# app.py
from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
import numpy as np
import cv2
import base64
import io

app = FastAPI(title="Detector de Corrosão Branca")

# PARA PROTOTIPO: permitir todas origens. Em produção restrinja ao domínio do frontend.
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

def process_image_bytes(img_bytes: bytes):
    # lê bytes em numpy + OpenCV
    nparr = np.frombuffer(img_bytes, np.uint8)
    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    if img is None:
        raise ValueError("Não foi possível decodificar a imagem.")

    # 1) Converter para HSV
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

    # 2) máscara do fundo preto (V baixo)
    lower_bg = np.array([0, 0, 0], dtype=np.uint8)
    upper_bg = np.array([180, 255, 50], dtype=np.uint8)
    mask_bg = cv2.inRange(hsv, lower_bg, upper_bg)

    # 3) objeto = invertendo máscara do fundo
    mask_obj = cv2.bitwise_not(mask_bg)

    # 4) limpar máscara (morfologia)
    kernel = np.ones((5, 5), np.uint8)
    mask_obj = cv2.morphologyEx(mask_obj, cv2.MORPH_OPEN, kernel)
    mask_obj = cv2.morphologyEx(mask_obj, cv2.MORPH_CLOSE, kernel)

    # 5) maior contorno (supõe um parafuso)
    contours, _ = cv2.findContours(mask_obj, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    if not contours:
        return {"error": "Nenhum objeto detectado"}

    largest = max(contours, key=cv2.contourArea)
    mask_clean = np.zeros_like(mask_obj)
    cv2.drawContours(mask_clean, [largest], -1, 255, cv2.FILLED)

    # 6) isolar objeto
    isolated = cv2.bitwise_and(img, img, mask=mask_clean)

    # 7) detectar corrosão BRANCA (S baixa, V alta)
    hsv_iso = cv2.cvtColor(isolated, cv2.COLOR_BGR2HSV)
    lower_white = np.array([0, 0, 180], dtype=np.uint8)
    upper_white = np.array([180, 60, 255], dtype=np.uint8)
    mask_white = cv2.inRange(hsv_iso, lower_white, upper_white)
    mask_white = cv2.bitwise_and(mask_white, mask_white, mask=mask_clean)

    # 8) métricas
    total_pixels = int(np.count_nonzero(mask_clean))
    corrosion_pixels = int(np.count_nonzero(mask_white))
    percent = (corrosion_pixels / max(1, total_pixels)) * 100.0

    # 9) preparar imagens para frontend (PNG base64)
    # isolado em RGB para visualização
    isolated_rgb = cv2.cvtColor(isolated, cv2.COLOR_BGR2RGB)
    corrosion_vis = cv2.bitwise_and(isolated_rgb, isolated_rgb, mask=mask_white)

    def to_data_uri(img_arr):
        # img_arr: RGB uint8
        bgr = cv2.cvtColor(img_arr, cv2.COLOR_RGB2BGR)
        ok, buf = cv2.imencode(".png", bgr)
        if not ok:
            return None
        b64 = base64.b64encode(buf.tobytes()).decode("ascii")
        return f"data:image/png;base64,{b64}"

    isolated_b64 = to_data_uri(isolated_rgb)
    corrosion_b64 = to_data_uri(corrosion_vis)

    return {
        "percent": round(percent, 4),
        "total_pixels": total_pixels,
        "corrosion_pixels": corrosion_pixels,
        "isolated_image": isolated_b64,
        "corrosion_image": corrosion_b64,
    }

@app.post("/analyze")
async def analyze(file: UploadFile = File(...)):
    content = await file.read()
    try:
        result = process_image_bytes(content)
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))
    return JSONResponse(result)

@app.get("/")
def read_root():
    return {"status": "ok"}