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|
| | import os |
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|
| | import cv2 |
| | import numpy as np |
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|
| | def minmax(data_array: np.ndarray, up_bound: float = None) -> np.ndarray: |
| | """ |
| | :: |
| | |
| | data_array = (data_array / up_bound) |
| | if min_value != max_value: |
| | data_array = (data_array - min_value) / (max_value - min_value) |
| | |
| | :param data_array: |
| | :param up_bound: if is not None, data_array will devided by it before the minmax ops. |
| | :return: |
| | """ |
| | if up_bound is not None: |
| | data_array = data_array / up_bound |
| | max_value = data_array.max() |
| | min_value = data_array.min() |
| | if max_value != min_value: |
| | data_array = (data_array - min_value) / (max_value - min_value) |
| | return data_array |
| |
|
| |
|
| | def clip_to_normalize(data_array: np.ndarray, clip_range: tuple = None) -> np.ndarray: |
| | clip_range = sorted(clip_range) |
| | if len(clip_range) == 3: |
| | clip_min, clip_mid, clip_max = clip_range |
| | assert 0 <= clip_min < clip_mid < clip_max <= 1, clip_range |
| | lower_array = data_array[data_array < clip_mid] |
| | higher_array = data_array[data_array > clip_mid] |
| | if lower_array.size > 0: |
| | lower_array = np.clip(lower_array, a_min=clip_min, a_max=1) |
| | max_lower = lower_array.max() |
| | lower_array = minmax(lower_array) * max_lower |
| | data_array[data_array < clip_mid] = lower_array |
| | if higher_array.size > 0: |
| | higher_array = np.clip(higher_array, a_min=0, a_max=clip_max) |
| | min_lower = higher_array.min() |
| | higher_array = minmax(higher_array) * (1 - min_lower) + min_lower |
| | data_array[data_array > clip_mid] = higher_array |
| | elif len(clip_range) == 2: |
| | clip_min, clip_max = clip_range |
| | assert 0 <= clip_min < clip_max <= 1, clip_range |
| | if clip_min != 0 and clip_max != 1: |
| | data_array = np.clip(data_array, a_min=clip_min, a_max=clip_max) |
| | data_array = minmax(data_array) |
| | elif clip_range is None: |
| | data_array = minmax(data_array) |
| | else: |
| | raise NotImplementedError |
| | return data_array |
| |
|
| |
|
| | def save_array_as_image(data_array: np.ndarray, save_name: str, save_dir: str, to_minmax: bool = False): |
| | """ |
| | save the ndarray as a image |
| | |
| | Args: |
| | data_array: np.float32 the max value is less than or equal to 1 |
| | save_name: with special suffix |
| | save_dir: the dirname of the image path |
| | to_minmax: minmax the array |
| | """ |
| | if not os.path.exists(save_dir): |
| | os.makedirs(save_dir) |
| | save_path = os.path.join(save_dir, save_name) |
| | if data_array.dtype != np.uint8: |
| | if data_array.max() > 1: |
| | raise Exception("the range of data_array has smoe errors") |
| | data_array = (data_array * 255).astype(np.uint8) |
| | if to_minmax: |
| | data_array = minmax(data_array, up_bound=255) |
| | data_array = (data_array * 255).astype(np.uint8) |
| | cv2.imwrite(save_path, data_array) |
| |
|
| |
|
| | def resize(image_array: np.ndarray, height, width, interpolation=cv2.INTER_LINEAR): |
| | h, w = image_array.shape[:2] |
| | if h == height and w == width: |
| | return image_array |
| |
|
| | resized_image_array = cv2.resize(image_array, dsize=(width, height), interpolation=interpolation) |
| | return resized_image_array |
| |
|
| |
|
| | def ms_resize(img, scales, base_h=None, base_w=None, interpolation=cv2.INTER_LINEAR): |
| | assert isinstance(scales, (list, tuple)) |
| | if base_h is None: |
| | base_h = img.shape[0] |
| | if base_w is None: |
| | base_w = img.shape[1] |
| | return [resize(img, height=int(base_h * s), width=int(base_w * s), interpolation=interpolation) for s in scales] |
| |
|
