From 354b8ea3a247d70c95b3ed939ca92c5cb421ef54 Mon Sep 17 00:00:00 2001 From: geoyee Date: Thu, 3 Mar 2022 20:15:17 +0800 Subject: [PATCH] [Fix] Clean chinese punctuation --- paddlers/datasets/raster.py | 19 +++++++++---------- paddlers/tools/yolo_cluster.py | 2 +- paddlers/transforms/batch_operators.py | 4 ++-- paddlers/transforms/img_decoder.py | 5 ++--- paddlers/transforms/operators.py | 16 ++++++++-------- paddlers/utils/convert.py | 12 ++++++------ requirements.txt | 3 ++- 7 files changed, 30 insertions(+), 31 deletions(-) diff --git a/paddlers/datasets/raster.py b/paddlers/datasets/raster.py index c3d9b03..ddec49a 100644 --- a/paddlers/datasets/raster.py +++ b/paddlers/datasets/raster.py @@ -27,25 +27,23 @@ class Raster: def __init__(self, path: str, band_list: Union[List[int], Tuple[int], None]=None, - is_sar: bool=False, # TODO: Remove this param - is_src: bool=False) -> None: + to_uint8: bool=False) -> None: """ Class of read raster. Args: path (str): The path of raster. band_list (Union[List[int], Tuple[int], None], optional): band list (start with 1) or None (all of bands). Defaults to None. - is_sar (bool, optional): The raster is SAR or not. Defaults to False. - is_src (bool, optional): - Return raw data or not (convert uint8/float32). Defaults to False. + to_uint8 (bool, optional): + Convert uint8 or return raw data. Defaults to False. """ super(Raster, self).__init__() if osp.exists(path): self.path = path - self.__src_data = gdal.Open(path) + self.__src_data = np.load(path) if path.split(".")[-1] == "npy" \ + else gdal.Open(path) self.__getInfo() - self.is_sar = is_sar - self.is_src = is_src + self.to_uint8 = to_uint8 self.setBands(band_list) else: raise ValueError("The path {0} not exists.".format(path)) @@ -107,11 +105,12 @@ class Raster: band_array.append(band_i) ima = np.stack(band_array, axis=0) if self.bands == 1: - if self.is_sar: + # the type is complex means this is a SAR data + if isinstance(type(ima[0, 0]), complex): ima = abs(ima) else: ima = ima.transpose((1, 2, 0)) - if self.is_src is False: + if self.to_uint8 is True: ima = raster2uint8(ima) return ima diff --git a/paddlers/tools/yolo_cluster.py b/paddlers/tools/yolo_cluster.py index 889058c..57d1f97 100644 --- a/paddlers/tools/yolo_cluster.py +++ b/paddlers/tools/yolo_cluster.py @@ -99,7 +99,7 @@ class YOLOAnchorCluster(BaseAnchorCluster): num_anchors (int): number of clusters dataset (DataSet): DataSet instance, VOC or COCO image_size (list or int): [h, w], being an int means image height and image width are the same. - cache (bool): whether using cache。 Defaults to True. + cache (bool): whether using cache. Defaults to True. cache_path (str or None, optional): cache directory path. If None, use `data_dir` of dataset. Defaults to None. iters (int, optional): iters of kmeans algorithm. Defaults to 300. gen_iters (int, optional): iters of genetic algorithm. Defaults to 1000. diff --git a/paddlers/transforms/batch_operators.py b/paddlers/transforms/batch_operators.py index 4affa14..3007bd2 100644 --- a/paddlers/transforms/batch_operators.py +++ b/paddlers/transforms/batch_operators.py @@ -69,7 +69,7 @@ class BatchRandomResize(Transform): """ Resize a batch of input to random sizes. - Attention：If interp is 'RANDOM', the interpolation method will be chose randomly. + Attention: If interp is 'RANDOM', the interpolation method will be chose randomly. Args: target_sizes (List[int], List[list or tuple] or Tuple[list or tuple]): @@ -108,7 +108,7 @@ class BatchRandomResize(Transform): class BatchRandomResizeByShort(Transform): """Resize a batch of input to random sizes with keeping the aspect ratio. - Attention：If interp is 'RANDOM', the interpolation method will be chose randomly. + Attention: If interp is 'RANDOM', the interpolation method will be chose randomly. Args: short_sizes (List[int], Tuple[int]): Target sizes of the shorter side of the image(s). diff --git a/paddlers/transforms/img_decoder.py b/paddlers/transforms/img_decoder.py index 92f0c27..5720d88 100644 --- a/paddlers/transforms/img_decoder.py +++ b/paddlers/transforms/img_decoder.py @@ -1,5 +1,3 @@ - - # copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -21,6 +19,7 @@ import copy import random import imghdr from PIL import Image + try: from collections.abc import Sequence except Exception: @@ -103,7 +102,7 @@ class ImgDecode(Transform): return cv2.imread(img_path, cv2.IMREAD_ANYDEPTH | cv2.IMREAD_ANYCOLOR | cv2.IMREAD_COLOR) else: - return cv2.imread(im_file, cv2.IMREAD_ANYDEPTH | + return