def create_stitched_video_from_scratch(fnString, pos):
stores = (fnString + '.21990443', fnString + '.21990445',
fnString + '.21990447', fnString + '.21990449')
cams_stores = {}
for fn in stores:
store = new_for_filename(op.join(BASE_DATA, fn))
print store.full_path
nFrames = store.frame_count
cams_stores[get_store_camera_serial(store)] = store
sorted_stores = [cams_stores[i] for i in best_order]
undistortions = [Undistort(i) for i in sorted_stores]
aligned = StoreAligner(*sorted_stores)
aligned.extract_common_frames(StoreAligner.MISSING_POLICY_DROP)
if args.truncate == True:
suffix = '.partial_stitch'
aligned._fns = aligned._fns[
2000:5000] # THIS IS HOW WE TRUNCATE. #FIXME hardcoded cut
elif args.truncate == False:
suffix = '.stitched'
if not os.path.exists(fnString + suffix):
os.mkdir(fnString + suffix)
out = imgstore.new_for_format('avc1/mp4',
mode='w',
basedir=fnString + suffix,
imgshape=s.panorama_shape,
imgdtype='uint8',
chunksize=500)
for n, (imgs, (fn, ts)) in enumerate(aligned.iter_imgs(return_times=True)):
_imgs = []
for i in range(len(undistortions)):
_imgs.append(undistortions[i].undistort(imgs[i]))
#with silence_stdout():
ok, img = s.stitch_images(*[ensure_color(i) for i in _imgs])
assert ok
out.add_image(img, fn, ts)
printProgressBar(n,
nFrames,
prefix='Stitching progress:',
stdoutpos=pos)
out.close()
return
def create_stitched_video_from_scratch(path):
DIRECTORY, fnString = path.rsplit('/', 1)
DIRECTORY = DIRECTORY + '/'
stores = (fnString + '.21990443', fnString + '.21990445',
fnString + '.21990447', fnString + '.21990449')
cams_stores = {}
for fn in stores:
store = new_for_filename(op.join(BASE_DATA, fn))
print store.full_path
cams_stores[get_store_camera_serial(store)] = store
sorted_stores = [cams_stores[i] for i in best_order]
undistortions = [Undistort(i) for i in sorted_stores]
aligned = StoreAligner(*sorted_stores)
aligned.extract_common_frames(StoreAligner.MISSING_POLICY_DROP)
if not os.path.exists(DIRECTORY + fnString + '.stitched'):
os.mkdir(DIRECTORY + fnString + '.stitched')
"""
out = new_for_format('avc1/mp4', DIRECTORY + fnString + '.stitched/metadata.yaml',
imgshape=s.panorama_shape,
imgdtype=np.uint8)
"""
out = imgstore.new_for_format('avc1/mp4',
mode='w',
basedir=DIRECTORY + fnString + '.stitched',
imgshape=s.panorama_shape,
imgdtype='uint8',
chunksize=500)
for n, (imgs, (fn, ts)) in enumerate(aligned.iter_imgs(return_times=True)):
_imgs = []
for i in range(len(undistortions)):
_imgs.append(undistortions[i].undistort(imgs[i]))
ok, img = s.stitch_images(*[ensure_color(i) for i in _imgs])
assert ok
out.add_image(img, fn, ts)
out.close()
return
def create_stitched_video_from_undistorted(fnString):
stores = (fnString + '.21990443_undistorted',
fnString + '.21990445_undistorted',
fnString + '.21990447_undistorted',
fnString + '.21990449_undistorted')
cams_stores = {}
for fn in stores:
store = new_for_filename(op.join(BASE_DATA, fn))
print store.full_path
cams_stores[get_store_camera_serial(store)] = store
sorted_stores = [cams_stores[i] for i in best_order]
aligned = StoreAligner(*sorted_stores)
aligned.extract_common_frames(StoreAligner.MISSING_POLICY_DROP)
if not os.path.exists(BASE_DATA + fnString + '.stitched'):
os.mkdir(BASE_DATA + fnString + '.stitched')
out = imgstore.new_for_format('avc1/mp4',
mode='w',
basedir=BASE_DATA + fnString,
imgshape=s.panorama_shape,
imgdtype='uint8',
chunksize=500)
for n, (fn, imgs) in enumerate(aligned.iter_imgs()):
ok, img = s.stitch_images(*[ensure_color(i) for i in imgs])
assert ok
out.add_image(img, fn, 0)
print n
out.close()
return
else:
calibs = ('stitch09_20180910_165817.21990443_undistorted',
'stitch09_20180910_165817.21990447_undistorted',
'stitch09_20180910_165817.21990445_undistorted',
'stitch09_20180910_165817.21990449_undistorted')
cams_imgs = {}
# load the first frame for the calibration
for fn in calibs:
with new_for_filename(op.join(BASE_CALIB, fn)) as store:
camera_serial = get_store_camera_serial(store)
img, _ = store.get_image(frame_number=None,
exact_only=True,
frame_index=0)
cams_imgs[camera_serial] = ensure_color(img)
sorted_imgs = [cams_imgs[i] for i in best_order]
s = Stitcher(use_gpu=True,
estimator_type="homography",
matcher_type="affine",
warp_type="plane")
# s.enable_exposure_compensation('gain_blocks')
# s.enable_seam_finding('gc_color')
#with silence_stdout():
ok = s.load_calibration(*sorted_imgs)
assert ok
