| |
| |
|
|
| import os |
|
|
| 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) |
|
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|
|
| 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 |
|
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|
|
| 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] |
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