Create app.py

app.py

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+# 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)