Add video playback functionality to MainWindow and update app configuration

README.md

@@ -39,5 +39,22 @@ Istotne ustawienia:
 - `detection.mode` - `best` rysuje najlepsza etykiete, `all` rysuje wszystkie wykrycia.
 - `detection.frame_stride` - YOLO uruchamiany co N klatek podczas aktywnego wykrywania.
 - `label_data.models`, `label_data.colors` - slowniki do walidacji tekstu z etykiety.
+- `ocr.enabled`, `ocr.engine` - wlaczenie OCR i wybor silnika: `none`, `tesseract`, `paddle`.
 
 Zdjecia trafiaja do `captures/photos`, filmy do `captures/videos`. Obok kazdego pliku media zapisywany jest JSON z aktualnym wynikiem detekcji/OCR.
+
+## Testowanie OCR poza aplikacja
+
+OCR mozna testowac na gotowych cropach bez uruchamiania kamery i YOLO:
+
+```bash
+.venv-lin/bin/python -m app.ocr.cli crop --engine none
+.venv-lin/bin/python -m app.ocr.cli crop --engine tesseract
+.venv-lin/bin/python -m app.ocr.cli crop --engine paddle --json
+```
+
+Backend PaddleOCR jest opcjonalny. Zaleznosci do testow PaddleOCR sa w osobnym pliku:
+
+```bash
+.venv-lin/bin/pip install -r requirements-ocr-paddle.txt
+```

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)
-        result.raw_text = text
+        ocr_result = self.ocr.read_label(frame_bgr, result.xyxy)
+        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,
-            self.config["label_data"].get("colors", []),
-            self.config["label_data"].get("models", []),
+            ocr_result.text,
+            label_cfg.get("colors", []),
+            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:
-            result.error = ocr_error
+        if ocr_result.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_code = next(
-        (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,
-    )
+    color_match = best_fuzzy_match(normalized, known_colors, color_min_score)
+    model_match = best_fuzzy_match(normalized, known_models, model_min_score)
 
     return ParsedLabel(
         order_number=order_match.group("order") if order_match else None,
-        color_code=color_code,
-        product_model=product_model,
+        color_code=color_match.value if color_match else None,
+        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,
     )

app/main_window.py

@@ -6,12 +6,13 @@ from typing import Any
 
 import cv2
 import numpy as np
-from PySide6.QtCore import Qt, Slot
+from PySide6.QtCore import Qt, QTimer, Slot
 from PySide6.QtGui import QAction, QImage, QPixmap
 from PySide6.QtWidgets import (
     QApplication,
     QHBoxLayout,
     QLabel,
+    QFileDialog,
     QMainWindow,
     QMessageBox,
     QPushButton,
@@ -42,6 +43,10 @@ class MainWindow(QMainWindow):
         self.fps_frame_count = 0
         self.fps_last_time = time.monotonic()
         self.display_fps = 0.0
+        self.video_capture: cv2.VideoCapture | None = None
+        self.video_timer = QTimer(self)
+        self.video_timer.timeout.connect(self._read_video_frame)
+        self.video_playing = False
         self.media_store = MediaStore(self.config, self.app_config)
         self.video_recorder = VideoRecorder(self.config, self.app_config)
 
