def calibrate(args):
"""
%prog calibrate calibrate.JPG boxsize
Calibrate pixel-inch ratio and color adjustment.
- `calibrate.JPG` is the photo containig a colorchecker
- `boxsize` is the measured size for the boxes on printed colorchecker, in
squared centimeter (cm2) units
"""
xargs = args[2:]
p = OptionParser(calibrate.__doc__)
opts, args, iopts = add_seeds_options(p, args)
if len(args) != 2:
sys.exit(not p.print_help())
imagefile, boxsize = args
boxsize = float(boxsize)
# Read in color checker
colorcheckerfile = op.join(datadir, "colorchecker.txt")
colorchecker = []
expected = 0
for row in open(colorcheckerfile):
boxes = row.split()
colorchecker.append(boxes)
expected += len(boxes)
folder = op.split(imagefile)[0]
objects = seeds([imagefile, "--outdir={0}".format(folder)] + xargs)
nseeds = len(objects)
logging.debug("Found {0} boxes (expected={1})".format(nseeds, expected))
assert expected - 4 <= nseeds <= expected + 4, \
"Number of boxes drastically different from {0}".format(expected)
# Calculate pixel-cm ratio
boxes = [t.area for t in objects]
reject = reject_outliers(boxes)
retained_boxes = [b for r, b in zip(reject, boxes) if not r]
mbox = np.median(retained_boxes) # in pixels
pixel_cm_ratio = (mbox / boxsize) ** .5
logging.debug("Median box size: {0} pixels. Measured box size: {1} cm2".\
format(mbox, boxsize))
logging.debug("Pixel-cm ratio: {0}".format(pixel_cm_ratio))
xs = [t.x for t in objects]
ys = [t.y for t in objects]
idx_xs = get_kmeans(xs, 6)
idx_ys = get_kmeans(ys, 4)
for xi, yi, s in zip(idx_xs, idx_ys, objects):
s.rank = (yi, xi)
objects.sort(key=lambda x: x.rank)
colormap = []
for s in objects:
x, y = s.rank
observed, expected = s.rgb, rgb_to_triplet(colorchecker[x][y])
colormap.append((np.array(observed), np.array(expected)))
# Color transfer
tr0 = np.eye(3).flatten()
print("Initial distance:", total_error(tr0, colormap), file=sys.stderr)
tr = fmin(total_error, tr0, args=(colormap,))
tr.resize((3, 3))
print("RGB linear transform:\n", tr, file=sys.stderr)
calib = {"PixelCMratio": pixel_cm_ratio,
"RGBtransform": tr.tolist()}
jsonfile = op.join(folder, "calibrate.json")
fw = must_open(jsonfile, "w")
print(json.dumps(calib, indent=4), file=fw)
fw.close()
logging.debug("Calibration specs written to `{0}`.".format(jsonfile))
return jsonfile
def remove_outliers(self, markers, function):
data = [function(x) for x in markers]
reject = reject_outliers(data)
clean_markers = [m for m, r in zip(markers, reject) if not r]
return clean_markers
def calibrate(args):
"""
%prog calibrate calibrate.JPG boxsize
Calibrate pixel-inch ratio and color adjustment.
- `calibrate.JPG` is the photo containig a colorchecker
- `boxsize` is the measured size for the boxes on printed colorchecker, in
squared centimeter (cm2) units
"""
xargs = args[2:]
p = OptionParser(calibrate.__doc__)
opts, args, iopts = add_seeds_options(p, args)
if len(args) != 2:
sys.exit(not p.print_help())
imagefile, boxsize = args
boxsize = float(boxsize)
# Read in color checker
colorcheckerfile = op.join(datadir, "colorchecker.txt")
colorchecker = []
expected = 0
for row in open(colorcheckerfile):
boxes = row.split()
colorchecker.append(boxes)
expected += len(boxes)
folder = op.split(imagefile)[0]
objects = seeds([imagefile, "--outdir={0}".format(folder)] + xargs)
nseeds = len(objects)
logging.debug("Found {0} boxes (expected={1})".format(nseeds, expected))
assert (
expected - 4 <= nseeds <= expected +
4), "Number of boxes drastically different from {0}".format(expected)
# Calculate pixel-cm ratio
boxes = [t.area for t in objects]
reject = reject_outliers(boxes)
retained_boxes = [b for r, b in zip(reject, boxes) if not r]
mbox = np.median(retained_boxes) # in pixels
pixel_cm_ratio = (mbox / boxsize)**0.5
logging.debug(
"Median box size: {0} pixels. Measured box size: {1} cm2".format(
mbox, boxsize))
logging.debug("Pixel-cm ratio: {0}".format(pixel_cm_ratio))
xs = [t.x for t in objects]
ys = [t.y for t in objects]
xs = [float(itemx) for itemx in xs]
ys = [float(itemy) for itemy in ys]
idx_xs = get_kmeans(xs, 6)
idx_ys = get_kmeans(ys, 4)
for xi, yi, s in zip(idx_xs, idx_ys, objects):
s.rank = (yi, xi)
objects.sort(key=lambda x: x.rank)
colormap = []
for s in objects:
x, y = s.rank
observed, expected = s.rgb, rgb_to_triplet(colorchecker[x][y])
colormap.append((np.array(observed), np.array(expected)))
# Color transfer
tr0 = np.eye(3).flatten()
print("Initial distance:", total_error(tr0, colormap), file=sys.stderr)
tr = fmin(total_error, tr0, args=(colormap, ))
tr.resize((3, 3))
print("RGB linear transform:\n", tr, file=sys.stderr)
calib = {"PixelCMratio": pixel_cm_ratio, "RGBtransform": tr.tolist()}
jsonfile = op.join(folder, "calibrate.json")
fw = must_open(jsonfile, "w")
print(json.dumps(calib, indent=4), file=fw)
fw.close()
logging.debug("Calibration specs written to `{0}`.".format(jsonfile))
return jsonfile
def remove_outliers(self, markers, function):
data = [function(x) for x in markers]
reject = reject_outliers(data)
clean_markers = [m for m, r in zip(markers, reject) if not r]
return clean_markers
