132 lines
4.9 KiB
Python
132 lines
4.9 KiB
Python
# 검사 판별 로직 — PatMax 결과 판독 + 모델 판별 + Pass/Fail 판정
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import cv2
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import numpy as np
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from db.sql_client import SQLClient
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from logic.products import model_display_label
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class Inspector:
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def __init__(self):
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self._pattern_cells: dict = {}
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def set_pattern_cells(self, cells: dict):
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"""DB 기반 PatMax 셀 매핑 (refresh_wk_results 시 갱신)."""
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self._pattern_cells = cells or {}
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# ── Python PatMax 매칭 (주 경로) ─────────────────────────────────── #
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def match_image(self, image_bytes: bytes, matcher: "PatternMatcher") -> dict:
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"""
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FTP로 받은 이미지 바이트를 Python PatternMatcher로 매칭.
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반환 형식은 read_patmax_results와 동일하여 identify_model에서 그대로 사용.
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"""
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if not image_bytes:
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return {}
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arr = np.frombuffer(image_bytes, dtype=np.uint8)
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img = cv2.imdecode(arr, cv2.IMREAD_UNCHANGED)
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if img is None:
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return {}
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all_scores = matcher.match_all(img)
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results = {}
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for pid, score in all_scores.items():
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info = matcher.get_product_info(pid)
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if info is None:
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continue
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results[f"PY_{pid}"] = {
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"matched": score >= matcher.score_threshold,
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"score": score,
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"model": info,
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"raw": f"python_match={score:.1f}",
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}
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return results
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# ── Cognex GV 셀 방식 (fallback) ────────────────────────────────── #
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def read_patmax_results(self, insight) -> dict:
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"""PatMax 결과 셀 조회 → #ERR이면 실패, 그 외 점수 파싱."""
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results = {}
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for cell, model_info in self._pattern_cells.items():
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try:
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insight._send(f"GV{cell}")
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code = insight._read_line()
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if code != "1":
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results[cell] = {
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"matched": False, "score": 0.0,
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"model": model_info, "raw": ""
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}
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continue
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value = insight._read_line()
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if "#ERR" in value or value.strip() == "":
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results[cell] = {
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"matched": False, "score": 0.0,
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"model": model_info, "raw": value
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}
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else:
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# "(736.1,742.0) -1.8 = 82.9" 형식에서 = 뒤 값 추출
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try:
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score = float(value.split("=")[-1].strip())
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except Exception:
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score = 0.0
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results[cell] = {
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"matched": True, "score": score,
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"model": model_info, "raw": value
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}
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except Exception as e:
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print(f"[PatMax] {cell} 읽기 오류: {e}")
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results[cell] = {
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"matched": False, "score": 0.0,
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"model": model_info, "raw": ""
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}
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return results
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# ── 공통: 모델 판별 + 판정 ──────────────────────────────────────── #
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def identify_model(self, results: dict,
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allowed_model_ids: "list | None" = None,
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allowed_article_ids: "set | None" = None) -> dict:
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"""매칭된 패턴 중 점수가 가장 높은 것을 선택해 허용 여부 판별."""
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matched_patterns = [
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(cell, info) for cell, info in results.items()
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if info["matched"]
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]
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if not matched_patterns:
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return {
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"matched": False, "in_allowed": False,
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"model": None, "score": 0.0,
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"cognex_pass": False, "status": "인식 불가"
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}
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_best_cell, best_info = max(matched_patterns, key=lambda x: x[1]["score"])
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model = best_info["model"]
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label = model_display_label(model)
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if allowed_article_ids is not None:
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in_allowed = (
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SQLClient._norm_id(model.get("article_id")) in allowed_article_ids
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)
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else:
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in_allowed = model["id"] in (allowed_model_ids or [])
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return {
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"matched": True,
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"in_allowed": in_allowed,
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"model": model,
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"score": best_info["score"],
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"cognex_pass": in_allowed,
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"status": (
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f"{label} ({best_info['score']:.1f}점)"
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if in_allowed
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else f"작업 대상 외: {label}"
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),
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}
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def judge(self, cognex_pass: bool, basler_pass: bool) -> str:
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return "PASS" if cognex_pass and basler_pass else "FAIL"
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