Enhance OCR configuration and integrate fuzzy matching for label parsing

app/config.py

@@ -41,10 +41,23 @@ DEFAULT_CONFIG: dict[str, Any] = {
     },
     "ocr": {
         "enabled": True,
+        "engine": "tesseract",
         "language": "eng",
         "tesseract_cmd": None,
+        "psm": 6,
+        "margin": 0,
         "threshold": True,
+        "paddle_threshold": False,
         "scale": 2.0,
+        "config": "",
+        "use_angle_cls": True,
+        "paddle": {
+            "enable_mkldnn": False,
+            "lang": "en",
+            "use_doc_orientation_classify": False,
+            "use_doc_unwarping": False,
+            "use_textline_orientation": False,
+        },
     },
     "capture": {
         "photos_dir": "captures/photos",
@@ -56,7 +69,12 @@ DEFAULT_CONFIG: dict[str, Any] = {
     "display": {
         "show_fps": True,
     },
-    "label_data": {"models": ["Regius", "Duvell"], "colors": ["T-NF-BLK-OUT-BST-G", "T-BLK-G"]},
+    "label_data": {
+        "model_min_score": 0.72,
+        "color_min_score": 0.72,
+        "models": ["Regius", "Duvell"],
+        "colors": ["T-NF-BLK-OUT-BST-G", "T-BLK-G"],
+    },
 }
 
 

app/detection.py

@@ -4,10 +4,10 @@ from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Any
 
-import cv2
 import numpy as np
 
 from app.label_parser import ParsedLabel, parse_label_text
+from app.ocr import create_ocr_engine
 
 
 @dataclass
@@ -18,6 +18,9 @@ class DetectionResult:
     raw_text: str = ""
     parsed: ParsedLabel | None = None
     error: str | None = None
+    ocr_engine: str | None = None
+    ocr_confidence: float | None = None
+    ocr_elapsed_ms: float | None = None
     all_boxes: list[dict[str, Any]] = field(default_factory=list)
 
     def to_metadata(self) -> dict[str, Any]:
@@ -28,6 +31,9 @@ class DetectionResult:
             "raw_text": self.raw_text,
             "parsed": self.parsed.to_dict() if self.parsed else None,
             "error": self.error,
+            "ocr_engine": self.ocr_engine,
+            "ocr_confidence": self.ocr_confidence,
+            "ocr_elapsed_ms": self.ocr_elapsed_ms,
             "all_boxes": self.all_boxes,
         }
 
@@ -72,6 +78,9 @@ class YoloLabelDetector:
         boxes = []
         names = getattr(self.model, "names", {})
         for result in results:
+            if result.boxes is None:
+                continue
+            
             for box in result.boxes:
                 x1, y1, x2, y2 = [int(v) for v in box.xyxy[0].tolist()]
                 confidence = float(box.conf[0])
@@ -106,78 +115,30 @@ class YoloLabelDetector:
         return result
 
 
-class TesseractOcr:
-    def __init__(self, config: dict[str, Any]) -> None:
-        self.config = config
-        self.load_error: str | None = None
-        self.pytesseract = None
-        self._load()
-
-    def _load(self) -> None:
-        if not self.config["ocr"].get("enabled", True):
-            return
-        try:
-            import pytesseract
-
-            command = self.config["ocr"].get("tesseract_cmd")
-            if command:
-                pytesseract.pytesseract.tesseract_cmd = command
-            self.pytesseract = pytesseract
-        except Exception as exc:
-            self.load_error = f"Nie mozna zaladowac pytesseract: {exc}"
-
-    def read_label(self, frame_bgr: np.ndarray, bbox: tuple[int, int, int, int]) -> tuple[str, str | None]:
-        if not self.config["ocr"].get("enabled", True):
-            return "", None
-        if self.pytesseract is None:
-            return "", self.load_error or "OCR nie jest zaladowany"
-
-        x1, y1, x2, y2 = bbox
-        h, w = frame_bgr.shape[:2]
-        x1, y1 = max(0, x1), max(0, y1)
-        x2, y2 = min(w, x2), min(h, y2)
-        if x2 <= x1 or y2 <= y1:
-            return "", "Nieprawidlowy bbox OCR"
-
-        roi = frame_bgr[y1:y2, x1:x2]
-        scale = float(self.config["ocr"].get("scale", 1.0))
-        if scale != 1.0:
-            roi = cv2.resize(roi, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
-
-        gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
-        if self.config["ocr"].get("threshold", True):
-            gray = cv2.GaussianBlur(gray, (3, 3), 0)
-            gray = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
-
-        try:
-            text = self.pytesseract.image_to_string(
-                gray,
-                lang=self.config["ocr"].get("language", "eng"),
-                config="--psm 6",
-            )
-        except Exception as exc:
-            return "", f"Blad OCR: {exc}"
-        return text, None
-
-
 class DetectionPipeline:
     def __init__(self, config: dict[str, Any], app_config: Any) -> None:
         self.config = config
         self.detector = YoloLabelDetector(config, app_config)
-        self.ocr = TesseractOcr(config)
+        self.ocr = create_ocr_engine(config)
 
