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cb35b9c04e
* fix type checking of inputs for np.array inputs type of np.ndarray is not checked correctly. * add docstr for /preprocessor.py add docstr about np.ndarray, shape and order
72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import math
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import os
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from typing import Any, Dict
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import cv2
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import numpy as np
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import PIL
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import torch
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from modelscope.metainfo import Preprocessors
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from modelscope.preprocessors import Preprocessor, load_image
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from modelscope.preprocessors.builder import PREPROCESSORS
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from modelscope.utils.config import Config
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from modelscope.utils.constant import Fields, ModeKeys, ModelFile
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@PREPROCESSORS.register_module(
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Fields.cv, module_name=Preprocessors.ocr_detection)
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class OCRDetectionPreprocessor(Preprocessor):
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def __init__(self, model_dir: str, mode: str = ModeKeys.INFERENCE):
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"""The base constructor for all ocr recognition preprocessors.
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Args:
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model_dir (str): model directory to initialize some resource
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mode: The mode for the preprocessor.
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"""
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super().__init__(mode)
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cfgs = Config.from_file(
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os.path.join(model_dir, ModelFile.CONFIGURATION))
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self.image_short_side = cfgs.model.inference_kwargs.image_short_side
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def __call__(self, inputs):
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"""process the raw input data
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Args:
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inputs:
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- A string containing an HTTP link pointing to an image
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- A string containing a local path to an image
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- An image loaded in PIL(PIL.Image.Image) or opencv(np.ndarray) directly, 3 channels RGB
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Returns:
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outputs: the preprocessed image
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"""
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if isinstance(inputs, str):
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img = np.array(load_image(inputs))
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elif isinstance(inputs, PIL.Image.Image):
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img = np.array(inputs)
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elif isinstance(inputs, np.ndarray):
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img = inputs
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else:
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raise TypeError(
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f'inputs should be either str, PIL.Image, np.array, but got {type(inputs)}'
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)
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img = img[:, :, ::-1]
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height, width, _ = img.shape
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if height < width:
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new_height = self.image_short_side
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new_width = int(math.ceil(new_height / height * width / 32) * 32)
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else:
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new_width = self.image_short_side
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new_height = int(math.ceil(new_width / width * height / 32) * 32)
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resized_img = cv2.resize(img, (new_width, new_height))
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resized_img = resized_img - np.array([123.68, 116.78, 103.94],
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dtype=np.float32)
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resized_img /= 255.
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resized_img = torch.from_numpy(resized_img).permute(
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2, 0, 1).float().unsqueeze(0)
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result = {'img': resized_img, 'org_shape': [height, width]}
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return result
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