Example #1
0
def getPrCurveMergeToOne(
    methods=['MY', 'DRFI', 'GMR', 'MEAN'],
    num=None,
):
    '''
    过时的方法 全部读入内存
    '''
    imgGt = reduce(
        lambda x, y: np.r_[x, y],
        map(
            lambda name: io.imread(IMG_DIR + name[:-3] + 'png').astype(np.bool)
            .ravel(), IMG_NAME_LIST[:num]))
    print 'imgGt OK!'
    resoultDic = dict(zip(methods, [None] * len(methods)))
    print resoultDic.keys()
    for name in IMG_NAME_LIST[:num]:
        dic = getCoarseDic(name, methods, COARSE_DIR)
        for k, v in dic.items():
            resoultDic[k] = np.r_[
                resoultDic[k],
                v.ravel()] if resoultDic[k] != None else v.ravel()
        print '%d/%d' % (IMG_NAME_LIST.index(name), num), name

    precisions, recalls = [], []
    for method in methods:
        #        p, r ,_ = precisionRecallCurve(imgGt,resoultDic[method])
        p, r, _ = precision_recall_curve(imgGt, resoultDic[method])
        precisions += [p]
        recalls += [r]
        print method
    return precisions, recalls, methods
Example #2
0
def grabCutTest():
    coarseMethods = ['MY4','QCUT','DRFI']
    imgInd = 1
    n_segments,compactness = 200,10
    imgName = IMG_NAME_LIST[imgInd]
    img,imgGt = readImg(imgName)
    coarseDic = getCoarseDic(imgName,coarseMethods)
    refinedImg = coarseDic['DRFI']
    
    mask = grabCut(img,refinedImg)
    imgCut = img*mask[:,:,np.newaxis]
    
    show([img,imgCut])
    show([refinedImg,mask])
Example #3
0
def integratImgsBy3wayTest():
    from algorithm import getSlic,getCoarseDic,integratImgsBy3way,buildMethodDic
    labelMap = getSlic(rgbImg,300)
    img = rgbImg
    m, n = labelMap.shape 
    
    coarseMethods = ['GT']
    coarseDic = getCoarseDic(imgName,coarseMethods)
#    show(coarseDic)   
    coarseImgs=coarseDic.values()       
    
    g()
    refinedImgs=map(lambda f:f(img,coarseImgs,labelMap),buildMethodDic.values())
    img = integratImgsBy3way(refinedImgs)
    show(img)
Example #4
0
def getRefindImgsTest():
    IMG_DIR = r'E:\3-experiment\SalBenchmark-master\Data\DataSet1\Imgs/'
    COARSE_DIR =r'E:\3-experiment\SalBenchmark-master\Data\DataSet1\Saliency/'
      
    IMG_DIR =  'test/'
    COARSE_DIR ='test/'
  
    IMG_NAME_LIST = filter(lambda x:x[-3:]=='jpg',listdir(IMG_DIR))
    coarseMethods = ['QCUT','DRFI']
    coarseMethods = ['GT']
    imgInd = 0
    n_segments,compactness = 200,10
    
    imgName = IMG_NAME_LIST[imgInd]
    img,imgGt = readImg(imgName)
    coarseDic = getCoarseDic(imgName,coarseMethods)
    #show(coarsesDic)   
    sumCoarseImg = getSumCoarseImg(coarseDic)
    coarseImgs=coarseDic.values()
    labelMap = getSlic(img,n_segments,compactness)
    
    rgb = img
    
    img = sk.color.rgb2lab(img)
    #show([mark_boundaries(img,labelMap),imgGt])
    # 获得4+4维  distance
    degreeVectors, Ws = getVectors(img, labelMap)
    vectors = getWeightSum(labelMap, degreeVectors, Ws)
    
    vectorsTrains = []
    coarseTrains = []
    for coarseImg in coarseImgs:
        coarseTrain, vectorsTrainTag = getCoarseTrain(coarseImg, labelMap)
        vectorsTrains += list(vectors[vectorsTrainTag])
        coarseTrains += list(coarseTrain)
    
    elm = getElm(np.array(vectorsTrains), np.array(coarseTrains))
    refined = elm.predict(vectors)[:,0]
    refinedImg = valueToLabelMap(labelMap,normalizing(refined))
    
    vectorsImg = valueToLabelMap(labelMap,normalizing(vectors.sum(1)))
    show([rgb,refinedImg])
    show([rgb,vectorsImg])
    show(vectorsImg-refinedImg)
    loga(vectorsImg-refinedImg)
    g()
Example #5
0
def my4test():
#if 1:
    from algorithm import *
    labelMap = getSlic(rgbImg,200)
    maxLabel = labelMap.max()+1
    m, n = labelMap.shape 
    
    coarseMethods = ['MEAN']
    coarseDic = getCoarseDic(imgName,coarseMethods)
#    show(coarseDic)   
    sumCoarseImg = getSumCoarseImg(coarseDic)
    coarseImgs=coarseDic.values()    

