def get_part_rotated_verts(ibs, part_rowid_list):
r"""
Returns:
rotated_vert_list (list): verticies after rotation by theta.
RESTful:
Method: GET
URL: /api/part/vert/rotated/
"""
import vtool as vt
vert_list = ibs.get_part_verts(part_rowid_list)
theta_list = ibs.get_part_thetas(part_rowid_list)
# Convex bounding boxes for verticies
bbox_list = vt.geometry.bboxes_from_vert_list(vert_list)
rot_list = [
vt.rotation_around_bbox_mat3x3(theta, bbox)
for theta, bbox in zip(theta_list, bbox_list)
]
rotated_vert_list = [
vt.transform_points_with_homography(rot,
np.array(verts).T).T.tolist()
for rot, verts in zip(rot_list, vert_list)
]
# vert_list = [eval(vertstr, {}, {}) for vertstr in vertstr_list]
return rotated_vert_list
def transform_non_affine(self, input_xy):
"""
The input and output are Nx2 numpy arrays.
"""
import vtool as vt
_xys = input_xy.T
xy_t = vt.transform_points_with_homography(self.H, _xys)
output_xy = xy_t.T
return output_xy
def transform_non_affine(self, input_xy):
"""
The input and output are Nx2 numpy arrays.
"""
import vtool as vt
_xys = input_xy.T
xy_t = vt.transform_points_with_homography(self.H, _xys)
output_xy = xy_t.T
return output_xy
def draw_keypoints(ax,
kpts_,
scale_factor=1.0,
offset=(0.0, 0.0),
rotation=0.0,
ell=True,
pts=False,
rect=False,
eig=False,
ori=False,
sifts=None,
siftkw={},
H=None,
**kwargs):
"""
draws keypoints extracted by pyhesaff onto a matplotlib axis
FIXME: There is probably a matplotlib bug here. If you specify two different
alphas in a collection, whatever the last alpha was gets applied to
everything
Args:
ax (mpl.Axes):
kpts (ndarray): keypoints [[x, y, a, c, d, theta], ...]
scale_factor (float):
offset (tuple):
rotation (float):
ell (bool):
pts (bool):
rect (bool):
eig (bool):
ori (bool):
sifts (None):
References:
http://stackoverflow.com/questions/28401788/transforms-non-affine-patch
CommandLine:
python -m wbia.plottool.mpl_keypoint draw_keypoints --show
Example:
>>> # ENABLE_DOCTEST
>>> from wbia.plottool.mpl_keypoint import * # NOQA
>>> from wbia.plottool.mpl_keypoint import _draw_patches, _draw_pts # NOQA
>>> import wbia.plottool as pt
>>> import vtool as vt
>>> imgBGR = vt.get_star_patch(jitter=True)
>>> H = np.array([[1, 0, 0], [.5, 2, 0], [0, 0, 1]])
>>> H = np.array([[.8, 0, 0], [0, .8, 0], [0, 0, 1]])
>>> H = None
>>> TAU = 2 * np.pi
>>> kpts_ = vt.make_test_image_keypoints(imgBGR, scale=.5, skew=2, theta=TAU / 8.0)
>>> scale_factor=1.0
>>> #offset=(0.0, -4.0)
>>> offset=(0.0, 0.0)
>>> rotation=0.0
>>> ell=True
>>> pts=True
>>> rect=True
>>> eig=True
>>> ori=True
>>> # make random sifts
>>> sifts = mpl_sift.testdata_sifts()
>>> siftkw = {}
>>> kwargs = dict(ori_color=[0, 1, 0], rect_color=[0, 0, 1],
>>> eig_color=[1, 1, 0], pts_size=.1)
>>> w, h = imgBGR.shape[0:2][::-1]
>>> imgBGR_ = imgBGR if H is None else vt.warpAffine(
>>> imgBGR, H, (int(w * .8), int(h * .8)))
>>> fig, ax = pt.imshow(imgBGR_ * 255)
>>> draw_keypoints(ax, kpts_, scale_factor, offset, rotation, ell, pts,
... rect, eig, ori, sifts, siftkw, H=H, **kwargs)
>>> pt.iup()
>>> pt.show_if_requested()
"""
import vtool.keypoint as ktool
if kpts_.shape[1] == 2:
