ppocr.PPRecognizer 类

Classes

`PPRecognizer (Recognizer)`

Methods

`init(self, model_name='ch_PP-OCRv3', *, cand_alphabet=None, model_fp=None, root='/home/docs/.cnocr', rec_image_shape='3, 32, 320', use_space_char=True, **kwargs)` `special`

来自 ppocr 的文本识别器。

Parameters:

Name	Type	Description	Default
`model_name`	`str`	模型名称。默认为 `ch_PP-OCRv3`	`'ch_PP-OCRv3'`
`cand_alphabet`	`Optional[Union[Collection, str]]`	待识别字符所在的候选集合。默认为 `None`，表示不限定识别字符范围	`None`
`model_fp`	`Optional[str]`	如果不使用系统自带的模型，可以通过此参数直接指定所使用的模型文件（'.ckpt' 文件）	`None`
`root`	`Union[str, Path]`	模型文件所在的根目录 Linux/Mac下默认值为 `~/.cnocr`，表示模型文件所处文件夹类似 `~/.cnocr/2.1/densenet_lite_136-fc` Windows下默认值为 `C:/Users/<username>/AppData/Roaming/cnocr`	`'/home/docs/.cnocr'`
`rec_image_shape`	`str`	输入图片尺寸，无需更改使用默认值即可。默认值：`"3, 32, 320"`	`'3, 32, 320'`
`use_space_char`	`bool`	是否使用空格字符，无需更改使用默认值即可。默认值：`True`	`True`
`**kwargs`		目前未被使用。	`{}`

Source code in cnocr/ppocr/pp_recognizer.py

def __init__(
    self,
    model_name: str = 'ch_PP-OCRv3',
    *,
    cand_alphabet: Optional[Union[Collection, str]] = None,
    model_fp: Optional[str] = None,
    root: Union[str, Path] = data_dir(),
    rec_image_shape: str = "3, 32, 320",
    use_space_char: bool = True,
    **kwargs
):
    """
    来自 ppocr 的文本识别器。

    Args:
        model_name (str): 模型名称。默认为 `ch_PP-OCRv3`
        cand_alphabet (Optional[Union[Collection, str]]): 待识别字符所在的候选集合。默认为 `None`，表示不限定识别字符范围
        model_fp (Optional[str]): 如果不使用系统自带的模型，可以通过此参数直接指定所使用的模型文件（'.ckpt' 文件）
        root (Union[str, Path]): 模型文件所在的根目录
            Linux/Mac下默认值为 `~/.cnocr`，表示模型文件所处文件夹类似 `~/.cnocr/2.1/densenet_lite_136-fc`
            Windows下默认值为 `C:/Users/<username>/AppData/Roaming/cnocr`
        rec_image_shape (str): 输入图片尺寸，无需更改使用默认值即可。默认值：`"3, 32, 320"`
        use_space_char (bool): 是否使用空格字符，无需更改使用默认值即可。默认值：`True`
        **kwargs: 目前未被使用。
    """
    self.rec_image_shape = [int(v) for v in rec_image_shape.split(",")]
    self.rec_algorithm = 'CRNN'
    self._model_name = model_name
    self._model_backend = 'onnx'

    vocab_fp = AVAILABLE_MODELS.get_vocab_fp(self._model_name, self._model_backend)
    self._assert_and_prepare_model_files(model_fp, root)
    postprocess_params = {
        'name': 'CTCLabelDecode',
        'character_dict_path': vocab_fp,
        'use_space_char': use_space_char,
        'cand_alphabet': cand_alphabet,
    }
    self.postprocess_op = build_post_process(postprocess_params)
    (
        self.predictor,
        self.input_tensor,
        self.output_tensors,
        self.config,
    ) = create_predictor(self._model_fp, 'rec', logger)
    self.use_onnx = True

`recognize(self, img_list, batch_size=1)`

Batch recognize characters from a list of one-line-characters images.

Parameters:

Name	Type	Description	Default
`img_list`	`List[Union[str, Path, np.ndarray]]`	list of images, in which each element should be a line image array with np.ndarray. Each element should be a tensor with values ranging from 0 to 255, and with shape [height, width] or [height, width, channel]. The optional channel should be 1 (gray image) or 3 (RGB-format color image). 注：img_list 不宜包含太多图片，否则同时导入这些图片会消耗很多内存。	required
`batch_size`	`int`	待处理图片很多时，需要分批处理，每批图片的数量由此参数指定。默认为 `1`。	`1`

Returns:

Type	Description
`list`	list of (chars, prob), such as [('第一行', 0.80), ('第二行', 0.75), ('第三行', 0.9)]

Source code in cnocr/ppocr/pp_recognizer.py

def recognize(
    self, img_list: List[Union[str, Path, np.ndarray]], batch_size: int = 1,
) -> List[Tuple[str, float]]:
    """
    Batch recognize characters from a list of one-line-characters images.

