def test_grabcut_on_aid(aid):
chip_fpath = ibs.get_annot_chip_fpath(aid)
probchip_fpath = ibs.get_annot_probchip_fpath(aid)
chip_img = cv2.imread(chip_fpath)
probchip_img = cv2.imread(probchip_fpath, flags=cv2.IMREAD_GRAYSCALE)
label_values = [cv2.GC_BGD, cv2.GC_PR_BGD, cv2.GC_PR_FGD, cv2.GC_FGD]
def probchip_to_grabcut_labels(probchip_img, w, h):
scaled_probchip = cv2.resize(probchip_img, dsize=(w, h))
mask = ((len(label_values) - 1) * (scaled_probchip / 255)).astype(np.uint8)
# Except for one center pixel
#mask[mask.shape[0] // 2, mask.shape[1] // 2] = 3
label_mask = mask.copy()
for index, value in enumerate(label_values):
label_mask[mask == index] = value
# No certainty
label_mask[label_mask == cv2.GC_FGD] = cv2.GC_PR_FGD
label_mask[label_mask == cv2.GC_BGD] = cv2.GC_PR_BGD
return label_mask
def grabcut_labels_to_probchip(label_mask):
image_mask = label_mask.copy()
label_colors = np.linspace(0, 255, len(label_values)).astype(np.uint8)
for value, color in zip(label_values, label_colors):
image_mask[label_mask == value] = (color)
return image_mask
def grabcut_from_probchip(chip_img, label_mask):
rect = (0, 0, w, h)
bgd_model = np.zeros((1, 13 * 5), np.float64)
fgd_model = np.zeros((1, 13 * 5), np.float64)
num_iters = 5
mode = cv2.GC_INIT_WITH_MASK
# label_mask is an outvar
label_mask_ = label_mask.copy()
print(label_values)
print(np.unique(label_mask_))
with ut.Timer('grabcut'):
cv2.grabCut(chip_img, label_mask_, rect, bgd_model, fgd_model, num_iters, mode=mode)
#is_foreground = (label_mask == cv2.GC_FGD) + (label_mask == cv2.GC_PR_FGD)
#is_foreground = (label_mask_ == cv2.GC_FGD) # + (label_mask == cv2.GC_PR_FGD)
return label_mask_
(h, w) = chip_img.shape[0:2]
label_mask = probchip_to_grabcut_labels(probchip_img, w, h)
label_mask_ = grabcut_from_probchip(chip_img, label_mask)
float_mask = grabcut_labels_to_probchip(label_mask_) / 255.0
segmented_chip = chip_img * float_mask[:, :, None]
next_pnum = df2.make_pnum_nextgen(2, 3)
df2.imshow(chip_img, fnum=1, pnum=next_pnum())
df2.imshow(probchip_img, fnum=1, pnum=next_pnum())
df2.imshow(grabcut_labels_to_probchip(label_mask), fnum=1, pnum=next_pnum())
df2.imshow(segmented_chip, fnum=1, pnum=next_pnum())
df2.imshow(255 * (float_mask), fnum=1, pnum=next_pnum())
df2.imshow(chip_img * (float_mask > .6)[:, :, None], fnum=1, pnum=next_pnum())
df2.present()
def present():
# In its own function for reloadableness
from plottool import draw_func2 as df2
return df2.present(max_rows=4, row_first=False)
def present():
# In its own function for reloadableness
from plottool import draw_func2 as df2
return df2.present(max_rows=4, row_first=False)
for bbox in bboxes:
gid_list2.append(gid)
bbox_list2.append(bbox)
# not using confidence nor img_conf here
if SPECIAL:
from plottool import viz_image2, fig_presenter
#from plottool import draw_func2 as df2
for gid in gid_list:
isthisgid = [gid == gid2 for gid2 in gid_list2]
