def preproc_cropped_chips(depc, cid_list, tipid_list, config=None):
"""
CommandLine:
python -m ibeis_flukematch.plugin --exec-preproc_cropped_chips --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis_flukematch.plugin import * # NOQA
>>> ibs, aid_list = testdata_humpbacks()
>>> config = CropChipConfig(crop_enabled=True)
>>> cid_list = ibs.depc.get_rowids('chips', aid_list, config)
>>> tipid_list = ibs.depc.get_rowids('Notch_Tips', aid_list, config)
>>> depc = ibs.depc
>>> list(preproc_cropped_chips(depc, cid_list, tipid_list, config))
>>> #cpid_list = ibs.depc.d.get_Cropped_Chips_rowids(aid_list, config)
>>> #cpid_list = ibs.depc.w.Cropped_Chips.get_rowids(aid_list, config)
>>> chip_list = ibs.depc.get('Cropped_Chips', aid_list, 'img', config)
>>> notch_tips = ibs.depc.get('Cropped_Chips', aid_list, ('notch', 'left', 'right'), config)
>>> import plottool as pt
>>> ut.ensure_pylab_qt4()
>>> for notch, chip, aid in ut.InteractiveIter(zip(notch_tips, chip_list, aid_list)):
>>> pt.reset()
>>> pt.imshow(chip)
>>> print(ibs.depc.get('Cropped_Chips', [aid], 'img', config, read_extern=False)[0])
>>> kpts_ = np.array(notch)
>>> pt.draw_kpts2(kpts_, pts=True, ell=False, pts_size=20)
>>> pt.update()
>>> ut.show_if_requested()
"""
# crop first
img_list = depc.get_native_property(const.CHIP_TABLE, cid_list, 'img')
tips_list = depc.get_native_property('Notch_Tips', tipid_list, ('left', 'notch', 'right'))
#imgpath_list = depc.get_native_property(const.CHIP_TABLE, cid_list, 'img',
# read_extern=False)
cropped_chip_dpath = depc.controller.get_chipdir() + '_crop'
ut.ensuredir(cropped_chip_dpath)
#crop_path_list = [ut.augpath(path, '_crop' + config.get_hashid())
# for path in imgpath_list]
#for img, tips, path in zip(img_list, tips_list, crop_path_list):
for img, tips in zip(img_list, tips_list):
left, notch, right = tips
bbox = (0, 0, img.shape[1], img.shape[0]) # default to bbox being whole image
chip_size = (img.shape[1], img.shape[0])
if left[0] > right[0]:
# HACK: Ugh, I don't like this
# TODO: maybe move this to infer_kp?
right, left = (left, right)
if config['crop_enabled']:
# figure out bbox (x, y, w, h) w/x, y on top left
# assume left is on the left note: this may not be a good assumption
# note: lol that's not a good assumption
# what do when network predicts left on right and right on left?
bbox = (left[0], # leftmost x value
0, # top of the image
(right[0] - left[0]), # width
img.shape[0], # height
)
if config['crop_dim_size'] is not None:
# we're only resizing in x, but after the crop
# as a result we need to make sure we use the image dimensions apparent to get the chip size
# we want to preserve the aspect ratio of the crop, not the whole image
new_x = config['crop_dim_size']
#ratio = bbox[2] / bbox[3] # w/h
#new_y = int(new_x / ratio)
#chip_size = (new_x, new_y)
