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VisRes

__init__ [SOURCE]

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def __init__(self, text_score: float = 0.5):
    pass

参数

  • text_score (float, optional): 文本识别结果置信度，值越大，把握越大。取值范围：[0, 1], 默认值是0.5。

__call__ [SOURCE]

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def __call__(
    self,
    img_content: Union[str, np.ndarray, bytes, Path, Image.Image],
    dt_boxes: np.ndarray,
    txts: Optional[Union[List[str], Tuple[str]]] = None,
    scores: Optional[Tuple[float]] = None,
    font_path: Optional[str] = None,
) -> np.ndarray:
    pass

参数

  • img_content (Union[str, np.ndarray, bytes, Path, Image.Image]): 输入图像。
  • dt_boxes (np.ndarray): 文本检测所得文本框。
  • txts (Optional[Union[List[str], Tuple[str]]], optional): 文本框对应的文本内容。默认为None。
  • scores (Optional[Tuple[float]], optional): 文本内容对应的置信度，默认为None。
  • font_path (Optional[str], optional): 字体文件路径，默认为None。

使用示例

可视化识别结果时，需要提供字体文件。下载链接：link。

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import cv2

from rapidocr_onnxruntime import RapidOCR, VisRes

engine = RapidOCR()
vis = VisRes()

image_path = "tests/test_files/ch_en_num.jpg"
img = cv2.imread(image_path)

result, elapse_list = engine(img)
boxes, txts, scores = list(zip(*result))
res = vis(img, boxes)
cv2.imwrite("only_vis_det.png", res)

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import cv2

from rapidocr_onnxruntime import RapidOCR, VisRes

engine = RapidOCR()
vis = VisRes()

image_path = "tests/test_files/ch_en_num.jpg"
img = cv2.imread(image_path)

result, elapse_list = engine(img)
boxes, txts, scores = list(zip(*result))

font_path="resources/fonts/FZYTK.TTF"
res = vis(img, boxes, txts, scores, font_path)
cv2.imwrite("vis_det_rec.png", res)

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