def show_keypoints(rchip, kpts, draw_ell=True, draw_pts=False, sel_fx=None, fnum=0,
pnum=None, color=None, rect=False, **kwargs):
df2.imshow(rchip, fnum=fnum, pnum=pnum, **kwargs)
_annotate_kpts(kpts, sel_fx, draw_ell, draw_pts, color=color, rect=rect)
ax = df2.gca()
ax._hs_viewtype = 'keypoints'
ax._hs_kpts = kpts
def _draw_chip(title, chip, px, *args, **kwargs):
df2.imshow(chip,
*args,
title=title,
fnum=fnum,
pnum=(3, 4, px),
**kwargs)
def test():
(hs, qcx, cx, fm, fs, rchip1, rchip2, kpts1, kpts2) = ld2.get_sv_test_data()
# READ IMAGE AND ROI
cx2_roi = hs.tables.cx2_roi
cx2_gx = hs.tables.cx2_gx
gx2_gname = hs.tables.gx2_gname
#---
roi = cx2_roi[cx]
gx = cx2_gx[cx]
img_fname = gx2_gname[gx]
img_path = os.path.join(hs.dirs.img_dir, img_fname)
#---
#print('Showing Input')
## Original Image
#df2.imshow(mask_input, fignum=1, plotnum=122)
#df2.present()
# Crop out chips
#chip = img[ry:(ry+rh), rx:(rx+rw)]
#chip_mask = mask[ry:(ry+rh), rx:(rx+rw)]
# IMAGE AND MASK
#df2.imshow(img, plotnum=121, fignum=2, doclf=True)
#df2.imshow(mask, plotnum=122, fignum=2)
# MASKED CHIP
#df2.figure(fignum=3, doclf=True)
#df2.imshow(chip, plotnum=131, fignum=3, doclf=True)
#df2.imshow(color_mask, plotnum=132, fignum=3)
#df2.imshow(seg_chip, plotnum=133, fignum=3)
seg_chip = segment(img_path, roi)
df2.show_img(hs, cx, fignum=1, plotnum=121, title='original', doclf=True)
df2.imshow(seg_chip, fignum=1, plotnum=122, title='segmented')
df2.present()
def draw_splash(self):
print('[back] draw_splash()')
img = imread('_frontend/splash.png')
fig = df2.figure()
print(fig)
print(fig is self.win.plotWidget.figure)
df2.imshow(img)
df2.update()
def draw_splash(self):
print('[back] draw_splash()')
img = imread('_frontend/splash.png')
fig = df2.figure()
print(fig)
print(fig is self.win.plotWidget.figure)
df2.imshow(img)
df2.update()
def show_chipres(hs, res, cx, fnum=None, pnum=None, sel_fm=[], in_image=False, **kwargs):
'shows single annotated match result.'
qcx = res.qcx
#cx2_score = res.get_cx2_score()
cx2_fm = res.get_cx2_fm()
cx2_fs = res.get_cx2_fs()
#cx2_fk = res.get_cx2_fk()
#printDBG('[viz.show_chipres()] Showing matches from %s' % (vs_str))
#printDBG('[viz.show_chipres()] fnum=%r, pnum=%r' % (fnum, pnum))
# Test valid cx
printDBG('[viz] show_chipres()')
if np.isnan(cx):
nan_img = np.zeros((32, 32), dtype=np.uint8)
title = '(q%s v %r)' % (hs.cidstr(qcx), cx)
df2.imshow(nan_img, fnum=fnum, pnum=pnum, title=title)
return
fm = cx2_fm[cx]
fs = cx2_fs[cx]
#fk = cx2_fk[cx]
#vs_str = hs.vs_str(qcx, cx)
# Read query and result info (chips, names, ...)
if in_image:
# TODO: rectify build_transform2 with cc2
# clean up so its not abysmal
rchip1, rchip2 = [hs.cx2_image(_) for _ in [qcx, cx]]
kpts1, kpts2 = cx2_imgkpts(hs, [qcx, cx])
else:
rchip1, rchip2 = hs.get_chip([qcx, cx])
kpts1, kpts2 = hs.get_kpts([qcx, cx])
# Build annotation strings / colors
lbl1 = 'q' + hs.cidstr(qcx)
lbl2 = hs.cidstr(cx)
if in_image:
