Skip to content

transform

data.transform.transform

End-to-end 데이터 변환을 지원하는 모듈입니다.

ImageSizeType module-attribute 

ImageSizeType = tuple[int, int]

InspectionSizeResizer 

InspectionSizeResizer(inspection_size_wh: Optional[ImageSizeType] = None, resizer: Optional[Resizer] = None)

Bases: ScaleResizer

원본 이미지가 inspection_size를 넘지 않도록 resize 했을 때의 image scale만큼 resize를 해주는 resizer를 생성하는 class 입니다.

inspection_size는 원본 이미지에 대해 적용하지만, 실제 연산은 ROI가 먼저 계산되기 때문에, 원본 이미지가 inspection_size를 넘지 않도록 resize 했을 때의 image scale을 미리 계산해두고, 해당 scale만큼 resize를 하는 resizer를 생성하여 계산합니다.

Source code in SaigeToolkit/data/transform/resize.py 
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
def __init__(
    self,
    inspection_size_wh: Optional[ImageSizeType] = None,
    resizer: Optional[Resizer] = None,
) -> None:

    validate_inspection_size_wh(inspection_size_wh)

    initial_params = {}
    if resizer is not None:
        initial_params["resampling"] = resizer.resampling
        initial_params["image_only"] = resizer.image_only

    super().__init__(**initial_params)

    self.inspection_size_wh = self._preprocess_size(inspection_size_wh)

inspection_size_wh instance-attribute 

inspection_size_wh = _preprocess_size(inspection_size_wh)

set_scale_from_data 

set_scale_from_data(**data) -> None
Source code in SaigeToolkit/data/transform/resize.py 
461
462
463
464
465
def set_scale_from_data(self, **data) -> None:
    if self.inspection_size_wh is not None:
        input_w, input_h = self.compute_input_size(data)
        inspection_w, inspection_h = self.inspection_size_wh
        self.scale = min(inspection_w / input_w, inspection_h / input_h, 1)

StackedResizerHandler 

StackedResizerHandler(resizer_list: List[Optional[Resizer]])

같은 image에 대해 여러번의 resize가 연속적으로 적용될 때, 여러개의 resize를 하나로 묶어서 최종 target size로 한번에 resize를 해주는 class입니다.

Source code in SaigeToolkit/data/transform/resize.py 
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
def __init__(
    self,
    resizer_list: List[Optional[Resizer]],
) -> None:
    self.resizer_list = resizer_list
    self.is_empty = len(resizer_list) == 0 or all(x is None for x in resizer_list)

    if not self.is_empty:
        resampling = set()
        image_only = set()
        use_cv2_for_numpy = set()
        for resizer in resizer_list:
            if resizer is not None:
                resampling.add(resizer.resampling)
                image_only.add(resizer.image_only)
                use_cv2_for_numpy.add(resizer.use_cv2_for_numpy)

        if len(resampling) > 1 or len(image_only) > 1 or len(use_cv2_for_numpy) > 1:
            raise StackedResizerHandlerParameterValueError

        self.resampling = resampling.pop()
        self.image_only = image_only.pop()
        self.use_cv2_for_numpy = use_cv2_for_numpy.pop()

resizer_list instance-attribute 

resizer_list = resizer_list

is_empty instance-attribute 

is_empty = len(resizer_list) == 0 or all((x is None) for x in resizer_list)

resampling instance-attribute 

resampling = pop()

image_only instance-attribute 

image_only = pop()

use_cv2_for_numpy instance-attribute 

use_cv2_for_numpy = pop()

__call__ 

__call__(return_revert_params: bool = False, ignore_round: bool = False, **data) -> Union[Dict, Tuple[Dict, Dict]]
Source code in SaigeToolkit/data/transform/resize.py 
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
def __call__(
    self, return_revert_params: bool = False, ignore_round: bool = False, **data
) -> Union[Dict, Tuple[Dict, Dict]]:
    if return_revert_params:
        revert_params = []

    if self.is_empty:
        revert_params = [None] * len(self.resizer_list)
    else:
        input_size = Resizer.compute_input_size(data)
        target_size = input_size

        for resizer in self.resizer_list:
            if resizer is None:
                if return_revert_params:
                    revert_params.append(None)
            else:
                revert_param = {"image_size_before_resize": target_size}
                target_size = resizer.compute_target_size(target_size, ignore_round)
                revert_param["image_size_after_resize"] = target_size
                if return_revert_params:
                    revert_params.append(revert_param)

        if list(input_size) != list(target_size):
            data = Resizer.apply_with_params(
                data=data,
                input_size=input_size,
                target_size=target_size,
                resampling=self.resampling,
                image_only=self.image_only,
                use_cv2_for_numpy=self.use_cv2_for_numpy,
            )

    if return_revert_params:
        return_value = (data, revert_params)
    else:
        return_value = data

    return return_value

ROIHandler 

ROIHandler(**kwargs)

ROI 기능을 수행합니다. 이미지에 대한 ROI 좌표를 계산해 크롭하고, 크롭된 이미지에 blind_mask를 적용해 마스크 영역의 픽셀 값을 0으로 치환합니다.

Source code in SaigeToolkit/data/transform/roi/roi_handler.py 
27
28
def __init__(self, **kwargs) -> None:
    self.set(**kwargs)

roi_calculator_types class-attribute instance-attribute 

roi_calculator_types = {'simple': RelativeBoxROI, 'advanced': PixelIntensityROI, 'auto': AutoRelativeBoxROI}

roi_calculator instance-attribute 

roi_calculator: ROICalculator

set 

set(mode: str, blind_mask: Union[None, ndarray, str], image_only: bool = False, discard_outer_polygons: bool = False, det_blind_mask_threshold: float = 0.5, **kwargs)
Source code in SaigeToolkit/data/transform/roi/roi_handler.py 
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
def set(
    self,
    mode: str,
    blind_mask: Union[None, np.ndarray, str],
    image_only: bool = False,
    discard_outer_polygons: bool = False,
    det_blind_mask_threshold: float = 0.5,
    **kwargs,
):
    if mode not in self.roi_calculator_types:
        raise ROIModeError