     def process(self, frame_bgr: np.ndarray) -> DetectionResult:
         result = self.detector.detect(frame_bgr)
         if result.xyxy is None:
             return result
 
-        text, ocr_error = self.ocr.read_label(frame_bgr, result.xyxy)
+        ocr_result = self.ocr.read_label(frame_bgr, result.xyxy)
-        result.raw_text = text
+        result.raw_text = ocr_result.text
+        result.ocr_engine = ocr_result.engine
+        result.ocr_confidence = ocr_result.confidence
+        result.ocr_elapsed_ms = ocr_result.elapsed_ms
+        label_cfg = self.config["label_data"]
         result.parsed = parse_label_text(
-            text,
+            ocr_result.text,
-            self.config["label_data"].get("colors", []),
+            label_cfg.get("colors", []),
-            self.config["label_data"].get("models", []),
+            label_cfg.get("models", []),
+            model_min_score=float(label_cfg.get("model_min_score", 0.72)),
+            color_min_score=float(label_cfg.get("color_min_score", 0.72)),
         )
-        if ocr_error:
+        if ocr_result.error:
-            result.error = ocr_error
+            result.error = ocr_result.error
         return result

app/fuzzy_match.py

@@ -0,0 +1,131 @@
+from __future__ import annotations
+
+import re
+import unicodedata
+from dataclasses import dataclass
+from difflib import SequenceMatcher
+
+
+@dataclass(frozen=True)
+class FuzzyMatch:
+    value: str
+    score: float
+    matched_text: str
+    coverage: float
+
+
+def compact_text(text: str) -> str:
+    normalized = unicodedata.normalize("NFKD", text)
+    ascii_text = normalized.encode("ascii", "ignore").decode("ascii")
+    return re.sub(r"[^A-Z0-9]+", "", ascii_text.upper())
+
+
+def token_text(text: str) -> list[str]:
+    normalized = unicodedata.normalize("NFKD", text)
+    ascii_text = normalized.encode("ascii", "ignore").decode("ascii")
+    return re.findall(r"[A-Z0-9]+", ascii_text.upper())
+
+
+def similarity(left: str, right: str) -> float:
+    if not left or not right:
+        return 0.0
+    return SequenceMatcher(None, left, right).ratio()
+
+
+def best_fuzzy_match(text: str, candidates: list[str], min_score: float = 0.72) -> FuzzyMatch | None:
+    best: FuzzyMatch | None = None
+    for candidate in candidates:
+        candidate_compact = compact_text(candidate)
+        if not candidate_compact:
+            continue
+
+        score, matched_text, coverage = best_candidate_score(text, candidate_compact)
+        match = FuzzyMatch(
+            value=candidate,
+            score=score,
+            matched_text=matched_text,
+            coverage=coverage,
+        )
+        if best is None or _is_better_match(match, best):
+            best = match
+
+    if best is None or best.score < min_score:
+        return None
+    return best
+
+
+def best_candidate_score(text: str, candidate_compact: str) -> tuple[float, str, float]:
+    full_compact = compact_text(text)
+    if candidate_compact in full_compact:
+        return 1.0, candidate_compact, 1.0
+
+    windows = candidate_windows(text, len(candidate_compact))
+    if not windows and full_compact:
+        windows = [full_compact]
+
+    best_score = 0.0
+    best_window = ""
+    best_coverage = 0.0
+    for window in windows:
+        coverage = min(len(window), len(candidate_compact)) / max(len(window), len(candidate_compact))
+        score = similarity(candidate_compact, window) * coverage * digit_match_weight(candidate_compact, window)
+        if score > best_score:
+            best_score = score
+            best_window = window
+            best_coverage = coverage
+    return best_score, best_window, best_coverage
+
+
+def digit_match_weight(candidate: str, matched_text: str) -> float:
+    candidate_digits = re.findall(r"\d", candidate)
+    matched_digits = re.findall(r"\d", matched_text)
+    if not candidate_digits or not matched_digits:
+        return 1.0
+    if candidate_digits == matched_digits:
+        return 1.05
+    return 0.7
+
+
+def candidate_windows(text: str, candidate_length: int) -> list[str]:
+    tokens = token_text(text)
+    windows: set[str] = set()
+
+    for token in tokens:
+        windows.add(token)
+
+    max_ngram = min(8, len(tokens))
+    for size in range(2, max_ngram + 1):
+        for index in range(0, len(tokens) - size + 1):
+            joined = "".join(tokens[index : index + size])
+            if _length_is_plausible(joined, candidate_length):
+                windows.add(joined)
+
+    full_compact = compact_text(text)
+    if full_compact:
+        min_len = max(1, int(candidate_length * 0.65))
+        max_len = max(min_len, int(candidate_length * 1.35))
+        for length in range(min_len, max_len + 1):
+            if length > len(full_compact):
+                continue
+            for index in range(0, len(full_compact) - length + 1):
+                windows.add(full_compact[index : index + length])
+
+    return sorted(windows)
+
+
+def _length_is_plausible(value: str, candidate_length: int) -> bool:
+    if not value:
+        return False
+    return int(candidate_length * 0.65) <= len(value) <= int(candidate_length * 1.6)
+
+
+def _is_better_match(match: FuzzyMatch, best: FuzzyMatch) -> bool:
+    if match.score > best.score + 0.03:
+        return True
+    if match.score < best.score - 0.03:
+        return False
+    if match.coverage > best.coverage + 0.05:
+        return True
+    if match.coverage < best.coverage - 0.05:
+        return False
+    return len(compact_text(match.value)) > len(compact_text(best.value))