@@ -119,12 +124,19 @@ class MainWindow(QMainWindow):
         )
         toolbar_layout = QHBoxLayout(self.toolbar)
         toolbar_layout.setContentsMargins(8, 6, 8, 6)
+        self.load_video_button = self._tool_button(QStyle.SP_DirOpenIcon, "Wczytaj film")
+        self.video_play_button = self._tool_button(QStyle.SP_MediaPlay, "Play/pauza filmu")
         self.photo_button = self._tool_button(QStyle.SP_DialogSaveButton, "Zrob zdjecie")
         self.record_button = self._tool_button(QStyle.SP_MediaPlay, "Start/stop nagrywania")
         self.settings_button = self._tool_button(QStyle.SP_FileDialogDetailedView, "Ustawienia obrazu")
+        toolbar_layout.addWidget(self.load_video_button)
+        toolbar_layout.addWidget(self.video_play_button)
         toolbar_layout.addWidget(self.photo_button)
         toolbar_layout.addWidget(self.record_button)
         toolbar_layout.addWidget(self.settings_button)
+        self.video_play_button.setEnabled(False)
+        self.load_video_button.clicked.connect(self.load_video)
+        self.video_play_button.clicked.connect(self.toggle_video_playback)
         self.photo_button.clicked.connect(self.take_photo)
         self.record_button.clicked.connect(self.toggle_recording)
         self.settings_button.clicked.connect(self.open_settings)
@@ -158,9 +170,10 @@ class MainWindow(QMainWindow):
     def closeEvent(self, event: Any) -> None:
         if self.video_recorder.is_recording:
             self.video_recorder.stop(self.current_metadata("video"))
-        self.worker.stop()
+        self.video_timer.stop()
+        self._close_video_capture()
+        self._stop_camera_worker()
         self.detection_worker.stop()
-        self.worker.wait(2000)
         self.detection_worker.wait(2000)
         super().closeEvent(event)
 
@@ -223,6 +236,41 @@ class MainWindow(QMainWindow):
         self.record_button.setIcon(self.style().standardIcon(QStyle.SP_MediaStop))
         self.statusBar().showMessage(f"Nagrywanie: {path}", 5000)
 
+    def load_video(self) -> None:
+        path, _ = QFileDialog.getOpenFileName(
+            self,
+            "Wczytaj film",
+            "",
+            "Filmy (*.mp4 *.avi *.mov *.mkv *.m4v);;Wszystkie pliki (*)",
+        )
+        if not path:
+            return
+
+        capture = cv2.VideoCapture(path)
+        if not capture.isOpened():
+            QMessageBox.warning(self, "Film", "Nie mozna otworzyc pliku wideo")
+            capture.release()
+            return
+
+        if self.video_recorder.is_recording:
+            self.video_recorder.stop(self.current_metadata("video"))
+            self.record_button.setIcon(self.style().standardIcon(QStyle.SP_MediaPlay))
+
+        self._stop_camera_worker()
+        self._close_video_capture()
+        self.video_capture = capture
+        self.video_play_button.setEnabled(True)
+        self._set_video_playing(False)
+        self.overlay_result = None
+        self.last_detection = None
+        self.result_text.setPlainText(f"Wczytano film: {path}")
+        self._read_video_frame()
+
+    def toggle_video_playback(self) -> None:
+        if self.video_capture is None:
+            return
+        self._set_video_playing(not self.video_playing)
+
     def open_settings(self) -> None:
         dialog = SettingsDialog(self.config, self)
         dialog.settings_saved.connect(self.save_camera_settings)
@@ -232,8 +280,55 @@ class MainWindow(QMainWindow):
     def save_camera_settings(self, camera_config: dict[str, Any]) -> None:
         self.config["camera"] = camera_config
         self.app_config.save(self.config)
+        if self.worker is not None:
             self.worker.update_camera_config(camera_config)
 