    # mode 변경된 경우 roi_calculator 인스턴스 새로 생성, 그렇지 않은 경우 set 호출
    if not hasattr(self, "mode") or self.mode != mode:
        self.mode = mode
        self.roi_calculator = self.roi_calculator_types[mode](**kwargs)
    else:
        self.roi_calculator.set(**kwargs)

    if isinstance(blind_mask, str):
        blind_mask = np.array(Image.open(blind_mask))
    if blind_mask is not None and (blind_mask.dtype != np.uint8 or blind_mask.ndim != 2):
        raise ROIBlindMaskValueError
    self.blind_mask = blind_mask
    if blind_mask is not None:
        if np.sum(blind_mask) / blind_mask.size > 0.3:
            self.masking_method = "np_where"
        else:
            # blind mask 영역 비율이 적을수록 inplace 방식이 빠름
            self.masking_method = "inplace"
    else:
        self.masking_method = None

    self.image_only = image_only

    self.discard_outer_polygons = discard_outer_polygons

    self.det_blind_mask_threshold = det_blind_mask_threshold

apply_crop 

apply_crop(image: Union[Image, ndarray, List[Union[Image, ndarray]]], return_revert_params: bool = False, warmup: bool = False, **data) -> Union[Dict, Tuple[Dict, Dict]]
Source code in SaigeToolkit/data/transform/roi/roi_handler.py 
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
def apply_crop(
    self,
    image: Union[Image.Image, np.ndarray, List[Union[Image.Image, np.ndarray]]],
    return_revert_params: bool = False,
    warmup: bool = False,
    **data,
) -> Union[Dict, Tuple[Dict, Dict]]:
    multipage = isinstance(image, list)

    roi_info = self.roi_calculator(
        image=image[0] if multipage else image,
        get_intermediate_results=False,
        warmup=warmup,
        **data,
    )
    roi_coordinates = roi_info["roi_coordinates"]  # [left, top, right, bottom]
    if roi_info["do_crop"] is False:
        revert_params = {
            "image_size_before_roi": (roi_coordinates[2], roi_coordinates[3]),
            "roi_coordinates": roi_coordinates,
            "image_size_after_roi": (roi_coordinates[2], roi_coordinates[3]),
        }
        data["image"] = image
        return (data, revert_params) if return_revert_params else data

    if multipage:
        image_size_before_roi = read_image_size(image[0])
        cropped_image = [crop(image_i, roi_coordinates) for image_i in image]
        image_size_after_roi = read_image_size(cropped_image[0])
    else:
        image_size_before_roi = read_image_size(image)
        cropped_image = crop(image, roi_coordinates)
        image_size_after_roi = read_image_size(cropped_image)

    data["image"] = cropped_image

    revert_params = {
        "image_size_before_roi": image_size_before_roi,
        "roi_coordinates": roi_coordinates,
        "image_size_after_roi": image_size_after_roi,
    }

    if self.image_only:
        return (data, revert_params) if return_revert_params else data

    # 라벨 크롭 & 마스킹
    if "mask" in data:
        data["mask"] = crop(data["mask"], roi_coordinates)

    if "bboxes" in data:
        data["bboxes"] = crop_box(data["bboxes"], roi_coordinates)

    if "polygons" in data:
        # NOTE: polygons에는 blind_mask가 적용되지 않습니다.
        new_polygons = []
        new_data = {key: [] for key in ["strings", "ignore"] if key in data}
        # TODO: add any keys to be updated which should have same length with polygons.

        for idx, polygon in enumerate(data["polygons"]):
            if self.discard_outer_polygons and not self._check_polygon_within_box(
                polygon, roi_coordinates
            ):
                continue

            new_polygons.append(polygon)
            for k, v in new_data.items():
                v.append(data[k][idx])

        new_polygons = translate_polygon(
            polygons=new_polygons,
            offset=roi_coordinates[:2],
        )

        data.update({"polygons": new_polygons, **new_data})

    return (data, revert_params) if return_revert_params else data

apply_mask 

apply_mask(image: Union[Image, ndarray, List], **data)
Source code in SaigeToolkit/data/transform/roi/roi_handler.py 
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
def apply_mask(
    self,
    image: Union[Image.Image, np.ndarray, List],
    **data,
):
    if self.blind_mask is None:
        data["image"] = image
        return data

    multipage = isinstance(image, List)

    # cv2 resize: 약간 부정확하지만 빠름. 여기서는 아주 정확할 필요없음.
    image_size = read_image_size(image[0] if multipage else image)
    blind_mask = cv2.resize(self.blind_mask, dsize=image_size, interpolation=cv2.INTER_NEAREST)
    bool_mask = blind_mask > 0

    if multipage:
        data["image"] = [
            fill_pixels_with_mask(
                image_i,
                bool_mask,
                value=0,
                method=self.masking_method,
            )
            for image_i in image
        ]
    else:
        data["image"] = fill_pixels_with_mask(
            image,
            bool_mask,
            value=0,
            method=self.masking_method,
        )

    if self.image_only:
        return data

    # 라벨 크롭 & 마스킹
    if "mask" in data:
        data["mask"] = fill_pixels_with_mask(
            data["mask"],
            bool_mask,
            value=0,
            method=self.masking_method,
        )

    if "bboxes" in data:
        indices_alive = []

        if len(data["bboxes"]):
            xyxy_bboxes = data["bboxes"].convert_coordinate("xyxy")

            for i, bbox in enumerate(xyxy_bboxes):
                x0, y0, x1, y1 = bbox

                label_area = (x1 - x0) * (y1 - y0)
                overlapping_area = np.sum(blind_mask[int(y0) : int(y1), int(x0) : int(x1)])

                if overlapping_area / label_area < self.det_blind_mask_threshold:
                    indices_alive.append(i)

        data["bboxes"] = data["bboxes"][indices_alive]

        if "labels" in data:
            data["labels"] = data["labels"][indices_alive]

    return data

__call__ 

__call__(return_revert_params: bool = False, warmup: bool = False, **data) -> Union[Dict, Tuple[Dict, Dict]]
Source code in SaigeToolkit/data/transform/roi/roi_handler.py 
214
215
216
217
218
219
220
221
222
223
224
225
226
227
def __call__(
    self,
    return_revert_params: bool = False,
    warmup: bool = False,
    **data,
) -> Union[Dict, Tuple[Dict, Dict]]:
    if return_revert_params:
        data, revert_params = self.apply_crop(return_revert_params=True, warmup=warmup, **data)
        data = self.apply_mask(**data)
        return data, revert_params
    else:
        data = self.apply_crop(return_revert_params=False, warmup=warmup, **data)
        data = self.apply_mask(**data)
        return data

_check_polygon_within_box 

_check_polygon_within_box(polygon: ndarray, roi_coordinates: List[int]) -> bool
Source code in SaigeToolkit/data/transform/roi/roi_handler.py 
229
230
231
232
233
234
235
236
237
238
239
240
241
242
def _check_polygon_within_box(self, polygon: np.ndarray, roi_coordinates: List[int]) -> bool:
    left, top, right, bottom = roi_coordinates

    def check_x_cord(p):
        return p >= left and p < right

    def check_y_cord(p):
        return p >= top and p < bottom

    def check_point_within_box(point_xy):
        x, y = point_xy
        return check_x_cord(x) and check_y_cord(y)

    return all(check_point_within_box(p) for p in polygon)

ImageLoader 

ImageLoader(image_mode: Union[str, List[str]] = 'RGB', to_numpy: bool = True)

여러 형태의 데이터를 입력으로 받아, 전처리 과정을 거쳐 PIL Image 혹은 np.ndarray 로 return 합니다. 현재 지원하는 데이터는 다음과 같습니다. - 데이터 타입: [image path, np.ndarray, PIL Image] - color: ["RGB", "Gray"] - bit: [8, 16]

입력으로 받은 데이터에 따른 출력값은 다음과 같습니다. | image path | np.ndarray | PIL | RGB; 8 | PIL(RGB;8) | PIL(RGB;8) | PIL(RGB;8) | RGB;16 | PIL(RGB;8) | PIL(RGB;8) | - | Gray; 8 | PIL(L;8) | PIL(L;8) | PIL(L;8) | Gray;16 | PIL(L;8) | PIL(L;8) | PIL(L;8) |

Note1

PIL은 RGB;16을 지원하지 않습니다. 따라서 PIL(RGB;16)은 입력으로 들어올 수 없습니다.