# HACK!
lbl1 = None
lbl2 = None
# Draws the chips and keypoint matches
kwargs_ = dict(fs=fs, lbl1=lbl1, lbl2=lbl2, fnum=fnum,
pnum=pnum, vert=hs.prefs.display_cfg.vert)
kwargs_.update(kwargs)
ax, xywh1, xywh2 = df2.show_chipmatch2(rchip1, rchip2, kpts1, kpts2, fm, **kwargs_)
x1, y1, w1, h1 = xywh1
x2, y2, w2, h2 = xywh2
if len(sel_fm) > 0:
# Draw any selected matches
_smargs = dict(rect=True, colors=df2.BLUE)
df2.draw_fmatch(xywh1, xywh2, kpts1, kpts2, sel_fm, **_smargs)
offset1 = (x1, y1)
offset2 = (x2, y2)
annotate_chipres(hs, res, cx, xywh2=xywh2, in_image=in_image, offset1=offset1, offset2=offset2, **kwargs)
return ax, xywh1, xywh2
def segment(img_path, roi):
img = cv2.imread(img_path)
# Grab cut parametrs
(w, h) = img.shape[:2]
[rx, ry, rw, rh] = roi
expand = 10
rect = (rx - expand, rw + expand, ry - expand, rh + expand)
mask_input = np.zeros((w, h), dtype=np.uint8)
bg_model = np.zeros((1, 13 * 5))
fg_model = np.zeros((1, 13 * 5))
num_iters = 5
# Make mask specify the ROI
mask_input[ry:(ry + rh), rx:(rx + rw)] = cv2.GC_PR_FGD
grabcut_mode = cv2.GC_INIT_WITH_MASK
# Do grab cut
(mask, bg_model, fg_model) = cv2.grabCut(img,
mask_input,
rect,
bg_model,
fg_model,
num_iters,
mode=grabcut_mode)
# Make the segmented chip
# Normalize mask
mask = np.array(mask, np.float64)
mask[mask == cv2.GC_BGD] = 0 #.1
mask[mask == cv2.GC_PR_BGD] = 0 #.25
mask[mask == cv2.GC_PR_FGD] = 1 #.75
mask[mask == cv2.GC_FGD] = 1
# Clean the mask
element = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10))
tmp_ = mask
tmp_ = cv2.erode(tmp_, element)
tmp_ = cv2.dilate(tmp_, element)
tmp_ = cv2.dilate(tmp_, element)
tmp_ = cv2.dilate(tmp_, element)
df2.imshow(tmp_)
df2.update()
mask = tmp_
# Reshape chip_mask to NxMx3
color_mask = np.tile(chip_mask.reshape(tuple(list(chip_mask.shape) + [1])),
(1, 1, 3))
seg_chip = np.array(chip, dtype=np.float) * color_mask
seg_chip = np.array(np.round(seg_chip), dtype=np.uint8)
return seg_chip
def show_keypoints(rchip,
kpts,
draw_ell=True,
draw_pts=False,
sel_fx=None,
fnum=0,
pnum=None,
color=None,
rect=False,
**kwargs):
df2.imshow(rchip, fnum=fnum, pnum=pnum, **kwargs)
_annotate_kpts(kpts, sel_fx, draw_ell, draw_pts, color=color, rect=rect)
ax = df2.gca()
ax._hs_viewtype = 'keypoints'
ax._hs_kpts = kpts
def segment(img_path, roi):
img = cv2.imread(img_path)
# Grab cut parametrs
(w, h) = img.shape[:2]
[rx, ry, rw, rh] = roi
expand = 10
rect = (rx-expand, rw+expand, ry-expand, rh+expand)
mask_input = np.zeros((w,h), dtype=np.uint8)
bg_model = np.zeros((1, 13 * 5))
fg_model = np.zeros((1, 13 * 5))
num_iters = 5
# Make mask specify the ROI
mask_input[ry:(ry+rh), rx:(rx+rw)] = cv2.GC_PR_FGD
grabcut_mode = cv2.GC_INIT_WITH_MASK
# Do grab cut
(mask, bg_model, fg_model) = cv2.grabCut(img, mask_input, rect,
bg_model, fg_model,
num_iters, mode=grabcut_mode)
# Make the segmented chip
# Normalize mask
mask = np.array(mask, np.float64)
mask[mask == cv2.GC_BGD] = 0#.1
mask[mask == cv2.GC_PR_BGD] = 0#.25
mask[mask == cv2.GC_PR_FGD] = 1#.75
mask[mask == cv2.GC_FGD] = 1
# Clean the mask
element = cv2.getStructuringElement(cv2.MORPH_CROSS,(10,10))
tmp_ = mask
tmp_ = cv2.erode(tmp_, element)
tmp_ = cv2.dilate(tmp_, element)
tmp_ = cv2.dilate(tmp_, element)
tmp_ = cv2.dilate(tmp_, element)
df2.imshow(tmp_); df2.update()
mask = tmp_
# Reshape chip_mask to NxMx3
color_mask = np.tile(chip_mask.reshape(tuple(list(chip_mask.shape)+[1])), (1, 1, 3))
seg_chip = np.array(chip, dtype=np.float) * color_mask
seg_chip = np.array(np.round(seg_chip), dtype=np.uint8)
return seg_chip
def show_image(hs, gx, sel_cxs=[], fnum=1, figtitle='Img', annote=True,
draw_lbls=True, **kwargs):
# Shows an image with annotations
gname = hs.tables.gx2_gname[gx]
title = 'gx=%r gname=%r' % (gx, gname)
img = hs.gx2_image(gx)
fig = df2.figure(fnum=fnum, docla=True)
fig, ax = df2.imshow(img, title=title, fnum=fnum, **kwargs)
ax._hs_viewtype = 'image'
_annotate_image(hs, ax, gx, sel_cxs, draw_lbls, annote)
df2.set_figtitle(figtitle)
def show_chip(hs,
cx=None,
allres=None,
res=None,
draw_ell=True,
draw_pts=False,
nRandKpts=None,
prefix='',
sel_fx=None,
color=None,
in_image=False,
sel_fx2=None,