+    def _read_video_frame(self) -> None:
+        if self.video_capture is None:
+            return
+
+        ok, frame = self.video_capture.read()
+        if not ok or frame is None:
+            self._set_video_playing(False)
+            self.video_capture.set(cv2.CAP_PROP_POS_FRAMES, 0)
+            self.statusBar().showMessage("Koniec filmu", 3000)
+            return
+
+        self.on_frame_ready(frame)
+
+    def _set_video_playing(self, playing: bool) -> None:
+        self.video_playing = playing
+        if self.video_capture is None:
+            self.video_timer.stop()
+            self.video_play_button.setIcon(self.style().standardIcon(QStyle.SP_MediaPlay))
+            self.video_play_button.setEnabled(False)
+            return
+
+        if playing:
+            fps = self.video_capture.get(cv2.CAP_PROP_FPS)
+            if fps <= 0:
+                fps = float(self.config["camera"].get("fps", 30))
+            interval_ms = max(1, int(round(1000 / fps)))
+            self.video_timer.start(interval_ms)
+            self.video_play_button.setIcon(self.style().standardIcon(QStyle.SP_MediaPause))
+        else:
+            self.video_timer.stop()
+            self.video_play_button.setIcon(self.style().standardIcon(QStyle.SP_MediaPlay))
+
+    def _close_video_capture(self) -> None:
+        self._set_video_playing(False)
+        if self.video_capture is not None:
+            self.video_capture.release()
+            self.video_capture = None
+        self.video_play_button.setEnabled(False)
+
+    def _stop_camera_worker(self) -> None:
+        if self.worker is None:
+            return
+        self.worker.stop()
+        self.worker.wait(2000)
+        self.worker = None
+
     def _maybe_request_detection(self, frame: np.ndarray) -> None:
         if not self.detecting:
             return
@@ -277,10 +372,18 @@ class MainWindow(QMainWindow):
             lines.append(f"Komunikat: {result.error}")
         if result.confidence is not None:
             lines.append(f"YOLO confidence: {result.confidence:.3f}")
+        if result.ocr_engine:
+            lines.append(f"OCR: {result.ocr_engine}")
+        if result.ocr_confidence is not None:
+            lines.append(f"OCR confidence: {result.ocr_confidence:.3f}")
+        if result.ocr_elapsed_ms is not None:
+            lines.append(f"OCR czas: {result.ocr_elapsed_ms:.0f} ms")
         if result.parsed:
             lines.append(f"Zamowienie: {result.parsed.order_number or '-'}")
-            lines.append(f"Kolor: {result.parsed.color_code or '-'}")
-            lines.append(f"Model: {result.parsed.product_model or '-'}")
+            color_score = _format_score(result.parsed.color_score)
+            model_score = _format_score(result.parsed.product_model_score)
+            lines.append(f"Kolor: {result.parsed.color_code or '-'}{color_score}")
+            lines.append(f"Model: {result.parsed.product_model or '-'}{model_score}")
         if result.raw_text:
             lines.append("")
             lines.append(result.raw_text)
@@ -326,7 +429,7 @@ class MainWindow(QMainWindow):
 
     def _draw_fps(self, frame_bgr: np.ndarray) -> None:
         label = f"FPS: {self.display_fps:.1f}"
-        cv2.rectangle(frame_bgr, (12, 12), (122, 46), (0, 0, 0), -1)
+        cv2.rectangle(frame_bgr, (12, 12), (142, 46), (0, 0, 0), -1)
         cv2.putText(
             frame_bgr,
             label,
@@ -344,3 +447,9 @@ def run_app(app_config: AppConfig) -> int:
     window = MainWindow(app_config)
     window.show()
     return app.exec()
+
+
+def _format_score(score: float | None) -> str:
+    if score is None:
+        return ""
+    return f" ({score:.2f})"

app/ocr/__init__.py

@@ -0,0 +1,4 @@
+from app.ocr.base import OcrEngine, OcrLine, OcrResult
+from app.ocr.factory import create_ocr_engine
+
+__all__ = ["OcrEngine", "OcrLine", "OcrResult", "create_ocr_engine"]