Note2

PIL은 "I;16" 모드로 Gray;16을 지원하지만, PIL 내부의 convert 함수를 써서 Gray;8로 변환시 값이 overflow 나는 issue가 있습니다.

Note3

이미지는 기본적으로 np.ndarray로 로드되며, PIL.Image.Image로 로드하려면 to_numpy=False를 세팅하세요.

Note4

image_mode는 "RGB", "L" 중 하나를 지원하며, multipage (이미지 리스트) 인 경우 각 페이지의 모드를 리스트로 입력하세요. 예시: 1, 3번째 페이지는 RGB 이고 2번째 페이지는 L 인 경우 image_mode = ["RGB", "L", "RGB"]

Source code in SaigeToolkit/data/transform/image_load.py 
44
45
46
47
48
49
50
def __init__(self, image_mode: Union[str, List[str]] = "RGB", to_numpy: bool = True) -> None:
    if isinstance(image_mode, str):
        image_mode = [image_mode]
    elif not set(image_mode).issubset(set(IMAGE_MODES)):
        raise ValueError
    self.image_mode = image_mode
    self.to_numpy = to_numpy

image_mode instance-attribute 

image_mode = image_mode

to_numpy instance-attribute 

to_numpy = to_numpy

__call__ 

__call__(image: Union[Image, ndarray, str, List], **data) -> Dict
Source code in SaigeToolkit/data/transform/image_load.py 
52
53
def __call__(self, image: Union[Image.Image, np.ndarray, str, List], **data) -> Dict:
    return self.call_method(image=image, **data)

call_method 

call_method(image: Union[Image, ndarray, str, List], **data) -> Dict

make [PIL Image or np.ndarray] from PIL.Image, np.ndarray, or path string

Parameters:

  • image (Union[Image, ndarray, str, List]) –

    PIL.Image, array, path string or list of it.

Returns:

  • Dict ( Dict ) –

    { "image": Union[PIL.Image.Image, np.ndarray, List[PIL.Image.Image], List[np.ndarray]], **input_dict, }

Source code in SaigeToolkit/data/transform/image_load.py 
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
def call_method(self, image: Union[Image.Image, np.ndarray, str, List], **data) -> Dict:
    """make [PIL Image or np.ndarray] from PIL.Image, np.ndarray, or path string

    Args:
        image (Union[Image.Image, np.ndarray, str, List]): PIL.Image, array, path string or list of it.

    Returns:
        Dict:
            {
                "image": Union[PIL.Image.Image, np.ndarray, List[PIL.Image.Image], List[np.ndarray]],
                **input_dict,
            }
    """
    multipage = isinstance(image, List)
    if not multipage:
        image = [image]

    if len(image) != len(self.image_mode):
        raise ValueError("number of images should match len(image_mode)")

    # 이미지들을 각자의 모드로 로드 (RGB 또는 L).
    loaded_images = []
    for image_i, image_mode_i in zip(image, self.image_mode):
        image_i = self.load(image_i)
        image_i = convert_image_mode(image=image_i, mode=image_mode_i, copy=False)
        loaded_images.append(image_i)

    if multipage:
        # multipage 이미지들의 사이즈가 모두 동일해야함.
        image_sizes = set(read_image_size(item) for item in loaded_images)
        if len(image_sizes) > 1:
            raise ValueError("sizef of images in multipage should be identical")
    else:
        loaded_images = loaded_images[0]

    data["image"] = loaded_images
    return data

load 

load(image: Union[Image, ndarray, str]) -> Union[Image, ndarray]
Source code in SaigeToolkit/data/transform/image_load.py 
 93
 94
 95
 96
 97
 98
 99
100
def load(self, image: Union[Image.Image, np.ndarray, str]) -> Union[Image.Image, np.ndarray]:
    if isinstance(image, Image.Image):
        image = self.load_from_pil(image)
    elif isinstance(image, np.ndarray):
        image = self.load_from_array(image)
    elif isinstance(image, str):
        image = self.load_from_path(image)
    return image

load_from_pil 

load_from_pil(image: Image) -> Union[Image, ndarray]
Source code in SaigeToolkit/data/transform/image_load.py 
102
103
104
105
106
107
108
109
def load_from_pil(self, image: Image.Image) -> Union[Image.Image, np.ndarray]:
    if self.to_numpy:
        image = np.array(image, dtype=np.uint16 if self._is_16bit(image) else np.uint8)

    if self._is_16bit(image):
        image = self._convert_16_to_8(image)

    return image

load_from_array 

load_from_array(array: ndarray) -> Union[Image, ndarray]
Source code in SaigeToolkit/data/transform/image_load.py 
111
112
113
114
115
116
117
118
119
120
121
122
123
def load_from_array(self, array: np.ndarray) -> Union[Image.Image, np.ndarray]:
    if array.ndim == 3 and array.shape[-1] == 1:  # grayscale shape HW1 -> HW
        array = np.squeeze(array, axis=2)

    if self._is_16bit(array):
        array = self._convert_16_to_8(array)

    if self.to_numpy:
        out = array
    else:
        out = Image.fromarray(array)

    return out

load_from_path 

load_from_path(path: str) -> Union[Image, ndarray]

Loading function using PIL.Image library. This function is introduced because {exif_transpose} must be processed. {exif_transpose} fix rotated image binary data using {EXIF} information. Without {exif_transpose}, network would accidently learn randomly rotated image data.

Parameters:

  • path (str) –

    path to image file

Returns:

  • Union[Image, ndarray] –

    Union[Image.Image, np.ndarray]: image

Source code in SaigeToolkit/data/transform/image_load.py 
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
def load_from_path(self, path: str) -> Union[Image.Image, np.ndarray]:
    """Loading function using PIL.Image library.
    This function is introduced because {exif_transpose} must be processed.
    {exif_transpose} fix rotated image binary data using {EXIF} information.
    Without {exif_transpose}, network would accidently learn randomly rotated image data.

    Args:
        path (str): path to image file

    Returns:
        Union[Image.Image, np.ndarray]: image
    """
    if path.endswith(SAIGE_GENERATED_IMAGE_EXTENSIONS):
        image = self._load_from_encrypted_file_path(path)
    else:
        image = self._load_from_plain_file_path(path)

    if self.to_numpy:
        image = np.array(image, dtype=np.uint16 if self._is_16bit(image) else np.uint8)

    if self._is_16bit(image):
        image = self._convert_16_to_8(image)

    return image

_load_from_plain_file_path staticmethod 

_load_from_plain_file_path(path: str) -> Image

Loading function using PIL.Image library. This function is introduced because {exif_transpose} must be processed. {exif_transpose} fix rotated image binary data using {EXIF} information. Without {exif_transpose}, network would accidently learn randomly rotated image data.

