HYUNJUNGLEE
b9342f6726
S-CANVAS (Saman Corp.) — DXF + DEM + AI 기반 3D 조감도 생성 엔진. ~24k LOC Python (scanvas_maker.py 7072 LOC GUI + 구조물 파서/빌더 다수). 이 커밋은 7-iter cleanup이 적용된 상태로 import: - F821 8 + B023 6: 비동기 lambda + except/loop 변수 캡처 NameError (Py3.13에서 reproduce 확인된 진짜 버그) - RUF012 4 + RUF013 1: ClassVar / implicit Optional 명시화 - F811/B905/B904/F401/F841/W293/F541/UP/SIM/RUF/PLR 700+ cleanup/modernization 신규 파일: - ruff.toml: target=py313, Korean unicode/저자 스타일/도메인 복잡도 무력화 - requirements-py313.txt: pyproj>=3.7, scipy>=1.14, numpy>=2.0.2 (Py3.13 wheel) - .gitignore: gcp-key.json, 캐시, 백업, 생성 이미지 제외 검증: ruff 0 errors, py_compile 0 errors, import 33/33 OK on Py3.13.13. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
289 lines
13 KiB
Python
289 lines
13 KiB
Python
"""Gemini(Nano Banana 등) 기반 조감도 AI 렌더링 워커.
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scanvas_maker.SCanvasApp의 백그라운드 스레드에서 호출되며, app 인스턴스를 통해
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상태(capture_image / job_logger / log / after / dxf_path / 등)에 접근한다.
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scanvas_maker로부터 ~264줄을 분리해 AI 호출 로직을 모듈 단위로 격리.
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공개 API:
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run_gemini_render(app, credential, prompt, use_vertex=False, location="us-central1")
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"""
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from __future__ import annotations
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import io
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import os
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import time as _time
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from pathlib import Path
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from tkinter import messagebox
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from PIL import Image
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import contextlib
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# Harness 의존 (선택적 — 없어도 동작)
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try:
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from harness.seed_manager import SeedManager
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from harness.logger import get_db_session
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_HARNESS_OK = True
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except Exception:
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SeedManager = None # type: ignore
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get_db_session = None # type: ignore
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_HARNESS_OK = False
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def run_gemini_render(app, credential: str, prompt: str,
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use_vertex: bool = False,
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location: str = "us-central1") -> None:
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"""Gemini 자동 호출 + Harness 통합. app = SCanvasApp 인스턴스.
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Args:
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app: scanvas_maker.SCanvasApp (상태/로그/UI scheduling 접근)
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credential: use_vertex=True이면 GCP Project ID, 아니면 API Key
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prompt: 렌더 프롬프트
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use_vertex: True면 Vertex AI (google-genai SDK), False면 API Key 경로
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location: Vertex AI region ("global"=gemini-3.x, "us-central1"=2.x)
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"""
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t_start = _time.time()
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job_id = None
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db = None
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dxf_hash = app._get_dxf_hash()
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prompt_hash = app._get_prompt_hash(prompt)
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prompt_ver = "v1"
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seed = 0
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if app.job_logger and _HARNESS_OK:
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try:
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db = get_db_session()
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job = app.job_logger.create_job(db, app.dxf_path or "unknown", dxf_hash)
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job_id = job.id
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seed = app.seed_mgr.get_or_create_seed(db, job_id, dxf_hash)
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if app.prompt_reg:
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prompt_ver = app.prompt_reg.latest_version() or "v1"
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app.job_logger.start_job(db, job_id, seed, prompt_ver, prompt_hash)
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app.after(0, lambda: app.log(
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f" Harness: job#{job_id}, {SeedManager.describe(seed)}, prompt={prompt_ver}"))
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except Exception as e:
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app.after(0, lambda e=e: app.log(f" Harness 초기화 경고: {e}"))
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try:
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from google import genai
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from google.genai import types as gtypes
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sdk_version = "vertex" if use_vertex else "new"
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except ImportError:
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if use_vertex:
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if app.job_logger and db and job_id:
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app.job_logger.fail_job(db, job_id, "google-genai SDK 미설치")
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app.after(0, lambda: messagebox.showerror("패키지 필요",
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"Vertex AI는 google-genai SDK가 필요합니다.\n"
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"pip install google-genai\n"
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"그리고 gcloud auth application-default login"))
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return
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try:
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import google.generativeai as genai_legacy # type: ignore
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sdk_version = "legacy"
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except ImportError:
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if app.job_logger and db and job_id:
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app.job_logger.fail_job(db, job_id, "google-genai SDK 미설치")
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app.after(0, lambda: messagebox.showerror("패키지 필요",
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"pip install google-generativeai\n또는\npip install google-genai"))
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return
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try:
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source_img = app.capture_image if app.capture_image else app.guide_image
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input_path = os.path.abspath("capture_for_ai.png")
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source_img.save(input_path)
