def main():
model_folder = sys.argv[1]
with open(model_folder + '/pose0.gp') as f:
gpmodel = pickle.load(f)
mosaic = TagMosaic(0.0254)
for imfile in sys.argv[2:]:
im = imread(imfile)
im = rgb2gray(im)
detections = [ TagDetectionEx(d) for d in get_tag_detections(im) ]
# Rectify tag detections and store in `c1` attribute
det_i = np.array([ d.c0 for d in detections ])
undist = gpmodel.predict(det_i)
for d, r in zip(detections, undist):
d.c1 = np.add(d.c0, r)
def line_fit_sqerr(points):
""" smallest eigen value of covariance """
cov = np.cov(points)
return np.linalg.eig(cov)[0].min()
# Row-wise average residual
tag_row = lambda d: mosaic.get_row(d.id)
detections.sort(key=tag_row)
sq_errs = []
for _, group in groupby(detections, key=tag_row):
rectified = np.array([ d.c1 for d in group ])
if len(rectified) < 3: continue
sq_errs.append( line_fit_sqerr(rectified.T) )
sq_errs = np.array(sq_errs)
row_rmse, row_rmaxse = np.sqrt(sq_errs.mean()), np.sqrt(sq_errs.max())
# Col-wise average residual
tag_col = lambda d: mosaic.get_col(d.id)
detections.sort(key=tag_col)
sq_errs = []
for _, group in groupby(detections, key=tag_col):
rectified = np.array([ d.c1 for d in group ])
if len(rectified) < 3: continue
sq_errs.append( line_fit_sqerr(rectified.T) )
sq_errs = np.array(sq_errs)
col_rmse, col_rmaxse = np.sqrt(sq_errs.mean()), np.sqrt(sq_errs.max())
print '[ %d, %.2f, %.2f ],' % (
int(imfile.split('/')[-2]),
np.sqrt(row_rmse**2 + col_rmse**2),
max(row_rmaxse, col_rmaxse))
def main():
model_folder = sys.argv[1]
with open(model_folder + '/classic.poly') as f:
model = pickle.load(f)
mosaic = TagMosaic(0.0254)
for imfile in sys.argv[2:]:
im = imread(imfile)
im = rgb2gray(im)
detections = [ TagDetectionEx(d) for d in get_tag_detections(im) ]
# Rectify tag detections and store in `c1` attribute
det_i = np.array([ d.c0 for d in detections ])
undist = np.array([ model.undistort(p) for p in det_i ])
for d, r in zip(detections, undist):
d.c1 = np.add(d.c0, r)
def line_fit_sqerr(points):
""" smallest eigen value of covariance """
cov = np.cov(points)
return np.linalg.eig(cov)[0].min()
# Row-wise average residual
tag_row = lambda d: mosaic.get_row(d.id)
detections.sort(key=tag_row)
sq_errs = []
for _, group in groupby(detections, key=tag_row):
rectified = np.array([ d.c1 for d in group ])
if len(rectified) < 3: continue
sq_errs.append( line_fit_sqerr(rectified.T) )
sq_errs = np.array(sq_errs)
row_rmse, row_rmaxse = np.sqrt(sq_errs.mean()), np.sqrt(sq_errs.max())
# Col-wise average residual
tag_col = lambda d: mosaic.get_col(d.id)
detections.sort(key=tag_col)
sq_errs = []
for _, group in groupby(detections, key=tag_col):
rectified = np.array([ d.c1 for d in group ])
if len(rectified) < 3: continue
sq_errs.append( line_fit_sqerr(rectified.T) )
sq_errs = np.array(sq_errs)
col_rmse, col_rmaxse = np.sqrt(sq_errs.mean()), np.sqrt(sq_errs.max())
print '[ %s, %.2f, %.2f ],' % (
imfile.split('/')[-2],
np.sqrt(row_rmse**2 + col_rmse**2),
max(row_rmaxse, col_rmaxse))
def main():
model_folder = sys.argv[1]