**kwargs):
printDBG('[viz] show_chip()')
if allres is not None:
res = allres.qcx2_res[cx]
if res is not None:
cx = res.qcx
if in_image:
rchip = hs.cx2_image(cx)
else:
rchip = kwargs['rchip'] if 'rchip' in kwargs else hs.get_chip(cx)
# Add info to title
title_list = []
title_list += [hs.cidstr(cx)]
# FIXME
#title_list += ['gname=%r' % hs.cx2_gname(cx)]
title_list += ['name=%r' % hs.cx2_name(cx)]
#title_list += [hs.num_indexed_gt_str(cx)]
if NO_LABEL_OVERRIDE:
title_str = ''
else:
title_str = prefix + ', '.join(title_list)
fig, ax = df2.imshow(rchip, title=title_str, **kwargs)
# Add user data to axis
ax._hs_viewtype = 'chip'
ax._hs_cx = cx
if draw_ell or draw_pts:
# FIXME
if in_image:
kpts = cx2_imgkpts(hs, [cx])[0]
else:
kpts = kwargs['kpts'] if 'kpts' in kwargs else hs.get_kpts(cx)
if sel_fx2 is not None:
sel_fx2 = np.array(sel_fx2)
kpts = kpts[sel_fx2]
if res is not None:
# Draw keypoints with groundtruth information
cx2_color = kwargs.get('cx2_color', None)
_annotate_qcx_match_results(hs, res, cx, kpts, cx2_color)
else:
# Just draw boring keypoints
_annotate_kpts(kpts, sel_fx, draw_ell, draw_pts, color, nRandKpts)
def test():
(hs, qcx, cx, fm, fs, rchip1, rchip2, kpts1,
kpts2) = ld2.get_sv_test_data()
# READ IMAGE AND ROI
cx2_roi = hs.tables.cx2_roi
cx2_gx = hs.tables.cx2_gx
gx2_gname = hs.tables.gx2_gname
#---
roi = cx2_roi[cx]
gx = cx2_gx[cx]
img_fname = gx2_gname[gx]
img_path = os.path.join(hs.dirs.img_dir, img_fname)
#---
#print('Showing Input')
## Original Image
#df2.imshow(mask_input, fignum=1, plotnum=122)
#df2.present()
# Crop out chips
#chip = img[ry:(ry+rh), rx:(rx+rw)]
#chip_mask = mask[ry:(ry+rh), rx:(rx+rw)]
# IMAGE AND MASK
#df2.imshow(img, plotnum=121, fignum=2, doclf=True)
#df2.imshow(mask, plotnum=122, fignum=2)
# MASKED CHIP
#df2.figure(fignum=3, doclf=True)
#df2.imshow(chip, plotnum=131, fignum=3, doclf=True)
#df2.imshow(color_mask, plotnum=132, fignum=3)
#df2.imshow(seg_chip, plotnum=133, fignum=3)
seg_chip = segment(img_path, roi)
df2.show_img(hs, cx, fignum=1, plotnum=121, title='original', doclf=True)
df2.imshow(seg_chip, fignum=1, plotnum=122, title='segmented')
df2.present()
def plot_score_matrix(allres):
print('[viz] plotting score matrix')
score_matrix = allres.score_matrix
title = 'Score Matrix\n' + allres.title_suffix
# Find inliers
#inliers = helpers.find_std_inliers(score_matrix)
#max_inlier = score_matrix[inliers].max()
# Trunate above 255
score_img = np.copy(score_matrix)
#score_img[score_img < 0] = 0
#score_img[score_img > 255] = 255
#dim = 0
#score_img = helpers.norm_zero_one(score_img, dim=dim)
# Make colors
scores = score_img.flatten()
colors = df2.scores_to_color(scores, logscale=True)
cmap = df2.scores_to_cmap(scores, colors)
df2.figure(fnum=FIGNUM, doclf=True, title=title)
# Show score matrix
df2.imshow(score_img, fnum=FIGNUM, cmap=cmap)
# Colorbar
df2.colorbar(scores, colors)
df2.set_xlabel('database')
df2.set_ylabel('queries')
def show_image(hs,
gx,
sel_cxs=[],
fnum=1,
figtitle='Img',
annote=True,
draw_lbls=True,
**kwargs):
# Shows an image with annotations
gname = hs.tables.gx2_gname[gx]
title = 'gx=%r gname=%r' % (gx, gname)
img = hs.gx2_image(gx)
fig = df2.figure(fnum=fnum, docla=True)
fig, ax = df2.imshow(img, title=title, fnum=fnum, **kwargs)
ax._hs_viewtype = 'image'
_annotate_image(hs, ax, gx, sel_cxs, draw_lbls, annote)
df2.set_figtitle(figtitle)
def draw_keypoint_patch(rchip, kp, sift=None, warped=False, **kwargs):
#print('--------------------')
#print('[extract] Draw Patch')
if warped:
wpatch, wkp = get_warped_patch(rchip, kp)
patch = wpatch
subkp = wkp
else:
patch, subkp = get_patch(rchip, kp)
#print('[extract] kp = '+str(kp))
#print('[extract] subkp = '+str(subkp))
#print('[extract] patch.shape = %r' % (patch.shape,))
color = (0, 0, 1)
fig, ax = df2.imshow(patch, **kwargs)
df2.draw_kpts2([subkp], ell_color=color, pts=True)
if not sift is None:
df2.draw_sift(sift, [subkp])
return ax
def draw_keypoint_patch(rchip, kp, sift=None, warped=False, **kwargs):
#print('--------------------')
#print('[extract] Draw Patch')
if warped:
wpatch, wkp = get_warped_patch(rchip, kp)
patch = wpatch
subkp = wkp
else:
