基於Python實現RLE格式分割標註檔案的格式轉換

2022-08-18 14:01:55

1.Airbus Ship Detection Challenge

url: https://www.kaggle.com/competitions/airbus-ship-detection

Find ships on satellite images as quickly as possible

Data Description

In this competition, you are required to locate ships in images, and put an aligned bounding box segment around the ships you locate. Many images do not contain ships, and those that do may contain multiple ships. Ships within and across images may differ in size (sometimes significantly) and be located in open sea, at docks, marinas, etc.

For this metric, object segments cannot overlap. There were a small percentage of images in both the Train and Test set that had slight overlap of object segments when ships were directly next to each other. Any segments overlaps were removed by setting them to background (i.e., non-ship) encoding. Therefore, some images have a ground truth may be an aligned bounding box with some pixels removed from an edge of the segment. These small adjustments will have a minimal impact on scoring, since the scoring evaluates over increasing overlap thresholds.

The train_ship_segmentations.csv file provides the ground truth (in run-length encoding format) for the training images. The sample_submission files contains the images in the test images.

Please click on each file / folder in the Data Sources section to get more information about the files.

kaggle competitions download -c airbus-ship-detection

2.資料展示

2.1 標註資料

該資料以csv格式儲存，具體如下：

2.2 圖象檔案

3.格式轉換

由於圖太多，暫時轉換10個

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import numpy as np  # linear algebra
import pandas as pd  # data processing, CSV file I/O (e.g. pd.read_csv)
from PIL import Image


# ref: https://www.kaggle.com/paulorzp/run-length-encode-and-decode
# 將圖片編碼成rle格式
def rle_encode(img, min_max_threshold=1e-3, max_mean_threshold=None):
    '''
    img: numpy array, 1 - mask, 0 - background
    Returns run length as string formated
    '''
    if np.max(img) < min_max_threshold:
        return ''  ## no need to encode if it's all zeros
    if max_mean_threshold and np.mean(img) > max_mean_threshold:
        return ''  ## ignore overfilled mask
    pixels = img.T.flatten()
    pixels = np.concatenate([[0], pixels, [0]])
    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
    runs[1::2] -= runs[::2]
    return ' '.join(str(x) for x in runs)


# 將圖片從rle解碼
def rle_decode(mask_rle, shape=(768, 768)):
    '''
    mask_rle: run-length as string formated (start length)
    shape: (height,width) of array to return
    Returns numpy array, 1 - mask, 0 - background
    '''
    s = mask_rle.split()
    starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]
    starts -= 1
    ends = starts + lengths
    img = np.zeros(shape[0] * shape[1], dtype=np.uint8)
    for lo, hi in zip(starts, ends):
        # img[lo:hi] = 1
        img[lo:hi] = 255 #方便視覺化
    return img.reshape(shape).T  # Needed to align to RLE direction


def masks_as_image(in_mask_list):
    # Take the individual ship masks and create a single mask array for all ships
    all_masks = np.zeros((768, 768), dtype=np.uint8)
    for mask in in_mask_list:
        if isinstance(mask, str):
            all_masks |= rle_decode(mask)
    return all_masks


# 將目標路徑下的rle檔案中所包含的所有rle編碼，儲存到save_img_dir中去
def rle_2_img(train_rle_dir, save_img_dir):
    masks = pd.read_csv(train_rle_dir)
    not_empty = pd.notna(masks.EncodedPixels)
    print(not_empty.sum(), 'masks in', masks[not_empty].ImageId.nunique(), 'images')
    print((~not_empty).sum(), 'empty images in', masks.ImageId.nunique(), 'total images')
    all_batchs = list(masks.groupby('ImageId'))
    train_images = []
    train_masks = []
    i = 0
    for img_id, mask in all_batchs[:10]:
        c_mask = masks_as_image(mask['EncodedPixels'].values)
        im = Image.fromarray(c_mask)
        im.save(save_img_dir + img_id.split('.')[0] + '.png')
        print(i, img_id.split('.')[0] + '.png')
        i += 1

    return train_images, train_masks


if __name__ == '__main__':
    rle_2_img('train_ship_segmentations_v2.csv',
              'mask/')

其中為了方便檢視，原計劃0為背景，1為mask，為了方便顯示，設定為255為mask。