app/ocr/base.py

@@ -0,0 +1,54 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Protocol
+
+import cv2
+import numpy as np
+
+
+@dataclass
+class OcrLine:
+    text: str
+    confidence: float | None = None
+    bbox: list[list[float]] | None = None
+
+
+@dataclass
+class OcrResult:
+    text: str = ""
+    confidence: float | None = None
+    lines: list[OcrLine] = field(default_factory=list)
+    error: str | None = None
+    elapsed_ms: float = 0.0
+    engine: str = "none"
+
+
+class OcrEngine(Protocol):
+    name: str
+
+    def read_label(self, frame_bgr: np.ndarray, bbox: tuple[int, int, int, int]) -> OcrResult:
+        ...
+
+
+def crop_bbox(frame_bgr: np.ndarray, bbox: tuple[int, int, int, int], margin: int = 0) -> np.ndarray | None:
+    x1, y1, x2, y2 = bbox
+    h, w = frame_bgr.shape[:2]
+    x1, y1 = max(0, x1 - margin), max(0, y1 - margin)
+    x2, y2 = min(w, x2 + margin), min(h, y2 + margin)
+    if x2 <= x1 or y2 <= y1:
+        return None
+    return frame_bgr[y1:y2, x1:x2]
+
+
+def prepare_ocr_image(image_bgr: np.ndarray, config: dict) -> np.ndarray:
+    scale = float(config.get("scale", 1.0))
+    if scale != 1.0:
+        image_bgr = cv2.resize(image_bgr, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
+
+    if not config.get("threshold", False):
+        return image_bgr
+
+    gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY)
+    gray = cv2.GaussianBlur(gray, (3, 3), 0)
+    return cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

app/ocr/cli.py

@@ -0,0 +1,106 @@
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from typing import Any
+
+import cv2
+
+from app.config import AppConfig
+from app.label_parser import parse_label_text
+from app.ocr import create_ocr_engine
+
+
+def iter_images(path: Path) -> list[Path]:
+    if path.is_file():
+        return [path]
+
+    extensions = {".jpg", ".jpeg", ".png", ".bmp", ".webp", ".tif", ".tiff"}
+    return sorted(item for item in path.iterdir() if item.is_file() and item.suffix.lower() in extensions)
+
+
+def result_to_dict(path: Path, result: Any, config: dict[str, Any]) -> dict[str, Any]:
+    label_cfg = config.get("label_data", {})
+    parsed = parse_label_text(
+        result.text,
+        label_cfg.get("colors", []),
+        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)),
+    )
+    return {
+        "file": str(path),
+        "engine": result.engine,
+        "elapsed_ms": round(result.elapsed_ms, 2),
+        "confidence": result.confidence,
+        "error": result.error,
+        "text": result.text,
+        "lines": [
+            {
+                "text": line.text,
+                "confidence": line.confidence,
+                "bbox": line.bbox,
+            }
+            for line in result.lines
+        ],
+        "parsed": parsed.to_dict(),
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="Test OCR backend on cropped label images.")
+    parser.add_argument("path", help="Image file or directory with crop images")
+    parser.add_argument("--config", default="app_config.json", help="Application config JSON path")
+    parser.add_argument(
+        "--engine",
+        choices=["none", "tesseract", "paddle"],
+        help="Override ocr.engine from config",
+    )
+    parser.add_argument("--no-threshold", action="store_true", help="Disable threshold preprocessing")
+    parser.add_argument("--scale", type=float, help="Override OCR scale")
+    parser.add_argument("--json", action="store_true", help="Print JSON output")
+    args = parser.parse_args()
+
+    app_config = AppConfig(Path(args.config))
+    config = app_config.data
+    if args.engine:
+        config["ocr"]["engine"] = args.engine
+        config["ocr"]["enabled"] = args.engine != "none"
+    if args.no_threshold:
+        config["ocr"]["threshold"] = False
+    if args.scale is not None:
+        config["ocr"]["scale"] = args.scale
+
+    engine = create_ocr_engine(config)
+    outputs = []
+    for image_path in iter_images(Path(args.path)):
+        image = cv2.imread(str(image_path), cv2.IMREAD_COLOR)
+        if image is None:
+            outputs.append({"file": str(image_path), "error": "Nie mozna odczytac obrazu"})
+            continue
+
+        h, w = image.shape[:2]
+        result = engine.read_label(image, (0, 0, w, h))
+        outputs.append(result_to_dict(image_path, result, config))
+
+    if args.json:
+        print(json.dumps(outputs, indent=2, ensure_ascii=False))
+        return 0
+
+    for output in outputs:
+        print(f"file: {output['file']}")
+        print(f"engine: {output.get('engine')}")
+        print(f"elapsed_ms: {output.get('elapsed_ms')}")
+        print(f"confidence: {output.get('confidence')}")
+        if output.get("error"):
+            print(f"error: {output['error']}")
+        print("text:")
+        print(output.get("text") or "")
+        print(f"parsed: {output.get('parsed')}")
+        print()
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