patch, subkp = get_patch(rchip, kp)
#print('[extract] kp = '+str(kp))
#print('[extract] subkp = '+str(subkp))
#print('[extract] patch.shape = %r' % (patch.shape,))
color = (0, 0, 1)
fig, ax = df2.imshow(patch, **kwargs)
df2.draw_kpts2([subkp], ell_color=color, pts=True)
if not sift is None:
df2.draw_sift(sift, [subkp])
return ax
def show_chip(hs, cx=None, allres=None, res=None, draw_ell=True,
draw_pts=False, nRandKpts=None, prefix='', sel_fx=None,
color=None, in_image=False, sel_fx2=None, **kwargs):
printDBG('[viz] show_chip()')
if allres is not None:
res = allres.qcx2_res[cx]
if res is not None:
cx = res.qcx
if in_image:
rchip = hs.cx2_image(cx)
else:
rchip = kwargs['rchip'] if 'rchip' in kwargs else hs.get_chip(cx)
# Add info to title
title_list = []
title_list += [hs.cidstr(cx)]
# FIXME
#title_list += ['gname=%r' % hs.cx2_gname(cx)]
title_list += ['name=%r' % hs.cx2_name(cx)]
#title_list += [hs.num_indexed_gt_str(cx)]
if NO_LABEL_OVERRIDE:
title_str = ''
else:
title_str = prefix + ', '.join(title_list)
fig, ax = df2.imshow(rchip, title=title_str, **kwargs)
# Add user data to axis
ax._hs_viewtype = 'chip'
ax._hs_cx = cx
if draw_ell or draw_pts:
# FIXME
if in_image:
kpts = cx2_imgkpts(hs, [cx])[0]
else:
kpts = kwargs['kpts'] if 'kpts' in kwargs else hs.get_kpts(cx)
if sel_fx2 is not None:
sel_fx2 = np.array(sel_fx2)
kpts = kpts[sel_fx2]
if res is not None:
# Draw keypoints with groundtruth information
cx2_color = kwargs.get('cx2_color', None)
_annotate_qcx_match_results(hs, res, cx, kpts, cx2_color)
else:
# Just draw boring keypoints
_annotate_kpts(kpts, sel_fx, draw_ell, draw_pts, color, nRandKpts)
def test(hs, qcx):
fx2_scale = sv2.keypoint_scale(hs.feats.cx2_kpts[qcx])
fx = fx2_scale.argsort()[::-1][40]
rchip = hs.get_chip(qcx)
kp = hs.feats.cx2_kpts[qcx][fx]
# Show full image and keypoint
df2.figure(fignum=9000, doclf=True)
df2.imshow(rchip, plotnum=(1,3,1))
df2.draw_kpts2([kp], ell_color=(1,0,0), pts=True)
# Show cropped image and keypoint
patch, subkp = get_patch(rchip, kp)
df2.imshow(patch, plotnum=(1,3,2))
df2.draw_kpts2([subkp], ell_color=(1,0,0), pts=True)
# Show warped image and keypoint
wpatch, wkp = get_warped_patch(rchip, kp)
df2.imshow(wpatch, plotnum=(1,3,3))
df2.draw_kpts2([wkp], ell_color=(1,0,0), pts=True)
#
df2.set_figtitle('warp test')
def show_splash(fnum=1, **kwargs):
#printDBG('[viz] show_splash()')
splash_fpath = io.splash_img_fpath()
img = io.imread(splash_fpath)
df2.imshow(img, fnum=fnum, **kwargs)
def viz_top_features(hs, res, low, high, fignum=0, draw_chips=True):
from collections import defaultdict
qcx = res.qcx
cx2_nx = hs.tables.cx2_nx
top_patches_list = get_top_scoring_patches(hs, res, low, high)
num_rows = high-low
num_cols = 4
if params.__MATCH_TYPE__ == 'vsmany':
num_cols = 6
# Initialize Figure
fig = df2.figure(fignum+1, plotnum=(num_rows, num_cols,1))
cx2_rchip = defaultdict(int)
cx2_kplist = defaultdict(list)
def draw_one_kp(patch, kp, plotx, color, desc=None):
fig, ax = df2.imshow(patch, plotnum=(num_rows, num_cols, plotx))
df2.draw_kpts2([kp], ell_color=color, pts=True)
if not desc is None and not '--nodesc' in sys.argv:
df2.draw_sift(desc, [kp])
df2.draw_border(ax, color, 1)
for tx, (patches1, patches2, patchesN, cx, feat_score) in enumerate(top_patches_list):
(kp1, subkp1, wkp1, patch1, wpatch1, cx1, desc1) = patches1
(kp2, subkp2, wkp2, patch2, wpatch2, cx2, desc2) = patches2
# draw on table
# Draw Query Keypoint
plotx = (tx*num_cols)
draw_one_kp(patch1, subkp1, plotx+1, df2.GREEN, desc1)
qnx = cx2_nx[qcx]
df2.plt.gca().set_xlabel('qcx=%r; qnx=%r' % (qcx, qnx))
draw_one_kp(wpatch1, wkp1, plotx+2, df2.GREEN, desc1)
# Draw ith < k match
draw_one_kp(patch2, subkp2, plotx+3, df2.BLUE, desc2)
nx = cx2_nx[cx]
df2.plt.gca().set_xlabel('cx=%r; nx=%r' % (cx, nx))
draw_one_kp(wpatch2, wkp2, plotx+4, df2.BLUE, desc2)
df2.plt.gca().set_xlabel('score=%r' % (feat_score))
# Draw k+1th match
if params.__MATCH_TYPE__ == 'vsmany':
(kpN, subkpN, wkpN, patchN, wpatchN, cxN, descN) = patchesN
draw_one_kp(patchN, subkpN, plotx+5, df2.ORANGE, descN)
nxN = cx2_nx[qcx]
df2.plt.gca().set_xlabel('cxN=%r; nxN=%r' % (cxN, nxN))
