return time.strftime(ID_FORMAT, time.gmtime(d["timestamp"]))
ims = OrderedDict((metadata_to_id(d),
cv2.imread(d["image_path"], cv2.IMREAD_GRAYSCALE))
for d in sorted(metadata, key=lambda d: d['timestamp']))
times = OrderedDict((metadata_to_id(d), d["timestamp"]) for d in metadata)
print "Filtering images which are too bright"
filtered_ims = OrderedDict((im_id, im) for im_id, im in ims.items()
if numpy.mean(im) <= args.max_brightness)
print "Extracting stars from {} / {} images".format(len(filtered_ims),
len(ims))
im_stars = OrderedDict()
for im_id, im in filtered_ims.items():
try:
im_stars[im_id] = list(stars.extract(im))
except stars.ExtractFailed as e:
print "Failed to extract stars for {}: {}".format(im_id, e)
print "Registering {} / {} images".format(len(im_stars), len(ims))
transforms = OrderedDict()
for im_id, reg_result in zip(im_stars.keys(),
reg.register_many(im_stars.values())):
try:
M = reg_result.result()
except reg.RegistrationFailed as e:
print "Failed to register {}: {}".format(im_id, e)
else:
transforms[im_id] = M
print "Stacking {} / {} images".format(len(transforms), len(ims))
ims = OrderedDict(
(metadata_to_id(d), cv2.imread(d["image_path"], cv2.IMREAD_GRAYSCALE))
for d in sorted(metadata, key=lambda d: d['timestamp']))
times = OrderedDict((metadata_to_id(d), d["timestamp"]) for d in metadata)
print "Filtering images which are too bright"
filtered_ims = OrderedDict((im_id, im) for im_id, im in ims.items()
if numpy.mean(im) <= args.max_brightness)
print "Extracting stars from {} / {} images".format(len(filtered_ims),
len(ims))
im_stars = OrderedDict()
for im_id, im in filtered_ims.items():
try:
im_stars[im_id] = list(stars.extract(im))
except stars.ExtractFailed as e:
print "Failed to extract stars for {}: {}".format(im_id, e)
print "Registering {} / {} images".format(len(im_stars), len(ims))
transforms = OrderedDict()
for im_id, reg_result in zip(im_stars.keys(),
reg.register_many(im_stars.values())):
try:
M = reg_result.result()
except reg.RegistrationFailed as e:
print "Failed to register {}: {}".format(im_id, e)
else:
transforms[im_id] = M
print "Stacking {} / {} images".format(len(transforms), len(ims))
lineType=cv2.CV_AA)
output_image("step{}.png".format(step_num), im1_copy, im2_copy)
step_num += 1
if __name__ == "__main__":
import sys
import cv2
import stars
if sys.argv[1] == "register_pair":
im1 = cv2.imread(sys.argv[2], cv2.IMREAD_GRAYSCALE)
im2 = cv2.imread(sys.argv[3], cv2.IMREAD_GRAYSCALE)
stars1 = stars.extract(im1)
stars2 = stars.extract(im2)
A = register_pair(stars1, stars2)
print A
if sys.argv[1] == "draw_correspondences":
im1 = cv2.imread(sys.argv[2], cv2.IMREAD_GRAYSCALE)
im2 = cv2.imread(sys.argv[3], cv2.IMREAD_GRAYSCALE)
stars1 = list(stars.extract(im1))
stars2 = list(stars.extract(im2))
correspondences = _find_correspondences(stars1, stars2)
_draw_correspondences(correspondences, im1, im2, stars1, stars2)
if sys.argv[1] == "register_many":