    img = sk.color.rgb2lab(rgbImg)
    # 获得4+4维  distance
    degreeVectors, Ws = getVectors(img, labelMap)
    weightSumVectors = getWeightSum(labelMap, degreeVectors, Ws)
    
    diffEdges,diffNeighbors = getAllDiffEdgeAndNeighbor(labelMap,Ws)
    vectors = np.append(weightSumVectors,diffEdges,1)
    vectors = np.append(vectors,diffNeighbors,1)

    vectorsTrains = []
    coarseTrains = []
    for coarseImg in coarseImgs:
        coarseTrain, vectorsTrainTag = getCoarseTrain(coarseImg, labelMap)
        vectorsTrains += list(vectors[vectorsTrainTag])
        coarseTrains += list(coarseTrain)
    
    elm = getElm(np.array(vectorsTrains), np.array(coarseTrains))
    refined = elm.predict(vectors)[:,0]
    refinedImg = valueToLabelMap(labelMap,normalizing(refined))
    show(mark_boundaries(sk.color.lab2rgb(img),labelMap))
    print diffEdges.shape
    show(valueToLabelMap(labelMap,diffEdges.sum(1)))
    show(valueToLabelMap(labelMap,diffNeighbors[:,:4].sum(1)))
#    show(valueToLabelMap(labelMap,diffNeighbors[:,4:].sum(1)))
#    show(valueToLabelMap(labelMap,diffNeighbors.sum(1)))
    show(valueToLabelMap(labelMap,vectors[:,4:8].sum(1)))
Example #6
0
def getPrCurve(
    methods=['DRFI', 'GMR', 'MEAN'],
    num=None,
):
    '''
    返回数据库前num张图片的平均PR曲线
    dataDic 结构
    img->[imgname]->[method]->auc,pr
    method->[method]->auc,pr
    '''

    num = num if num else len(IMG_NAME_LIST)
    dataDic = {'img': {}}
    for name in IMG_NAME_LIST[:num]:
        print '%d/%d' % (IMG_NAME_LIST.index(name), num), name
        imgGt = io.imread(IMG_DIR + name[:-3] + 'png').astype(np.bool).ravel()
        coarseDic = getCoarseDic(name, methods, COARSE_DIR)
        dataDic['img'][name] = {}

        for k, v in coarseDic.items():
            dataDic['img'][name][k] = {}
            _dic = dataDic['img'][name][k]
            coarseImg = v.ravel()
            p, r, _ = precisionRecallCurve(imgGt, coarseImg)
            _dic['pr'] = (p, r)
            _dic['auc'] = metrics.roc_auc_score(imgGt.ravel(), coarseImg)

    dataDic['method'] = dict(zip(methods, [{} for i in range(len(methods))]))
    precisions, recalls = [], []
    for method in methods:
        _dic = dataDic['method'][method]
        p, r = sum([v[method]['pr'][0] for k,v in dataDic['img'].items()])/num,\
                sum([v[method]['pr'][1] for k,v in dataDic['img'].items()])/num
        _dic['pr'] = (p, r)
        _dic['auc'] = metrics.auc(r, p)
        precisions += [p]
        recalls += [r]
    return precisions, recalls, methods, dataDic
Example #7
0
def saveImgData(imgName, methods):
    '''
    save data to LABEL_DATA_DIR
    return default method`s AUC
    '''
    if isinstance(imgName, int):
        imgName = IMG_NAME_LIST[imgName]
    imgGt = io.imread(IMG_DIR + imgName[:-3] + 'png') / 255.
    coarseDic = getCoarseDic(imgName, methods, COARSE_DIR)
    data = {}
    data['name'] = imgName

    data['method'] = {}
    for method, refindImg in coarseDic.items():
        data['method'][method] = getAucAndPr(imgGt, refindImg, method)

    dataName = LABEL_DATA_DIR + imgName[:-3] + 'data'
    AUC_METHOD = "DRFI"
    data['aucMethod'] = AUC_METHOD

    data['auc'] = data['method'][AUC_METHOD][0]
    saveData(data, dataName)
    return data['auc']
Example #8
0
def aucBug():
    #    from saliency import *
    #    from algorithm import *
    #    from analysis import *
    ll = [('184.jpg', 0.83881506713016329), ('138.jpg', 0.84727243912368833),
          ('122.jpg', 0.88417034903928537), ('153.jpg', 0.92387361400695434)]
    name = ll[0][0]
    imgGt = io.imread(IMG_DIR + name[:-3] + 'png').astype(np.bool)  #.ravel()
    methods = ['DISC2', 'DRFI', 'GMR', 'MEAN']
    method = 'DISC2'
    coarseDic = getCoarseDic(name, methods, COARSE_DIR)
    print coarseDic.keys()
    #    show(coarseDic.values())
    resoult = coarseDic[method]
    show([resoult])
    show(imgGt)
    ##imgGt.ravel()
    #resoult.ravel()
    p, r, _ = precisionRecallCurve(imgGt, resoult)
    #l = zip(resoult,imgGt)
    print method, 'auc', metrics.roc_auc_score(imgGt.ravel(), resoult.ravel())
    plt.plot(p, r)
    plt.show()