# pad out structure if only xy given
kpts = np.zeros((len(kpts_), 6))
kpts[:, 0:2] = kpts_
kpts[:, 2] = 1
kpts[:, 4] = 1
kpts_ = kpts
if scale_factor is None:
scale_factor = 1.0
# print('[mpl_keypoint.draw_keypoints] kwargs = ' + ut.repr2(kwargs))
# ellipse and point properties
pts_size = kwargs.get('pts_size', 2)
pts_alpha = kwargs.get('pts_alpha', 1.0)
ell_alpha = kwargs.get('ell_alpha', 1.0)
ell_linewidth = kwargs.get('ell_linewidth', 2)
ell_color = kwargs.get('ell_color', None)
if ell_color is None:
ell_color = [1, 0, 0]
# colors
pts_color = kwargs.get('pts_color', ell_color)
rect_color = kwargs.get('rect_color', ell_color)
eig_color = kwargs.get('eig_color', ell_color)
ori_color = kwargs.get('ori_color', ell_color)
# linewidths
eig_linewidth = kwargs.get('eig_linewidth', ell_linewidth)
rect_linewidth = kwargs.get('rect_linewidth', ell_linewidth)
ori_linewidth = kwargs.get('ori_linewidth', ell_linewidth)
# Offset keypoints
assert len(kpts_) > 0, 'cannot draw no keypoints1'
kpts = ktool.offset_kpts(kpts_, offset, scale_factor)
assert len(kpts) > 0, 'cannot draw no keypoints2'
# Build list of keypoint shape transforms from unit circles to ellipes
invVR_aff2Ds = get_invVR_aff2Ds(kpts, H=H)
try:
if sifts is not None:
# SIFT descriptors
pass_props(
kwargs,
siftkw,
'bin_color',
'arm1_color',
'arm2_color',
'arm1_lw',
'arm2_lw',
'stroke',
'arm_alpha',
'arm_alpha',
'multicolored_arms',
)
mpl_sift.draw_sifts(ax, sifts, invVR_aff2Ds, **siftkw)
if rect:
# Bounding Rectangles
rect_patches = rectangle_actors(invVR_aff2Ds)
_draw_patches(ax, rect_patches, rect_color, ell_alpha,
rect_linewidth)
if ell:
# Keypoint shape
ell_patches = ellipse_actors(invVR_aff2Ds)
_draw_patches(ax, ell_patches, ell_color, ell_alpha, ell_linewidth)
if eig:
# Shape eigenvectors
eig_patches = eigenvector_actors(invVR_aff2Ds)
_draw_patches(ax, eig_patches, eig_color, ell_alpha, eig_linewidth)
if ori:
# Keypoint orientation
ori_patches = orientation_actors(kpts, H=H)
_draw_patches(ax, ori_patches, ori_color, ell_alpha, ori_linewidth,
ori_color)
if pts:
# Keypoint locations
_xs, _ys = ktool.get_xys(kpts)
if H is not None:
# adjust for homogrpahy
import vtool as vt
_xs, _ys = vt.transform_points_with_homography(
H, np.vstack((_xs, _ys)))
pts_patches = _draw_pts(ax, _xs, _ys, pts_size, pts_color,
pts_alpha)
if pts_patches is not None:
_draw_patches(ax, pts_patches, 'none', pts_alpha, pts_size,
pts_color)
except ValueError as ex:
ut.printex(ex, '\n[mplkp] !!! ERROR')
# print('_oris.shape = %r' % (_oris.shape,))
# print('_xs.shape = %r' % (_xs.shape,))
# print('_iv11s.shape = %r' % (_iv11s.shape,))
raise
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 wbia_plugin_kaggle7_chip_depc(depc, aid_list, config):
r"""
Refine localizations for CurvRank with Dependency Cache (depc)
CommandLine:
python -m wbia_kaggle7._plugin --test-wbia_plugin_kaggle7_chip_depc
python -m wbia_kaggle7._plugin --test-wbia_plugin_kaggle7_chip_depc:0
Example:
>>> # DISABLE_DOCTEST
>>> from wbia_kaggle7._plugin import * # NOQA
>>> import wbia
>>> from wbia.init import sysres
>>> dbdir = sysres.ensure_testdb_kaggle7()