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app.after(0, lambda: app.log(f" 입력 이미지: {source_img.size}"))
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with open(input_path, "rb") as f:
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img_bytes = f.read()
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render_prompt = (
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f"Transform this aerial/satellite terrain capture into a photorealistic "
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f"bird's-eye view architectural rendering. "
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f"CRITICAL: Preserve the EXACT terrain layout, roads, water, structures. "
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f"The scene may combine a high-detail DXF area (center) with a lower-detail "
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f"real DEM+satellite outer ring (edges). Render BOTH areas seamlessly — "
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f"the outer ring is actual surrounding terrain, NOT filler to crop out. "
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f"Keep the full image frame; do NOT trim to the central bbox. "
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f"Dark road areas = freshly paved asphalt. Cut slopes = exposed earth. "
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f"Enhance vegetation textures, water reflections, natural lighting. "
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f"Style: {prompt}"
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)
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app.after(0, lambda: app.log(
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f" Gemini API 호출 중... (SDK: {sdk_version}, "
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f"loc: {location if use_vertex else '-'})"))
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rendered = None
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if sdk_version == "vertex":
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try:
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client = genai.Client(
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vertexai=True,
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project=credential,
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location=location,
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)
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except Exception as ce:
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err = str(ce)[:160]
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app.after(0, lambda: app.log(
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f" Vertex AI 클라이언트 생성 실패: {err}"))
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has_sa = bool(os.environ.get("GOOGLE_APPLICATION_CREDENTIALS"))
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if has_sa:
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app.after(0, lambda: app.log(
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" → gcp-key.json 경로 확인. 서비스 계정에 "
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"aiplatform.user 권한이 있는지 확인하세요."))
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else:
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app.after(0, lambda: app.log(
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" → gcp-key.json을 프로젝트 루트에 배치하거나 "
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"gcloud auth application-default login 실행"))
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if app.job_logger and db and job_id:
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app.job_logger.fail_job(db, job_id, "Vertex 인증 실패")
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return
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model_priority = [
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("gemini-3.1-flash-image-preview", location),
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("gemini-3-pro-image-preview", location),
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("gemini-2.5-flash-image", "us-central1"),
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("gemini-2.5-flash-image-preview", "us-central1"),
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("gemini-2.0-flash-preview-image-generation", "us-central1"),
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]
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current_loc = location
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for model_name, model_loc in model_priority:
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if model_loc != current_loc:
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try:
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client = genai.Client(
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vertexai=True,
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project=credential,
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location=model_loc,
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)
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current_loc = model_loc
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except Exception:
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continue
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try:
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response = client.models.generate_content(
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model=model_name,
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contents=[
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gtypes.Part.from_bytes(data=img_bytes, mime_type="image/png"),
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render_prompt
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],
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config=gtypes.GenerateContentConfig(
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response_modalities=["IMAGE"],
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)
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)
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for part in response.candidates[0].content.parts:
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if hasattr(part, 'inline_data') and part.inline_data and \
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part.inline_data.mime_type.startswith("image/"):
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rendered = Image.open(io.BytesIO(part.inline_data.data))
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break
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if rendered:
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_m, _l = model_name, current_loc
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app.after(0, lambda _m=_m, _l=_l: app.log(
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f" [Vertex] 모델 {_m} @ {_l} 성공"))
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break
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except Exception as exc:
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_m, _e = model_name, str(exc)[:120]
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app.after(0, lambda _m=_m, _e=_e: app.log(f" [Vertex] {_m}: {_e}"))
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elif sdk_version == "new":
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client = genai.Client(api_key=credential)
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for model_name in ["gemini-2.5-flash-image",
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"gemini-2.0-flash-preview-image-generation"]:
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try:
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response = client.models.generate_content(
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model=model_name,
