85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
from typing import List, Optional, Tuple
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import cv2
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import numpy as np
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def resize_keep_aspect(image: np.ndarray, target_width: int) -> np.ndarray:
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if target_width <= 0:
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return image
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h, w = image.shape[:2]
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if w == target_width:
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return image
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scale = target_width / float(w)
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new_size = (target_width, int(round(h * scale)))
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return cv2.resize(image, new_size, interpolation=cv2.INTER_AREA)
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def enhance_barcode_stripes(gray: np.ndarray, gaussian_ksize: int, sobel_ksize: int) -> np.ndarray:
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g = gray
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if gaussian_ksize and gaussian_ksize > 1:
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g = cv2.GaussianBlur(g, (gaussian_ksize, gaussian_ksize), 0)
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# 使用水平 Sobel 捕捉垂直边缘（条纹）
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grad_x = cv2.Sobel(g, cv2.CV_32F, 1, 0, ksize=sobel_ksize)
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grad_x = cv2.convertScaleAbs(grad_x)
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return grad_x
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def binarize_image(img: np.ndarray, method: str = "otsu") -> np.ndarray:
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if method == "adaptive":
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return cv2.adaptiveThreshold(
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img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 31, 10
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)
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# 默认 OTSU
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_, th = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
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return th
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def morph_close(img: np.ndarray, kernel_size: int) -> np.ndarray:
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if kernel_size <= 1:
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return img
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_size, kernel_size))
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closed = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
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return closed
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def find_barcode_roi(binary: np.ndarray, original_shape: Tuple[int, int], min_area_ratio: float, min_wh_ratio: float) -> Optional[Tuple[np.ndarray, Tuple[int, int, int, int]]]:
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h, w = original_shape
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contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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image_area = h * w
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candidates: List[Tuple[float, Tuple[int, int, int, int]]] = []
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for cnt in contours:
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x, y, cw, ch = cv2.boundingRect(cnt)
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area = cw * ch
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if area / image_area < min_area_ratio:
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continue
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wh_ratio = cw / float(ch + 1e-6)
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if wh_ratio < min_wh_ratio:
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continue
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candidates.append((area, (x, y, cw, ch)))
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if not candidates:
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return None
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candidates.sort(key=lambda t: t[0], reverse=True)
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_, bbox = candidates[0]
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x, y, cw, ch = bbox
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roi = binary[y : y + ch, x : x + cw]
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return roi, bbox
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def warp_barcode_region(gray: np.ndarray, bbox: Tuple[int, int, int, int], target_height: int, crop_bottom_ratio: float = 0.0) -> np.ndarray:
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x, y, cw, ch = bbox
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crop = gray[y : y + ch, x : x + cw]
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# 去除底部数字区域干扰
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if 0 < crop_bottom_ratio < 1:
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hb = int(round(ch * (1.0 - crop_bottom_ratio)))
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hb = max(10, min(ch, hb))
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crop = crop[:hb, :]
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if target_height <= 0:
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return crop
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scale = target_height / float(ch)
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target_width = int(round(cw * scale))
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warped = cv2.resize(crop, (target_width, target_height), interpolation=cv2.INTER_CUBIC)
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return warped
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