Mask-RCNN:教你如何製做本身的數據集進行像素級的目標檢測

時間 2019-11-30

標籤 mask rcnn 如何製做本身數據進行像素目標檢測简体版

原文原文鏈接

概述

Mask-RCNN,是一個處於像素級別的目標檢測手段.目標檢測的發展主要歷程大概是：RCNN,Fast-RCNN,Fster-RCNN,Darknet,YOLO,YOLOv2,YOLO3（參考目標檢測：keras-yolo3之製做VOC數據集訓練指南）,Mask-RCNN.本文參考的論文來源於https://arxiv.org/abs/1703.06870.html

下面，開始製做用於Mask訓練的數據集。python

首先展現一下成果，因爲我的設備有限，cpu僅迭代5次的結果。linux

使用labelme進行圖片標註

注意：

　　**標註以前將圖片的名字經過linux或者python腳本更名，改成有序便可，個人命名格式爲升序，下面爲linux腳本。git

i=1; for x in *; do mv $x $i.png; let i=i+1; done

　　**將全部圖片的尺寸改成600*800.(通常設置爲2的整數次冪，不然，後序訓練時會報錯).腳本自取https://github.com/hyhouyong/Mask-RCNN/blob/master/train_data/resize.pygithub

pip install labelme
labelme

1.新建文件夾train_data,並建立子文件夾json，將標註後的json格式的文件放入該文件夾中json

2.當你安裝lableme的時候，默認安裝到了Anaconda目錄下/envs/名字/Scripts/下，使用labelme_json_to_dataset.exe將json文件轉化爲5個文件app

　　轉化方法，切換到labelme安裝目錄下，執行：post

labelme_json_to_dataset.exe [文件名]

注意：文件名爲絕對路徑 . eg:(chineseocr) D:\anaconda\envs\chineseocr\Scripts>labelme_json_to_dataset.exe F:\samples\shapes\train_data\json\1.json測試

　　***這樣只能一次轉化一個json文件，故開始批量轉。ui

　　　　切換到D:\anaconda\envs\py3.6\Lib\site-packages\labelme\cli下，修改json_to_dataset.py，而後切換到Scripts，執行命令：

labelme_json_to_dataset.exe [存放json文件夾的絕對路徑]

　　***生成的json文件夾會在當前目錄，將文件夾拷貝到train_data下的labelme_json文件夾中

import argparse
import json
import os
import os.path as osp
import warnings
 
import PIL.Image
import yaml
 
from labelme import utils
import base64
 
def main():
    warnings.warn("This script is aimed to demonstrate how to convert the\n"
                  "JSON file to a single image dataset, and not to handle\n"
                  "multiple JSON files to generate a real-use dataset.")
    parser = argparse.ArgumentParser()
    parser.add_argument('json_file')
    parser.add_argument('-o', '--out', default=None)
    args = parser.parse_args()
 
    json_file = args.json_file
    if args.out is None:
        out_dir = osp.basename(json_file).replace('.', '_')
        out_dir = osp.join(osp.dirname(json_file), out_dir)
    else:
        out_dir = args.out
    if not osp.exists(out_dir):
        os.mkdir(out_dir)
 
    count = os.listdir(json_file) 
    for i in range(0, len(count)):
        path = os.path.join(json_file, count[i])
        if os.path.isfile(path):
            data = json.load(open(path))
            
            if data['imageData']:
                imageData = data['imageData']
            else:
                imagePath = os.path.join(os.path.dirname(path), data['imagePath'])
                with open(imagePath, 'rb') as f:
                    imageData = f.read()
                    imageData = base64.b64encode(imageData).decode('utf-8')
            img = utils.img_b64_to_arr(imageData)
            label_name_to_value = {'_background_': 0}
            for shape in data['shapes']:
                label_name = shape['label']
                if label_name in label_name_to_value:
                    label_value = label_name_to_value[label_name]
                else:
                    label_value = len(label_name_to_value)
                    label_name_to_value[label_name] = label_value
            
            # label_values must be dense
            label_values, label_names = [], []
            for ln, lv in sorted(label_name_to_value.items(), key=lambda x: x[1]):
                label_values.append(lv)
                label_names.append(ln)
            assert label_values == list(range(len(label_values)))
            
            lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)
            
            captions = ['{}: {}'.format(lv, ln)
                for ln, lv in label_name_to_value.items()]
            lbl_viz = utils.draw_label(lbl, img, captions)
            
            out_dir = osp.basename(count[i]).replace('.', '_')
            out_dir = osp.join(osp.dirname(count[i]), out_dir)
            if not osp.exists(out_dir):
                os.mkdir(out_dir)
 
            PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
            #PIL.Image.fromarray(lbl).save(osp.join(out_dir, 'label.png'))
            utils.lblsave(osp.join(out_dir, 'label.png'), lbl)
            PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))
 
            with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
                for lbl_name in label_names:
                    f.write(lbl_name + '\n')
 
            warnings.warn('info.yaml is being replaced by label_names.txt')
            info = dict(label_names=label_names)
            with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
                yaml.safe_dump(info, f, default_flow_style=False)
 
            print('Saved to: %s' % out_dir)
if __name__ == '__main__':
    main()