candidates.py 文件源码

def unet_candidates():
    cands = glob.glob('%s/*.png' % sys.argv[1]) #"/razberry/workspace/dsb_nodule_detection.109fd54/*.png
    #df = pd.DataFrame(columns=['seriesuid','coordX','coordY','coordZ','class'])
    data = []
    imname = ""
    origin = []
    spacing = []
    nrimages = 0
    for name in tqdm(cands):

        #image = imread(name)
        image_t = imread(name)
        image_t = image_t.transpose()
        #Thresholding
        image_t[image_t<THRESHOLD] = 0
        image_t[image_t>0] = 1
        #erosion
        selem = morphology.disk(1)
        image_eroded = image_t
        image_eroded = morphology.binary_erosion(image_t,selem=selem)

        label_im, nb_labels = ndimage.label(image_eroded)
        imname3 = os.path.split(name)[1].replace('.png','')

        splitted = imname3.split("_")
        slice = splitted[1]
        imname2 = splitted[0][:-1]
        centers = []
        for i in xrange(1,nb_labels+1):
            blob_i = np.where(label_im==i,1,0)
            mass = center_of_mass(blob_i)
            centers.append([mass[1],mass[0]])


        if imname2 != imname:
            if os.path.isfile("../data/1_1_1mm_512_x_512_annotation_masks/spacings/{0}.pickle".format(imname2)):
                with open("../data/1_1_1mm_512_x_512_annotation_masks/spacings/{0}.pickle".format(imname2), 'rb') as handle:
                    dic = pickle.load(handle)
                    origin = dic["origin"]
                    spacing = dic["spacing"]

            imname = imname2
            nrimages +=1

        for center in centers:
            # coords = voxel_2_world([int(slice),center[1]+(512-324)*0.5,center[0]+(512-324)*0.5],origin,spacing)
            coords = [int(slice),center[1],center[0]]
            data.append([imname2,coords[2],coords[1],coords[0],'?'])

        #if nrimages == 5:
        #    break

    df = pd.DataFrame(data,columns=CANDIDATES_COLUMNS)

    save_candidates('%s/candidates.csv' % work_dir, df)