Parameters:

  • path (str) –

    path to image file

Returns:

  • Image –

    Image.Image: image

Source code in SaigeToolkit/data/transform/image_load.py 
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
@staticmethod
def _load_from_plain_file_path(path: str) -> Image.Image:
    """Loading function using PIL.Image library.
    This function is introduced because {exif_transpose} must be processed.
    {exif_transpose} fix rotated image binary data using {EXIF} information.
    Without {exif_transpose}, network would accidently learn randomly rotated image data.

    Args:
        path (str): path to image file

    Returns:
        Image.Image: image
    """

    try:
        with open(path, "rb") as f:
            image = Image.open(f)
            image = ImageOps.exif_transpose(image)
    except Exception as e:
        raise InvalidImageFileError(f"[path] {path}") from e
    return image

_load_from_encrypted_file_path staticmethod 

_load_from_encrypted_file_path(path: str) -> Image

Loading function using PIL.Image library and decrypting encrypted image file.

Parameters:

  • path (str) –

    encrypted image file path

Returns:

  • Image –

    Image.Image: decrypted image

Source code in SaigeToolkit/data/transform/image_load.py 
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
@staticmethod
def _load_from_encrypted_file_path(path: str) -> Image.Image:
    """Loading function using PIL.Image library and decrypting encrypted image file.

    Args:
        path (str): encrypted image file path

    Returns:
        Image.Image: decrypted image
    """
    try:
        image = decrypt_load_image(path)
    except Exception as e:
        raise InvalidImageFileError(f"[path] {path}") from e
    return image

_convert_16_to_8 staticmethod 

_convert_16_to_8(image: Union[Image, ndarray]) -> Union[Image, ndarray]

이미지 픽셀당 비트수를 16에서 8로 변화합니다. 입출력 데이터 타입이 같습니다. (pil로 받으면 pil을 ndarray로 받을 시 ndarray를 출력합니다.)

Source code in SaigeToolkit/data/transform/image_load.py 
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
@staticmethod
def _convert_16_to_8(image: Union[Image.Image, np.ndarray]) -> Union[Image.Image, np.ndarray]:
    """
    이미지 픽셀당 비트수를 16에서 8로 변화합니다.
    입출력 데이터 타입이 같습니다. (pil로 받으면 pil을 ndarray로 받을 시 ndarray를 출력합니다.)
    """

    def _convert_16_to_8_np(array: np.ndarray) -> np.ndarray:
        return (array >> 8).astype(np.uint8)

    if isinstance(image, Image.Image):
        assert "I" in image.mode
        array_16bit = np.array(image, dtype=np.uint16)
        array_8bit = _convert_16_to_8_np(array_16bit)
        ret_image = Image.fromarray(array_8bit)
    elif isinstance(image, np.ndarray):
        assert image.dtype == np.uint16
        ret_image = _convert_16_to_8_np(image)
    else:
        raise NotImplementedError

    return ret_image

_is_16bit staticmethod 

_is_16bit(image: Union[Image, ndarray]) -> bool
Source code in SaigeToolkit/data/transform/image_load.py 
211
212
213
214
215
216
217
@staticmethod
def _is_16bit(image: Union[Image.Image, np.ndarray]) -> bool:
    if isinstance(image, Image.Image) and "I" in image.mode:
        return True
    elif isinstance(image, np.ndarray) and image.dtype == np.uint16:
        return True
    return False

OversizedImageHandling

Bases: Enum

RESIZE_TO_FIT class-attribute instance-attribute 

RESIZE_TO_FIT = 'resize_to_fit'

DO_NOT_RESIZE class-attribute instance-attribute 

DO_NOT_RESIZE = 'do_not_resize'

values classmethod 

values() -> List[str]
Source code in SaigeToolkit/data/transform/transform.py 
31
32
33
@classmethod
def values(cls) -> List[str]:
    return [item.value for item in cls]

Transform 

Transform(image_mode: Union[str, List[str]] = 'RGB', inspection_size_wh: Optional[ImageSizeType] = None, roi: Optional[Dict] = None, resize: Optional[Dict] = None, augmentation: Optional[Dict] = None, roi_mask_first: bool = True, oversized_image_handling: str = 'resize_to_fit', skip_round_resize_if_undersized: bool = False)
Source code in SaigeToolkit/data/transform/transform.py 
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
def __init__(
    self,
    image_mode: Union[str, List[str]] = "RGB",
    inspection_size_wh: Optional[ImageSizeType] = None,
    roi: Optional[Dict] = None,
    resize: Optional[Dict] = None,
    augmentation: Optional[Dict] = None,
    roi_mask_first: bool = True,  # 하위 호환성을 위해 roi_mask_first=True를 기본값으로 설정
    oversized_image_handling: str = "resize_to_fit",  # 하위 호환성을 위해 기본값을 "resize_to_fit"으로 설정
    skip_round_resize_if_undersized: bool = False,  # 하위 호환성을 위해 기본값을 False로 설정
):
    self.roi_mask_first = roi_mask_first
    self.image_loader = ImageLoader(image_mode=image_mode)
    self.roi_handler = ROIHandler(**roi) if roi is not None else None
    self.base_resizer = build_resizer(**resize) if resize is not None else None

    # inspection_size_wh와 oversized_image_handling에 따라서 inspection_size_resizer와 stacked_resizer_handler를 설정합니다.
    self.inspection_size_resizer: Optional[InspectionSizeResizer] = None
    self.stacked_resizer_handler: StackedResizerHandler = None

    # inspection_size_wh setter에서 inspection_size_resizer와 stacked_resizer_handler 생성
    self.inspection_size_wh = inspection_size_wh

    self.oversized_image_handling = oversized_image_handling
    self.skip_round_resize_if_undersized = skip_round_resize_if_undersized

    if augmentation is None:
        self.augmentation = None
    elif "_target_" in augmentation:
        self.augmentation = build_augmentation(augmentation)
    else:
        raise NotImplementedError("Old version of augmentation config is not supported.")

roi_mask_first instance-attribute 

roi_mask_first = roi_mask_first

image_loader instance-attribute 

image_loader = ImageLoader(image_mode=image_mode)

roi_handler instance-attribute 

roi_handler = ROIHandler(**roi) if roi is not None else None

base_resizer instance-attribute 

base_resizer = build_resizer(**resize) if resize is not None else None

inspection_size_resizer instance-attribute 

inspection_size_resizer: Optional[InspectionSizeResizer] = None

stacked_resizer_handler instance-attribute 

stacked_resizer_handler: StackedResizerHandler = None

skip_round_resize_if_undersized instance-attribute 

skip_round_resize_if_undersized = skip_round_resize_if_undersized

augmentation instance-attribute 

augmentation = None

inspection_size_wh property writable 

inspection_size_wh: Optional[ImageSizeType]

oversized_image_handling property writable 

oversized_image_handling: OversizedImageHandling

Operation

Bases: str, Enum

ROI class-attribute instance-attribute 
ROI = auto()
InspectionSize class-attribute instance-attribute 
InspectionSize = auto()
Resize class-attribute instance-attribute 
Resize = auto()

_set_inspection_size_resizer_and_stacked_resizer_handler 

_set_inspection_size_resizer_and_stacked_resizer_handler() -> None

inspection_size_wh와 oversized_image_handling에 따라서 inspection_size_resizer와 stacked_resizer_handler 설정합니다.

