def do_sampling(pat_df, out_dir):
for pat,side,view in pat_df.index.unique():
full_fn = const_filename(pat, side, view, full_img_dir)
# import pdb; pdb.set_trace()
try:
if target_width is None:
full_img = read_resize_img(
full_fn, target_height=target_height)
else:
full_img = read_resize_img(
full_fn, target_size=(target_height, target_width))
img_id = '_'.join([pat, side, view])
print "ID:%s, read image of size=%s" % (img_id, full_img.shape),
if segment_breast:
full_img, bbox = imprep.segment_breast(full_img)
print "size after segmentation=%s" % (str(full_img.shape))
sys.stdout.flush()
# Read mask image(s).
abn_path = roi_mask_path_df.loc[pat].loc[side].loc[view]
if isinstance(abn_path, pd.Series):
abn_num = [abn_path['abn_num']]
pathology = [abn_path['pathology']]
itypes = [abn_path['type']]
else:
abn_num = abn_path['abn_num']
pathology = abn_path['pathology']
itypes = abn_path['type']
bkg_sampled = False
for abn, path, itype in zip(abn_num, pathology, itypes):
mask_fn = const_filename(pat, side, view, roi_mask_dir, itype, abn)
if target_width is None:
mask_img = read_resize_img(
mask_fn, target_height=target_height, gs_255=True)
else:
mask_img = read_resize_img(
mask_fn, target_size=(target_height, target_width),
gs_255=True)
if segment_breast:
mask_img = crop_img(mask_img, bbox)
# sample using mask and full image.
nb_hns_ = nb_hns if not bkg_sampled else 0
if nb_hns_ > 0:
hns_sampled = sample_blob_negatives(
full_img, mask_img, out_dir, img_id,
abn, blob_detector, patch_size, neg_cutoff,
nb_hns_, 0, bkg_dir, verbose)
else:
hns_sampled = 0
pos = path.startswith('MALIGNANT')
nb_bkg_ = nb_bkg - hns_sampled if not bkg_sampled else 0
sample_patches(full_img, mask_img, out_dir, img_id, abn, pos,
patch_size, pos_cutoff, neg_cutoff,
nb_bkg_, nb_abn, hns_sampled, itype,
bkg_dir, calc_pos_dir, calc_neg_dir,
mass_pos_dir, mass_neg_dir, verbose)
bkg_sampled = True
except AttributeError:
print "Read image error: %s" % (full_fn)
except ValueError:
print "Error sampling from ROI mask image: %s" % (mask_fn)
def do_sampling(pat_df, out_dir):
for patient_id, side, view in pat_df.index.unique():
cur_desc = description_df.loc[patient_id].loc[side].loc[view]
abn_ids = cur_desc["abnormality id"]
pathologies = cur_desc["pathology"]
abn_types = cur_desc["abnormality type"]
if isinstance(cur_desc, pd.Series):
abn_ids = [abn_ids]
pathologies = [pathologies]
abn_types = [abn_types]
# Read mask image(s).
bkg_sampled = False
for abn_id, pathology, abn_type in zip(abn_ids, pathologies, abn_types):
# NOTE csv not reliable due to formatting error, and there are missing files.
# image_path = cur_desc["image file path"]
# mask_path = cur_desc["ROI mask file path"]
try:
full_image, mask_image = get_image_and_mask(
patient_id=patient_id,
side=side,
view=view,
image_dir=image_dir,
roi_mask_dir=roi_mask_dir,
abn_type=abn_type,
abn_id=abn_id,
target_height=target_height,
target_width=target_width
)
image_id = "_".join([patient_id, side, view])
print "ID:%s, read image of size=%s" % (image_id, full_image.shape)
if segment_breast:
full_image, bbox = imprep.segment_breast(full_image)
print "size after segmentation=%s" % (str(full_image.shape))
mask_image = crop_img(mask_image, bbox)
# sample using mask and full image.
nb_hns_ = nb_hns if not bkg_sampled else 0
if nb_hns_ > 0:
hns_sampled = sample_blob_negatives(
full_image, mask_image, out_dir, image_id,
abn_id, blob_detector, patch_size, neg_cutoff,
nb_hns_, 0, bkg_dir, verbose)
else:
hns_sampled = 0
pos = pathology.startswith("MALIGNANT")
nb_bkg_ = nb_bkg - hns_sampled if not bkg_sampled else 0
sample_patches(full_image, mask_image, out_dir, image_id, abn_id, pos,
patch_size, pos_cutoff, neg_cutoff,
nb_bkg_, nb_abn, hns_sampled, abn_type,
bkg_dir, calc_pos_dir, calc_neg_dir,
mass_pos_dir, mass_neg_dir, verbose)
bkg_sampled = True
except RuntimeError as exception:
print exception
def do_sampling(pat_df, out_dir):
for pat, side, view in pat_df.index.unique():
full_fn = const_filename(pat, side, view, full_img_dir)
# import pdb; pdb.set_trace()
try:
full_img = read_resize_img(full_fn,
target_height=target_height)
img_id = '_'.join([pat, side, view])
print "ID:%s, read image of size=%s" % (img_id,
full_img.shape),
full_img, bbox = imprep.segment_breast(full_img)
print "size after segmentation=%s" % (str(full_img.shape))
sys.stdout.flush()