cv2.imread(img_path, cv2.IMREAD_ANYDEPTH | cv2.IMREAD_ANYCOLOR) elif ext == '.npy': return np.load(img_path) diff --git a/paddlers/transforms/operators.py b/paddlers/transforms/operators.py index 415fd2c..5c18672 100644 --- a/paddlers/transforms/operators.py +++ b/paddlers/transforms/operators.py @@ -204,9 +204,9 @@ class Resize(Transform): """ Resize input. - - If target_size is an int，resize the image(s) to (target_size, target_size). + - If target_size is an int, resize the image(s) to (target_size, target_size). - If target_size is a list or tuple, resize the image(s) to target_size. - Attention：If interp is 'RANDOM', the interpolation method will be chose randomly. + Attention: If interp is 'RANDOM', the interpolation method will be chose randomly. Args: target_size (int, List[int] or Tuple[int]): Target size. If int, the height and width share the same target_size. @@ -315,7 +315,7 @@ class RandomResize(Transform): """ Resize input to random sizes. - Attention：If interp is 'RANDOM', the interpolation method will be chose randomly. + Attention: If interp is 'RANDOM', the interpolation method will be chose randomly. Args: target_sizes (List[int], List[list or tuple] or Tuple[list or tuple]): @@ -356,7 +356,7 @@ class ResizeByShort(Transform): """ Resize input with keeping the aspect ratio. - Attention：If interp is 'RANDOM', the interpolation method will be chose randomly. + Attention: If interp is 'RANDOM', the interpolation method will be chose randomly. Args: short_size (int): Target size of the shorter side of the image(s). @@ -395,7 +395,7 @@ class RandomResizeByShort(Transform): """ Resize input to random sizes with keeping the aspect ratio. - Attention：If interp is 'RANDOM', the interpolation method will be chose randomly. + Attention: If interp is 'RANDOM', the interpolation method will be chose randomly. Args: short_sizes (List[int]): Target size of the shorter side of the image(s). @@ -833,8 +833,8 @@ class RandomCrop(Transform): class RandomScaleAspect(Transform): """ Crop input image(s) and resize back to original sizes. - Args： - min_scale (float)：Minimum ratio between the cropped region and the original image. + Args: + min_scale (float): Minimum ratio between the cropped region and the original image. If 0, image(s) will not be cropped. Defaults to .5. aspect_ratio (float): Aspect ratio of cropped region. Defaults to .33. """ @@ -1230,7 +1230,7 @@ class RandomBlur(Transform): """ Randomly blur input image(s). - Args： + Args: prob (float): Probability of blurring. """ diff --git a/paddlers/utils/convert.py b/paddlers/utils/convert.py index 4b161e1..2964abf 100644 --- a/paddlers/utils/convert.py +++ b/paddlers/utils/convert.py @@ -39,7 +39,7 @@ def raster2uint8(image: np.ndarray) -> np.ndarray: # 2% linear stretch def _two_percentLinear(image: np.ndarray, max_out: int=255, min_out: int=0) -> np.ndarray: def _gray_process(gray, maxout=max_out, minout=min_out): - # Get the corresponding gray level at 98% histogram + # get the corresponding gray level at 98% histogram high_value = np.percentile(gray, 98) low_value = np.percentile(gray, 2) truncated_gray = np.clip(gray, a_min=low_value, a_max=high_value) @@ -55,7 +55,7 @@ def _two_percentLinear(image: np.ndarray, max_out: int=255, min_out: int=0) -> n return np.uint8(result) -# Simple image standardization +# simple image standardization def _sample_norm(image: np.ndarray, NUMS: int=65536) -> np.ndarray: stretches = [] if len(image.shape) == 3: @@ -69,14 +69,14 @@ def _sample_norm(image: np.ndarray, NUMS: int=65536) -> np.ndarray: return np.uint8(stretched_img * 255) -# Histogram equalization +# histogram equalization def _stretch(ima: np.ndarray, NUMS: int) -> np.ndarray: hist = _histogram(ima, NUMS) lut = [] for bt in range(0, len(hist), NUMS): - # Step size + # step size step = reduce(operator.add, hist[bt : bt + NUMS]) / (NUMS - 1) - # Create balanced lookup table + # create balanced lookup table n = 0 for i in range(NUMS): lut.append(n / step) @@ -85,7 +85,7 @@ def _stretch(ima: np.ndarray, NUMS: int) -> np.ndarray: return ima -# Calculate histogram +# calculate histogram def _histogram(ima: np.ndarray, NUMS: int) -> np.ndarray: bins = list(range(0, NUMS)) flat = ima.flat diff --git a/requirements.txt b/requirements.txt index 804ab1b..d08cf53 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,9 +8,10 @@ paddleslim == 2.2.1 shapely paddlepaddle-gpu >= 2.2.0 opencv-python -scikit-learn==0.20.3 +scikit-learn == 0.20.3 lap motmetrics matplotlib chardet openpyxl +GDAL >= 3.2.2 \ No newline at end of file