bbox(): cut and save annotations about pylidc HOT 11 CLOSED

import numpy as np
import matplotlib.pyplot as plt

import pylidc as pl
from pylidc.utils import consensus


# Query for a scan, and convert it to an array volume.
scan = pl.query(pl.Scan).filter(pl.Scan.patient_id == 'LIDC-IDRI-0001').first()
vol = scan.to_volume()

nodules = scan.cluster_annotations()
annotations = nodules[0]
consensus_mask, consensus_bbox, _ = consensus(
    annotations,
    clevel=0.5,
    pad=[(20,20), (20,20), (0,0)]
)

k = consensus_mask.shape[-1] // 2

plt.subplot(121)
plt.imshow(vol[consensus_bbox][:, :, k], cmap='gray')
plt.subplot(122)
plt.imshow(consensus_mask[:, :, k], cmap='gray')
plt.show()

for scan in pl.query(pl.Scan):
    # annotation_groups is a list of of lists of Annotation's
    annotation_groups = scan.cluster_annotations()

for nodule_annotations in annotation_group:
    # Only consider nodules with 4 annotators and have >= 50% indicating GGO
    if (len(nodule_annotations) == 4 and
            sum([a.texture == 1 for a in nodule_annotations]) >= 2):
        # Do whatever you want, e.g.,
        # consensus(nodule_annotations)

for scan in pl.query(pl.Scan):
        annotation_groups = scan.cluster_annotations()
        vol = scan.to_volume()
        for nodule_annotations in annotation_groups:
            if (len(nodule_annotations) >= 2 and sum([a.texture == 1 for a in nodule_annotations]) >= 1):
                consensus_mask, consensus_bbox, _ = consensus(
                    nodule_annotations,
                    clevel=0.5,
                    pad=[(5,5), (5,5), (0,0)]
                )
                image = np.asarray(vol[consensus_bbox][:, :, :]).transpose(2,0,1)
                mask_image = np.float32(np.array(consensus_mask[:, :, :])).transpose(2,0,1)