From d0596bdfcdf1e0f55b5d19a9fb701606d8d5e3de Mon Sep 17 00:00:00 2001 From: iqballx Date: Thu, 12 Mar 2026 16:40:32 +0700 Subject: [PATCH 1/3] chore: clean up code structure and remove unused code blocks --- .python-version | 1 + MANIFEST.in | 1 - pyproject.toml | 55 +++ setup.cfg | 42 --- setup.py | 4 - uv.lock | 948 ++++++++++++++++++++++++++++++++++++++++++++++++ 6 files changed, 1004 insertions(+), 47 deletions(-) create mode 100644 .python-version delete mode 100644 MANIFEST.in create mode 100644 pyproject.toml delete mode 100644 setup.cfg delete mode 100644 setup.py create mode 100644 uv.lock diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index c0759df..0000000 --- a/MANIFEST.in +++ /dev/null @@ -1 +0,0 @@ -recursive-include mrzscanner * diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..7fd5013 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,55 @@ +[project] +name = "mrzscanner_docsaid" +version = "1.0.7" +description = "A Python package for recognizing and extracting MRZ from visa and passport documents." +readme = "README.md" +license = "Apache-2.0" +requires-python = ">=3.10,<3.13" +classifiers = [ + "Development Status :: 5 - Production/Stable", + "License :: OSI Approved :: Apache Software License", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Topic :: Software Development :: Libraries", + "Topic :: Software Development :: Libraries :: Python Modules", +] +dependencies = [ + "capybara_docsaid[onnxruntime]>=1.0.0,<2.0.0", +] + +[project.urls] +Repository = "https://github.com/DocsaidLab/MRZScanner.git" + +[dependency-groups] +dev = [ + "pytest>=7.0", + "numpy>=1.24", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["mrzscanner"] + +[tool.hatch.build.targets.sdist] +include = ["mrzscanner/**"] +exclude = [ + "data", + "docker", + "demo", + "docs", + "model", + "model.*", + "tests", + "benchmark", + "reference", +] + +[tool.pytest.ini_options] +testpaths = ["tests"] diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 2def89b..0000000 --- a/setup.cfg +++ /dev/null @@ -1,42 +0,0 @@ -[metadata] -name = mrzscanner_docsaid -version = attr: mrzscanner.__version__ -description = A Python package for recognizing and extracting MRZ from visa and passport documents. -long_description = file: README.md -long_description_content_type = text/markdown -license = Apache License 2.0 -classifiers= - Development Status :: 5 - Production/Stable - License :: OSI Approved :: Apache Software License - Intended Audience :: Developers - Intended Audience :: 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+++++++------- mrzscanner/rec/infer.py | 56 ++++++------- mrzscanner/scanner.py | 116 ++++++++++++-------------- mrzscanner/spotting/infer.py | 60 ++++++------- tests/det/test_detection_infer.py | 9 +- tests/rec/test_recognition_infer.py | 68 +++++++++------ tests/spotting/test_spotting_infer.py | 67 ++++++++++----- 10 files changed, 248 insertions(+), 200 deletions(-) diff --git a/README.md b/README.md index aeb53b0..f925e31 100644 --- a/README.md +++ b/README.md @@ -214,7 +214,7 @@ Here are the advanced configuration options during initialization: - **cuda**: Use GPU for computation (requires appropriate hardware support). ```python - from capybara import Backend + from capybara.runtime import Backend model = MRZScanner(backend=Backend.cuda) # Use CUDA backend # diff --git a/README_tw.md b/README_tw.md index 8949bf3..8bdc68d 100644 --- a/README_tw.md +++ b/README_tw.md @@ -214,7 +214,7 @@ result = model(img, do_postprocess=True) - **cuda**：使用 GPU 進行運算（需要適當的硬體支援）。 ```python - from capybara import Backend + from capybara.runtime import Backend model = MRZScanner(backend=Backend.cuda) # 使用 CUDA 後端 # diff --git a/mrzscanner/__init__.py b/mrzscanner/__init__.py index 40c51b7..309d4d4 100644 --- a/mrzscanner/__init__.py +++ b/mrzscanner/__init__.py @@ -1,4 +1,6 @@ +from importlib.metadata import version + from .scanner import ErrorCodes, ModelType, MRZScanner, SpottingInference from .utils import replace_digits, replace_letters, replace_sex -__version__ = '1.0.7' +__version__ = version("mrzscanner_docsaid") diff --git a/mrzscanner/det/infer.py b/mrzscanner/det/infer.py index 0a33f60..1c0fd77 100644 --- a/mrzscanner/det/infer.py +++ b/mrzscanner/det/infer.py @@ -1,43 +1,46 @@ +from pathlib import Path from typing import Tuple import capybara as cb import cv2 import numpy as np +from capybara.onnxengine import ONNXEngine +from capybara.runtime import Backend +from capybara.utils import download_from_google DIR = cb.get_curdir(__file__) -__all__ = ['Inference'] +__all__ = ["Inference"] class Inference: - configs = { - '20250222': { - 'model_path': 'mrz_detection_20250222_fp32.onnx', - 'file_id': '1D--D7glsse51oIdcyerZoYs88iokOmqU', - 'img_size_infer': (256, 256), + "20250222": { + "model_path": "mrz_detection_20250222_fp32.onnx", + "file_id": "1D--D7glsse51oIdcyerZoYs88iokOmqU", + "img_size_infer": (256, 256), } } def __init__( self, gpu_id: int = 