    Args:
        img_list (List[Union[str, Path, np.ndarray]]):
            list of images, in which each element should be a line image array with np.ndarray.
            Each element should be a tensor with values ranging from 0 to 255,
            and with shape [height, width] or [height, width, channel].
            The optional channel should be 1 (gray image) or 3 (RGB-format color image).
            注：img_list 不宜包含太多图片，否则同时导入这些图片会消耗很多内存。
        batch_size: 待处理图片很多时，需要分批处理，每批图片的数量由此参数指定。默认为 `1`。

    Returns:
        list: list of (chars, prob), such as
        [('第一行', 0.80), ('第二行', 0.75), ('第三行', 0.9)]
    """
    if len(img_list) == 0:
        return []

    img_list = [self._prepare_img(img) for img in img_list]

    img_num = len(img_list)
    # Calculate the aspect ratio of all text bars
    width_list = []
    for img in img_list:
        width_list.append(img.shape[1] / float(img.shape[0]))
    # Sorting can speed up the recognition process
    indices = np.argsort(np.array(width_list))
    rec_res = [['', 0.0]] * img_num
    for beg_img_no in range(0, img_num, batch_size):
        end_img_no = min(img_num, beg_img_no + batch_size)
        norm_img_batch = []
        max_wh_ratio = 0
        for ino in range(beg_img_no, end_img_no):
            h, w = img_list[indices[ino]].shape[0:2]
            wh_ratio = w * 1.0 / h
            max_wh_ratio = max(max_wh_ratio, wh_ratio)
        for ino in range(beg_img_no, end_img_no):
            if self.rec_algorithm != "SRN" and self.rec_algorithm != "SAR":
                norm_img = self.resize_norm_img(
                    img_list[indices[ino]], max_wh_ratio
                )
                norm_img = norm_img[np.newaxis, :]
                norm_img_batch.append(norm_img)
        norm_img_batch = np.concatenate(norm_img_batch)
        norm_img_batch = norm_img_batch.copy()

        input_dict = dict()
        input_dict[self.input_tensor.name] = norm_img_batch
        outputs = self.predictor.run(self.output_tensors, input_dict)
        preds = outputs[0]

        rec_result = self.postprocess_op(preds)
        for rno in range(len(rec_result)):
            rec_res[indices[beg_img_no + rno]] = rec_result[rno]
    return rec_res

`resize_norm_img(self, img, max_wh_ratio)`

Parameters:

Name	Type	Description	Default
`img`		with shape of (height, width, channel)	required
`max_wh_ratio`			required

Source code in cnocr/ppocr/pp_recognizer.py

def resize_norm_img(self, img, max_wh_ratio):
    """

    Args:
        img (): with shape of (height, width, channel)
        max_wh_ratio ():

    Returns:

    """
    imgC, imgH, imgW = self.rec_image_shape

    assert imgC == img.shape[2]
    imgW = int((32 * max_wh_ratio))
    if self.use_onnx:
        w = self.input_tensor.shape[3:][0]
        if w is not None and w > 0:
            imgW = w
    h, w = img.shape[:2]
    ratio = w / float(h)
    if math.ceil(imgH * ratio) > imgW:
        resized_w = imgW
    else:
        resized_w = int(math.ceil(imgH * ratio))
    # resized_image = cv2.resize(img, (resized_w, imgH))
    resized_image = resize_img(
        img.transpose((2, 0, 1)), target_h_w=(imgH, resized_w), return_torch=False
    ).transpose((1, 2, 0))
    resized_image = resized_image.astype('float32')
    resized_image = resized_image.transpose((2, 0, 1)) / 255
    resized_image -= 0.5
    resized_image /= 0.5
    padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
    padding_im[:, :, 0:resized_w] = resized_image
    return padding_im

ppocr.PPRecognizer 类

Classes

PPRecognizer (Recognizer)

Methods

__init__(self, model_name='ch_PP-OCRv3', *, cand_alphabet=None, model_fp=None, root='/home/docs/.cnocr', rec_image_shape='3, 32, 320', use_space_char=True, **kwargs) special

recognize(self, img_list, batch_size=1)

resize_norm_img(self, img, max_wh_ratio)

`PPRecognizer (Recognizer)`

`init(self, model_name='ch_PP-OCRv3', *, cand_alphabet=None, model_fp=None, root='/home/docs/.cnocr', rec_image_shape='3, 32, 320', use_space_char=True, **kwargs)` `special`

`recognize(self, img_list, batch_size=1)`

`resize_norm_img(self, img, max_wh_ratio)`