def create_sliceinfo_w(images_fn,
                       labels_fn,
                       regint_fn,
                       kernels,
                       i,
                       slice_no=None):
    # figure out the biggest slice
    if slice_no == None:
        slice_no = find_biggest_slice(path + labels_fn[i])
    print("Creating Weighted Slice Info... {}/{}: Slice {}".format(
        i + 1, len(images_fn), slice_no))

    # get the slice, label, and associated orientations
    slice_im, slice_im_or = get_nifti_slice(path + images_fn[i], slice_no)
    slice_lb, slice_lb_or = get_nifti_slice(path + labels_fn[i], slice_no)
    slice_ro, slice_ro_or = get_nifti_slice(path + regint_fn[i], slice_no)

    # if crop, we crop the image down
    if crop:
        crop_x, crop_y = crop
        start_x = slice_im.shape[0] / 2 - crop_x / 2
        end_x = start_x + crop_x
        start_y = slice_im.shape[1] / 2 - crop_y / 2
        end_y = start_y + crop_y

        slice_im = slice_im[start_x:end_x, start_y:end_y]
        slice_lb = slice_lb[start_x:end_x, start_y:end_y]
        slice_ro = slice_ro[start_x:end_x, start_y:end_y]

    # figure out the principal patches
    pc_payload = (slice_im, slice_lb)
    patches_m_pc, patches_n_pc, vals_m, vals_n = create_pc_patches_w(
        *pc_payload)

    # compute gabor features for the patches
    feats_m = []
    feats_n = []
    intens_m = []
    intens_n = []
    for patch in patches_m_pc:
        feats_m.append(compute_feats(patch, kernels))
        intens_m.append(compute_intens(patch))
    for patch in patches_n_pc:
        feats_n.append(compute_feats(patch, kernels))
        intens_n.append(compute_intens(patch))

    # package it into a SliceInfo object
    si_payload = (images_fn[i], slice_no, slice_im, slice_im_or, slice_lb,
                  slice_lb_or, slice_ro, slice_ro_or, patches_m_pc,
                  patches_n_pc, feats_m, feats_n, intens_m, intens_n, vals_m,
                  vals_n)

    return SliceInfo(*si_payload)
def create_sliceinfo_w(images_fn, labels_fn, regint_fn, kernels, i, slice_no=None):
    # figure out the biggest slice
    if slice_no == None:    
        slice_no = find_biggest_slice(path + labels_fn[i])
    print("Creating Weighted Slice Info... {}/{}: Slice {}".format(i+1, len(images_fn), slice_no))
    
    # get the slice, label, and associated orientations
    slice_im, slice_im_or = get_nifti_slice(path + images_fn[i], slice_no)
    slice_lb, slice_lb_or = get_nifti_slice(path + labels_fn[i], slice_no)
    slice_ro, slice_ro_or = get_nifti_slice(path + regint_fn[i], slice_no)
    
    # if crop, we crop the image down
    if crop:    
        crop_x, crop_y = crop
        start_x = slice_im.shape[0] / 2 - crop_x / 2
        end_x = start_x + crop_x
        start_y = slice_im.shape[1] / 2 - crop_y / 2
        end_y = start_y + crop_y
    
        slice_im = slice_im[start_x:end_x, start_y:end_y]
        slice_lb = slice_lb[start_x:end_x, start_y:end_y]
        slice_ro = slice_ro[start_x:end_x, start_y:end_y]
            
    # figure out the principal patches
    pc_payload = (slice_im, slice_lb)
    patches_m_pc, patches_n_pc, vals_m, vals_n = create_pc_patches_w(*pc_payload)
    
    # compute gabor features for the patches
    feats_m = []
    feats_n = []
    intens_m = []
    intens_n = []
    for patch in patches_m_pc:
        feats_m.append(compute_feats(patch, kernels))
        intens_m.append(compute_intens(patch))
    for patch in patches_n_pc:
        feats_n.append(compute_feats(patch, kernels))
        intens_n.append(compute_intens(patch))
    
    # package it into a SliceInfo object
    si_payload = (images_fn[i], slice_no, 
                  slice_im, slice_im_or,
                  slice_lb, slice_lb_or,
                  slice_ro, slice_ro_or,
                  patches_m_pc, patches_n_pc,
                  feats_m, feats_n,
                  intens_m, intens_n,
                  vals_m, vals_n) 
                  
    return SliceInfo(*si_payload)
    filenames_label = []

    for fn in os.listdir(path):
        if "CTV" in fn:
            filenames_label.append(path + fn)
        else:
            filenames.append(path + fn)
    
    #filenames = [path + "anon_mr_150420_30sec.nii.gz"]
    #filenames_label = [path + "anon_mr_150420_30sec.nii-label.nrrd.nii.gz"]
    #slice_no = 128-105
    
    slice_infos = []
    
    for i, fn in enumerate(filenames):
        slice_no = find_biggest_slice(filenames_label[i])
        if DEBUG:
            print("Processing: ", fn, ", Slice ", slice_no)
        slice_info = process(fn, filenames_label[i], slice_no)
        slice_infos.append(slice_info)
        
    if REPORTS:
        for sl in slice_infos:
            sl.save_report("reports/")

else:
    with open('slice_infos.pkl', 'rb') as f:
        slice_infos = dill.load(f)
        
#if CLASSIFY:
#    # we need to go through each slice and collect the masked features and
Пример #4
0
    filenames_label = []

    for fn in os.listdir(path):
        if "CTV" in fn:
            filenames_label.append(path + fn)
        else:
            filenames.append(path + fn)

    #filenames = [path + "anon_mr_150420_30sec.nii.gz"]
    #filenames_label = [path + "anon_mr_150420_30sec.nii-label.nrrd.nii.gz"]
    #slice_no = 128-105

    slice_infos = []

    for i, fn in enumerate(filenames):
        slice_no = find_biggest_slice(filenames_label[i])
        if DEBUG:
            print("Processing: ", fn, ", Slice ", slice_no)
        slice_info = process(fn, filenames_label[i], slice_no)
        slice_infos.append(slice_info)

    if REPORTS:
        for sl in slice_infos:
            sl.save_report("reports/")

else:
    with open('slice_infos.pkl', 'rb') as f:
        slice_infos = dill.load(f)

if CLASSIFY:
    # we need to go through each slice and collect the masked features and