# Read mask image(s).
abn_path = roi_mask_path_df.loc[pat].loc[side].loc[view]
if isinstance(abn_path, pd.Series):
abn_num = [abn_path['abn_num']]
pathology = [abn_path['pathology']]
itypes = [abn_path['type']]
else:
abn_num = abn_path['abn_num']
pathology = abn_path['pathology']
itypes = abn_path['type']
bkg_sampled = False
for abn, path, itype in zip(abn_num, pathology, itypes):
mask_fn = const_filename(pat, side, view, roi_mask_dir,
itype, abn)
mask_img = read_resize_img(mask_fn,
target_height=target_height,
gs_255=True)
mask_img = crop_img(mask_img, bbox)
# sample using mask and full image.
nb_hns_ = nb_hns if not bkg_sampled else 0
if nb_hns_ > 0:
hns_sampled = sample_blob_negatives(
full_img, mask_img, out_dir, img_id, abn,
blob_detector, patch_size, neg_cutoff, nb_hns_, 0,
bkg_dir, verbose)
else:
hns_sampled = 0
pos = path.startswith('MALIGNANT')
nb_bkg_ = nb_bkg - hns_sampled if not bkg_sampled else 0
sample_patches(full_img, mask_img, out_dir, img_id, abn,
pos, patch_size, pos_cutoff, neg_cutoff,
nb_bkg_, nb_abn, hns_sampled, itype,
bkg_dir, calc_pos_dir, calc_neg_dir,
mass_pos_dir, mass_neg_dir, verbose)
bkg_sampled = True
except AttributeError:
print "Read image error: %s" % (full_fn)
except ValueError:
print "Error sampling from ROI mask image: %s" % (mask_fn)
def do_sampling(pat_df, out_dir):
#遍历病人信息
for pat, side, view in pat_df.index.unique():
#文件名命名
full_fn = const_filename(pat, side, view, full_img_dir)
# import pdb; pdb.set_trace()
try:
#如果未指定宽度则根据高度调整图片大小
if target_width is None:
full_img = read_resize_img(full_fn,
target_height=target_height)
else:
#根据宽高调整大小
full_img = read_resize_img(full_fn,
target_size=(target_height,
target_width))
#命名图片ID
img_id = '_'.join([pat, side, view])
print(
"ID:%s, read image of size=%s" %
(img_id, full_img.shape), )
#图像中分割出乳腺为true
if segment_breast:
#获取乳腺图像和坐标宽高
full_img, bbox = imprep.segment_breast(full_img)
#输出图像宽高通道数
print("size after segmentation=%s" % (str(full_img.shape)))
sys.stdout.flush()
# Read mask image(s).读取掩码图
#获取异常路径
abn_path = roi_mask_path_df.loc[pat].loc[side].loc[view]
#无论此路径是否为pd.Series类型，都要进行异常数，病理（良恶性），类型（肿块或钙化点）的数据获取
if isinstance(abn_path, pd.Series):
abn_num = [abn_path['abn_num']]
pathology = [abn_path['pathology']]
itypes = [abn_path['type']]
else:
abn_num = abn_path['abn_num']
pathology = abn_path['pathology']
itypes = abn_path['type']
#
bkg_sampled = False
#遍历刚才获取的数据，转为掩码文件
for abn, path, itype in zip(abn_num, pathology, itypes):
mask_fn = const_filename(pat, side, view, roi_mask_dir,
itype, abn)
if target_width is None:
mask_img = read_resize_img(mask_fn,
target_height=target_height,
gs_255=True)
else:
mask_img = read_resize_img(mask_fn,
target_size=(target_height,
target_width),
gs_255=True)
if segment_breast:
mask_img = crop_img(mask_img, bbox)
# sample using mask and full image.样本使用掩码和全图
#获取难类数
nb_hns_ = nb_hns if not bkg_sampled else 0
if nb_hns_ > 0:
#返回阴性斑点采样得到的背景样本数
hns_sampled = sample_blob_negatives(
full_img, mask_img, out_dir, img_id, abn,
blob_detector, patch_size, neg_cutoff, nb_hns_, 0,
bkg_dir, verbose)
else:
hns_sampled = 0
pos = path.startswith('MALIGNANT')
#背景数为总背景数-阴性斑点采样数
nb_bkg_ = nb_bkg - hns_sampled if not bkg_sampled else 0
#对块进行采样
sample_patches(full_img, mask_img, out_dir, img_id, abn,
pos, patch_size, pos_cutoff, neg_cutoff,
nb_bkg_, nb_abn, hns_sampled, itype,
bkg_dir, calc_pos_dir, calc_neg_dir,
mass_pos_dir, mass_neg_dir, verbose)
bkg_sampled = True
except AttributeError:
print("Read image error: %s" % (full_fn))
except ValueError:
print("Error sampling from ROI mask image: %s" % (mask_fn))