>>> ibs = wbia.opendb(dbdir=dbdir, allow_newdir=True)
>>> aid_list = ibs.get_image_aids(1)
>>> images = ibs.depc_annot.get('KaggleSevenChip', aid_list, 'image')
>>> image = images[0]
>>> assert ut.hash_data(image) in ['imlkoiskkykpbwozghmpidlqwbmglzhw']
"""
padding = config['chip_padding']
tips_list = depc.get('Notch_Tips', aid_list)
size_list = depc.get('chips', aid_list, ('width', 'height'))
config_ = {
'dim_size': 1550,
'resize_dim': 'width',
'ext': '.jpg',
}
chip_list = depc.get('chips', aid_list, 'img', config=config_, ensure=True)
tps = cv2.createThinPlateSplineShapeTransformer()
zipped = list(zip(aid_list, tips_list, size_list, chip_list))
for aid, tip_list, size, chip in zipped:
h0, w0, c0 = chip.shape
notch = tip_list[0].copy()
left = tip_list[1].copy()
right = tip_list[2].copy()
size = np.array(size, dtype=np.float32)
notch /= size
left /= size
right /= size
size = np.array([w0, h0], dtype=np.float32)
notch *= size
left *= size
right *= size
chip_ = chip.copy()
h0, w0, c0 = chip_.shape
left += padding
notch += padding
right += padding
pad = np.zeros((h0, padding, 3), dtype=chip_.dtype)
chip_ = np.hstack((pad, chip_, pad))
h, w, c = chip_.shape
pad = np.zeros((padding, w, 3), dtype=chip_.dtype)
chip_ = np.vstack((pad, chip_, pad))
h, w, c = chip_.shape
delta = right - left
radian = np.arctan2(delta[1], delta[0])
degree = np.degrees(radian)
M = cv2.getRotationMatrix2D((left[1], left[0]), degree, 1)
chip_ = cv2.warpAffine(chip_, M, (w, h), flags=cv2.INTER_LANCZOS4)
H = np.vstack((M, [0, 0, 1]))
vert_list = np.array([notch, left, right])
vert_list_ = vt.transform_points_with_homography(H, vert_list.T).T
notch, left, right = vert_list_
left[0] -= padding // 2
left[1] -= padding // 2
notch[1] += padding // 2
right[0] += padding // 2
right[1] -= padding // 2
sshape = np.array([left, notch, right], np.float32)
tshape = np.array([[0, 0], [w0 // 2, h0], [w0, 0]], np.float32)
sshape = sshape.reshape(1, -1, 2)
tshape = tshape.reshape(1, -1, 2)
matches = [
cv2.DMatch(0, 0, 0),
cv2.DMatch(1, 1, 0),
cv2.DMatch(2, 2, 0),
]
tps.clear()
tps.estimateTransformation(tshape, sshape, matches)
chip_ = tps.warpImage(chip_)
chip_ = chip_[:h0, :w0, :]
chip_h, chip_w = chip_.shape[:2]
yield (
chip_,
chip_w,
chip_h,
)
def fix_annotation_orientation(ibs, min_percentage=0.95):
"""
Fixes the annotations that are outside the bounds of the image due to a
changed image orientation flag in the database
CommandLine:
python -m ibeis.scripts.fix_annotation_orientation_issue fix_annotation_orientation
Example:
>>> # ENABLE_DOCTEST
>>> import ibeis
>>> from ibeis.scripts.fix_annotation_orientation_issue import * # NOQA
>>> ibs = ibeis.opendb()
>>> unfixable_gid_list = fix_annotation_orientation(ibs)
>>> assert len(unfixable_gid_list) == 0
"""
from vtool import exif
def bbox_overlap(bbox1, bbox2):
ax1 = bbox1[0]
ay1 = bbox1[1]
ax2 = bbox1[0] + bbox1[2]
ay2 = bbox1[1] + bbox1[3]
bx1 = bbox2[0]
by1 = bbox2[1]
bx2 = bbox2[0] + bbox2[2]
by2 = bbox2[1] + bbox2[3]
overlap_x = max(0, min(ax2, bx2) - max(ax1, bx1))
overlap_y = max(0, min(ay2, by2) - max(ay1, by1))
return overlap_x * overlap_y
