# Test 2: LOKALNY deficyt glebi — komora = region maski znacznie glebiej niz
# jego otoczenie (obrecz siedziska), nie niz globalna mediana.
# deficit(px) = max glebi w promieniu R - glebi(px); komora ma duzy deficit,
# bo jej sasiedztwo siega jasnego obrysu; oparcie/dalekie siedzisko - maly.
import cv2
import numpy as np
import torch
from PIL import Image

from transformers import AutoImageProcessor, AutoModelForDepthEstimation

MODEL_ID = "depth-anything/Depth-Anything-V2-Small-hf"
IMG = r"C:\xampp\htdocs\vilmax\vilmal\tmp\recolor-svc-storage-granat-v4.png"
MATTE = r"C:\xampp\htdocs\vilmax\vilmal\tmp\birefnet-matte-6413.png"

proc = AutoImageProcessor.from_pretrained(MODEL_ID)
model = AutoModelForDepthEstimation.from_pretrained(MODEL_ID).to("cuda").eval()

img = Image.open(IMG).convert("RGB")
W, H = img.size
inputs = proc(images=img, return_tensors="pt").to("cuda")
with torch.no_grad():
    d = model(**inputs).predicted_depth[0].cpu().numpy()
d = cv2.resize(d, (W, H), interpolation=cv2.INTER_LINEAR)
d = (d - d.min()) / max(1e-6, d.max() - d.min())

matte = np.asarray(Image.open(MATTE).convert("L").resize((W, H), Image.BILINEAR), np.float32) / 255.0
mask = (matte > 0.5).astype(np.uint8)

# lokalny maks glebi TYLKO w obrebie maski: tlo=0 przed dylatacja,
# wynik ograniczony do maski — komora "wyciaga" jasna obrecz siedziska
R = 260  # ~8% szerokosci — musi przekroczyc szerokosc obreczy komory
k = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * R + 1, 2 * R + 1))
d_in = d * mask
local_max = cv2.dilate(d_in, k)
deficit = np.where(mask > 0, local_max - d, 0).astype(np.float32)

for th in (0.15, 0.2, 0.25):
    rec = (deficit > th).astype(np.uint8)
    n, lab, stats, _ = cv2.connectedComponentsWithStats(rec, 8)
    big = [(int(s[cv2.CC_STAT_AREA]), (int(s[cv2.CC_STAT_LEFT]), int(s[cv2.CC_STAT_TOP]),
            int(s[cv2.CC_STAT_WIDTH]), int(s[cv2.CC_STAT_HEIGHT])))
           for s in stats[1:] if s[cv2.CC_STAT_AREA] > 5000]
    big.sort(reverse=True)
    print(f"th={th}: px={int(rec.sum())} ({rec.sum()/max(1,mask.sum()):.1%} maski) komponenty>5k: {big[:6]}")

th = 0.2
rec = (deficit > th).astype(np.uint8)
out = np.asarray(img, np.uint8).copy()
out[rec > 0] = (out[rec > 0] * 0.35 + np.array([255, 60, 60]) * 0.65).astype(np.uint8)
Image.fromarray(out).resize((W // 3, H // 3)).save(r"C:\xampp\htdocs\vilmax\vilmal\tmp\depth-cavity-local.png")
Image.fromarray((np.clip(deficit, 0, 1) * 255).astype(np.uint8)).resize((W // 3, H // 3)).save(
    r"C:\xampp\htdocs\vilmax\vilmal\tmp\depth-deficit-map.png"
)
print("overlay zapisany")