0, - backend: cb.Backend = cb.Backend.cpu, - model_cfg: str = '20250222', - **kwargs + backend: Backend = Backend.cpu, + model_cfg: str = "20250222", + **kwargs, ): - self.root = DIR / 'ckpt' + self.root = DIR / "ckpt" self.model_cfg = model_cfg self.cfg = cfg = self.configs[model_cfg] - self.image_size = cfg['img_size_infer'] - model_path = self.root / cfg['model_path'] - if not cb.Path(model_path).exists(): - cb.download_from_google( - cfg['file_id'], model_path.name, str(DIR / 'ckpt')) + self.image_size = cfg["img_size_infer"] + model_path = self.root / cfg["model_path"] + if not Path(model_path).exists(): + download_from_google(cfg["file_id"], model_path.name, str(DIR / "ckpt")) - self.model = cb.ONNXEngine(model_path, gpu_id, backend, **kwargs) - self.input_key = list(self.model.input_infos.keys())[0] - self.output_key = list(self.model.output_infos.keys())[0] + self.model = ONNXEngine(model_path, gpu_id, backend, **kwargs) + info = self.model.summary() + self.input_key = info["inputs"][0]["name"] + self.output_key = info["outputs"][0]["name"] def preprocess(self, img: np.ndarray, normalize: bool = False): @@ -46,20 +49,28 @@ def preprocess(self, img: np.ndarray, normalize: bool = False): pad = (img.shape[1] - img.shape[0]) // 2 padding = (pad, pad, 0, 0) img = cv2.copyMakeBorder( - img, *padding, cv2.BORDER_CONSTANT, value=(0, 0, 0)) + img, *padding, cv2.BORDER_CONSTANT, value=(0, 0, 0) + ) else: pad = (img.shape[0] - img.shape[1]) // 2 padding = (0, 0, pad, pad) img = cv2.copyMakeBorder( - img, *padding, cv2.BORDER_CONSTANT, value=(0, 0, 0)) + img, *padding, cv2.BORDER_CONSTANT, value=(0, 0, 0) + ) tensor = cb.imresize(img, size=self.image_size) - tensor = np.transpose(tensor, axes=(2, 0, 1)).astype('float32') + tensor = np.transpose(tensor, axes=(2, 0, 1)).astype("float32") tensor = tensor[None] / 255.0 if normalize else tensor[None] - return {self.input_key: tensor}, (img.shape[0], img.shape[1]), (padding[2], padding[0]) + return ( + {self.input_key: tensor}, + (img.shape[0], img.shape[1]), + (padding[2], padding[0]), + ) - def postprocess(self, hmap: np.ndarray, img_size: Tuple[int, int], shift: Tuple[int, int]) -> np.ndarray: + def postprocess( + self, hmap: np.ndarray, img_size: Tuple[int, int], shift: Tuple[int, int] + ) -> np.ndarray: hmap = np.uint8(hmap * 255) hmap = cb.imresize(hmap, size=img_size) hmap = cb.imbinarize(hmap) @@ -75,12 +86,9 @@ def postprocess(self, hmap: np.ndarray, img_size: Tuple[int, int], shift: Tuple[ return poly def __call__(self, img: np.ndarray, normalize: bool = True) -> np.ndarray: - tensor, img_size, shift = self.preprocess( - img, normalize=normalize) + tensor, img_size, shift = self.preprocess(img, normalize=normalize) x = self.model(**tensor) polygon = self.postprocess( - hmap=x[self.output_key][0], - img_size=img_size, - shift=shift + hmap=x[self.output_key][0], img_size=img_size, shift=shift ) return polygon diff --git a/mrzscanner/rec/infer.py b/mrzscanner/rec/infer.py index ca4dbd2..8a65f73 100644 --- a/mrzscanner/rec/infer.py +++ b/mrzscanner/rec/infer.py @@ -1,62 +1,62 @@ +from pathlib import Path from typing import Any, Callable, List, Tuple import capybara as cb import numpy as np +from capybara.onnxengine import ONNXEngine +from capybara.runtime import Backend +from capybara.utils import download_from_google from ..utils import DecodeMode, TextDecoder DIR = cb.get_curdir(__file__) -__all__ = ['Inference'] +__all__ = ["Inference"] class Inference: - configs = { - '20250221': { - 'model_path': 'mrz_recognition_20250221_fp32.onnx', - 'file_id': '16t-kYHoBnI72MWDMCQ0K8GEyb90Tx2Rp', - 'img_size_infer': (64, 640), + "20250221": { + "model_path": "mrz_recognition_20250221_fp32.onnx", + "file_id": "16t-kYHoBnI72MWDMCQ0K8GEyb90Tx2Rp", + "img_size_infer": (64, 640), }, } def __init__( self, gpu_id: int = 0, - backend: cb.Backend = cb.Backend.cpu, - model_cfg: str = '20250221', - delimeter: str = '', - **kwargs + backend: Backend = Backend.cpu, + model_cfg: str = "20250221", + delimeter: str = "", + **kwargs, ): - self.root = DIR / 'ckpt' + self.root = DIR / "ckpt" self.model_cfg = model_cfg self.cfg = cfg = self.configs[model_cfg] - self.image_size = cfg['img_size_infer'] + self.image_size = cfg["img_size_infer"] self.delimeter = delimeter - model_path = self.root / cfg['model_path'] - if not cb.Path(model_path).exists(): - cb.download_from_google( - cfg['file_id'], model_path.name, str(DIR / 'ckpt')) + model_path = self.root / cfg["model_path"] + if not Path(model_path).exists(): + download_from_google(cfg["file_id"], model_path.name, str(DIR / "ckpt")) - self.model = cb.ONNXEngine(model_path, gpu_id, backend, **kwargs) - self.input_key = list(self.model.input_infos.keys())[0] - self.output_key = list(self.model.output_infos.keys())[0] + self.model = ONNXEngine(model_path, gpu_id, backend, **kwargs) + info = self.model.summary() + self.input_key = info["inputs"][0]["name"] + self.output_key = info["outputs"][0]["name"] - keys = ["", "", delimeter] + \ - list("ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789<") - chars_dict = { - k: i - for i, k in enumerate(keys) - } + keys = ["", "", delimeter] + list( + "ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789<" + ) + chars_dict = {k: i for i, k in enumerate(keys)} self.text_dec = TextDecoder( - chars_dict=chars_dict, - decode_mode=DecodeMode.Normal + chars_dict=chars_dict, decode_mode=DecodeMode.Normal ) def preprocess(self, img: np.ndarray, normalize: bool = False): tensor = cb.imresize(img, size=tuple(self.image_size)) - tensor = np.transpose(tensor, axes=(2, 0, 1)).astype('float32') + tensor = np.transpose(tensor, axes=(2, 0, 1)).astype("float32") tensor = tensor[None] / 255.0 if normalize else tensor[None] return {self.input_key: tensor} diff --git a/mrzscanner/scanner.py b/mrzscanner/scanner.py index 6c3ee9c..114566e 100644 --- a/mrzscanner/scanner.py +++ b/mrzscanner/scanner.py @@ -3,14 +3,15 @@ import capybara as cb import numpy as np +from capybara.runtime import Backend +from capybara.vision.improc import is_numpy_img from .det import Inference as DetectionInference from .rec import Inference as RecognitionInference from .spotting import Inference as SpottingInference from .utils import replace_digits, replace_letters, replace_sex -__all__ = [ - 'MRZScanner', 'ModelType', 'SpottingInference', 'ErrorCodes'] +__all__ = ["MRZScanner", "ModelType", "SpottingInference", "ErrorCodes"] class ModelType(cb.EnumCheckMixin, Enum): @@ -21,26 +22,27 @@ class ModelType(cb.EnumCheckMixin, Enum): class ErrorCodes(Enum): - NO_ERROR = 'No error.' - INVALID_INPUT_FORMAT = 'Invalid input format.' - POSTPROCESS_FAILED_LINE_COUNT = 'Postprocess failed, number of lines not 2 or 3.' - POSTPROCESS_FAILED_TD1_LENGTH = 'Postprocess failed, length of lines not 30 when `doc_type` is TD1.' - POSTPROCESS_FAILED_TD2_TD3_LENGTH = 'Postprocess failed, length of lines not 36 or 44 when `doc_type` is TD2 or TD3.' + NO_ERROR = "No error." + INVALID_INPUT_FORMAT = "Invalid input format." + POSTPROCESS_FAILED_LINE_COUNT = "Postprocess failed, number of lines not 2 or 3." + POSTPROCESS_FAILED_TD1_LENGTH = ( + "Postprocess failed, length of lines not 30 when `doc_type` is TD1." + ) + POSTPROCESS_FAILED_TD2_TD3_LENGTH = "Postprocess failed, length of lines not 36 or 44 when `doc_type` is TD2 or TD3." class MRZScanner: - def __init__( self, model_type: ModelType = ModelType.two_stage, - spotting_cfg: str = '20240919', - detection_cfg: str = '20250222', - recognition_cfg: str = '20250221', - backend: cb.Backend = cb.Backend.cpu, + spotting_cfg: str = "20240919", + detection_cfg: str = "20250222", + recognition_cfg: str = "20250221", + backend: Backend = Backend.cpu, gpu_id: int = 0, - **kwargs + **kwargs, ) -> None: - """ Initialize MRZScanner. + """Initialize MRZScanner. Args: model_type (ModelType): Model type. @@ -73,46 +75,37 @@ def __init__( self._init_recognition(recognition_cfg, gpu_id, backend, **kwargs) else: raise ValueError( - f'Invalid model_type: {model_type}, valid model_types: {list(ModelType)}' + f"Invalid model_type: {model_type}, valid model_types: {list(ModelType)}" ) def _init_spotting(self, model_cfg, gpu_id, backend, **kwargs): valid_model_cfgs = list(SpottingInference.configs.keys()) if model_cfg not in valid_model_cfgs: raise ValueError( - f'Invalid spotting_cfg: {model_cfg}, valid configs: {valid_model_cfgs}' + f"Invalid spotting_cfg: {model_cfg}, valid configs: {valid_model_cfgs}" ) self.scanner = SpottingInference( - gpu_id=gpu_id, - backend=backend, - model_cfg=model_cfg, - **kwargs + gpu_id=gpu_id, backend=backend, model_cfg=model_cfg, **kwargs ) def _init_detection(self, model_cfg, gpu_id, backend, **kwargs): valid_model_cfgs = list(DetectionInference.configs.keys()) if model_cfg not in valid_model_cfgs: raise ValueError( - f'Invalid detection_cfg: {model_cfg}, valid configs: {valid_model_cfgs}' + f"Invalid detection_cfg: {model_cfg}, valid configs: {valid_model_cfgs}" ) self.detector = DetectionInference( - gpu_id=gpu_id, - backend=backend, - model_cfg=model_cfg, - **kwargs + gpu_id=gpu_id, backend=backend, model_cfg=model_cfg, **kwargs ) def _init_recognition(self, model_cfg, gpu_id, backend, **kwargs): valid_model_cfgs = list(RecognitionInference.configs.keys()) if model_cfg not in valid_model_cfgs: raise ValueError( - f'Invalid recognition_cfg: {model_cfg}, valid configs: {valid_model_cfgs}' + f"Invalid recognition_cfg: {model_cfg}, valid configs: {valid_model_cfgs}" ) self.recognizer = RecognitionInference( - gpu_id=gpu_id, - backend=backend, - model_cfg=model_cfg, - **kwargs + gpu_id=gpu_id, backend=backend, model_cfg=model_cfg, **kwargs ) def list_models(self) -> List[str]: @@ -120,40 +113,41 @@ def list_models(self) -> List[str]: detection_models = list(DetectionInference.configs.keys()) recognition_models = list(RecognitionInference.configs.keys()) infos = { - 'spotting': spotting_models, - 'detection': detection_models, - 