draw_one_kp(wpatchN, wkpN, plotx+6, df2.ORANGE, descN)
# Get other info
cx2_rchip[cx] = hs.get_chip(cx)
cx2_kplist[qcx].append(kp1)
cx2_kplist[cx].append(kp2)
# Draw annotations on
df2.figure(plotnum=(num_rows, num_cols,1), title='Query Patch')
df2.figure(plotnum=(num_rows, num_cols,2), title='Query Patch')
df2.figure(plotnum=(num_rows, num_cols,3), title='Result Patch')
df2.figure(plotnum=(num_rows, num_cols,4), title='Result Warped')
if params.__MATCH_TYPE__ == 'vsmany':
df2.figure(plotnum=(num_rows, num_cols,5), title='Normalizer Patch')
df2.figure(plotnum=(num_rows, num_cols,6), title='Normalizer Warped')
df2.set_figtitle('Top '+str(low)+' to '+str(high)+' scoring matches')
if not draw_chips:
return
#
# Draw on full images
cx2_rchip[qcx] = hs.get_chip(qcx)
cx_keys = cx2_kplist.keys()
cx_vals = map(len, cx2_kplist.values())
cx_list = [x for (y,x) in sorted(zip(cx_vals, cx_keys))][::-1]
num_chips = len(cx_list)
pltnum_fn = lambda ix: (int(np.ceil(num_chips/2)), 2, ix)
plotnum = pltnum_fn(1)
fig2 = df2.figure(fignum-1, plotnum=plotnum)
for ix, cx in enumerate(cx_list):
plotnum = pltnum_fn(ix+1)
fig2 = df2.figure(fignum-1, plotnum=plotnum)
rchip = cx2_rchip[cx]
fig, ax = df2.imshow(rchip)
title_pref = ''
color = df2.BLUE
if res.qcx == cx:
title_pref = 'q'
color = df2.GREEN
df2.draw_kpts2(cx2_kplist[cx], ell_color=color, ell_alpha=1)
df2.draw_border(ax, color, 2)
nx = cx2_nx[cx]
ax.set_title(title_pref+'cx = %r; nx=%r' % (cx, nx))
gname = hs.get_gname(cx)
ax.set_xlabel(gname)
rchip_fpath = 'tpl/extern_feat/zebra.jpg' rchip_fpath = os.path.realpath(rchip_fpath) rchip = cv2.cvtColor(cv2.imread(rchip_fpath, flags=cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB) outname = extern_feat.compute_perdoch_text_feats(rchip_fpath) #outname = compute_inria_text_feats(rchip_fpath, 'harris', 'sift') # Keep the wrong way to compare kpts0, desc = extern_feat.read_text_feat_file(outname) invE = extern_feat.expand_invET(kpts0[:,2:5].T)[0] kpts1 = extern_feat.fix_kpts_hack(kpts0[:], method=1) A1 = extern_feat.expand_acd(kpts1[:,2:5])[0] #kpts, desc = filter_kpts_scale(kpts, desc) df2.figure(1, doclf=True) df2.DARKEN = .5 #---- df2.imshow(rchip, plotnum=(2,3,1), title='0 before (inverse square root)') draw_kpts3(kpts0.copy(),0) #---- df2.imshow(rchip, plotnum=(2,3,2), title='1 just inverse') draw_kpts3(kpts1.copy(),1) #---- df2.imshow(rchip, plotnum=(2,3,3), title='2 identity') draw_kpts3(kpts1.copy(), 2) df2.update() #---- df2.imshow(rchip, plotnum=(2,3,4), title='3 identity') draw_kpts3(kpts1.copy(), 3) df2.update()
def draw_one_kp(patch, kp, plotx, color, desc=None):
fig, ax = df2.imshow(patch, plotnum=(num_rows, num_cols, plotx))
df2.draw_kpts2([kp], ell_color=color, pts=True)
if not desc is None and not '--nodesc' in sys.argv:
df2.draw_sift(desc, [kp])
df2.draw_border(ax, color, 1)
def get_kp_border(rchip, kp):
np.set_printoptions(precision=8)
df2.reset()
df2.figure(9003, docla=True, doclf=True)
def _plotpts(data, px, color=df2.BLUE, label=''):
#df2.figure(9003, docla=True, pnum=(1, 1, px))
df2.plot2(data.T[0], data.T[1], '-', '', color=color, label=label)
df2.update()
def _plotarrow(x, y, dx, dy, color=df2.BLUE, label=''):
ax = df2.gca()
arrowargs = dict(head_width=.5, length_includes_head=True, label='')
arrow = df2.FancyArrow(x, y, dx, dy, **arrowargs)
arrow.set_edgecolor(color)
arrow.set_facecolor(color)
ax.add_patch(arrow)
df2.update()
def _2x2_eig(M2x2):
(evals, evecs) = np.linalg.eig(M2x2)
l1, l2 = evals
v1, v2 = evecs
return l1, l2, v1, v2
#-----------------------
# INPUT
#-----------------------
# We are given the keypoint in invA format
(x, y, ia11, ia21, ia22), ia12 = kp, 0
# invA2x2 is a transformation from points on a unit circle to the ellipse
invA2x2 = np.array([[ia11, ia12], [ia21, ia22]])
#-----------------------
# DRAWING
#-----------------------
# Lets start off by drawing the ellipse that we are goign to work with
# Create unit circle sample
tau = 2 * np.pi
theta_list = np.linspace(0, tau, 1000)
cicrle_pts = np.array([(np.cos(t), np.sin(t)) for t in theta_list])
ellipse_pts = invA2x2.dot(cicrle_pts.T).T
_plotpts(ellipse_pts, 0, df2.BLACK, label='invA2x2.dot(unit_circle)')
