def main():
stitcher = Stitcher()
while True:
leftCam = cv2.VideoCapture(2)
rearCam = cv2.VideoCapture(1)
rightCam = cv2.VideoCapture(0)
#ap = argparse.ArgumentParser()
#ap.add_argument('-i', '--input-dir', required=True, help='Path to image input directory')
#args = vars(ap.parse_args())
#dir = args['input_dir'].strip(' ')
#input_dir = os.path.join(os.getcwd(), dir)
#image_files = sorted(os.listdir(input_dir))
#image_files = [os.path.join(input_dir, x) for x in image_files]
#images = [imutils.resize(cv2.imread(x), width=400) for x in image_files]
images = []
images.append(leftCam.read()[1])
images.append(rearCam.read()[1])
images.append(rightCam.read()[1])
images = [imutils.resize(cv2.flip(x, 1), width=400) for x in images]
try:
result = stitcher.stitch(images)
cv2.imshow("Stitched", result)
except RuntimeError:
cv2.waitKey(100)
continue
cv2.waitKey(100)
def main():
parser = argparse.ArgumentParser(
description='A small utility that creates a big map.')
parser.add_argument(
'--start',
required=True,
help=
'top-left point of the region of interest (comma-separated lat/lng pair)'
)
parser.add_argument(
'--end',
required=True,
help=
'bottom-right point of the region of interest (comma-separated lat/lng pair)'
)
parser.add_argument('--zoom', type=int, required=True, help='zoom level')
parser.add_argument('--out', required=True, help='output directory')
parser.add_argument('--transit',
help='enable transit layer',
action='store_true')
args = parser.parse_args()
start = map(float, args.start.split(','))
end = map(float, args.end.split(','))
tile_size_px = 500
print('[bigmapmaker] Starting.')
screenshotter = Screenshotter(start, end, args.zoom, args.out,
args.transit, tile_size_px)
screenshotter.fetch_tiles()
print('[bigmapmaker] Done with fetching, moving on to stitching.')
stitcher = Stitcher(args.out, tile_size_px)
stitcher.stitch()
print('[bigmapmaker] Done.')
def template(directory: str,
in_file: str,
out_file: str = None,
passes: int = -1,
logger=None):
OUT_PREFIX = 'out_'
FEEDBACK = 'Finished stitching in {0}'
ERROR = 'Error: {0}!'
if out_file == None:
out_file = OUT_PREFIX + in_file
try:
Stitcher(directory, in_file, out_file, passes, logger)
if logger is None:
print(FEEDBACK.format(os.path.join(directory, out_file)))
else:
logger.info(FEEDBACK.format(os.path.join(directory, out_file)))
except Exception as e:
if logger is None:
print(ERROR.format(e))
else:
logger.error(e)
raise
print()
return
def process(self):
while (True):
start_time = time.time()
# Capture frame-by-frame
self.extFrames.clear()
for cam in self.extCams:
ret, frame = cam.read()
frame = self.f.unwarp(frame)
self.extFrames.append(frame)
# Our operations on the frame come here
# Display the resulting frame
if self.stitch:
# s = CVStitcher(args.debug)
s = Stitcher(self.debug)
pano = s.stitch(self.extFrames)
# cv2.resize(pano, (320, 180), interpolation=cv2.INTER_AREA)
cv2.imshow("pano", pano)
if self.debug:
for i in range(len(self.extCams)):
cv2.imshow(
'frame' + str(i),
cv2.resize(self.extFrames[i], (480, 270),
interpolation=cv2.INTER_AREA))
for cam in self.extCams:
print(self.decode_fourcc(cam.get(cv2.CAP_PROP_FOURCC)))
if cv2.waitKey(1) & 0xFF == ord('q'):
break
fps = 1.0 / (time.time() - start_time)
print("[INFO] approx. FPS: {:.2f}".format(fps))