app/label_parser.py

@@ -3,8 +3,12 @@ from __future__ import annotations
 import re
 from dataclasses import dataclass, asdict
 
+from app.fuzzy_match import best_fuzzy_match
+
 
 ORDER_RE = re.compile(r"\b(?P<order>\d{4}/\d{4}/(?:[1-9]|[1-9]\d))\b")
+DEFAULT_MODEL_MIN_SCORE = 0.72
+DEFAULT_COLOR_MIN_SCORE = 0.72
 
 
 @dataclass
@@ -13,8 +17,10 @@ class ParsedLabel:
     color_code: str | None
     product_model: str | None
     raw_text: str
+    color_score: float | None = None
+    product_model_score: float | None = None
 
-    def to_dict(self) -> dict[str, str | None]:
+    def to_dict(self) -> dict[str, str | float | None]:
         return asdict(self)
 
 
@@ -22,23 +28,24 @@ def normalize_ocr_text(text: str) -> str:
     return " ".join(text.replace("\n", " ").replace("\r", " ").split())
 
 
-def parse_label_text(text: str, known_colors: list[str], known_models: list[str]) -> ParsedLabel:
+def parse_label_text(
+    text: str,
+    known_colors: list[str],
+    known_models: list[str],
+    model_min_score: float = DEFAULT_MODEL_MIN_SCORE,
+    color_min_score: float = DEFAULT_COLOR_MIN_SCORE,
+) -> ParsedLabel:
     normalized = normalize_ocr_text(text)
     order_match = ORDER_RE.search(normalized)
 
-    normalized_upper = normalized.upper()
+    color_match = best_fuzzy_match(normalized, known_colors, color_min_score)
-    color_code = next(
+    model_match = best_fuzzy_match(normalized, known_models, model_min_score)
-        (color for color in known_colors if color.upper() in normalized_upper),
-        None,
-    )
-    product_model = next(
-        (model for model in known_models if re.search(rf"\b{re.escape(model)}\b", normalized, re.I)),
-        None,
-    )
 
     return ParsedLabel(
         order_number=order_match.group("order") if order_match else None,
-        color_code=color_code,
+        color_code=color_match.value if color_match else None,
-        product_model=product_model,
+        product_model=model_match.value if model_match else None,
         raw_text=normalized,
+        color_score=color_match.score if color_match else None,
+        product_model_score=model_match.score if model_match else None,
     )