try:
#print("[cropped-chips] %s: bbox: %r, l/n/r %r" % (path, bbox,tips))
chip_size = vt.get_scaled_size_with_width(new_x, bbox[2], bbox[3])
except OverflowError:
print("[cropped chip] WARNING: Probably got a bad keypoint prediction: bbox: %r" % (bbox,))
yield None
M = vt.get_image_to_chip_transform(bbox, chip_size, 0)
with ut.embed_on_exception_context:
new_img = cv2.warpAffine(img, M[:-1, :], chip_size)
notch_, left_, right_ = vt.transform_points_with_homography(M, np.array([notch, left, right]).T).T
notch_ = bound_point(notch_, chip_size)
left_ = bound_point(left_, chip_size)
right_ = bound_point(right_, chip_size)
#vt.imwrite(path, new_img)
yield (new_img, bbox[2], bbox[3], M, notch_, left_, right_)
def preproc_cropped_chips(depc, cid_list, tipid_list, config=None):
"""
CommandLine:
python -m ibeis_flukematch.plugin --exec-preproc_cropped_chips --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis_flukematch.plugin import * # NOQA
>>> ibs, aid_list = testdata_humpbacks()
>>> config = CropChipConfig(crop_enabled=True)
>>> cid_list = ibs.depc.get_rowids('chips', aid_list, config)
>>> tipid_list = ibs.depc.get_rowids('Notch_Tips', aid_list, config)
>>> depc = ibs.depc
>>> list(preproc_cropped_chips(depc, cid_list, tipid_list, config))
>>> #cpid_list = ibs.depc.d.get_Cropped_Chips_rowids(aid_list, config)
>>> #cpid_list = ibs.depc.w.Cropped_Chips.get_rowids(aid_list, config)
>>> chip_list = ibs.depc.get('Cropped_Chips', aid_list, 'img', config)
>>> notch_tips = ibs.depc.get('Cropped_Chips', aid_list, ('notch', 'left', 'right'), config)
>>> import plottool as pt
>>> ut.ensure_pylab_qt4()
>>> for notch, chip, aid in ut.InteractiveIter(zip(notch_tips, chip_list, aid_list)):
>>> pt.reset()
>>> pt.imshow(chip)
>>> print(ibs.depc.get('Cropped_Chips', [aid], 'img', config, read_extern=False)[0])
>>> kpts_ = np.array(notch)
>>> pt.draw_kpts2(kpts_, pts=True, ell=False, pts_size=20)
>>> pt.update()
>>> ut.show_if_requested()
"""
# crop first
img_list = depc.get_native_property(const.CHIP_TABLE, cid_list, 'img')
tips_list = depc.get_native_property('Notch_Tips', tipid_list,
('left', 'notch', 'right'))
#imgpath_list = depc.get_native_property(const.CHIP_TABLE, cid_list, 'img',
# read_extern=False)
cropped_chip_dpath = depc.controller.get_chipdir() + '_crop'
ut.ensuredir(cropped_chip_dpath)
#crop_path_list = [ut.augpath(path, '_crop' + config.get_hashid())
# for path in imgpath_list]
#for img, tips, path in zip(img_list, tips_list, crop_path_list):
for img, tips in zip(img_list, tips_list):
left, notch, right = tips
bbox = (0, 0, img.shape[1], img.shape[0]
) # default to bbox being whole image
chip_size = (img.shape[1], img.shape[0])
if left[0] > right[0]:
# HACK: Ugh, I don't like this
# TODO: maybe move this to infer_kp?
right, left = (left, right)
if config['crop_enabled']:
# figure out bbox (x, y, w, h) w/x, y on top left
# assume left is on the left note: this may not be a good assumption
# note: lol that's not a good assumption
# what do when network predicts left on right and right on left?
bbox = (
left[0], # leftmost x value
0, # top of the image
(right[0] - left[0]), # width
img.shape[0], # height
)
if config['crop_dim_size'] is not None:
# we're only resizing in x, but after the crop
# as a result we need to make sure we use the image dimensions apparent to get the chip size
# we want to preserve the aspect ratio of the crop, not the whole image
new_x = config['crop_dim_size']
#ratio = bbox[2] / bbox[3] # w/h
#new_y = int(new_x / ratio)
#chip_size = (new_x, new_y)
try:
#print("[cropped-chips] %s: bbox: %r, l/n/r %r" % (path, bbox,tips))
chip_size = vt.get_scaled_size_with_width(
new_x, bbox[2], bbox[3])
except OverflowError:
print(
"[cropped chip] WARNING: Probably got a bad keypoint prediction: bbox: %r"
% (bbox, ))
yield None
M = vt.get_image_to_chip_transform(bbox, chip_size, 0)
with ut.embed_on_exception_context:
new_img = cv2.warpAffine(img, M[:-1, :], chip_size)
notch_, left_, right_ = vt.transform_points_with_homography(
M,
np.array([notch, left, right]).T).T
notch_ = bound_point(notch_, chip_size)
left_ = bound_point(left_, chip_size)
right_ = bound_point(right_, chip_size)
#vt.imwrite(path, new_img)
yield (new_img, bbox[2], bbox[3], M, notch_, left_, right_)
def compute_labels_localizations(depc, loc_id_list, config=None):
r"""
Extracts the detections for a given input image
Args:
depc (ibeis.depends_cache.DependencyCache):
loc_id_list (list): list of localization rowids
config (dict): (default = None)
Yields:
(float, str): tup
CommandLine:
ibeis compute_labels_localizations
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.core_images import * # NOQA
>>> import ibeis
>>> defaultdb = 'PZ_MTEST'
>>> ibs = ibeis.opendb(defaultdb=defaultdb)
>>> depc = ibs.depc_image
>>> gid_list = ibs.get_valid_gids()[0:100]
>>> depc.delete_property('labeler', gid_list)
>>> results = depc.get_property('labeler', gid_list, None)
>>> results = depc.get_property('labeler', gid_list, 'species')
>>> print(results)
"""
from ibeis.algo.detect.labeler.labeler import label_chip_list
print('[ibs] Process Localization Labels')
print('config = %r' % (config,))
# Get controller
ibs = depc.controller
depc = ibs.depc_image
gid_list_ = depc.get_ancestor_rowids('localizations', loc_id_list, 'images')
assert len(gid_list_) == len(loc_id_list)
# Grab the localizations
bboxes_list = depc.get_native('localizations', loc_id_list, 'bboxes')
thetas_list = depc.get_native('localizations', loc_id_list, 'thetas')
gids_list = [
np.array([gid] * len(bbox_list))
for gid, bbox_list in zip(gid_list_, bboxes_list)
]
# Flatten all of these lists for efficiency
bbox_list = ut.flatten(bboxes_list)
theta_list = ut.flatten(thetas_list)
gid_list = ut.flatten(gids_list)
bbox_size_list = ut.take_column(bbox_list, [2, 3])
newsize_list = [(128, 128)] * len(bbox_list)
# Checks
invalid_flags = [w == 0 or h == 0 for (w, h) in bbox_size_list]
invalid_bboxes = ut.compress(bbox_list, invalid_flags)
assert len(invalid_bboxes) == 0, 'invalid bboxes=%r' % (invalid_bboxes,)
# Build transformation from image to chip
M_list = [
vt.get_image_to_chip_transform(bbox, new_size, theta)
for bbox, theta, new_size in zip(bbox_list, theta_list, newsize_list)
]
# Extract "chips"
flags = cv2.INTER_LANCZOS4
borderMode = cv2.BORDER_CONSTANT
warpkw = dict(flags=flags, borderMode=borderMode)
last_gid = None
chip_list = []
for gid, new_size, M in zip(gid_list, newsize_list, M_list):
if gid != last_gid:
img = ibs.get_images(gid)
last_gid = gid
chip = cv2.warpAffine(img, M[0:2], tuple(new_size), **warpkw)
# cv2.imshow('', chip)
# cv2.waitKey()
assert chip.shape[0] == 128 and chip.shape[1] == 128
chip_list.append(chip)
# Get the results from the algorithm
result_list = label_chip_list(chip_list)
assert len(gid_list) == len(result_list)
# Group the results
group_dict = {}
for gid, result in zip(gid_list, result_list):
if gid not in group_dict:
group_dict[gid] = []
group_dict[gid].append(result)
assert len(gid_list_) == len(group_dict.keys())
# Return the results
for gid in gid_list_:
result_list = group_dict[gid]
zipped_list = zip(*result_list)
ret_tuple = (
np.array(zipped_list[0]),
np.array(zipped_list[1]),
np.array(zipped_list[2]),
np.array(zipped_list[3]),
np.array(zipped_list[4]),
list(zipped_list[5]),
)
# print(ret_tuple[:-1])
# print('-------')
yield ret_tuple