Source code in SaigeToolkit/data/transform/transform.py 
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
def _set_inspection_size_resizer_and_stacked_resizer_handler(self) -> None:
    """
    inspection_size_wh와 oversized_image_handling에 따라서 inspection_size_resizer와 stacked_resizer_handler 설정합니다.
    """

    # XXX: inspection size가 설정되어 있고, oversized_image_handling이 resize_to_fit일 경우에 InspectionSizeResizer 생성합니다.
    #     inspection size가 설정되어 있어도 oversized_image_handling이 do_not_resize일 경우에는 inspection_size에 맞추는 resize를 하지 않습니다.
    if (
        self.inspection_size_wh
        and self.oversized_image_handling == OversizedImageHandling.RESIZE_TO_FIT
    ):
        self.inspection_size_resizer = InspectionSizeResizer(
            inspection_size_wh=self.inspection_size_wh, resizer=self.base_resizer
        )
    else:
        self.inspection_size_resizer = None

    self.stacked_resizer_handler = StackedResizerHandler(
        resizer_list=[self.inspection_size_resizer, self.base_resizer]
    )

__call__ 

__call__(data: Dict, warmup: bool = False) -> Dict

data Dict에 transform을 적용합니다.

Parameters:

  • data (Dict) –

    data Dictionary

  • warmup (bool, default: False ) –

    InferenceHandler warmup시에 사용하는 파라미터 입니다. True일 경우, 현재 transform을 적용했을 때 나올 수 있는 가장 큰 image size로 transform을 적용합니다. Transform operation 중 ROI의 경우 input image에 따라 output size가 매번 바뀔 수 있기 때문에 해당 옵션이 추가되었습니다. Defaults to False.

Returns:

  • Dict ( Dict ) –

    transform이 적용된 데이터 Dict입니다.

Source code in SaigeToolkit/data/transform/transform.py 
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
def __call__(self, data: Dict, warmup: bool = False) -> Dict:
    """data Dict에 transform을 적용합니다.

    Args:
        data (Dict): data Dictionary
        warmup (bool, optional): InferenceHandler warmup시에 사용하는 파라미터 입니다. True일 경우,
                                    현재 transform을 적용했을 때 나올 수 있는 가장 큰 image size로
                                    transform을 적용합니다.
                                 Transform operation 중 ROI의 경우 input image에 따라 output size가
                                    매번 바뀔 수 있기 때문에 해당 옵션이 추가되었습니다.
                                 Defaults to False.

    Returns:
        Dict: transform이 적용된 데이터 Dict입니다.
    """
    # transform_params 설정
    if "transform_params" not in data:
        data["transform_params"] = {
            "operation_stack": [
                self.Operation.ROI,
                self.Operation.InspectionSize,
                self.Operation.Resize,
            ],
            "operation_params": {},
        }

    operation_params: Dict = data["transform_params"]["operation_params"]

    # load PIL Image
    data = self.image_loader(**data)

    # image가 설정된 inspection_size_wh보다 작은지 확인합니다.
    _undersized = False
    if self.inspection_size_wh:
        multipage = isinstance(data["image"], List)
        input_size = read_image_size(data["image"][0] if multipage else data["image"])

        _undersized = (input_size[0] < self.inspection_size_wh[0]) and (
            input_size[1] < self.inspection_size_wh[1]
        )

    # XXX: 원본 이미지가 inspection_size를 넘지 않도록 resize 했을 때의 image scale 계산
    if self.inspection_size_resizer is not None:
        self.inspection_size_resizer.set_scale_from_data(**data)

    # ROI crop 적용
    if self.roi_handler is not None:
        data, roi_revert_params = self.roi_handler.apply_crop(
            return_revert_params=True, warmup=warmup, **data
        )
        if self.roi_mask_first:
            data = self.roi_handler.apply_mask(**data)

        if self.Operation.ROI in operation_params:
            raise KeyError
        operation_params[self.Operation.ROI] = roi_revert_params

    ignore_round_resize = False
    # XXX: inspection size보다 작고, skip_round_resize_if_undersized가 True일 경우 round resize를 skip합니다.
    # Transform 바깥에서 작은 이미지에 padding 처리를 하는 경우, 시간 손실을 줄이기 위하여 추가되었습니다.
    if _undersized and self.skip_round_resize_if_undersized:
        ignore_round_resize = True

    # inspection_size와 resize_factor를 하나로 묶어서 resize
    data, revert_param_list = self.stacked_resizer_handler(
        return_revert_params=True, ignore_round=ignore_round_resize, **data
    )
    inspection_size_revert_params, resizer_revert_params = revert_param_list

    if inspection_size_revert_params is not None:
        if self.Operation.InspectionSize in operation_params:
            raise KeyError
        operation_params[self.Operation.InspectionSize] = inspection_size_revert_params

    if resizer_revert_params is not None:
        if self.Operation.Resize in operation_params:
            raise KeyError
        operation_params[self.Operation.Resize] = resizer_revert_params

    # # ROI blind mask 적용
    if self.roi_handler is not None and not self.roi_mask_first:
        data = self.roi_handler.apply_mask(**data)

    # data augmentation
    if self.augmentation is not None:
        data = self.augmentation(**data)

    return data

get_resize_scale classmethod 

get_resize_scale(transform_params: Dict) -> List[float]

before_transform_image_size -> after_transform_input_size가 되기 위한 scale을 구합니다. before_transform_image_size * scale = after_transform_input_size

Parameters:

  • transform_params (Dict) –

    transform시에 저장해둔 parameter 입니다.

Returns:

  • List[float] –

    List[float]: before_transform_image_size * scale = after_transform_input_size인 scale scale: [scale_width, scale_height]

Source code in SaigeToolkit/data/transform/transform.py 
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
@classmethod
def get_resize_scale(cls, transform_params: Dict) -> List[float]:
    """before_transform_image_size -> after_transform_input_size가 되기 위한 scale을 구합니다.
    before_transform_image_size * scale = after_transform_input_size

    Args:
        transform_params (Dict): transform시에 저장해둔 parameter 입니다.