orient_dict = exif.ORIENTATION_DICT_INVERSE
good_orient_list = [
exif.ORIENTATION_UNDEFINED,
exif.ORIENTATION_000,
]
good_orient_key_list = [
orient_dict.get(good_orient) for good_orient in good_orient_list
]
assert None not in good_orient_key_list
gid_list = ibs.get_valid_gids()
orient_list = ibs.get_image_orientation(gid_list)
flag_list = [orient not in good_orient_key_list for orient in orient_list]
# Filter based on based gids
unfixable_gid_list = []
gid_list = ut.filter_items(gid_list, flag_list)
if len(gid_list) > 0:
args = (len(gid_list), )
print('Found %d images with non-standard orientations' % args)
aids_list = ibs.get_image_aids(gid_list,
is_staged=None,
__check_staged__=False)
size_list = ibs.get_image_sizes(gid_list)
invalid_gid_list = []
zipped = zip(gid_list, orient_list, aids_list, size_list)
for gid, orient, aid_list, (w, h) in zipped:
image = ibs.get_images(gid)
h_, w_ = image.shape[:2]
if h != h_ or w != w_:
ibs._set_image_sizes([gid], [w_], [h_])
orient_str = exif.ORIENTATION_DICT[orient]
image_bbox = (0, 0, w, h)
verts_list = ibs.get_annot_rotated_verts(aid_list)
invalid = False
for aid, vert_list in zip(aid_list, verts_list):
annot_bbox = vt.bbox_from_verts(vert_list)
overlap = bbox_overlap(image_bbox, annot_bbox)
area = annot_bbox[2] * annot_bbox[3]
percentage = overlap / area
if percentage < min_percentage:
args = (gid, orient_str, aid, overlap, area, percentage)
print(
'\tInvalid GID %r, Orient %r, AID %r: Overlap %0.2f, Area %0.2f (%0.2f %%)'
% args)
invalid = True
# break
if invalid:
invalid_gid_list.append(gid)
invalid_gid_list = list(set(invalid_gid_list))
if len(invalid_gid_list) > 0:
args = (
len(invalid_gid_list),
len(gid_list),
invalid_gid_list,
)
print('Found %d / %d images with invalid annotations = %r' % args)
orient_list = ibs.get_image_orientation(invalid_gid_list)
aids_list = ibs.get_image_aids(invalid_gid_list,
is_staged=None,
__check_staged__=False)
size_list = ibs.get_image_sizes(invalid_gid_list)
zipped = zip(invalid_gid_list, orient_list, aids_list, size_list)
for invalid_gid, orient, aid_list, (w, h) in zipped:
orient_str = exif.ORIENTATION_DICT[orient]
image_bbox = (0, 0, w, h)
args = (
invalid_gid,
len(aid_list),
)
print('Fixing GID %r with %d annotations' % args)
theta = np.pi / 2.0
tx = 0.0
ty = 0.0
if orient == orient_dict.get(exif.ORIENTATION_090):
theta *= 1.0
tx = w
elif orient == orient_dict.get(exif.ORIENTATION_180):
theta *= 2.0
tx = w
ty = h
elif orient == orient_dict.get(exif.ORIENTATION_270):
theta *= -1.0
ty = h
else:
raise ValueError('Unrecognized invalid orientation')
H = np.array([[np.cos(theta), -np.sin(theta), tx],
[np.sin(theta), np.cos(theta), ty],
[0.0, 0.0, 1.0]])
# print(H)
verts_list = ibs.get_annot_rotated_verts(aid_list)
for aid, vert_list in zip(aid_list, verts_list):
vert_list = np.array(vert_list)
# print(vert_list)
vert_list = vert_list.T
transformed_vert_list = vt.transform_points_with_homography(
H, vert_list)
transformed_vert_list = transformed_vert_list.T
# print(transformed_vert_list)
ibs.set_annot_verts([aid], [transformed_vert_list],
update_visual_uuids=False)
current_theta = ibs.get_annot_thetas(aid)
