Пример #1
0
def run(input_dir, phantom, suffix=".raw", output_dir="../data/simresults"):
    """
    Compute the variance in three ROIs of several volumes in the given input
    directory. Pass a suffix if desired.
    Run this in in scripts/ if no output_dir specified.
        phantom | string, prefix to output filename
    """
    from constants import NOISELEVEL_FILE
    OUTPUT_FNAME = NOISELEVEL_FILE.format(phantom)
    raw_files = [f for f in os.listdir(input_dir) if f.endswith(suffix)]

    d = OrderedDict()

    for f in tqdm(sorted(raw_files)):
        img = np.fromfile(
                os.path.join(input_dir, f),
                dtype=np.float16).reshape((250, 512, 311))
        variances = compute_var_from_volume(img)
        # Fetch frame and flux info from filename.
        filtermode = find_filtermode(f)
        frms = f[:3]
        flux = f[8:13]
        d[f] = (filtermode, frms, flux) + variances

    if d:
        outpath = os.path.join(output_dir, OUTPUT_FNAME)
        # output file will be sorted because first loop runs over sorted filenames.
        write_header = not os.path.exists(outpath)
        with open(outpath, 'a+') as f:
            if write_header:
                f.write("#fmode,fmrs,flux,var1,var2,var3\n")
            for vtuple in d.values():
                # CSV style
                f.write("{},{},{},{},{},{}\n".format(*vtuple))
Пример #2
0
def run(input_dir, phantom, gt_path, 
        mask_path=None, 
        dtypes=(np.float32, np.float16, np.uint8),
        suffix=".raw",
        output_dir="../data/simresults"
        ):
    """
    Compute RMSE on a set of volumes in the given `input_dir`. Only files
    ending with `suffix` are considered. 
    Specify `gt_path` and `mask_path` to create ground truth and mask arrays
    prior to looping over files. 
    `dtypes` order: ground truth, experiment, mask
    """
    import os
    files = [f for f in os.listdir(input_dir) if f.endswith(suffix)]

    import re 
    tv_grad_pattern = re.compile(".*-(\d+\.\d+)tv")

    from utils import find_filtermode, find_recomode
    results = []
    ground_truth = np.fromfile(gt_path, dtype=dtypes[0])
    mask = None if mask_path is None else np.fromfile(mask_path, dtype=dtypes[2])
    for f in files:
        rmse = compute_rmse_from_exp_path(
                os.path.join(input_dir, f), 
                ground_truth,
                mask,
                dtype=dtypes[1]
                )
        filMode = find_filtermode(f)
        frms = f[:3]
        flux = f[8:13]
        tvGrad = tv_grad_pattern.search(f).group(1)
        recoMode = find_recomode(f)

        results.append((
            int(filMode), 
            int(frms),
            int(flux),
            float(tvGrad),
            float(rmse),
            int(recoMode),
            # still hardcoded...
            3
            ))

    from constants import RMSE_FILE
    rmse_filepath = os.path.join(output_dir, RMSE_FILE.format(phantom))
    write_header = not os.path.exists(rmse_filepath)
    with open(rmse_filepath, 'a+') as rf:
        if write_header:
            rf.write("#fmode,frms,flux,tvGrad,rmse,recoMode,numIter\n")
        for result in results:
            rf.write(
                    ','.join([str(r) for r in result]) + '\n'
                    )
Пример #3
0
def run(input_dir, phantom, roi_name, suffix=".raw", 
        output_dir="../data/simresults", **kwargs):
    """
    Script to compute the ROI edge sharpness of several volumes in the given
    input directory. Pass a suffix if desired.
    Results are written to file. Manually specify parameters!
    Run this in in scripts/ if no output_dir specified.
        phantom | string, prefix to output filename
        width | int, pixel width of region selected for ES computation, DEPRECATED
        input_dir, output_dir | string 
        roi_name | name of ROI, taken from EDGESHARPNESS_ROIS
        kwargs | passed to `compute_es_from_volume`: prefilter_sigma 
                 passed to `compute_es_from_profile`: p0
    """

    #input_dir = "/media/metzner/Z/Data/Head/forbild-phantoms/simresults/1noise"
    raw_files = [f for f in os.listdir(input_dir) if f.endswith(suffix)]

    prefilter_sigma = kwargs.get("prefilter_sigma", 0)
    kwargs.pop("return_fig", None)
    from constants import EDGESHARPNESS_FILE
    output_fname = EDGESHARPNESS_FILE.format(
            phantom, 
            roi_name,
            prefilter_sigma
            )

    results = []

    for fname in tqdm(sorted(raw_files)):
        #fname = "479frms_5e+05flux_1noise_nofilter_fdk.raw"
        volume = np.fromfile(os.path.join(input_dir, fname), dtype=np.float16).reshape((250,512,311))

        edge_sharpness, edge_sharpness_delta = compute_es_from_volume(volume,
                roi_name=roi_name, return_fig=False, **kwargs)

        frms = fname[:3]
        flux = fname[8:13]
        filtermode = find_filtermode(fname)

        results.append((filtermode, frms, flux, edge_sharpness, edge_sharpness_delta))

    if results:
        outpath = os.path.join(output_dir, output_fname)
        write_header = not os.path.exists(outpath)
        with open(outpath, 'a+') as f:
            if write_header:
                f.write("#fmode,frms,flux,shrpns,uncrty\n")
            for result in results:
                f.write(','.join([str(i) for i in result]) + '\n')