    Returns:
        List[float]: before_transform_image_size * scale = after_transform_input_size인 scale
                     scale: [scale_width, scale_height]
    """
    operation_params: Dict = transform_params["operation_params"]
    inspection_size_revert_params = operation_params.get(cls.Operation.InspectionSize, None)
    resizer_revert_params = operation_params.get(cls.Operation.Resize, None)

    if inspection_size_revert_params is None and resizer_revert_params is None:
        resize_scale = [1.0, 1.0]
    else:
        if inspection_size_revert_params is None:
            image_size_before_resize = resizer_revert_params["image_size_before_resize"]
            image_size_after_resize = resizer_revert_params["image_size_after_resize"]
        elif resizer_revert_params is None:
            image_size_before_resize = inspection_size_revert_params["image_size_before_resize"]
            image_size_after_resize = inspection_size_revert_params["image_size_after_resize"]
        else:
            image_size_before_resize = inspection_size_revert_params["image_size_before_resize"]
            image_size_after_resize = resizer_revert_params["image_size_after_resize"]

        resize_scale = (
            np.array(image_size_after_resize) / np.array(image_size_before_resize)
        ).tolist()

    return resize_scale

_get_next_revert_operation staticmethod 

_get_next_revert_operation(transform_params: Dict) -> Optional[Operation]

다음으로 할 revert operation을 가져옵니다. operation_stack에서 operation이 제거되지는 않습니다.

Parameters:

  • transform_params (Dict) –

    transform시에 저장해둔 parameter 입니다.

Returns:

  • Optional[Operation] –

    Optional[Operation]: 남아있는 revert operation이 있으면 operation을 없으면 None을 반환합니다.

Source code in SaigeToolkit/data/transform/transform.py 
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
@staticmethod
def _get_next_revert_operation(transform_params: Dict) -> Optional[Operation]:
    """다음으로 할 revert operation을 가져옵니다. operation_stack에서 operation이 제거되지는 않습니다.

    Args:
        transform_params (Dict): transform시에 저장해둔 parameter 입니다.

    Returns:
        Optional[Operation]: 남아있는 revert operation이 있으면 operation을 없으면 None을 반환합니다.
    """
    operation_stack: List = transform_params["operation_stack"]
    next_operation = None
    if len(operation_stack) > 0:
        next_operation = operation_stack[-1]

    return next_operation

revert classmethod 

revert(data: Dict, transform_params: Dict, operation: Optional[Operation] = None) -> Dict

summary

Parameters:

  • data (Dict) –

    revert operation을 적용할 data dictionary입니다. data는 다음과 같은 구조를 지닙니다. { "key1": { "data" (Union[np.ndarray, torch.Tensor, List[Dict]]): 실제 data입니다. "data_type" (str): 해당 data의 type 입니다. 현재 ["array", "objects"]를 지원합니다. }, "key2": { "data" (Union[np.ndarray, torch.Tensor, List[Dict]]): 실제 data입니다. "data_type" (str): 해당 data의 type 입니다. 현재 ["array", "objects"]를 지원합니다. }, ... }

  • transform_params (Dict) –

    transform시에 저장해둔 parameter 입니다.

  • operation (Optional[Operation], default: None ) –

    transform에서 revert가 가능한 operation 입니다. Enum class인 Transform.Operation에 있는 항목들을 지원합니다. operation이 None이 아닐 시, 해당 operation 까지 revert를 적용하고, None일 시, 그 다음 revert operation을 적용합니다. Defaults to None.

Raises:

  • RevertOperationNotFoundError –

    args로 넣은 operation이 남은 revert operation 중에 없을 때 에러를 발생합니다.

Returns:

  • Dict ( Dict ) –

    revert operation이 적용된 data dictionary 입니다. 구조는 Args의 data와 같습니다.

Source code in SaigeToolkit/data/transform/transform.py 
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
@classmethod
def revert(
    cls,
    data: Dict,
    transform_params: Dict,
    operation: Optional[Operation] = None,
) -> Dict:
    """_summary_

    Args:
        data (Dict): revert operation을 적용할 data dictionary입니다. data는 다음과 같은 구조를 지닙니다.
            {
                "key1": {
                    "data" (Union[np.ndarray, torch.Tensor, List[Dict]]): 실제 data입니다.
                    "data_type" (str): 해당 data의 type 입니다. 현재 ["array", "objects"]를 지원합니다.
                },
                "key2": {
                    "data" (Union[np.ndarray, torch.Tensor, List[Dict]]): 실제 data입니다.
                    "data_type" (str): 해당 data의 type 입니다. 현재 ["array", "objects"]를 지원합니다.
                },
                ...
            }
        transform_params (Dict): transform시에 저장해둔 parameter 입니다.
        operation (Optional[Operation], optional): transform에서 revert가 가능한 operation 입니다.
                                                    Enum class인 Transform.Operation에 있는 항목들을 지원합니다.
                                                    operation이 None이 아닐 시, 해당 operation 까지 revert를 적용하고,
                                                    None일 시, 그 다음 revert operation을 적용합니다.
                                                    Defaults to None.

    Raises:
        RevertOperationNotFoundError: args로 넣은 operation이 남은 revert operation 중에 없을 때 에러를 발생합니다.

    Returns:
        Dict: revert operation이 적용된 data dictionary 입니다. 구조는 Args의 data와 같습니다.
    """
    # operation이 None인 경우 다음 revert operation 하나를 진행
    if operation is None:
        data = cls._revert(data=data, transform_params=transform_params)
    # operation이 None이 아닌 경우
    else:
        # 해당 operation이 operation_stack에 있는지 확인
        operation_stack: List = transform_params["operation_stack"]
        if operation not in operation_stack:
            raise RevertOperationNotFoundError
        # 해당 operation까지 revert를 적용
        while cls._get_next_revert_operation(transform_params=transform_params) != operation:
            data = cls._revert(data=data, transform_params=transform_params)
        data = cls._revert(data=data, transform_params=transform_params)

    return data

_revert classmethod 

_revert(data: Dict, transform_params: Dict) -> Dict
Source code in SaigeToolkit/data/transform/transform.py 
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
@classmethod
def _revert(
    cls,
    data: Dict,
    transform_params: Dict,
) -> Dict:
    operation_stack: List = transform_params["operation_stack"]
    operation_params: Dict = transform_params["operation_params"]

    if cls._get_next_revert_operation(transform_params) is None:
        raise RevertOperationStackEmptyError

    next_revert_operation = operation_stack.pop()
    revert_params = operation_params.pop(next_revert_operation, None)

    next_revert_operation = {
        cls.Operation.ROI: cls._revert_roi,
        cls.Operation.Resize: cls._revert_resize,
        cls.Operation.InspectionSize: cls._revert_resize,
    }[next_revert_operation]

    result = {}
    for k, v in data.items():
        result[k] = {
            "data": next_revert_operation(revert_params=revert_params, **v),
            "data_type": v["data_type"],
        }

    return result

_revert_resize staticmethod 

_revert_resize(data: Optional[Union[Tensor, ndarray, List[Dict]]], revert_params: Optional[Dict], data_type: str) -> Union[Tensor, ndarray, List[Dict]]
Source code in SaigeToolkit/data/transform/transform.py 
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
@staticmethod
def _revert_resize(
    data: Optional[Union[torch.Tensor, np.ndarray, List[Dict]]],
    revert_params: Optional[Dict],
    data_type: str,
) -> Union[torch.Tensor, np.ndarray, List[Dict]]:
    if data is not None and revert_params is not None:
        image_size_before_resize = revert_params["image_size_before_resize"]
        image_size_after_resize = revert_params["image_size_after_resize"]

