feat: 초기 프로젝트 구조 추가

logic/inspector.py

@@ -0,0 +1,120 @@
+# 검사 판별 로직 — PatMax 결과 판독 + 모델 판별 + Pass/Fail 판정
+import cv2
+import numpy as np
+
+# Cognex 카메라 셀 매핑 (GV 방식 fallback용으로 유지)
+PATTERN_RESULT_CELLS = {
+    "A27":  {"id": 1, "name": "LOW REF",     "model": "LX3", "type": "RH"},
+    "A77":  {"id": 2, "name": "LOW REF",     "model": "LX3", "type": "LH"},
+    "A127": {"id": 3, "name": "LOW REF NAS", "model": "LX3", "type": "RH"},
+    "A177": {"id": 4, "name": "LOW REF NAS", "model": "LX3", "type": "LH"},
+}
+
+
+class Inspector:
+
+    # ── Python PatMax 매칭 (주 경로) ─────────────────────────────────── #
+
+    def match_image(self, image_bytes: bytes, matcher: "PatternMatcher") -> dict:
+        """
+        FTP로 받은 이미지 바이트를 Python PatternMatcher로 매칭.
+        반환 형식은 read_patmax_results와 동일하여 identify_model에서 그대로 사용.
+        """
+        if not image_bytes:
+            return {}
+
+        arr = np.frombuffer(image_bytes, dtype=np.uint8)
+        img = cv2.imdecode(arr, cv2.IMREAD_UNCHANGED)
+        if img is None:
+            return {}
+
+        all_scores = matcher.match_all(img)
+        results    = {}
+
+        for pid, score in all_scores.items():
+            info = matcher.get_product_info(pid)
+            if info is None:
+                continue
+            results[f"PY_{pid}"] = {
+                "matched": score >= matcher.score_threshold,
+                "score":   score,
+                "model":   info,
+                "raw":     f"python_match={score:.1f}",
+            }
+
+        return results
+
+    # ── Cognex GV 셀 방식 (fallback) ────────────────────────────────── #
+
+    def read_patmax_results(self, insight) -> dict:
+        """A27/A77/A127/A177 셀 조회 → #ERR이면 실패, 그 외 점수 파싱."""
+        results = {}
+        for cell, model_info in PATTERN_RESULT_CELLS.items():
+            try:
+                insight._send(f"GV{cell}")
+                code = insight._read_line()
+                if code != "1":
+                    results[cell] = {
+                        "matched": False, "score": 0.0,
+                        "model": model_info, "raw": ""
+                    }
+                    continue
+                value = insight._read_line()
+                if "#ERR" in value or value.strip() == "":
+                    results[cell] = {
+                        "matched": False, "score": 0.0,
+                        "model": model_info, "raw": value
+                    }
+                else:
+                    # "(736.1,742.0) -1.8 = 82.9" 형식에서 = 뒤 값 추출
+                    try:
+                        score = float(value.split("=")[-1].strip())
+                    except Exception:
+                        score = 0.0
+                    results[cell] = {
+                        "matched": True, "score": score,
+                        "model": model_info, "raw": value
+                    }
+            except Exception as e:
+                print(f"[PatMax] {cell} 읽기 오류: {e}")
+                results[cell] = {
+                    "matched": False, "score": 0.0,
+                    "model": model_info, "raw": ""
+                }
+        return results
+
+    # ── 공통: 모델 판별 + 판정 ──────────────────────────────────────── #
+
+    def identify_model(self, results: dict, allowed_model_ids: list) -> dict:
+        """매칭된 패턴 중 점수가 가장 높은 것을 선택해 허용 모델 여부 판별."""
+        matched_patterns = [
+            (cell, info) for cell, info in results.items()
+            if info["matched"]
+        ]
+
+        if not matched_patterns:
+            return {
+                "matched": False, "in_allowed": False,
+                "model": None, "score": 0.0,
+                "cognex_pass": False, "status": "인식 불가"
+            }
+
+        _best_cell, best_info = max(matched_patterns, key=lambda x: x[1]["score"])
+        model      = best_info["model"]
+        in_allowed = model["id"] in allowed_model_ids
+
+        return {
+            "matched":    True,
+            "in_allowed": in_allowed,
+            "model":      model,
+            "score":      best_info["score"],
+            "cognex_pass": in_allowed,
+            "status": (
+                f"{model['name']} {model['model']} {model['type']} ({best_info['score']:.1f}점)"
+                if in_allowed
+                else f"허용 외 모델: {model['name']} {model['model']} {model['type']}"
+            ),
+        }
+
+    def judge(self, cognex_pass: bool, basler_pass: bool) -> str:
+        return "PASS" if cognex_pass and basler_pass else "FAIL"