAGE_CUT")
    if not verdict: verdict=["KEEP"]
    print(f"'{NEGATIVE_PROMPTS[idx]}' area={a}({a/obj:.0%}) band={bf:.3f} dE={de:.1f} -> {'+'.join(verdict)}")