'recognition': recognition_models + "spotting": spotting_models, + "detection": detection_models, + "recognition": recognition_models, } return infos def postprocess(self, results: np.ndarray) -> List[str]: if (doc_type := len(results)) not in [2, 3]: - return [''], ErrorCodes.POSTPROCESS_FAILED_LINE_COUNT + return [""], ErrorCodes.POSTPROCESS_FAILED_LINE_COUNT if doc_type == 3: # TD1 if len(results[0]) != 30 or len(results[1]) != 30 or len(results[2]) != 30: - return [''], ErrorCodes.POSTPROCESS_FAILED_TD1_LENGTH + return [""], ErrorCodes.POSTPROCESS_FAILED_TD1_LENGTH # Line1 doc = results[0][0:2] country = replace_digits(results[0][2:5]) doc_number = results[0][5:14] doc_number_hash = replace_letters(results[0][14]) optional = results[0][15:30] - results[0] = f'{doc}{country}{doc_number}{doc_number_hash}{optional}' + results[0] = f"{doc}{country}{doc_number}{doc_number_hash}{optional}" # Line2 birth_date = replace_letters(results[1][0:7]) sex = replace_sex(results[1][7]) expiry_date = replace_letters(results[1][8:15]) nationality = replace_digits(results[1][15:18]) optional = results[1][18:30] - results[1] = f'{birth_date}{sex}{expiry_date}{nationality}{optional}' + results[1] = f"{birth_date}{sex}{expiry_date}{nationality}{optional}" return results, ErrorCodes.NO_ERROR elif doc_type == 2: # TD2 or TD3 - - if not ((len(results[0]) == 36 and len(results[1]) == 36) or - (len(results[0]) == 44 and len(results[1]) == 44)): - return [''], ErrorCodes.POSTPROCESS_FAILED_TD2_TD3_LENGTH + if not ( + (len(results[0]) == 36 and len(results[1]) == 36) + or (len(results[0]) == 44 and len(results[1]) == 44) + ): + return [""], ErrorCodes.POSTPROCESS_FAILED_TD2_TD3_LENGTH # Line2 doc_number = results[1][0:9] @@ -163,30 +157,34 @@ def postprocess(self, results: np.ndarray) -> List[str]: sex = replace_sex(results[1][20]) expiry_date = replace_letters(results[1][21:28]) optional = results[1][28:] - results[1] = f'{doc_number}{doc_number_hash}{nationality}{birth_date}{sex}{expiry_date}{optional}' + results[1] = ( + f"{doc_number}{doc_number_hash}{nationality}{birth_date}{sex}{expiry_date}{optional}" + ) return results, ErrorCodes.NO_ERROR def __repr__(self) -> str: if self.model_type == ModelType.spotting and self.scanner: - return f'{self.scanner.__class__.__name__}(\n{self.scanner.model})' + return f"{self.scanner.__class__.__name__}(\n{self.scanner.model})" elif self.model_type == ModelType.detection and self.detector: - return f'{self.detector.__class__.__name__}(\n{self.detector.model})' + return f"{self.detector.__class__.__name__}(\n{self.detector.model})" elif self.model_type == ModelType.recognition and self.recognizer: - return f'{self.recognizer.__class__.__name__}(\n{self.recognizer.model})' - elif self.model_type == ModelType.two_stage and self.detector and self.recognizer: + return f"{self.recognizer.__class__.__name__}(\n{self.recognizer.model})" + elif ( + self.model_type == ModelType.two_stage and self.detector and self.recognizer + ): return ( - f'{self.detector.__class__.__name__}(\n{self.detector.model}) \n\n' - f'{self.recognizer.__class__.__name__}(\n{self.recognizer.model})' + f"{self.detector.__class__.__name__}(\n{self.detector.model}) \n\n" + f"{self.recognizer.__class__.__name__}(\n{self.recognizer.model})" ) - return 'MRZScanner(Uninitialized or Invalid Model)' + return "MRZScanner(Uninitialized or Invalid Model)" def __call__( self, img: np.ndarray, do_center_crop: bool = False, - do_postprocess: bool = False + do_postprocess: bool = False, ) -> List[str]: - """ Run MRZScanner. + """Run MRZScanner. Args: img (np.ndarray): Image. @@ -199,18 +197,18 @@ def __call__( Raises: ErrorCodes: If invalid input format. """ - if not cb.is_numpy_img(img): + if not is_numpy_img(img): return { - 'mrz_polygon': [], - 'mrz_texts': [''], - 'msg': ErrorCodes.INVALID_INPUT_FORMAT + "mrz_polygon": [], + "mrz_texts": [""], + "msg": ErrorCodes.INVALID_INPUT_FORMAT, } if do_center_crop: ori_h, ori_w = img.shape[:2] img = cb.centercrop(img) new_h, new_w = img.shape[:2] - shift = ((ori_w - new_w)//2, (ori_h - new_h)//2) + shift = ((ori_w - new_w) // 2, (ori_h - new_h) // 2) mrz_polygon, mrz_texts = None, None if self.model_type == ModelType.spotting: @@ -231,8 +229,4 @@ def __call__( if do_postprocess and self.model_type != ModelType.detection: mrz_texts, msg = self.postprocess(mrz_texts) - return { - 'mrz_polygon': mrz_polygon, - 'mrz_texts': mrz_texts, - 'msg': msg - } + return {"mrz_polygon": mrz_polygon, "mrz_texts": mrz_texts, "msg": msg} diff --git a/mrzscanner/spotting/infer.py b/mrzscanner/spotting/infer.py index 71c871f..54e3e3e 100644 --- a/mrzscanner/spotting/infer.py +++ b/mrzscanner/spotting/infer.py @@ -1,57 +1,55 @@ +from pathlib import Path from typing import List import capybara as cb import cv2 import numpy as np +from capybara.onnxengine import ONNXEngine +from capybara.runtime import Backend +from capybara.utils import download_from_google from ..utils import