# Cleanly exit.
for cam in self.extCams:
cam.release()
cv2.destroyAllWindows()
def stitchFolder(self, targetFolder):
stitcherHandler = Stitcher()
filesToStitch = []
onlyFiles = [
f for f in os.listdir(targetFolder)
if isfile(join(targetFolder, f))
]
for files in onlyFiles:
if (files.endswith(".jpg")):
filesToStitch.append(targetFolder + "/" + files)
if (len(filesToStitch) < 2):
return
parentName = os.path.split(os.path.dirname(filesToStitch[0]))[1]
outputFile = os.path.join(os.path.dirname(filesToStitch[0]),
parentName + ".tiff")
logFile = os.path.join(os.path.dirname(filesToStitch[0]),
parentName + "_log.txt")
filesToStitch.sort()
print(filesToStitch)
print(outputFile)
print("Logging to: " + logFile)
if (outputFile is None):
exit()
stitcherHandler.stitchFileList(filesToStitch, outputFile, logFile,
self.progressCallback,
self.maskBox.IsChecked(),
self.scalePath,
self.verticalCore.IsChecked())
if (self.archiveImages.IsChecked()):
self.ArchiveQueue.put(targetFolder)
def __init__(self, path=None):
# Initialize stitcher object and pdf object
self.path = path
if (path == None):
self.stitcher = None
else:
self.stitcher = Stitcher(self.path)
self.quilt = None
def homography_matrices(video):
stitcher = Stitcher()
initialized = False
for i, frame in enumerate(video):
stdout.write('{}\r'.format(i))
stdout.flush()
if initialized is False:
initialized = True
homography_matrices = np.array([np.identity(3)])
prev_frame = frame
continue
if i % 20 == 0:
homography_matrices = np.append(homography_matrices,
[np.identity(3)],
axis=0)
prev_frame = frame
continue
H = Stitcher().find_homography(frame, prev_frame)
homography_matrices = np.append(homography_matrices, [H], axis=0)
initialized = False
for i, frame in enumerate(video):
stdout.write('{}\r'.format(i))
stdout.flush()
if initialized is False:
initialized = True
reduced_H = np.identity(3)
prev_frame = frame
continue
if i % 20 == 0:
H = Stitcher().find_homography(frame, prev_frame)
reduced_H = reduced_H @ H
prev_frame = frame
# H = reduced_H @ H
homography_matrices[i] = reduced_H @ homography_matrices[i]
return homography_matrices
def main(_):
assert FLAGS.out
assert FLAGS.db and os.path.exists(FLAGS.db)
picpac_config = dict(
seed=2016,
#loop=True,
shuffle=True,
reshuffle=True,
#resize_width=256,
#resize_height=256,
round_div=FLAGS.stride,
batch=1,
split=1,
split_fold=0,
annotate='json',
channels=FLAGS.channels,
stratify=True,
pert_color1=20,
pert_angle=20,
pert_min_scale=0.8,
pert_max_scale=1.2,
#pad=False,
pert_hflip=True,
pert_vflip=True,
channel_first=False # this is tensorflow specific
# Caffe's dimension order is different.
)
stream = picpac.ImageStream(FLAGS.db,
perturb=False,
loop=False,
**picpac_config)
gal = Gallery(FLAGS.out)
cc = 0
with Model(FLAGS.model, name=FLAGS.name, prob=True) as model:
for images, _, _ in stream:
#images *= 600.0/1500
#images -= 800
#images *= 3000 /(2000-800)
_, H, W, _ = images.shape
if FLAGS.max_size:
if max(H, W) > FLAGS.max_size:
continue
if FLAGS.patch:
stch = Stitcher(images, FLAGS.patch)
probs = stch.stitch(model.apply(stch.split()))
else:
probs = model.apply(images)
cc += 1
save(gal.next(), images, probs)
if FLAGS.max and cc >= FLAGS.max:
break
gal.flush()
pass
def __init__(self, bot):
self.bot = bot
self.sql_query = SQLQuery(initialize_connection())
self.manager = Manager()
self.stitcher = Stitcher()
self.dbl_token = config('DBL_TOKEN')
self.webhook_auth_token = config('ALICE_WEBHOOK_AUTH_TOKEN')
self.dblpy = dbl.DBLClient(self.bot,
self.dbl_token,
autopost=True,
webhook_path='/dblwebhook',
webhook_auth=self.webhook_auth_token,
webhook_port=environ.get("PORT", 8000))
def startNoah(self, path):
#data is a string with the text
self.stitcher = Stitcher(path)
self.stitcher.stitch()
self.pdfifier = PDFifier()
self.quilt = self.stitcher.get_quilt()
self.image_num = 0
image_map = QPixmap(self.quilt[0].slide_path)
data = self.fix_text(self.quilt[0].script)
self.slideImage.setPixmap(image_map)
self.textArea.setText('\n'.join(data))
self.repaint()
def stitch(img1, img2, sigma=2.0, levels=None, flag_fast=False):
if flag_fast:
if img1.shape[1] > 400:
img1 = cv2.resize(
img1,
(width, int(img1.shape[0] * float(width) / img1.shape[1])))
if img2.shape[1] > 400:
img2 = cv2.resize(
img2,
(width, int(img2.shape[0] * float(width) / img2.shape[1])))
# stitch the images together to create a panorama
stitcher = Stitcher()
return stitcher.stitch([img1, img2], sigma=sigma, levels=levels)
def write(self, name='fullfile.csv'):
stitch = Stitcher(self.fpath)
files = len(self.successful) - 1
try:
stitch.import_vf(files)
except Exception as e:
print("VF files failed. Reason: {}".format(e))
try:
stitch.import_franklin()
except:
print("Whoops, no franklin.")
