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src/detect.py

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+"""
+ROI detection: full passport image -> bounding box of the dotted series/number strip.
+Uses a YOLO model fine-tuned on Ukrainian passport images (class: "Dotted").
+"""
+
+from pathlib import Path
+import cv2
+import numpy as np
+
+
+def load_detector(model_path: str | Path):
+    from ultralytics import YOLO
+    return YOLO(str(model_path))
+
+
+def detect_roi(image: np.ndarray, model, conf: float = 0.30) -> tuple[int, int, int, int] | None:
+    """
+    Detect the dotted passport series/number strip in a full passport image.
+
+    Args:
+        image:  BGR image (numpy array) or grayscale — will be converted as needed.
+        model:  loaded YOLO model (from load_detector).
+        conf:   minimum confidence threshold.
+
+    Returns:
+        (x1, y1, x2, y2) pixel coordinates of the best detection,
+        or None if nothing found.
+    """
+    results = model(image, conf=conf, verbose=False)
+    boxes = results[0].boxes
+
+    if boxes is None or len(boxes) == 0:
+        return None
+
+    # Pick highest-confidence box
+    best = boxes[boxes.conf.argmax()]
+    x1, y1, x2, y2 = map(int, best.xyxy[0].tolist())
+    return x1, y1, x2, y2
+
+
+def crop_roi(image: np.ndarray, box: tuple[int, int, int, int],
+             padding: float = 0.05) -> np.ndarray:
+    """
+    Crop the ROI from the image with optional relative padding.
+
+    Args:
+        image:   BGR or grayscale image.
+        box:     (x1, y1, x2, y2)
+        padding: fraction of box size to add as margin on each side.
+
+    Returns:
+        Cropped numpy array (same channels as input).
+    """
+    h, w = image.shape[:2]
+    x1, y1, x2, y2 = box
+
+    pw = int((x2 - x1) * padding)
+    ph = int((y2 - y1) * padding)
+
+    x1 = max(0, x1 - pw)
+    y1 = max(0, y1 - ph)
+    x2 = min(w, x2 + pw)
+    y2 = min(h, y2 + ph)
+
+    return image[y1:y2, x1:x2]