bbox_list = utool.filter_items(bbox_list2, isthisgid)
img = ibs.get_images(gid)
fig = viz_image2.show_image(img, bbox_list=bbox_list)
fig_presenter.present()
#fig_presenter.all_figures_bring_to_front()
#ibs.detect_random_forest(gid_list, 'zebra_grevys')
return locals()
if __name__ == '__main__':
multiprocessing.freeze_support() # For windows
main_locals = ibeis.main(defaultdb='testdb1', gui=False)
ibs = main_locals['ibs'] # IBEIS Control
test_locals = utool.run_test(TEST_DETECT, ibs)
execstr = utool.execstr_dict(test_locals, 'test_locals')
exec(execstr)
if SPECIAL:
from plottool import df2
df2.present()
raw_input()
def test_grabcut_on_aid(aid):
chip_fpath = ibs.get_annot_chip_fpath(aid)
probchip_fpath = ibs.get_annot_probchip_fpath(aid)
chip_img = vt.imread(chip_fpath)
probchip_img = vt.imread(probchip_fpath, grayscale=True)
label_values = [cv2.GC_BGD, cv2.GC_PR_BGD, cv2.GC_PR_FGD, cv2.GC_FGD]
def probchip_to_grabcut_labels(probchip_img, w, h):
scaled_probchip = cv2.resize(probchip_img, dsize=(w, h))
mask = ((len(label_values) - 1) * (scaled_probchip / 255)).astype(
np.uint8)
# Except for one center pixel
#mask[mask.shape[0] // 2, mask.shape[1] // 2] = 3
label_mask = mask.copy()
for index, value in enumerate(label_values):
label_mask[mask == index] = value
# No certainty
label_mask[label_mask == cv2.GC_FGD] = cv2.GC_PR_FGD
label_mask[label_mask == cv2.GC_BGD] = cv2.GC_PR_BGD
return label_mask
def grabcut_labels_to_probchip(label_mask):
image_mask = label_mask.copy()
label_colors = np.linspace(0, 255,
len(label_values)).astype(np.uint8)
for value, color in zip(label_values, label_colors):
image_mask[label_mask == value] = (color)
return image_mask
def grabcut_from_probchip(chip_img, label_mask):
rect = (0, 0, w, h)
bgd_model = np.zeros((1, 13 * 5), np.float64)
fgd_model = np.zeros((1, 13 * 5), np.float64)
num_iters = 5
mode = cv2.GC_INIT_WITH_MASK
# label_mask is an outvar
label_mask_ = label_mask.copy()
print(label_values)
print(np.unique(label_mask_))
with ut.Timer('grabcut'):
cv2.grabCut(chip_img,
label_mask_,
rect,
bgd_model,
fgd_model,
num_iters,
mode=mode)
#is_foreground = (label_mask == cv2.GC_FGD) + (label_mask == cv2.GC_PR_FGD)
#is_foreground = (label_mask_ == cv2.GC_FGD) # + (label_mask == cv2.GC_PR_FGD)
return label_mask_
(h, w) = chip_img.shape[0:2]
label_mask = probchip_to_grabcut_labels(probchip_img, w, h)
label_mask_ = grabcut_from_probchip(chip_img, label_mask)
float_mask = grabcut_labels_to_probchip(label_mask_) / 255.0
segmented_chip = chip_img * float_mask[:, :, None]
next_pnum = df2.make_pnum_nextgen(2, 3)
df2.imshow(chip_img, fnum=1, pnum=next_pnum())
df2.imshow(probchip_img, fnum=1, pnum=next_pnum())
df2.imshow(grabcut_labels_to_probchip(label_mask),
fnum=1,
pnum=next_pnum())
df2.imshow(segmented_chip, fnum=1, pnum=next_pnum())
df2.imshow(255 * (float_mask), fnum=1, pnum=next_pnum())
df2.imshow(chip_img * (float_mask > .6)[:, :, None],
fnum=1,
pnum=next_pnum())
df2.present()