        if list(image_size_after_resize) != list(image_size_before_resize):
            if data_type == "array":
                data = resize_array(array=data, target_size=image_size_before_resize)
            elif data_type == "objects":
                resize_scale = (
                    np.array(image_size_before_resize) / np.array(image_size_after_resize)
                ).tolist()
                data = scale_segments_properties(segments_properties=data, scale=resize_scale)
            elif data_type == "polygons":
                data = resize_polygon(
                    polygons=data,
                    image_size=image_size_after_resize,
                    tw=image_size_before_resize[0],
                    th=image_size_before_resize[1],
                )
            else:
                raise NotImplementedError

    return data

_revert_roi staticmethod 

_revert_roi(data: Optional[Union[Tensor, ndarray, List[Dict]]], revert_params: Optional[Dict], data_type: str) -> Union[Tensor, ndarray, List[Dict]]
Source code in SaigeToolkit/data/transform/transform.py 
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
@staticmethod
def _revert_roi(
    data: Optional[Union[torch.Tensor, np.ndarray, List[Dict]]],
    revert_params: Optional[Dict],
    data_type: str,
) -> Union[torch.Tensor, np.ndarray, List[Dict]]:
    if data is not None and revert_params is not None:
        image_size_before_roi = revert_params["image_size_before_roi"]
        image_size_after_roi = revert_params["image_size_after_roi"]

        if list(image_size_before_roi) != list(image_size_after_roi):
            image_width, image_height = image_size_before_roi
            roi_left, roi_top, roi_right, roi_bottom = revert_params["roi_coordinates"]
            pad_left = roi_left
            pad_top = roi_top
            pad_right = image_width - roi_right
            pad_bottom = image_height - roi_bottom

            if data_type == "array":
                data = add_constant_margin_array(
                    array=data,
                    left=pad_left,
                    top=pad_top,
                    right=pad_right,
                    bottom=pad_bottom,
                    value=0,
                )
            elif data_type == "objects":
                data = translate_segments_properties(
                    segments_properties=data,
                    left=pad_left,
                    top=pad_top,
                )
            elif data_type == "polygons":
                data = translate_polygon(
                    polygons=data,
                    offset=(-pad_left, -pad_top),
                )
            else:
                raise NotImplementedError

    return data

is_oversized staticmethod 

is_oversized(transform_params: Optional[Dict]) -> bool

InspectionSize 연산에서 resize 되었는지 여부를 판단

Source code in SaigeToolkit/data/transform/transform.py 
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
@staticmethod
def is_oversized(transform_params: Optional[Dict]) -> bool:
    """InspectionSize 연산에서 resize 되었는지 여부를 판단"""
    if transform_params is None:
        return False

    inspection_size_params = get_tree_node_with_default(
        transform_params, ["operation_params", Transform.Operation.InspectionSize], None
    )
    if inspection_size_params is None:
        return False

    size1 = tuple(inspection_size_params["image_size_before_resize"])
    size2 = tuple(inspection_size_params["image_size_after_resize"])

    if size1 != size2:
        return True
    else:
        return False

build_augmentation 

build_augmentation(config: Dict) -> Augmentation
Source code in SaigeToolkit/data/transform/augmentation/builder.py 
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
def build_augmentation(config: Dict) -> Augmentation:
    logger.info(f"[AUGMENTATION]\n{pprint.pformat(config, sort_dicts=False)}")

    config = dict(config.items())
    _target_ = config.pop("_target_")
    target_class = _types[_target_]

    # Build transform
    if issubclass(target_class, ImageTransform):
        transform = target_class(**config)

    # Build compose
    elif issubclass(target_class, BaseCompose):
        transform_configs = config.pop("transforms", [])

        _transforms = []
        for transform_config in transform_configs:
            _transform = build_transform(**transform_config)
            _transforms.append(_transform)
        config["transforms"] = _transforms

        transform = build_compose(_target_, **config)

    else:
        raise NotImplementedError(f"Unsupported augmentation type: {_target_}")

    return Augmentation(transform)

validate_inspection_size_wh 

validate_inspection_size_wh(inspection_size_wh: ImageSizeType) -> None

inspection_size_wh가 유효한지 확인합니다. inspection_size_wh는 (width, height)로 모두 1 이상의 int여야 합니다.

유효하지 않을 경우, error를 발생시킵니다.

Parameters:

  • inspection_size_wh (Optional[ImageSizeType]) –

    검사할 inspection_size_wh.

Raises:

  • InspectionSizeTypeError –

    맞지 않는 타입일 경우 발생합니다.

  • InspectionSizeValueError –

    값이 1 이상이 아닐 경우 발생합니다.

Source code in SaigeToolkit/data/transform/parameter_validation.py 
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
def validate_inspection_size_wh(inspection_size_wh: ImageSizeType) -> None:
    """inspection_size_wh가 유효한지 확인합니다.
    inspection_size_wh는 (width, height)로 모두 1 이상의 int여야 합니다.

    유효하지 않을 경우, error를 발생시킵니다.

    Args:
        inspection_size_wh (Optional[ImageSizeType], optional): 검사할 inspection_size_wh.

    Raises:
        InspectionSizeTypeError: 맞지 않는 타입일 경우 발생합니다.
        InspectionSizeValueError: 값이 1 이상이 아닐 경우 발생합니다.
    """
    # 타입 체크
    valid_type = inspection_size_wh is None or (  # inspection_size는 None 이거나 혹은
        len(inspection_size_wh) == 2  # length가 2이면서 (width, height)
        and all([isinstance(v, int) for v in inspection_size_wh])  # 값은 모두 int여야 한다.
    )
    if not valid_type:
        raise InspectionSizeTypeError

    # 값 체크, inspection_size_wh의 값은 모두 1 이상이어야 한다.
    valid_value = inspection_size_wh is None or all([value >= 1 for value in inspection_size_wh])
    if not valid_value:
        raise InspectionSizeValueError

resize_polygon 

resize_polygon(polygons: PolygonType, image_size: Sequence[int], tw: int, th: int) -> PolygonType

Resize polygon from (w, h) to (tw, th)

Source code in SaigeToolkit/data/transform/polygon_function.py 
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
def resize_polygon(polygons: PolygonType, image_size: Sequence[int], tw: int, th: int) -> PolygonType:
    """Resize polygon from (w, h) to (tw, th)"""
    if len(polygons) == 0:
        return polygons

    w, h = image_size
    if w == tw and h == th:
        return polygons

    dtype = polygons[0].dtype

    w_scale = tw / w
    h_scale = th / h

    new_polygons = deepcopy(polygons)

    for polygon in new_polygons:
        polygon[:, 0] = polygon[:, 0] * w_scale
        polygon[:, 1] = polygon[:, 1] * h_scale
        polygon = polygon.astype(dtype)

    return new_polygons

translate_polygon 

translate_polygon(polygons: PolygonType, offset: Sequence[int]) -> PolygonType

Translate polyfon from [(x1, y1), ... ] to [(x1 - x_offset), (y1 - y_offset), ...]