app/ocr/factory.py

@@ -0,0 +1,24 @@
+from __future__ import annotations
+
+from typing import Any
+
+from app.ocr.base import OcrEngine
+from app.ocr.none import NoOcrEngine
+from app.ocr.paddle import PaddleOcrEngine
+from app.ocr.tesseract import TesseractOcrEngine
+
+
+def create_ocr_engine(config: dict[str, Any]) -> OcrEngine:
+    ocr_cfg = config.get("ocr", {})
+    if not ocr_cfg.get("enabled", True):
+        return NoOcrEngine(ocr_cfg)
+
+    engine = str(ocr_cfg.get("engine", "tesseract")).lower()
+    if engine in {"none", "off", "disabled"}:
+        return NoOcrEngine(ocr_cfg)
+    if engine == "tesseract":
+        return TesseractOcrEngine(ocr_cfg)
+    if engine == "paddle":
+        return PaddleOcrEngine(ocr_cfg)
+
+    raise ValueError(f"Nieznany silnik OCR: {engine}")

app/ocr/none.py

@@ -0,0 +1,15 @@
+from __future__ import annotations
+
+import numpy as np
+
+from app.ocr.base import OcrResult
+
+
+class NoOcrEngine:
+    name = "none"
+
+    def __init__(self, config: dict) -> None:
+        self.config = config
+
+    def read_label(self, frame_bgr: np.ndarray, bbox: tuple[int, int, int, int]) -> OcrResult:
+        return OcrResult(engine=self.name)