DecodeMode, TextDecoder DIR = cb.get_curdir(__file__) -__all__ = ['Inference'] +__all__ = ["Inference"] class Inference: - configs = { - '20240919': { - 'model_path': 'mobilenetv4_conv_small_bifpn1_l6_d256_p12345_finetune_20240919_fp32.onnx', - 'file_id': '1WVFHyyjhbBHttY_fIaSO_xHG97tL6m5c', - 'img_size_infer': (512, 512), + "20240919": { + "model_path": "mobilenetv4_conv_small_bifpn1_l6_d256_p12345_finetune_20240919_fp32.onnx", + "file_id": "1WVFHyyjhbBHttY_fIaSO_xHG97tL6m5c", + "img_size_infer": (512, 512), }, } def __init__( self, gpu_id: int = 0, - backend: cb.Backend = cb.Backend.cpu, - model_cfg: str = '20240919', - **kwargs + backend: Backend = Backend.cpu, + model_cfg: str = "20240919", + **kwargs, ) -> None: - self.root = DIR / 'ckpt' + self.root = DIR / "ckpt" self.model_cfg = model_cfg self.cfg = cfg = self.configs[model_cfg] - self.image_size = cfg['img_size_infer'] - model_path = self.root / cfg['model_path'] - if not cb.Path(model_path).exists(): - cb.download_from_google( - cfg['file_id'], model_path.name, str(DIR / 'ckpt')) + self.image_size = cfg["img_size_infer"] + model_path = self.root / cfg["model_path"] + if not Path(model_path).exists(): + download_from_google(cfg["file_id"], model_path.name, str(DIR / "ckpt")) - self.model = cb.ONNXEngine(model_path, gpu_id, backend, **kwargs) - self.input_key = list(self.model.input_infos.keys())[0] - self.output_key = list(self.model.output_infos.keys())[0] + self.model = ONNXEngine(model_path, gpu_id, backend, **kwargs) + info = self.model.summary() + self.input_key = info["inputs"][0]["name"] + self.output_key = info["outputs"][0]["name"] # Text en/de-coding - keys = ["", ""] + \ - list("ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789<&") - chars_dict = { - k: i - for i, k in enumerate(keys) - } + keys = ["", ""] + list("ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789<&") + chars_dict = {k: i for i, k in enumerate(keys)} self.text_dec = TextDecoder( - chars_dict=chars_dict, - decode_mode=DecodeMode.Normal + chars_dict=chars_dict, decode_mode=DecodeMode.Normal ) def preprocess(self, img: np.ndarray, normalize: bool) -> np.ndarray: @@ -61,15 +59,17 @@ def preprocess(self, img: np.ndarray, normalize: bool) -> np.ndarray: pad = (img.shape[1] - img.shape[0]) // 2 padding = (pad, pad, 0, 0) img = cv2.copyMakeBorder( - img, *padding, cv2.BORDER_CONSTANT, value=(0, 0, 0)) + img, *padding, cv2.BORDER_CONSTANT, value=(0, 0, 0) + ) else: pad = (img.shape[0] - img.shape[1]) // 2 padding = (0, 0, pad, pad) img = cv2.copyMakeBorder( - img, *padding, cv2.BORDER_CONSTANT, value=(0, 0, 0)) + img, *padding, cv2.BORDER_CONSTANT, value=(0, 0, 0) + ) tensor = cb.imresize(img, size=tuple(self.image_size)) - tensor = np.transpose(tensor, axes=(2, 0, 1)).astype('float32') + tensor = np.transpose(tensor, axes=(2, 0, 1)).astype("float32") tensor = tensor[None] / 255.0 if normalize else tensor[None] return {self.input_key: tensor} @@ -79,5 +79,5 @@ def __call__(self, img: np.ndarray, normalize: bool = True) -> List[str]: x = self.model(**tensor) x = x[self.output_key].argmax(-1) result = self.text_dec(x)[0] - result = result.split('&') + result = result.split("&") return result diff --git a/tests/det/test_detection_infer.py b/tests/det/test_detection_infer.py index bf4d7dc..a7df959 100644 --- a/tests/det/test_detection_infer.py +++ b/tests/det/test_detection_infer.py @@ -1,6 +1,6 @@ -import capybara as cb import numpy as np import pytest +from capybara.runtime import Backend from mrzscanner.det.infer import Inference @@ -9,12 +9,12 @@ def fake_inference(): # 建立 Inference 物件，方便後續測試使用 # 這邊的 GPU ID 與 backend 可根據環境需求自行調整 - return Inference(gpu_id=0, backend=cb.Backend.cpu, model_cfg='20250222') + return Inference(gpu_id=0, backend=Backend.cpu, model_cfg="20250222") def test_inference_init(fake_inference): # 測試初始化參數是否正確 - assert fake_inference.model_cfg == '20250222' + assert fake_inference.model_cfg == "20250222" assert fake_inference.image_size == (256, 256) assert fake_inference.input_key is not None assert fake_inference.output_key is not None @@ -89,8 +89,7 @@ def test_postprocess_single_polygon(fake_inference): def test_call_with_mock(fake_inference, monkeypatch): # 假設不想依賴真實模型推理結果，我們可以 mock 其輸出 mock_output = { - fake_inference.output_key: np.random.rand( - 1, 256, 256).astype(np.float32) + fake_inference.output_key: np.random.rand(1, 256, 256).astype(np.float32) } def mock_model_call(*args, **kwargs): diff --git a/tests/rec/test_recognition_infer.py b/tests/rec/test_recognition_infer.py index a82288e..7eda76e 100644 --- a/tests/rec/test_recognition_infer.py +++ b/tests/rec/test_recognition_infer.py @@ -4,6 +4,7 @@ import numpy as np import pytest +import mrzscanner.rec.infer as rec_infer from mrzscanner.rec.infer import Inference @@ -12,8 +13,12 @@ def __init__(self, model_path, gpu_id, backend, **kwargs): self.model_path = model_path self.gpu_id = gpu_id self.backend = backend - self.input_infos = {"input": "dummy"} - self.output_infos = {"output": "dummy"} + + def summary(self): + return { + "inputs": [{"name": "input", "dtype": "", "shape": []}], + "outputs": [{"name": "output", "dtype": "", "shape": []}], + } def __call__(self, **kwargs): # 傳回一個 dummy 預測結果，其 shape 為 (1, 序列長度, 類別數) @@ -32,6 +37,7 @@ def dummy_download(file_id, file_name, target_dir): # 紀錄下載呼叫，不做實際動作 pass + # Dummy Path 物件 @@ -43,20 +49,22 @@ def __init__(self, path, exists_flag=True): def exists(self): return self.exists_flag + # --- 測試案例 --- # 測試 __init__ 當模型檔案存在時不會呼叫下載 def test_init_no_download(tmp_path, monkeypatch): - monkeypatch.setattr(cb, "get_curdir", lambda _: tmp_path) - monkeypatch.setattr(cb, "Path", lambda p: DummyPath(p, exists_flag=True)) + monkeypatch.setattr(rec_infer, "DIR", tmp_path) + monkeypatch.setattr(rec_infer, "Path", lambda p: DummyPath(p, exists_flag=True)) download_called = {"called": False} def dummy_download_google(file_id, file_name, target_dir): download_called["called"] = True - monkeypatch.setattr(cb, "download_from_google", dummy_download_google) - monkeypatch.setattr(cb, "ONNXEngine", DummyONNXEngine) + + monkeypatch.setattr(rec_infer, "download_from_google", dummy_download_google) + monkeypatch.setattr(rec_infer, "ONNXEngine", DummyONNXEngine) monkeypatch.setattr(cb, "imresize", dummy_imresize) inf = Inference() @@ -68,30 +76,33 @@ def dummy_download_google(file_id, file_name, target_dir): # 驗證 text_dec 已正確建立 assert hasattr(inf, "text_dec") + # 測試 __init__ 當模型檔案不存在時會呼叫下載 def test_init_with_download(tmp_path, monkeypatch): - monkeypatch.setattr(cb, "get_curdir", lambda _: tmp_path) - monkeypatch.setattr(cb, "Path", lambda p: DummyPath(p, exists_flag=False)) + monkeypatch.setattr(rec_infer, "DIR", tmp_path) + monkeypatch.setattr(rec_infer, "Path", lambda p: DummyPath(p, exists_flag=False)) download_called = {"called": False} def dummy_download_google(file_id, file_name, target_dir): download_called["called"] = True - monkeypatch.setattr(cb, "download_from_google", dummy_download_google) - monkeypatch.setattr(cb, "ONNXEngine", DummyONNXEngine) + + monkeypatch.setattr(rec_infer, "download_from_google", dummy_download_google) + monkeypatch.setattr(rec_infer, "ONNXEngine", DummyONNXEngine) monkeypatch.setattr(cb, "imresize", dummy_imresize) Inference() assert download_called["called"] + # 測試 preprocess 方法 (啟用 normalization) def test_preprocess_normalize(tmp_path, monkeypatch): - monkeypatch.setattr(cb, "get_curdir", lambda _: tmp_path) - monkeypatch.setattr(cb, "Path", lambda p: DummyPath(p, exists_flag=True)) - monkeypatch.setattr(cb, "ONNXEngine", DummyONNXEngine) + monkeypatch.setattr(rec_infer, "DIR", tmp_path) + monkeypatch.setattr(rec_infer, "Path", lambda p: DummyPath(p, exists_flag=True)) + monkeypatch.setattr(rec_infer, "ONNXEngine", DummyONNXEngine) monkeypatch.setattr(cb, "imresize", dummy_imresize) inf = Inference() @@ -104,13 +115,14 @@ def test_preprocess_normalize(tmp_path, monkeypatch): # normalization 應該將數值壓縮至 [0, 1] assert tensor.max() <= 1.0 + # 測試 preprocess 方法 (未啟用 normalization) def test_preprocess_no_normalize(tmp_path, monkeypatch): - monkeypatch.setattr(cb, "get_curdir", lambda _: tmp_path) - monkeypatch.setattr(cb, "Path", lambda p: DummyPath(p, exists_flag=True)) - monkeypatch.setattr(cb, "ONNXEngine", DummyONNXEngine) + monkeypatch.setattr(rec_infer, "DIR", tmp_path) + monkeypatch.setattr(rec_infer, "Path", lambda p: DummyPath(p, exists_flag=True)) + monkeypatch.setattr(rec_infer, "ONNXEngine", DummyONNXEngine) monkeypatch.setattr(cb, "imresize", dummy_imresize) inf = Inference() @@ -121,13 +133,14 @@ def test_preprocess_no_normalize(tmp_path, monkeypatch): # 未 normalization 時，tensor 中應有值大於 1 assert tensor.max() > 1.0 + # 測試 postprocess 方法 def test_postprocess(monkeypatch, tmp_path): - monkeypatch.setattr(cb, "get_curdir", lambda _: tmp_path) - monkeypatch.setattr(cb, "Path", lambda p: DummyPath(p, exists_flag=True)) - monkeypatch.setattr(cb, "ONNXEngine", DummyONNXEngine) + monkeypatch.setattr(rec_infer, "DIR", tmp_path) + monkeypatch.setattr(rec_infer, "Path", lambda p: DummyPath(p, exists_flag=True)) + monkeypatch.setattr(rec_infer, "ONNXEngine", DummyONNXEngine) inf = Inference() # 覆寫 text_dec，使其回傳固定字串 @@ -138,22 +151,29 @@ def test_postprocess(monkeypatch, tmp_path): result = inf.postprocess(dummy_pred) assert result == "ABCDEF" + # 測試 __call__ 方法，檢查整個流程 def test_call(monkeypatch, tmp_path): - monkeypatch.setattr(cb, "get_curdir", lambda _: tmp_path) - monkeypatch.setattr(cb, "Path", lambda p: DummyPath(p, exists_flag=True)) + monkeypatch.setattr(rec_infer, "DIR", tmp_path) + monkeypatch.setattr(rec_infer, "Path", lambda p: DummyPath(p, exists_flag=True)) # 使用一個自訂的 Dummy ONNXEngine class DummyONNXEngineCall: def __init__(self, model_path, gpu_id, backend, **kwargs): - self.input_infos = {"input": "dummy"} - self.output_infos = {"output": "dummy"} + pass + + def summary(self): + return { + "inputs": [{"name": "input", "dtype": "", "shape": []}], + "outputs": [{"name": "output", "dtype": "", "shape": []}], + } def __call__(self, **kwargs): return {"output": np.zeros((1, 5, 10), dtype=np.float32)} - monkeypatch.setattr(cb, "ONNXEngine", DummyONNXEngineCall) + + monkeypatch.setattr(rec_infer, "ONNXEngine", DummyONNXEngineCall) monkeypatch.setattr(cb, "imresize", dummy_imresize) inf = Inference() diff --git a/tests/spotting/test_spotting_infer.py b/tests/spotting/test_spotting_infer.py index e111b11..192f860 100644 --- a/tests/spotting/test_spotting_infer.py +++ b/tests/spotting/test_spotting_infer.py @@ -6,6 +6,7 @@ import numpy as np import pytest +import mrzscanner.spotting.infer as spotting_infer from mrzscanner.spotting.infer import Inference @@ -14,8 +15,12 @@ def __init__(self, model_path, gpu_id, backend, **kwargs): self.model_path = model_path self.gpu_id = gpu_id self.backend = backend - self.input_infos = {"input": "dummy"} - self.output_infos = {"output": "dummy"} + + def summary(self): + return { + "inputs": [{"name": "input", "dtype": "", "shape": []}], + "outputs": [{"name": "output", "dtype": "", "shape": []}], + } def __call__(self, **kwargs): # Return a dummy output array with shape (1, 10, 20) @@ -43,18 +48,22 @@ def exists(self): def __truediv__(self, other): return f"{self.path}/{other}" + # --- Tests --- def test_init_no_download(tmp_path, monkeypatch): - monkeypatch.setattr(cb, "get_curdir", lambda _: tmp_path) - monkeypatch.setattr(cb, "Path", lambda p: DummyPath(p, exists_flag=True)) + monkeypatch.setattr(spotting_infer, "DIR", tmp_path) + monkeypatch.setattr( + spotting_infer, "Path", lambda p: DummyPath(p, exists_flag=True) + ) download_called = {"called": False} def dummy_download_google(file_id, file_name, target_dir): download_called["called"] = True - monkeypatch.setattr(cb, "download_from_google", dummy_download_google) - monkeypatch.setattr(cb, "ONNXEngine", DummyONNXEngine) + + monkeypatch.setattr(spotting_infer, "download_from_google", dummy_download_google) + monkeypatch.setattr(spotting_infer, "ONNXEngine", DummyONNXEngine) monkeypatch.setattr(cb, "imresize", dummy_imresize) inf = Inference() @@ -66,14 +75,17 @@ def dummy_download_google(file_id, file_name, target_dir): def test_init_with_download(tmp_path, monkeypatch): - monkeypatch.setattr(cb, "get_curdir", lambda _: tmp_path) - monkeypatch.setattr(cb, "Path", lambda p: DummyPath(p, exists_flag=False)) + monkeypatch.setattr(spotting_infer, "DIR", tmp_path) + monkeypatch.setattr( + spotting_infer, "Path", lambda p: DummyPath(p, exists_flag=False) + ) download_called = {"called": False} def dummy_download_google(file_id, file_name, target_dir): download_called["called"] = True - monkeypatch.setattr(cb, "download_from_google", dummy_download_google) - monkeypatch.setattr(cb, "ONNXEngine", DummyONNXEngine) + + monkeypatch.setattr(spotting_infer, "download_from_google", dummy_download_google) + monkeypatch.setattr(spotting_infer, "ONNXEngine", DummyONNXEngine) monkeypatch.setattr(cb, "imresize", dummy_imresize) Inference() @@ -82,9 +94,11 @@ def dummy_download_google(file_id, file_name, target_dir): def test_preprocess_padding_horizontal(tmp_path, monkeypatch): # Test when image height < width (H < W) - monkeypatch.setattr(cb, "get_curdir", lambda _: tmp_path) - monkeypatch.setattr(cb, "Path", lambda p: DummyPath(p, exists_flag=True)) - monkeypatch.setattr(cb, "ONNXEngine", DummyONNXEngine) + monkeypatch.setattr(spotting_infer, "DIR", tmp_path) + monkeypatch.setattr( + spotting_infer, "Path", lambda p: DummyPath(p, exists_flag=True) + ) + monkeypatch.setattr(spotting_infer, "ONNXEngine", DummyONNXEngine) monkeypatch.setattr(cb, "imresize", dummy_imresize) record = {} @@ -95,6 +109,7 @@ def dummy_copyMakeBorder(img, top, bottom, left, right, borderType, value): record["left"] = left record["right"] = right return img + monkeypatch.setattr(cv2, "copyMakeBorder", dummy_copyMakeBorder) inf = Inference() @@ -114,9 +129,11 @@ def dummy_copyMakeBorder(img, top, bottom, left, right, borderType, value): def test_preprocess_padding_vertical(tmp_path, monkeypatch): # Test when image height >= width (H >= W) - monkeypatch.setattr(cb, "get_curdir", lambda _: tmp_path) - monkeypatch.setattr(cb, "Path", lambda p: DummyPath(p, exists_flag=True)) - monkeypatch.setattr(cb, "ONNXEngine", DummyONNXEngine) + monkeypatch.setattr(spotting_infer, "DIR", tmp_path) + monkeypatch.setattr( + spotting_infer, "Path", lambda p: DummyPath(p, exists_flag=True) + ) + monkeypatch.setattr(spotting_infer, "ONNXEngine", DummyONNXEngine) monkeypatch.setattr(cb, "imresize", dummy_imresize) record = {} @@ -127,6 +144,7 @@ def dummy_copyMakeBorder(img, top, bottom, left, right, borderType, value): record["left"] = left record["right"] = right return img + monkeypatch.setattr(cv2, "copyMakeBorder", dummy_copyMakeBorder) inf = Inference() @@ -145,19 +163,26 @@ def dummy_copyMakeBorder(img, top, bottom, left, right, borderType, value): def test_call(tmp_path, monkeypatch): - monkeypatch.setattr(cb, "get_curdir", lambda _: tmp_path) - monkeypatch.setattr(cb, "Path", lambda p: DummyPath(p, exists_flag=True)) + monkeypatch.setattr(spotting_infer, "DIR", tmp_path) + monkeypatch.setattr( + spotting_infer, "Path", lambda p: DummyPath(p, exists_flag=True) + ) class DummyONNXEngineCall: def __init__(self, model_path, gpu_id, backend, **kwargs): - self.input_infos = {"input": "dummy"} - self.output_infos = {"output": "dummy"} + pass + + def summary(self): + return { + "inputs": [{"name": "input", "dtype": "", "shape": []}], + "outputs": [{"name": "output", "dtype": "", "shape": []}], + } def __call__(self, **kwargs): # Return a dummy output array. return {"output": np.zeros((1, 10, 20), dtype=np.float32)} - monkeypatch.setattr(cb, "ONNXEngine", DummyONNXEngineCall) + monkeypatch.setattr(spotting_infer, "ONNXEngine", DummyONNXEngineCall) monkeypatch.setattr(cb, "imresize", dummy_imresize) inf = Inference() From 497ae59c387e0c3080453bc8f82775a248c9150d Mon Sep 17 00:00:00 2001 From: iqballx Date: Thu, 12 Mar 2026 16:47:51 +0700 Subject: [PATCH 3/3] chore: update capybara_docsaid dependencies and add optional GPU support --- pyproject.toml | 6 +++++- uv.lock | 39 ++++++++++++++++++++++++++++++++++++++- 2 files changed, 43 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 7fd5013..b4a31ff 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,9 +18,13 @@ classifiers = [ "Topic :: Software Development :: Libraries :: Python Modules", ] dependencies = [ - "capybara_docsaid[onnxruntime]>=1.0.0,<2.0.0", + "capybara_docsaid>=1.0.0,<2.0.0", ] +[project.optional-dependencies] +cpu = ["capybara_docsaid[onnxruntime]>=1.0.0,<2.0.0"] +gpu = ["capybara_docsaid[onnxruntime-gpu]>=1.0.0,<2.0.0"] + [project.urls] Repository = "https://github.com/DocsaidLab/MRZScanner.git" diff --git a/uv.lock b/uv.lock index d8406f4..4f74881 100644 --- a/uv.lock +++ b/uv.lock @@ -53,6 +53,11 @@ onnxruntime = [ { name = "onnxruntime" }, { name = "onnxslim" }, ] +onnxruntime-gpu = [ + { name = "onnx" }, + { name = "onnxruntime-gpu" }, + { name = "onnxslim" }, +] [[package]] name = "certifi" @@ -249,8 +254,16 @@ name = "mrzscanner-docsaid" version = "1.0.7" source = { editable = "." } dependencies = [ + { name = "capybara-docsaid" }, +] + +[package.optional-dependencies] +cpu = [ { name = "capybara-docsaid", extra = ["onnxruntime"] }, ] +gpu = [ + { name = "capybara-docsaid", extra = ["onnxruntime-gpu"] }, +] [package.dev-dependencies] dev = [ @@ -260,7 +273,12 @@ dev = [ ] [package.metadata] -requires-dist = [{ name = "capybara-docsaid", extras = ["onnxruntime"], specifier = ">=1.0.0,<2.0.0" }] +requires-dist = [ + { name = "capybara-docsaid", specifier = ">=1.0.0,<2.0.0" }, + { name = "capybara-docsaid", extras = ["onnxruntime"], marker = "extra == 'cpu'", specifier = ">=1.0.0,<2.0.0" }, + { name = "capybara-docsaid", extras = ["onnxruntime-gpu"], marker = "extra == 'gpu'", specifier = ">=1.0.0,<2.0.0" }, +] +provides-extras = ["cpu", "gpu"] [package.metadata.requires-dev] dev = [ @@ -423,6 +441,25 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/36/b4/3f1c71ce1d3d21078a6a74c5483bfa2b07e41a8d2b8fb1e9993e6a26d8d3/onnxruntime-1.22.0-cp312-cp312-win_amd64.whl", hash = "sha256:c0d534a43d1264d1273c2d4f00a5a588fa98d21117a3345b7104fa0bbcaadb9a", size = 12692233, upload-time = "2025-05-12T21:26:16.963Z" }, ] +[[package]] +name = "onnxruntime-gpu" +version = "1.24.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "flatbuffers" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.4.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "packaging" }, + { name = "protobuf" }, + { name = "sympy" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/28/f4/c8050f3f4916ab6c75432724f0ba51c1548dc1c3d66d40c0f8a9611e370f/onnxruntime_gpu-1.24.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ac922633819e1cdc81c9b3a28b5e37d788805307bbaa708a01a3d7150e345625", size = 252750845, upload-time = "2026-03-05T16:35:33.604Z" }, + { url = "https://files.pythonhosted.org/packages/07/b7/81e8936354651915192a362a1718253c6d03da6b902a95237aa392b1d260/onnxruntime_gpu-1.24.3-cp311-cp311-win_amd64.whl", hash = "sha256:0fe6ece3042db149f36f4991cbebd19a690b7ffd82af89450a261b47f4704a37", size = 207192429, upload-time = "2026-03-05T16:39:57.015Z" }, + { url = "https://files.pythonhosted.org/packages/24/fa/58ceca812214c9c1a286407c376e42e0b7de3e2c6e14b61cdf3caf6d6d9c/onnxruntime_gpu-1.24.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:537bdd6d95006a9200ae81f2e73ba9e621e723fdf0deb5901e2e62fb2cccf876", size = 252756089, upload-time = "2026-03-05T16:35:46.004Z" }, + { url = "https://files.pythonhosted.org/packages/3c/07/2f36920b513bd8939e25591153e37d9cfda94115bd119f2874da0750fce2/onnxruntime_gpu-1.24.3-cp312-cp312-win_amd64.whl", hash = "sha256:d72065b3ab5fdaef74d8b6b8f39b7ce20d89731610e3e63cb40e997d3dce177e", size = 207197001, upload-time = "2026-03-05T16:40:05.691Z" }, +] + [[package]] name = "onnxslim" version = "0.1.87"