new_theta = current_theta + theta
ibs.set_annot_thetas(aid,
new_theta,
update_visual_uuids=False)
fixed_vert_list = ibs.get_annot_rotated_verts(aid)
fixed_annot_bbox = vt.bbox_from_verts(fixed_vert_list)
fixed_overlap = bbox_overlap(image_bbox, fixed_annot_bbox)
fixed_area = fixed_annot_bbox[2] * fixed_annot_bbox[3]
fixed_percentage = fixed_overlap / fixed_area
args = (invalid_gid, orient_str, aid, fixed_overlap,
fixed_area, fixed_percentage)
print(
'\tFixing GID %r, Orient %r, AID %r: Overlap %0.2f, Area %0.2f (%0.2f %%)'
% args)
if fixed_percentage < min_percentage:
print('\tWARNING: FIXING DID NOT CORRECT AID %r' %
(aid, ))
unfixable_gid_list.append(gid)
print('Un-fixable gid_list = %r' % (unfixable_gid_list, ))
return unfixable_gid_list
for location, color in zip(location_list, color_list):
cv2.circle(chip1_, location, 5, color=color)
chip_filename = 'img_aid_%d_1.png' % (aid, )
chip_filepath = join(notch_path, chip_filename)
cv2.imwrite(chip_filepath, chip1_)
delta = right - left
radian = np.arctan2(delta[1], delta[0])
degree = np.degrees(radian)
M = cv2.getRotationMatrix2D((left[1], left[0]), degree, 1)
chip2 = cv2.warpAffine(chip1, M, (w, h), flags=cv2.INTER_LANCZOS4)
H = np.vstack((M, [0, 0, 1]))
vert_list = np.array([notch, left, right])
vert_list_ = vt.transform_points_with_homography(H, vert_list.T).T
notch, left, right = vert_list_
location_list = [
tuple(map(int, np.around(notch))),
tuple(map(int, np.around(left))),
tuple(map(int, np.around(right))),
]
chip2_ = chip2.copy()
for location, color in zip(location_list, color_list):
cv2.circle(chip2_, location, 5, color=color)
chip_filename = 'img_aid_%d_2.png' % (aid, )
chip_filepath = join(notch_path, chip_filename)
cv2.imwrite(chip_filepath, chip2_)
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 draw_keypoints(ax, kpts_, scale_factor=1.0, offset=(0.0, 0.0), rotation=0.0,
ell=True, pts=False, rect=False, eig=False, ori=False,
sifts=None, siftkw={}, H=None, **kwargs):
"""
draws keypoints extracted by pyhesaff onto a matplotlib axis
FIXME: There is probably a matplotlib bug here. If you specify two different
alphas in a collection, whatever the last alpha was gets applied to
everything
Args:
ax (mpl.Axes):
kpts (ndarray): keypoints [[x, y, a, c, d, theta], ...]
scale_factor (float):
offset (tuple):
rotation (float):
ell (bool):
pts (bool):
rect (bool):
eig (bool):
ori (bool):
sifts (None):
References:
http://stackoverflow.com/questions/28401788/using-homogeneous-transforms-non-affine-with-matplotlib-patches
CommandLine:
python -m plottool.mpl_keypoint --test-draw_keypoints --show
Example:
>>> # ENABLE_DOCTEST
>>> from plottool.mpl_keypoint import * # NOQA
>>> from plottool.mpl_keypoint import _draw_patches, _draw_pts # NOQA
>>> import plottool as pt
>>> import vtool as vt
>>> imgBGR = vt.get_star_patch(jitter=True)
>>> H = np.array([[1, 0, 0], [.5, 2, 0], [0, 0, 1]])
>>> H = None
>>> TAU = 2 * np.pi
>>> kpts_ = vt.make_test_image_keypoints(imgBGR, scale=.5, skew=2, theta=TAU / 8.0)
>>> scale_factor=1.0
>>> offset=(0.0, 0.0)
>>> rotation=0.0
>>> ell=True
>>> pts=True
>>> rect=True
>>> eig=True