l1, l2, v1, v2 = _2x2_eig(invA2x2)
dx1, dy1 = (v1 * l1)
dx2, dy2 = (v2 * l2)
_plotarrow(0, 0, dx1, dy1, color=df2.ORANGE, label='invA2x2 e1')
_plotarrow(0, 0, dx2, dy2, color=df2.RED, label='invA2x2 e2')
#-----------------------
# REPRESENTATION
#-----------------------
# A2x2 is a transformation from points on the ellipse to a unit circle
A2x2 = np.linalg.inv(invA2x2)
# Points on a matrix satisfy (x).T.dot(E2x2).dot(x) = 1
E2x2 = A2x2.T.dot(A2x2)
#Lets check our assertion: (x).T.dot(E2x2).dot(x) = 1
checks = [pt.T.dot(E2x2).dot(pt) for pt in ellipse_pts]
assert all([abs(1 - check) < 1E-11 for check in checks])
#-----------------------
# CONIC SECTIONS
#-----------------------
# All of this was from the Perdoch paper, now lets move into conic sections
# We will use the notation from wikipedia
# http://en.wikipedia.org/wiki/Conic_section
# http://en.wikipedia.org/wiki/Matrix_representation_of_conic_sections
# The matrix representation of a conic is:
((A, B, B_, C), (D, E, F)) = (E2x2.flatten(), (0, 0, 1))
assert B == B_, 'matrix should by symmetric'
# A_Q is our conic section (aka ellipse matrix)
A_Q = np.array(((A, B / 2, D / 2), (B / 2, C, E / 2), (D / 2, E / 2, F)))
assert np.linalg.det(A_Q) != 0, 'degenerate conic'
# As long as det(A_Q) is not 0 it is not degenerate and we can work with the
# minor 2x2 matrix
A_33 = np.array(((A, B / 2), (B / 2, C)))
# (det == 0)->parabola, (det < 0)->hyperbola, (det > 0)->ellipse
assert np.linalg.det(A_33) > 0, 'conic is not an ellipse'
# Centers are obtained by solving for where the gradient of the quadratic
# becomes 0. Without going through the derivation the calculation is...
# These should be 0, 0 if we are at the origin, or our original x, y
# coordinate specified by the keypoints. I'm doing the calculation just for
# shits and giggles
x_center = (B * E - (2 * C * D)) / (4 * A * C - B**2)
y_center = (D * B - (2 * A * E)) / (4 * A * C - B**2)
#=================
# DRAWING
#=================
# Now we are going to determine the major and minor axis
# of this beast. It just the center augmented by the eigenvecs
l1, l2, v1, v2 = _2x2_eig(A_33)
dx1, dy1 = 0 - (v1 / np.sqrt(l1))
dx2, dy2 = 0 - (v2 / np.sqrt(l2))
_plotarrow(0, 0, dx1, dy1, color=df2.BLUE)
_plotarrow(0, 0, dx2, dy2, color=df2.BLUE)
# The angle between the major axis and our x axis is:
x_axis = np.array([1, 0])
theta = np.arccos(x_axis.dot(evec1))
# The eccentricity is determined by:
nu = 1
numer = 2 * np.sqrt((A - C)**2 + B**2)
denom = nu * (A + C) + np.sqrt((A - C)**2 + B**2)
eccentricity = np.sqrt(numer / denom)
from scipy.special import ellipeinc
# Algebraic form of connic
#assert (a * (x ** 2)) + (b * (x * y)) + (c * (y ** 2)) + (d * x) + (e * y) + (f) == 0
#---------------------
invA = np.array([[a, 0], [c, d]])
Ashape = np.linalg.inv(np.array([[a, 0], [c, d]]))
Ashape /= np.sqrt(np.linalg.det(Ashape))
tau = 2 * np.pi
nSamples = 100
theta_list = np.linspace(0, tau, nSamples)
# Create unit circle sample
cicrle_pts = np.array([(np.cos(t), np.sin(t)) for t in theta_list])
circle_hpts = np.hstack([cicrle_pts, np.ones((len(cicrle_pts), 1))])
# Transform as if the unit cirle was the warped patch
ashape_pts = Ashape.dot(cicrle_pts.T).T
inv = np.linalg.inv
svd = np.linalg.svd
U, S_, V = svd(Ashape)
S = np.diag(S_)
pxl_list3 = invA.dot(cicrle_pts[:, 0:2].T).T
pxl_list4 = invA.dot(ashape_pts[:, 0:2].T).T
pxl_list5 = invA.T.dot(cicrle_pts[:, 0:2].T).T
pxl_list6 = invA.T.dot(ashape_pts[:, 0:2].T).T
pxl_list7 = inv(V).dot(ashape_pts[:, 0:2].T).T
pxl_list8 = inv(U).dot(ashape_pts[:, 0:2].T).T
df2.draw()
def _plot(data, px, title=''):
df2.figure(9003, docla=True, pnum=(2, 4, px))
df2.plot2(data.T[0], data.T[1], '.', title)
df2.figure(9003, doclf=True)
_plot(cicrle_pts, 1, 'unit circle')
_plot(ashape_pts, 2, 'A => circle shape')
_plot(pxl_list3, 3)
_plot(pxl_list4, 4)
_plot(pxl_list5, 5)
_plot(pxl_list6, 6)
_plot(pxl_list7, 7)
_plot(pxl_list8, 8)
df2.draw()
invA = np.array([[a, 0, x], [c, d, y], [0, 0, 1]])
pxl_list = invA.dot(circle_hpts.T).T[:, 0:2]
df2.figure(9002, doclf=True)
df2.imshow(rchip)
df2.plot2(pxl_list.T[0], pxl_list.T[1], '.')
