通过简单的api调用就可以实现对物体在图像中进行训练及识别，切割，定位的轻量级，面向小白的框架

package org.wlld.naturalLanguage.languageCreator; import org.wlld.entity.DyStateModel; import org.wlld.entity.KeyWordForSentence; import org.wlld.gameRobot.Action; import org.wlld.gameRobot.DynamicProgramming; import org.wlld.gameRobot.DynamicState; import java.util.*; public class CatchKeyWord {//抓取关键词 private DynamicProgramming dynamicProgramming = new DynamicProgramming();//保存它的状态集合 private List<String> keyWords = new ArrayList<>();//保存词列表 private List<String> finishWords = new ArrayList<>();//终结态词集合 private Set<String> noList = new HashSet<>();//禁止词集合 private double proTh = 0.1;//收益阈值 public void setProTh(double proTh) { this.proTh = proTh; } public void study(List<KeyWordForSentence> keyWordForSentenceList) { int size = keyWordForSentenceList.size(); List<DynamicState> dynamicStateList = dynamicProgramming.getDynamicStateList(); DynamicState dynamicState0 = new DynamicState(new int[]{0}); dynamicState0.setFinish(true); dynamicStateList.add(dynamicState0); for (int i = 0; i < size; i++) {//添加禁止词 KeyWordForSentence keyWords = keyWordForSentenceList.get(i); noList.add(keyWords.getKeyWord()); } for (int i = 0; i < size; i++) { KeyWordForSentence keyWords = keyWordForSentenceList.get(i); String sentence = keyWords.getSentence();//句子 String keyWord = keyWords.getKeyWord();//关键词 int startIndex = getIndex(sentence, keyWord); if (startIndex >= 0) { creatID(sentence, startIndex, startIndex + keyWord.length() - 1); } } Map<Integer, Action> actionMap = dynamicProgramming.getActionMap(); WordRight wordRight = new WordRight(keyWords, finishWords); WordLeft wordLeft = new WordLeft(keyWords, finishWords); wordRight.setActionId(1); wordLeft.setActionId(2); actionMap.put(1, wordRight); actionMap.put(2, wordLeft); dynamicProgramming.gameStart();//探索中奖 dynamicProgramming.strategyStudy();//研究策略中奖 } private WordsValue isContinuity(int start1, int end1, int start2, int end2) { boolean isContinuity = false; WordsValue wordsValue = null; if (end1 + 1 == start2 || end2 + 1 == start1) {//相接 isContinuity = true; } else if ((start2 >= start1 && start2 <= end1) || (end2 >= start1 && end2 <= end1) || (start1 >= start2 && start1 <= end2) || (end1 >= start2 && end1 <= end2)) {//相交 isContinuity = true; } if (isContinuity) { wordsValue = new WordsValue(); wordsValue.isMerge = false; if (start1 > start2) { wordsValue.startIndex = start2; } else { wordsValue.startIndex = start1; } if (end1 > end2) { wordsValue.endIndex = end1; } else { wordsValue.endIndex = end2; } } return wordsValue; } private void mergeWord(List<WordsValue> myDyList) {//合并状态 for (int i = 0; i < myDyList.size(); i++) { WordsValue dynamicState = myDyList.get(i); if (!dynamicState.isMerge) { for (int j = 0; j < myDyList.size(); j++) { if (j != i) { int startIndex = dynamicState.startIndex;//开始下标 int endIndex = dynamicState.endIndex;//结束下标 boolean isFinish = dynamicState.isFinish; double value = dynamicState.value; WordsValue dynamic = myDyList.get(j); int myStart = dynamic.startIndex; int myEnd = dynamic.endIndex; WordsValue wordsValue = isContinuity(startIndex, endIndex, myStart, myEnd); if (wordsValue != null) {//可进行合并 dynamic.isMerge = true; if (isFinish || dynamic.isFinish) { wordsValue.isFinish = true; } else { wordsValue.isFinish = false; } if (wordsValue.isFinish) { wordsValue.value = 10; } else { if (dynamic.value > value) { wordsValue.value = dynamic.value; } else { wordsValue.value = value; } } dynamicState = wordsValue; } } } myDyList.set(i, dynamicState);//替换合并后的结果 } } for (int i = 0; i < myDyList.size(); i++) { if (myDyList.get(i).isMerge) { myDyList.remove(i); i--; } } } public KeyWordModel getModel() { KeyWordModel keyWordModel = new KeyWordModel(); List<DyStateModel> dyStateModels = new ArrayList<>(); List<DynamicState> dynamicStateList = dynamicProgramming.getDynamicStateList(); int size = dynamicStateList.size(); for (int i = 0; i < size; i++) { DynamicState dynamicState = dynamicStateList.get(i); DyStateModel dyStateModel = new DyStateModel(); dyStateModel.setId(dynamicState.getStateId()[0]); dyStateModel.setFinish(dynamicState.isFinish()); dyStateModel.setValue(dynamicState.getValue()); dyStateModels.add(dyStateModel); } keyWordModel.setDynamicStateList(dyStateModels); keyWordModel.setKeyWords(keyWords); return keyWordModel; } public void insertModel(KeyWordModel keyWordModel) { List<String> myKeyWords = keyWordModel.getKeyWords(); List<DyStateModel> dynamicStates = keyWordModel.getDynamicStateList(); List<DynamicState> dynamicStateList = dynamicProgramming.getDynamicStateList(); int size = myKeyWords.size(); for (int i = 0; i < size; i++) { keyWords.add(myKeyWords.get(i)); } int s = dynamicStates.size(); for (int i = 0; i < s; i++) { DyStateModel modelDy = dynamicStates.get(i); DynamicState dynamicState = new DynamicState(new int[]{modelDy.getId()}); dynamicState.setValue(modelDy.getValue()); dynamicState.setFinish(modelDy.isFinish()); dynamicStateList.add(dynamicState); } } private void insertValue(WordsValue wordsValue, DynamicState dynamicState, int startIndex, int endIndex) {//传输数值 wordsValue.isFinish = dynamicState.isFinish(); wordsValue.value = dynamicState.getValue(); wordsValue.startIndex = startIndex; wordsValue.endIndex = endIndex; wordsValue.id = dynamicState.getStateId()[0]; wordsValue.isMerge = false; } private List<WordsValue> getBestDyRight(String sentence) { List<WordsValue> myDyList = new ArrayList<>(); List<DynamicState> dynamicStateList = dynamicProgramming.getDynamicStateList(); int size = sentence.length() - 1; for (int i = size; i >= 0; i--) { WordsValue maxDy = null; //0,1,2,3 for (int j = i; j >= 0; j--) {//我是好人 String word = sentence.substring(j, i + 1);//对该词进行收益判定 DynamicState dynamicState = getDynamicState(word, dynamicStateList); if (dynamicState != null && dynamicState.getValue() > proTh) {