app/ocr/paddle.py

@@ -0,0 +1,153 @@
+from __future__ import annotations
+
+import time
+from typing import Any
+
+import numpy as np
+
+from app.ocr.base import OcrLine, OcrResult, crop_bbox, prepare_ocr_image
+
+
+class PaddleOcrEngine:
+    name = "paddle"
+
+    def __init__(self, config: dict) -> None:
+        self.config = config
+        self.load_error: str | None = None
+        self.ocr: Any = None
+        self._load()
+
+    def _load(self) -> None:
+        try:
+            from paddleocr import PaddleOCR
+        except Exception as exc:
+            self.load_error = f"Nie mozna zaimportowac PaddleOCR: {exc}"
+            return
+
+        paddle_cfg = dict(self.config.get("paddle", {}))
+        paddle_cfg.setdefault("lang", self.config.get("language", "en"))
+        try:
+            self.ocr = PaddleOCR(**paddle_cfg)
+        except Exception as exc:
+            self.load_error = f"Nie mozna zaladowac PaddleOCR: {exc}"
+
+    def read_label(self, frame_bgr: np.ndarray, bbox: tuple[int, int, int, int]) -> OcrResult:
+        started = time.perf_counter()
+        if self.ocr is None:
+            return OcrResult(
+                error=self.load_error or "PaddleOCR nie jest zaladowany",
+                elapsed_ms=self._elapsed_ms(started),
+                engine=self.name,
+            )
+
+        margin = int(self.config.get("margin", 0))
+        roi = crop_bbox(frame_bgr, bbox, margin=margin)
+        if roi is None:
+            return OcrResult(
+                error="Nieprawidlowy bbox OCR",
+                elapsed_ms=self._elapsed_ms(started),
+                engine=self.name,
+            )
+
+        preprocess_config = {
+            **self.config,
+            "threshold": bool(self.config.get("paddle_threshold", False)),
+        }
+        image = prepare_ocr_image(roi, preprocess_config)
+        try:
+            raw_result = self._run_ocr(image)
+        except Exception as exc:
+            return OcrResult(
+                error=f"Blad PaddleOCR: {exc}",
+                elapsed_ms=self._elapsed_ms(started),
+                engine=self.name,
+            )
+
+        lines = self._parse_lines(raw_result)
+        text = "\n".join(line.text for line in lines)
+        confidences = [line.confidence for line in lines if line.confidence is not None]
+        confidence = sum(confidences) / len(confidences) if confidences else None
+        return OcrResult(
+            text=text,
+            confidence=confidence,
+            lines=lines,
+            elapsed_ms=self._elapsed_ms(started),
+            engine=self.name,
+        )
+
+    def _run_ocr(self, image: np.ndarray) -> Any:
+        if hasattr(self.ocr, "predict"):
+            return self.ocr.predict(image)
+        try:
+            return self.ocr.ocr(image, cls=bool(self.config.get("use_angle_cls", True)))
+        except TypeError:
+            return self.ocr.ocr(image)
+
+    def _parse_lines(self, raw_result: Any) -> list[OcrLine]:
+        if raw_result is None:
+            return []
+
+        lines: list[OcrLine] = []
+        for item in self._iter_result_items(raw_result):
+            parsed = self._parse_item(item)
+            if parsed is not None and parsed.text.strip():
+                lines.append(parsed)
+        return lines
+
+    def _iter_result_items(self, raw_result: Any) -> list[Any]:
+        if isinstance(raw_result, dict):
+            texts = raw_result.get("rec_texts") or raw_result.get("texts")
+            scores = raw_result.get("rec_scores") or raw_result.get("scores") or []
+            boxes = raw_result.get("rec_polys") or raw_result.get("dt_polys") or raw_result.get("boxes") or []
+            if texts:
+                return [
+                    (boxes[index] if index < len(boxes) else None, (text, scores[index] if index < len(scores) else None))
+                    for index, text in enumerate(texts)
+                ]
+            return []
+
+        if isinstance(raw_result, list) and len(raw_result) == 1 and isinstance(raw_result[0], list):
+            return raw_result[0]
+        if isinstance(raw_result, list):
+            items = []
+            for result in raw_result:
+                if isinstance(result, dict):
+                    items.extend(self._iter_result_items(result))
+                elif isinstance(result, list):
+                    items.extend(result)
+                else:
+                    items.append(result)
+            return items
+        return [raw_result]
+
+    def _parse_item(self, item: Any) -> OcrLine | None:
+        if not isinstance(item, (list, tuple)):
+            return None
+
+        if len(item) >= 2 and isinstance(item[1], (list, tuple)) and item[1]:
+            text = str(item[1][0])
+            confidence = self._to_float(item[1][1]) if len(item[1]) > 1 else None
+            bbox = self._to_bbox(item[0])
+            return OcrLine(text=text, confidence=confidence, bbox=bbox)
+
+        if len(item) >= 2 and isinstance(item[0], str):
+            return OcrLine(text=str(item[0]), confidence=self._to_float(item[1]))
+
+        return None
+
+    def _to_float(self, value: Any) -> float | None:
+        try:
+            return float(value)
+        except (TypeError, ValueError):
+            return None
+
+    def _to_bbox(self, value: Any) -> list[list[float]] | None:
+        if value is None:
+            return None
+        try:
+            return [[float(point[0]), float(point[1])] for point in value]
+        except (TypeError, ValueError, IndexError):
+            return None
+
+    def _elapsed_ms(self, started: float) -> float:
+        return (time.perf_counter() - started) * 1000.0