df2.draw()
vals = [cv2.getRectSubPix(rchip, (1, 1), tuple(pxl)) for pxl in pxl_list]
return vals
def show_chipres(hs,
res,
cx,
fnum=None,
pnum=None,
sel_fm=[],
in_image=False,
**kwargs):
'shows single annotated match result.'
qcx = res.qcx
#cx2_score = res.get_cx2_score()
cx2_fm = res.get_cx2_fm()
cx2_fs = res.get_cx2_fs()
#cx2_fk = res.get_cx2_fk()
#printDBG('[viz.show_chipres()] Showing matches from %s' % (vs_str))
#printDBG('[viz.show_chipres()] fnum=%r, pnum=%r' % (fnum, pnum))
# Test valid cx
printDBG('[viz] show_chipres()')
if np.isnan(cx):
nan_img = np.zeros((32, 32), dtype=np.uint8)
title = '(q%s v %r)' % (hs.cidstr(qcx), cx)
df2.imshow(nan_img, fnum=fnum, pnum=pnum, title=title)
return
fm = cx2_fm[cx]
fs = cx2_fs[cx]
#fk = cx2_fk[cx]
#vs_str = hs.vs_str(qcx, cx)
# Read query and result info (chips, names, ...)
if in_image:
# TODO: rectify build_transform2 with cc2
# clean up so its not abysmal
rchip1, rchip2 = [hs.cx2_image(_) for _ in [qcx, cx]]
kpts1, kpts2 = cx2_imgkpts(hs, [qcx, cx])
else:
rchip1, rchip2 = hs.get_chip([qcx, cx])
kpts1, kpts2 = hs.get_kpts([qcx, cx])
# Build annotation strings / colors
lbl1 = 'q' + hs.cidstr(qcx)
lbl2 = hs.cidstr(cx)
if in_image:
# HACK!
lbl1 = None
lbl2 = None
# Draws the chips and keypoint matches
kwargs_ = dict(fs=fs,
lbl1=lbl1,
lbl2=lbl2,
fnum=fnum,
pnum=pnum,
vert=hs.prefs.display_cfg.vert)
kwargs_.update(kwargs)
ax, xywh1, xywh2 = df2.show_chipmatch2(rchip1, rchip2, kpts1, kpts2, fm,
**kwargs_)
x1, y1, w1, h1 = xywh1
x2, y2, w2, h2 = xywh2
if len(sel_fm) > 0:
# Draw any selected matches
_smargs = dict(rect=True, colors=df2.BLUE)
df2.draw_fmatch(xywh1, xywh2, kpts1, kpts2, sel_fm, **_smargs)
offset1 = (x1, y1)
offset2 = (x2, y2)
annotate_chipres(hs,
res,
cx,
xywh2=xywh2,
in_image=in_image,
offset1=offset1,
offset2=offset2,
**kwargs)
return ax, xywh1, xywh2
def _draw_chip(title, chip, px, *args, **kwargs):
df2.imshow(chip, *args, title=title, fnum=fnum, pnum=(3, 4, px), **kwargs)
def show_splash(fnum=1, **kwargs):
#printDBG('[viz] show_splash()')
splash_fpath = io.splash_img_fpath()
img = io.imread(splash_fpath)
df2.imshow(img, fnum=fnum, **kwargs)
def get_kp_border(rchip, kp):
np.set_printoptions(precision=8)
df2.reset()
df2.figure(9003, docla=True, doclf=True)
def _plotpts(data, px, color=df2.BLUE, label=''):
#df2.figure(9003, docla=True, pnum=(1, 1, px))
df2.plot2(data.T[0], data.T[1], '-', '', color=color, label=label)
df2.update()
def _plotarrow(x, y, dx, dy, color=df2.BLUE, label=''):
ax = df2.gca()
arrowargs = dict(head_width=.5, length_includes_head=True, label='')
arrow = df2.FancyArrow(x, y, dx, dy, **arrowargs)
arrow.set_edgecolor(color)
arrow.set_facecolor(color)
ax.add_patch(arrow)
df2.update()
def _2x2_eig(M2x2):
(evals, evecs) = np.linalg.eig(M2x2)
l1, l2 = evals
v1, v2 = evecs
return l1, l2, v1, v2
#-----------------------
# INPUT
#-----------------------
# We are given the keypoint in invA format
(x, y, ia11, ia21, ia22), ia12 = kp, 0
# invA2x2 is a transformation from points on a unit circle to the ellipse
invA2x2 = np.array([[ia11, ia12],
[ia21, ia22]])
#-----------------------
# DRAWING
#-----------------------
# Lets start off by drawing the ellipse that we are goign to work with
# Create unit circle sample
tau = 2 * np.pi
theta_list = np.linspace(0, tau, 1000)
cicrle_pts = np.array([(np.cos(t), np.sin(t)) for t in theta_list])
ellipse_pts = invA2x2.dot(cicrle_pts.T).T
_plotpts(ellipse_pts, 0, df2.BLACK, label='invA2x2.dot(unit_circle)')
l1, l2, v1, v2 = _2x2_eig(invA2x2)
dx1, dy1 = (v1 * l1)
dx2, dy2 = (v2 * l2)
_plotarrow(0, 0, dx1, dy1, color=df2.ORANGE, label='invA2x2 e1')
_plotarrow(0, 0, dx2, dy2, color=df2.RED, label='invA2x2 e2')
#-----------------------
# REPRESENTATION
#-----------------------
# A2x2 is a transformation from points on the ellipse to a unit circle
A2x2 = np.linalg.inv(invA2x2)