test_image = sys.argv[2]
zoom_levels = [
float(f.split('/')[-2]) for f in iglob(model_folder + '/*/')
]
zoom_levels = np.array(sorted(zoom_levels))
test_zoom = float(test_image.split('/')[-2])
test_zoom_pos = np.searchsorted(zoom_levels, test_zoom)
zoom0 = zoom_levels[test_zoom_pos - 1]
zoom1 = zoom_levels[test_zoom_pos]
with open('%s/%03d/classic.poly' % (model_folder, zoom0)) as f:
model0 = pickle.load(f)
with open('%s/%03d/classic.poly' % (model_folder, zoom1)) as f:
model1 = pickle.load(f)
def interpolate_predict(query_points):
predict0 = np.array([model0.undistort(q) for q in query_points])
predict1 = np.array([model1.undistort(q) for q in query_points])
t = (zoom1 - test_zoom) / (zoom1 - zoom0)
return t * predict0 + (1. - t) * predict1
im = imread(test_image)
im = rgb2gray(im)
detections = [TagDetectionEx(d) for d in get_tag_detections(im)]
# Rectify tag detections and store in `c1` attribute
det_i = np.array([d.c0 for d in detections])
undist = interpolate_predict(det_i)
for d, r in zip(detections, undist):
d.c1 = np.add(d.c0, r)
def line_fit_sqerr(points):
""" smallest eigen value of covariance """
cov = np.cov(points)
return np.linalg.eig(cov)[0].min()
mosaic = TagMosaic(0.0254)
# Row-wise average residual
tag_row = lambda d: mosaic.get_row(d.id)
detections.sort(key=tag_row)
sq_errs = []
for _, group in groupby(detections, key=tag_row):
rectified = np.array([d.c1 for d in group])
if len(rectified) < 3: continue
sq_errs.append(line_fit_sqerr(rectified.T))
sq_errs = np.array(sq_errs)
row_rmse, row_rmaxse = np.sqrt(sq_errs.mean()), np.sqrt(sq_errs.max())
# Col-wise average residual
tag_col = lambda d: mosaic.get_col(d.id)
detections.sort(key=tag_col)
sq_errs = []
for _, group in groupby(detections, key=tag_col):
rectified = np.array([d.c1 for d in group])
if len(rectified) < 3: continue
sq_errs.append(line_fit_sqerr(rectified.T))
sq_errs = np.array(sq_errs)
col_rmse, col_rmaxse = np.sqrt(sq_errs.mean()), np.sqrt(sq_errs.max())
print '( %d, %.2f, %.2f ),' % (int(
test_image.split('/')[-2]), np.sqrt(row_rmse**2 + col_rmse**2),
max(row_rmaxse, col_rmaxse))
def main():
model_folder = sys.argv[1]
test_image = sys.argv[2]
zoom_levels = [ float(f.split('/')[-2]) for f in iglob(model_folder + '/*/') ]
zoom_levels = np.array(sorted(zoom_levels))
test_zoom = float(test_image.split('/')[-2])
test_zoom_pos = np.searchsorted(zoom_levels, test_zoom)
zoom0 = zoom_levels[test_zoom_pos-1]
zoom1 = zoom_levels[test_zoom_pos]
with open('%s/%03d/pose0.gp' % (model_folder,zoom0)) as f:
gpmodel0 = pickle.load(f)
with open('%s/%03d/pose0.gp' % (model_folder,zoom1)) as f:
gpmodel1 = pickle.load(f)
def interpolate_predict(query_points):
predict0 = gpmodel0.predict(query_points)
predict1 = gpmodel1.predict(query_points)
t = (zoom1-test_zoom) / (zoom1-zoom0)
return t*predict0 + (1.-t)*predict1
im = imread(test_image)
im = rgb2gray(im)
detections = [ TagDetectionEx(d) for d in get_tag_detections(im) ]
# Rectify tag detections and store in `c1` attribute
det_i = np.array([ d.c0 for d in detections ])