try:
stitch.import_hamilton()
except Exception as e:
print("whoops, no Hamilton: {}".format(e))
stitch.write(fname=name)
def stitch(self):
stitcherHandler = Stitcher()
print(self.fileList)
print(self.outputFile)
if (self.outputFile is None):
exit()
parentName = os.path.split(os.path.dirname(self.fileList[0]))[1]
outputFile = os.path.join(
os.path.dirname(self.fileList[0]),
parentName + str(len(self.fileList)) + ".tiff")
self.finishedFiles = []
self.finishedFiles.append(outputFile)
_thread.start_new_thread(
stitcherHandler.stitchFileList,
(self.fileList, outputFile, self.progressCallback,
self.maskImages.get()))
def run(self, path):
self.stitcher = Stitcher(path)
self.stitcher.stitch()
self.pdfifier = PDFifier()
self.quilt = self.stitcher.get_quilt()
name = self.random_string(16)
self.pdfifier.makePDF(self.quilt, name)
filename = name
if (os.path.isfile(path + ".mp3")):
os.remove(path + ".mp3")
if (os.path.isdir("audio_segments")):
shutil.rmtree("audio_segments")
if (os.path.isdir("slides")):
shutil.rmtree("slides")
return filename
def __init__(self, logger, ip, port, configfps, ingestproxy, mcip):
self._logger = logger
self._ip = ip
self._port = port
# read all config files and add channels
for configfp in configfps:
Channel.append(configfp)
# create shared memdb
self._memdb = MemDB()
# handler class for responding to http requests
self._myhandler = makehandlerclass(self._logger.getChild("HTTPServer"), ingestproxy, mcip, self._memdb)
# self._myhandler.protocol_version = "HTTP/1.1" #-->Do not use HTTP1.1 because handler does not support 206 and byte requests easily.
self._myhandler.server_version = "m2u"
self._httpd = None
# stitcher for concatenating RTP slices into fragments
self._stitcher = Stitcher(name="stitcher", args=(self._logger.getChild("Stitcher"), self._memdb))
def stitchFolder(self, targetFolder):
stitcherHandler = Stitcher()
filesToStitch = []
onlyFiles = [
f for f in os.listdir(targetFolder)
if isfile(join(targetFolder, f))
]
for files in onlyFiles:
if (files.endswith(".jpg")):
filesToStitch.append(targetFolder + "/" + files)
parentName = os.path.split(os.path.dirname(filesToStitch[0]))[1]
outputFile = os.path.join(os.path.dirname(filesToStitch[0]),
parentName + ".tiff")
print(filesToStitch)
print(outputFile)
if (outputFile is None):
exit()
_thread.start_new_thread(
stitcherHandler.stitchFileList,
(filesToStitch, outputFile, "test.txt", self.progressCallback,
self.maskImages.get(), None))
"Stack several image files to create digital long exposure photographies")
parser.add_argument("--align",
action="store_true",
help="run only the aligner, do not compress")
parser.add_argument("--transform",
action="store_true",
help="run only the aligner and transform, do not compress")
parser.add_argument("--stitch",
action="store_true",
help="stitch images for panoramic formats")
args = parser.parse_args()
# --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---
aligner = Aligner()
stitcher = Stitcher()
stacker = Stacker(aligner)
input_images_aligner = []
input_images_stitcher = []
input_images_stacker = []
# transform to absolute paths
BASE_DIR = os.path.dirname(os.path.realpath(__file__))
# --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---
# init aligner
if args.align or args.transform:
# expand all paths
u0 = np.zeros((ny, nx, nF[0]))
v0 = np.zeros((ny, nx, nF[0]))
for i in range(nF[0].astype(int)):
u0[:, :, i] = i / 5.
v0[:, :, i] = 2 * i / 5.
#Initialize data
us = np.zeros((ny, nx, np.sum(nF)))
vs = np.zeros((ny, nx, np.sum(nF)))
us[:, :, 0:nF[0]] = u0
vs[:, :, 0:nF[0]] = v0
#Load in optic flow data
vidx = 1
#Load MFSF data
u1 = np.zeros((ny, nx, nF[vidx]))
v1 = np.zeros((ny, nx, nF[vidx]))
for i in range(nF[0].astype(int)):
v1[:, :, i] = -i / 5.
#Make a Stitcher
thestitch = Stitcher(u1, v1)
self = thestitch
(u, v) = thestitch.run(u0, v0)
us[:, :, np.sum(nF[0:vidx]):np.sum(nF[0:vidx + 1])] = u
vs[:, :, np.sum(nF[0:vidx]):np.sum(nF[0:vidx + 1])] = v
#Save output matrix
#mdict = {'u':us, 'v':vs, 'parmsOF':params, 'info':info}
#savemat(args.fn_out, mdict)
from stitcher import Stitcher image_list = ["cam4.png", "cam8.png"] #,"images/image_3.png"] stitcher = Stitcher(image_list) stitcher.process_images()