# Points on a matrix satisfy (x).T.dot(E2x2).dot(x) = 1
E2x2 = A2x2.T.dot(A2x2)
#Lets check our assertion: (x).T.dot(E2x2).dot(x) = 1
checks = [pt.T.dot(E2x2).dot(pt) for pt in ellipse_pts]
assert all([abs(1 - check) < 1E-11 for check in checks])
#-----------------------
# CONIC SECTIONS
#-----------------------
# All of this was from the Perdoch paper, now lets move into conic sections
# We will use the notation from wikipedia
# http://en.wikipedia.org/wiki/Conic_section
# http://en.wikipedia.org/wiki/Matrix_representation_of_conic_sections
# The matrix representation of a conic is:
((A, B, B_, C), (D, E, F)) = (E2x2.flatten(), (0, 0, 1))
assert B == B_, 'matrix should by symmetric'
# A_Q is our conic section (aka ellipse matrix)
A_Q = np.array((( A, B / 2, D / 2),
(B / 2, C, E / 2),
(D / 2, E / 2, F)))
assert np.linalg.det(A_Q) != 0, 'degenerate conic'
# As long as det(A_Q) is not 0 it is not degenerate and we can work with the
# minor 2x2 matrix
A_33 = np.array((( A, B / 2),
(B / 2, C)))
# (det == 0)->parabola, (det < 0)->hyperbola, (det > 0)->ellipse
assert np.linalg.det(A_33) > 0, 'conic is not an ellipse'
# Centers are obtained by solving for where the gradient of the quadratic
# becomes 0. Without going through the derivation the calculation is...
# These should be 0, 0 if we are at the origin, or our original x, y
# coordinate specified by the keypoints. I'm doing the calculation just for
# shits and giggles
x_center = (B * E - (2 * C * D)) / (4 * A * C - B ** 2)
y_center = (D * B - (2 * A * E)) / (4 * A * C - B ** 2)
#=================
# DRAWING
#=================
# Now we are going to determine the major and minor axis
# of this beast. It just the center augmented by the eigenvecs
l1, l2, v1, v2 = _2x2_eig(A_33)
dx1, dy1 = 0 - (v1 / np.sqrt(l1))
dx2, dy2 = 0 - (v2 / np.sqrt(l2))
_plotarrow(0, 0, dx1, dy1, color=df2.BLUE)
_plotarrow(0, 0, dx2, dy2, color=df2.BLUE)
# The angle between the major axis and our x axis is:
x_axis = np.array([1, 0])
theta = np.arccos(x_axis.dot(evec1))
# The eccentricity is determined by:
nu = 1
numer = 2 * np.sqrt((A - C) ** 2 + B ** 2)
denom = nu * (A + C) + np.sqrt((A - C) ** 2 + B ** 2)
eccentricity = np.sqrt(numer / denom)
from scipy.special import ellipeinc
# Algebraic form of connic
#assert (a * (x ** 2)) + (b * (x * y)) + (c * (y ** 2)) + (d * x) + (e * y) + (f) == 0
#---------------------
invA = np.array([[a, 0],
[c, d]])
Ashape = np.linalg.inv(np.array([[a, 0],
[c, d]]))
Ashape /= np.sqrt(np.linalg.det(Ashape))
tau = 2 * np.pi
nSamples = 100
theta_list = np.linspace(0, tau, nSamples)
# Create unit circle sample
cicrle_pts = np.array([(np.cos(t), np.sin(t)) for t in theta_list])
circle_hpts = np.hstack([cicrle_pts, np.ones((len(cicrle_pts), 1))])
# Transform as if the unit cirle was the warped patch
ashape_pts = Ashape.dot(cicrle_pts.T).T
inv = np.linalg.inv
svd = np.linalg.svd
U, S_, V = svd(Ashape)
S = np.diag(S_)
pxl_list3 = invA.dot(cicrle_pts[:, 0:2].T).T
pxl_list4 = invA.dot(ashape_pts[:, 0:2].T).T
pxl_list5 = invA.T.dot(cicrle_pts[:, 0:2].T).T
pxl_list6 = invA.T.dot(ashape_pts[:, 0:2].T).T
pxl_list7 = inv(V).dot(ashape_pts[:, 0:2].T).T
pxl_list8 = inv(U).dot(ashape_pts[:, 0:2].T).T
df2.draw()
def _plot(data, px, title=''):
df2.figure(9003, docla=True, pnum=(2, 4, px))
df2.plot2(data.T[0], data.T[1], '.', title)
df2.figure(9003, doclf=True)
_plot(cicrle_pts, 1, 'unit circle')
_plot(ashape_pts, 2, 'A => circle shape')
_plot(pxl_list3, 3)
_plot(pxl_list4, 4)
_plot(pxl_list5, 5)
_plot(pxl_list6, 6)
_plot(pxl_list7, 7)
_plot(pxl_list8, 8)
df2.draw()
invA = np.array([[a, 0, x],
[c, d, y],
[0, 0, 1]])
pxl_list = invA.dot(circle_hpts.T).T[:, 0:2]
df2.figure(9002, doclf=True)
df2.imshow(rchip)
df2.plot2(pxl_list.T[0], pxl_list.T[1], '.')
df2.draw()
vals = [cv2.getRectSubPix(rchip, (1, 1), tuple(pxl)) for pxl in pxl_list]
return vals