>>> ori=True
>>> # make random sifts
>>> sifts = mpl_sift.testdata_sifts()
>>> siftkw = {}
>>> kwargs = dict(ori_color=[0, 1, 0], rect_color=[0, 0, 1], eig_color=[1, 1, 0], pts_size=.1)
>>> w, h = imgBGR.shape[0:2][::-1]
>>> imgBGR_ = imgBGR if H is None else vt.warpAffine(imgBGR, H, (w * 2, h * 2))
>>> fig, ax = pt.imshow(imgBGR_ * 255)
>>> draw_keypoints(ax, kpts_, scale_factor, offset, rotation, ell, pts,
... rect, eig, ori, sifts, siftkw, H=H, **kwargs)
>>> pt.iup()
>>> pt.show_if_requested()
"""
if kpts_.shape[1] == 2:
# pad out structure if only xy given
kpts = np.zeros((len(kpts_), 6))
kpts[:, 0:2] = kpts_
kpts[:, 2] = 1
kpts[:, 4] = 1
kpts_ = kpts
if scale_factor is None:
scale_factor = 1.0
#print('[mpl_keypoint.draw_keypoints] kwargs = ' + ut.dict_str(kwargs))
# ellipse and point properties
pts_size = kwargs.get('pts_size', 2)
pts_alpha = kwargs.get('pts_alpha', 1.0)
ell_alpha = kwargs.get('ell_alpha', 1.0)
ell_linewidth = kwargs.get('ell_linewidth', 2)
ell_color = kwargs.get('ell_color', None)
if ell_color is None:
ell_color = [1, 0, 0]
# colors
pts_color = kwargs.get('pts_color', ell_color)
rect_color = kwargs.get('rect_color', ell_color)
eig_color = kwargs.get('eig_color', ell_color)
ori_color = kwargs.get('ori_color', ell_color)
# linewidths
eig_linewidth = kwargs.get('eig_linewidth', ell_linewidth)
rect_linewidth = kwargs.get('rect_linewidth', ell_linewidth)
ori_linewidth = kwargs.get('ori_linewidth', ell_linewidth)
# Offset keypoints
assert len(kpts_) > 0, 'cannot draw no keypoints1'
kpts = ktool.offset_kpts(kpts_, offset, scale_factor)
assert len(kpts) > 0, 'cannot draw no keypoints2'
# Build list of keypoint shape transforms from unit circles to ellipes
invVR_aff2Ds = get_invVR_aff2Ds(kpts, H=H)
try:
if sifts is not None:
# SIFT descriptors
pass_props(kwargs, siftkw, 'bin_color', 'arm1_color', 'arm2_color',
'arm1_lw', 'arm2_lw', 'arm_alpha', 'arm_alpha', 'multicolored_arms')
mpl_sift.draw_sifts(ax, sifts, invVR_aff2Ds, **siftkw)
if rect:
# Bounding Rectangles
rect_patches = rectangle_actors(invVR_aff2Ds)
_draw_patches(ax, rect_patches, rect_color, ell_alpha, rect_linewidth)
if ell:
# Keypoint shape
ell_patches = ellipse_actors(invVR_aff2Ds)
_draw_patches(ax, ell_patches, ell_color, ell_alpha, ell_linewidth)
if eig:
# Shape eigenvectors
eig_patches = eigenvector_actors(invVR_aff2Ds)
_draw_patches(ax, eig_patches, eig_color, ell_alpha, eig_linewidth)
if ori:
# Keypoint orientation
ori_patches = orientation_actors(kpts, H=H)
_draw_patches(ax, ori_patches, ori_color, ell_alpha, ori_linewidth, ori_color)
if pts:
# Keypoint locations
_xs, _ys = ktool.get_xys(kpts)
if H is not None:
# adjust for homogrpahy
import vtool as vt
_xs, _ys = vt.transform_points_with_homography(H, np.vstack((_xs, _ys)))
pts_patches = _draw_pts(ax, _xs, _ys, pts_size, pts_color, pts_alpha)
if pts_patches is not None:
_draw_patches(ax, pts_patches, 'none', pts_alpha, pts_size, pts_color)
except ValueError as ex:
ut.printex(ex, '\n[mplkp] !!! ERROR')
#print('_oris.shape = %r' % (_oris.shape,))
#print('_xs.shape = %r' % (_xs.shape,))
#print('_iv11s.shape = %r' % (_iv11s.shape,))
raise