import io import os import gdown import base64 import cv2 import numpy as np from PIL import Image from typing import Optional from fastapi import FastAPI, UploadFile, File, Form from fastapi.responses import JSONResponse from fastapi.middleware.cors import CORSMiddleware from detectron2.engine import DefaultPredictor from detectron2.config import get_cfg from detectron2.projects.point_rend import add_pointrend_config # ------------------------------- # FastAPI setup # ------------------------------- app = FastAPI(title="Rooftop Segmentation API") app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) # ------------------------------- # Available epsilons # ------------------------------- EPSILONS = [0.01, 0.005, 0.004, 0.003, 0.001] @app.get("/epsilons") def get_epsilons(): return {"epsilons": EPSILONS} # ------------------------------- # Google Drive model download (irregular-flat) # ------------------------------- MODEL_PATH_IRREGULAR = "/tmp/model_irregular_flat.pth" DRIVE_FILE_ID = "1GO_Ut-N2e2we8t9mnsysb0P1qMsBF8FW" def download_irregular_model(): if not os.path.exists(MODEL_PATH_IRREGULAR): url = f"https://drive.google.com/uc?id={DRIVE_FILE_ID}" tmp_dir = "/tmp/gdown" os.makedirs(tmp_dir, exist_ok=True) os.environ["GDOWN_CACHE_DIR"] = tmp_dir print("Downloading irregular-flat Detectron2 model...") gdown.download(url, MODEL_PATH_IRREGULAR, quiet=False, fuzzy=True, use_cookies=False) print("Download complete.") else: print("Irregular-flat model already exists, skipping download.") download_irregular_model() if os.path.exists(MODEL_PATH_IRREGULAR): print("Irregular-flat model is ready at", MODEL_PATH_IRREGULAR) else: print("Irregular-flat model NOT found! Something went wrong!") # ------------------------------- # Detectron2 model setup # ------------------------------- def setup_model_rect(weights_path: str): cfg = get_cfg() add_pointrend_config(cfg) cfg_path = "detectron2/projects/PointRend/configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_3x_coco.yaml" cfg.merge_from_file(cfg_path) cfg.MODEL.ROI_HEADS.NUM_CLASSES = 2 cfg.MODEL.POINT_HEAD.NUM_CLASSES = cfg.MODEL.ROI_HEADS.NUM_CLASSES cfg.MODEL.WEIGHTS = weights_path cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 cfg.MODEL.DEVICE = "cpu" return DefaultPredictor(cfg) def setup_model_irregular(weights_path: str): cfg = get_cfg() add_pointrend_config(cfg) cfg_path = "detectron2/projects/PointRend/configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_3x_coco.yaml" cfg.merge_from_file(cfg_path) cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1 cfg.MODEL.POINT_HEAD.NUM_CLASSES = cfg.MODEL.ROI_HEADS.NUM_CLASSES cfg.MODEL.WEIGHTS = weights_path cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 cfg.MODEL.DEVICE = "cpu" return DefaultPredictor(cfg) # Load models predictor_rect = setup_model_rect("/app/model_rect_final.pth") predictor_irregular_flat = setup_model_irregular(MODEL_PATH_IRREGULAR) # ------------------------------- # Utility functions # ------------------------------- def im_to_b64_png(im: np.ndarray) -> str: _, buffer = cv2.imencode(".png", im) return base64.b64encode(buffer).decode() def extract_polygon(mask: np.ndarray, epsilon_ratio: float = 0.004): mask_uint8 = (mask * 255).astype(np.uint8) contours, _ = cv2.findContours(mask_uint8, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) if not contours: return None c = max(contours, key=cv2.contourArea) epsilon = epsilon_ratio * cv2.arcLength(c, True) polygon = cv2.approxPolyDP(c, epsilon, True) return polygon.reshape(-1, 2) def overlay_polygon(im: np.ndarray, polygon: Optional[np.ndarray], vertex_color=(0,0,255), line_color=(0,255,0)): overlay = im.copy() if polygon is not None: # Draw polygon outline (thin) cv2.polylines(overlay, [polygon.astype(np.int32)], True, line_color, thickness=2) # Draw vertices for i, (x, y) in enumerate(polygon): cv2.circle(overlay, (int(x), int(y)), 4, vertex_color, -1) # Draw vertex index (black number) cv2.putText(overlay, str(i+1), (int(x)+5, int(y)-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (20,20,20), 1, cv2.LINE_AA) # Display vertex count on top vertex_count = len(polygon) cv2.putText(overlay, f"num_vertices = {vertex_count}", (20, 35), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (20,20,20), 2, cv2.LINE_AA) return overlay # ------------------------------- # API endpoints # ------------------------------- @app.get("/") def root(): return {"message": "Rooftop Segmentation API is running!"} @app.post("/predict") async def predict( file: UploadFile = File(...), rooftop_type: str = Form(...), epsilon: float = Form(0.004) ): contents = await file.read() try: im_pil = Image.open(io.BytesIO(contents)).convert("RGB") except Exception as e: return JSONResponse(status_code=400, content={"error": "Invalid image", "detail": str(e)}) im = np.array(im_pil)[:, :, ::-1].copy() # RGB -> BGR if rooftop_type.lower() == "rectangular": predictor = predictor_rect model_used = "model_rect_final.pth" elif rooftop_type.lower() == "irregular": predictor = predictor_irregular_flat model_used = "model_irregular_flat.pth" else: return JSONResponse(status_code=400, content={"error": "Invalid rooftop_type. Choose 'rectangular' or 'irregular'."}) outputs = predictor(im) instances = outputs["instances"].to("cpu") if len(instances) == 0: return { "polygon": None, "vertices": None, "vertex_count": 0, "image": None, "model_used": model_used, "rooftop_type": rooftop_type, "epsilon": epsilon } idx = int(instances.scores.argmax().item()) raw_mask = instances.pred_masks[idx].numpy().astype(np.uint8) polygon = extract_polygon(raw_mask, epsilon_ratio=epsilon) vertex_count = len(polygon) if polygon is not None else 0 overlay = overlay_polygon(im, polygon) img_b64 = im_to_b64_png(overlay) return { "polygon": polygon.tolist() if polygon is not None else None, "vertex_count": vertex_count, "image": img_b64, "model_used": model_used, "rooftop_type": rooftop_type, "epsilon": epsilon }