app/ocr/tesseract.py

@@ -0,0 +1,104 @@
+from __future__ import annotations
+
+import time
+
+import numpy as np
+
+from app.ocr.base import OcrLine, OcrResult, crop_bbox, prepare_ocr_image
+
+
+class TesseractOcrEngine:
+    name = "tesseract"
+
+    def __init__(self, config: dict) -> None:
+        self.config = config
+        self.load_error: str | None = None
+        self.pytesseract = None
+        self._load()
+
+    def _load(self) -> None:
+        try:
+            import pytesseract
+
+            command = self.config.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]) -> OcrResult:
+        started = time.perf_counter()
+        if self.pytesseract is None:
+            return OcrResult(
+                error=self.load_error or "OCR Tesseract nie jest zaladowany",
+                elapsed_ms=self._elapsed_ms(started),
+                engine=self.name,
+            )
+
+        margin = int(self.config.get("margin", 0))
+        roi = crop_bbox(frame_bgr, bbox, margin=margin)
+        if roi is None:
+            return OcrResult(
+                error="Nieprawidlowy bbox OCR",
+                elapsed_ms=self._elapsed_ms(started),
+                engine=self.name,
+            )
+
+        image = prepare_ocr_image(roi, self.config)
+        psm = int(self.config.get("psm", 6))
+        language = self.config.get("language", "eng")
+        extra_config = str(self.config.get("config", "")).strip()
+        tesseract_config = f"--psm {psm}"
+        if extra_config:
+            tesseract_config = f"{tesseract_config} {extra_config}"
+
+        try:
+            text = self.pytesseract.image_to_string(
+                image,
+                lang=language,
+                config=tesseract_config,
+            )
+        except Exception as exc:
+            return OcrResult(
+                error=f"Blad OCR Tesseract: {exc}",
+                elapsed_ms=self._elapsed_ms(started),
+                engine=self.name,
+            )
+
+        confidence = self._mean_confidence(image, language, tesseract_config)
+        return OcrResult(
+            text=text,
+            confidence=confidence,
+            lines=[OcrLine(text=line) for line in text.splitlines() if line.strip()],
+            elapsed_ms=self._elapsed_ms(started),
+            engine=self.name,
+        )
+
+    def _mean_confidence(self, image: np.ndarray, language: str, tesseract_config: str) -> float | None:
+        if self.pytesseract is None:
+            return None
+        try:
+            data = self.pytesseract.image_to_data(
+                image,
+                lang=language,
+                config=tesseract_config,
+                output_type=self.pytesseract.Output.DICT,
+            )
+        except Exception:
+            return None
+
+        values = []
+        for raw_conf in data.get("conf", []):
+            try:
+                confidence = float(raw_conf)
+            except (TypeError, ValueError):
+                continue
+            if confidence >= 0:
+                values.append(confidence / 100.0)
+        if not values:
+            return None
+        return sum(values) / len(values)
+
+    def _elapsed_ms(self, started: float) -> float:
+        return (time.perf_counter() - started) * 1000.0

app_config.json

@@ -20,19 +20,32 @@
     }
   },
   "detection": {
-    "model_path": "models/best.pt",
+    "model_path": "models/best_v1.pt",
     "confidence_threshold": 0.25,
     "mode": "best",
-    "frame_stride": 5,
+    "frame_stride": 30,
     "image_size": 640,
     "device": "cpu"
   },
   "ocr": {
     "enabled": true,
+    "engine": "paddle",
     "language": "eng",
     "tesseract_cmd": null,
+    "psm": 6,
+    "margin": 0,
     "threshold": true,
-    "scale": 2.0
+    "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",
@@ -45,13 +58,33 @@
     "show_fps": true
   },
   "label_data": {
+    "model_min_score": 0.72,
+    "color_min_score": 0.72,
     "models": [
-      "Regius",
-      "Duvell"
+      "Regius 6",
+      "Regius 7",
+      "Duvell 6",
+      "Duvell 7",
+      "Duvell Elite 6",
+      "Duvell Elite 7"
     ],
     "colors": [
       "T-NF-BLK-OUT-BST-G",
-      "T-BLK-G"
+      "T-BLK-G",
+      "T-BLK-S",
+      "T-BLK-M",
+      "M-BLK-G",
+      "M-BLK-S",
+      "M-BLK-M",
+      "T-CST-G",
+      "T-CST-S",
+      "T-CST-M",
+      "T-ANTIQUE-G",
+      "T-ANTIQUE-S",
+      "T-ANTIQUE-M",
+      "T-NAT-G",
+      "T-NAT-S",
+      "T-NAT-M"
     ]
   }
 }

requirements-ocr-paddle.txt

@@ -0,0 +1,2 @@
+paddlepaddle
+paddleocr