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contents=[
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gtypes.Part.from_bytes(data=img_bytes, mime_type="image/png"),
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render_prompt
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],
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config=gtypes.GenerateContentConfig(
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response_modalities=["IMAGE", "TEXT"],
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)
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)
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for part in response.candidates[0].content.parts:
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if hasattr(part, 'inline_data') and part.inline_data and \
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part.inline_data.mime_type.startswith("image/"):
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rendered = Image.open(io.BytesIO(part.inline_data.data))
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break
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if rendered:
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_m = model_name
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app.after(0, lambda _m=_m: app.log(f" 모델 {_m} 성공"))
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break
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except Exception as exc:
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_m, _e = model_name, str(exc)[:80]
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app.after(0, lambda _m=_m, _e=_e: app.log(f" {_m}: {_e}"))
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else:
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genai_legacy.configure(api_key=credential)
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pil_img = Image.open(io.BytesIO(img_bytes))
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for model_name in ["gemini-2.5-flash-image",
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"gemini-2.0-flash-preview-image-generation"]:
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try:
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model = genai_legacy.GenerativeModel(model_name)
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response = model.generate_content(
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[pil_img, render_prompt]
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)
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if hasattr(response, 'candidates') and response.candidates:
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for part in response.candidates[0].content.parts:
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if (hasattr(part, 'inline_data') and part.inline_data
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and part.inline_data.mime_type.startswith("image/")):
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rendered = Image.open(io.BytesIO(part.inline_data.data))
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break
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if rendered:
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_m = model_name
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app.after(0, lambda _m=_m: app.log(f" 모델 {_m} 성공"))
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break
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except Exception as exc:
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_m, _e = model_name, str(exc)[:80]
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app.after(0, lambda _m=_m, _e=_e: app.log(f" {_m}: {_e}"))
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if not rendered:
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if app.job_logger and db and job_id:
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app.job_logger.fail_job(db, job_id, "Gemini 이미지 생성 실패")
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app.after(0, lambda: app.log(" Gemini 이미지 생성 실패. API Key와 모델을 확인하세요."))
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app.after(0, lambda: app.set_status("이미지 생성 실패", "#E74C3C"))
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return
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# 출력 화질 후처리 — Step 4에서 고른 HD/FHD/UHD 로 리사이즈
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tgt = getattr(app, "target_resolution", None)
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if tgt and tgt[0] > 0 and tgt[1] > 0 and rendered.size != tuple(tgt):
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src_size = rendered.size
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app.after(0, lambda s=src_size, t=tgt: app.log(
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f" 화질 리사이즈: {s[0]}x{s[1]} → {t[0]}x{t[1]}"))
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rendered = rendered.resize(tuple(tgt), Image.LANCZOS)
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output_path = "rendered_birdseye.png"
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rendered.save(output_path)
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latency_ms = (_time.time() - t_start) * 1000
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quality_score = 0.0
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if app.quality_val:
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try:
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vr = app.quality_val.validate(Path(output_path))
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quality_score = vr.score
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app.after(0, lambda: app.log(f" 품질검증: {vr.summary}"))
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except Exception as e:
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app.after(0, lambda e=e: app.log(f" 품질검증 오류: {e}"))
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if app.job_logger and db and job_id:
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with contextlib.suppress(Exception):
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app.job_logger.complete_job(db, job_id, output_path, quality_score, latency_ms)
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app.after(0, lambda: app.log(
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f" Gemini 렌더링 완료! → {output_path} ({rendered.size}) "
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f"[{latency_ms:.0f}ms, 품질={quality_score:.2f}]"))
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app.after(0, lambda: app.set_status("AI 렌더링 완료", "#2ECC71"))
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app.after(0, lambda: app._show_rendered_result(output_path))
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except Exception as e:
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if app.job_logger and db and job_id:
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with contextlib.suppress(Exception):
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app.job_logger.fail_job(db, job_id, str(e))
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err_msg = str(e)[:300]
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app.after(0, lambda: app.log(f" Gemini 오류: {err_msg}"))
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app.after(0, lambda: app.set_status("렌더링 실패", "#E74C3C"))
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app.after(0, lambda: messagebox.showerror("Gemini 오류", f"API 호출 오류:\n{err_msg}"))
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finally:
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if db:
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with contextlib.suppress(Exception):
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db.close()
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