undist = interpolate_predict(det_i)
for d, r in zip(detections, undist):
d.c1 = np.add(d.c0, r)
def line_fit_sqerr(points):
""" smallest eigen value of covariance """
cov = np.cov(points)
return np.linalg.eig(cov)[0].min()
mosaic = TagMosaic(0.0254)
# Row-wise average residual
tag_row = lambda d: mosaic.get_row(d.id)
detections.sort(key=tag_row)
sq_errs = []
for _, group in groupby(detections, key=tag_row):
rectified = np.array([ d.c1 for d in group ])
if len(rectified) < 3: continue
sq_errs.append( line_fit_sqerr(rectified.T) )
sq_errs = np.array(sq_errs)
row_rmse, row_rmaxse = np.sqrt(sq_errs.mean()), np.sqrt(sq_errs.max())
# Col-wise average residual
tag_col = lambda d: mosaic.get_col(d.id)
detections.sort(key=tag_col)
sq_errs = []
for _, group in groupby(detections, key=tag_col):
rectified = np.array([ d.c1 for d in group ])
if len(rectified) < 3: continue
sq_errs.append( line_fit_sqerr(rectified.T) )
sq_errs = np.array(sq_errs)
col_rmse, col_rmaxse = np.sqrt(sq_errs.mean()), np.sqrt(sq_errs.max())
print '[ %d, %.2f, %.2f ],' % (
int(test_image.split('/')[-2]),
np.sqrt(row_rmse**2 + col_rmse**2),
max(row_rmaxse, col_rmaxse))
def main():
matplotlib.rcParams.update({'font.size': 8})
model_folder = sys.argv[1]
with open(model_folder + '/pose0.gp') as f:
gpmodel = pickle.load(f)
mosaic = TagMosaic(0.0254)
for num, imfile in enumerate(sys.argv[2:14]):
im = imread(imfile)
im = rgb2gray(im)
detections = [ TagDetectionEx(d) for d in get_tag_detections(im) ]
print imfile
# Rectify tag detections and store in `c1` attribute
det_i = np.array([ d.c0 for d in detections ])
undist = gpmodel.predict(det_i)
rectified = det_i + undist
for d, r in zip(detections, undist):
d.c1 = np.add(d.c0, r)
plt.subplot(3, 4, num+1)
plt.axis('off')
plt.plot(det_i[:,0], det_i[:,1], 'o', markersize=3, markeredgecolor='b', markerfacecolor='c')
plt.plot(rectified[:,0], rectified[:,1], 'ko', markersize=3)
def line_fit_sqerr(points):
""" smallest eigen value of covariance """
cov = np.cov(points)
return np.linalg.eig(cov)[0].min()
# Plot rows
tag_row = lambda d: mosaic.get_row(d.id)
detections.sort(key=tag_row)
sq_errs = []
colors = cycle(['#922428', '#CC2529'])
for _, group in groupby(detections, key=tag_row):
rectified = np.array([d.c1 for d in group])
plt.plot(rectified[:,0], rectified[:,1], '-', color=colors.next())
if len(rectified) < 3: continue
sq_errs.append( line_fit_sqerr(rectified.T) )
sq_errs = np.array(sq_errs)
row_rmse = np.sqrt(sq_errs.max())
# Plot cols
tag_col = lambda d: mosaic.get_col(d.id)
detections.sort(key=tag_col)
sq_errs = []
colors = cycle(['#6B4C9A', '#396AB1'])
for _, group in groupby(detections, key=tag_col):
rectified = np.array([d.c1 for d in group])
plt.plot(rectified[:,0], rectified[:,1], '-', color=colors.next())
if len(rectified) < 3: continue
sq_errs.append( line_fit_sqerr(rectified.T) )
sq_errs = np.array(sq_errs)
col_rmse = np.sqrt(sq_errs.max())
plt.title( '%s (%.1f, %.1f)' % (imfile.split('/')[-1], row_rmse, col_rmse) )
plt.show()