Source code in SaigeToolkit/data/transform/polygon_function.py 
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
def translate_polygon(
    polygons: PolygonType,
    offset: Sequence[int],
) -> PolygonType:
    """Translate polyfon from [(x1, y1), ... ] to [(x1 - x_offset), (y1 - y_offset), ...]"""
    if len(polygons) == 0:
        return polygons

    if offset[0] == 0 and offset[1] == 0:
        return polygons

    dtype = polygons[0].dtype

    x_offset, y_offset = np.array(offset).astype(dtype)

    new_polygons = deepcopy(polygons)

    for polygon in new_polygons:
        polygon[:, 0] -= x_offset
        polygon[:, 1] -= y_offset

    return new_polygons

build_resizer 

build_resizer(**kwargs) -> Resizer
Source code in SaigeToolkit/data/transform/resize.py 
14
15
16
17
18
19
20
21
22
23
24
25
26
27
def build_resizer(**kwargs) -> "Resizer":

    # Check if multiple resizer parameters are given
    resizer_params = [kwargs.get(key) for key in Resizer.registry.keys()]
    if sum(param is not None for param in resizer_params) > 1:
        raise ResizerParameterValueError

    # Return the resizer instance if the resizer type is given
    for resizer_type in Resizer.registry.keys():
        if kwargs.get(resizer_type) is not None:
            return Resizer.registry[resizer_type](**kwargs)

    # Return the default resizer instance if no resizer type is given
    return Resizer(**kwargs)

read_image_size 

read_image_size(image: Union[Image, Tensor, ndarray]) -> ImageSizeType

image size: (W, H)

Source code in SaigeToolkit/data/transform/image_function.py 
11
12
13
14
15
16
17
18
19
20
def read_image_size(image: Union[Image.Image, torch.Tensor, np.ndarray]) -> ImageSizeType:
    """image size: (W, H)"""
    if isinstance(image, Image.Image):
        return image.size
    elif isinstance(image, torch.Tensor) and image.ndim in (2, 3, 4):  # HW, CHW, BCHW
        return (image.shape[-1], image.shape[-2])
    elif isinstance(image, np.ndarray) and image.ndim in (2, 3):  # HW, HWC
        return (image.shape[1], image.shape[0])
    else:
        raise NotImplementedError

resize_array 

resize_array(array: Union[Tensor, ndarray], target_size: ImageSizeType, resampling: str = 'nearest') -> Union[Tensor, ndarray]

[HW, BHW]의 array를 resize합니다.

Parameters:

  • array (Union[Tensor, ndarray]) –

    array data [HW, BHW]

  • target_size (ImageSizeType) –

    [W, H]

  • resampling (str, default: 'nearest' ) –

    resampling method. Defaults to "nearest".

Returns:

  • Union[Tensor, ndarray] –

    Union[torch.Tensor, np.ndarray]: resized array

Source code in SaigeToolkit/data/transform/image_function.py 
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
def resize_array(
    array: Union[torch.Tensor, np.ndarray],
    target_size: ImageSizeType,
    resampling: str = "nearest",
) -> Union[torch.Tensor, np.ndarray]:
    """[HW, BHW]의 array를 resize합니다.

    Args:
        array (Union[torch.Tensor, np.ndarray]): array data [HW, BHW]
        target_size (ImageSizeType): [W, H]
        resampling (str, optional): resampling method. Defaults to "nearest".

    Returns:
        Union[torch.Tensor, np.ndarray]: resized array
    """
    is_np_array = isinstance(array, np.ndarray)
    if is_np_array:  # np.ndarray -> torch.Tensor
        array = torch.tensor(array)

    is_two_dim = array.ndim == 2
    if is_two_dim:  # HW -> 1HW
        array = array.unsqueeze(0)

    array = torch.nn.functional.interpolate(
        array.unsqueeze(1),  # BHW -> B1HW
        size=(target_size[1], target_size[0]),
        mode=RESAMPLE_TORCH[resampling],
        antialias=None if resampling == "nearest" else True,
    ).squeeze(1)

    if is_two_dim:  # 1HW -> HW
        array = array.squeeze(0)

    if is_np_array:  # torch.Tensor -> np.ndarray
        array = array.numpy()

    return array

add_constant_margin_array 

add_constant_margin_array(array: Union[Tensor, ndarray], left: int, top: int, right: int, bottom: int, value: Union[float, int]) -> Union[Tensor, ndarray]

array에 left, top, right, bottom 만큼 constant value로 padding 합니다.

Parameters:

  • array (Union[Tensor, ndarray]) –

    array data [HW, BHW]

  • left (int) –

    array의 왼쪽 padding size 입니다.

  • top (int) –

    array의 위쪽 padding size 입니다.

  • right (int) –

    array의 오른쪽 padding size 입니다.

  • bottom (int) –

    array의 아래쪽 padding size 입니다.

  • value (Union[float, int]) –

    padding된 영역에 들어갈 value 입니다.

Source code in SaigeToolkit/data/transform/image_function.py 
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
def add_constant_margin_array(
    array: Union[torch.Tensor, np.ndarray],
    left: int,
    top: int,
    right: int,
    bottom: int,
    value: Union[float, int],
) -> Union[torch.Tensor, np.ndarray]:
    """array에 left, top, right, bottom 만큼 constant value로 padding 합니다.

    Args:
        array (Union[torch.Tensor, np.ndarray]): array data [HW, BHW]
        left (int): array의 왼쪽 padding size 입니다.
        top (int): array의 위쪽 padding size 입니다.
        right (int): array의 오른쪽 padding size 입니다.
        bottom (int): array의 아래쪽 padding size 입니다.
        value (Union[float, int]): padding된 영역에 들어갈 value 입니다.
    """
    if isinstance(array, np.ndarray):
        is_ndarray = True
        array = torch.from_numpy(array)
    else:
        is_ndarray = False

    result = torch.nn.functional.pad(array, (left, right, top, bottom), value=value)

    if is_ndarray:
        result = result.numpy(force=True)

    return result

scale_segments_properties 

scale_segments_properties(segments_properties: List[Dict], scale: Sequence[float]) -> List[Dict]
Source code in SaigeToolkit/data/dataclass/segment.py 
431
432
433
434
def scale_segments_properties(segments_properties: List[Dict], scale: Sequence[float]) -> List[Dict]:
    return [
        scale_segment_properties(segment_properties, scale) for segment_properties in segments_properties
    ]

translate_segments_properties 

translate_segments_properties(segments_properties: List[Dict], left: int, top: int, **kwargs) -> List[Dict]
Source code in SaigeToolkit/data/dataclass/segment.py 
482
483
484
485
486
487
488
489
490
491
def translate_segments_properties(
    segments_properties: List[Dict],
    left: int,
    top: int,
    **kwargs,
) -> List[Dict]:
    return [
        translate_segment_properties(segment_properties, left, top, **kwargs)
        for segment_properties in segments_properties
    ]