def main(args):
capture = cv2.VideoCapture(args.video)
W = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
H = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
FPS = int(capture.get(cv2.CAP_PROP_FPS))
if args.save:
writer = cv2.VideoWriter(args.save, cv2.VideoWriter_fourcc(*"mp4v"),
FPS, (W, H))
if W > 1024:
downscale = 1024.0 / W
H = int(H * downscale)
W = 1024
if not args.headless:
display2d = Display2D(W, H)
ret, frame = capture.read()
if not ret:
print("Fatal - failed to load video")
exit(-1)
matcher = FrameMatcher()
prevframe = Frame(frame)
while capture.isOpened():
ret, frame = capture.read()
if not ret:
print("No more frames to read")
break
frame = Frame(frame)
if frame.roi is not None and prevframe.roi is not None:
matches = matcher(frame, prevframe)
print('matches:', len(matches))
else:
print('no ROI')
if not args.headless:
display2d.paint(cv2.resize(frame.getAnnotated(), (W, H)))
if args.save:
writer.write(frame.getAnnotated())
prevframe = frame
cap.set(cv2.CAP_PROP_POS_FRAMES, int(os.getenv("SEEK")))
if W > 1024:
downscale = 1024.0 / W
F *= downscale
H = int(H * downscale)
W = 1024
print("using camera %dx%d with F %f" % (W, H, F))
# camera intrinsics
K = np.array([[F, 0, W // 2], [0, F, H // 2], [0, 0, 1]])
Kinv = np.linalg.inv(K)
# create 2-D display
if os.getenv("HEADLESS") is None:
disp2d = Display2D(W, H)
slam = SLAM(W, H, K)
"""
mapp.deserialize(open('map.json').read())
while 1:
disp3d.paint(mapp)
time.sleep(1)
"""
gt_pose = None
if len(sys.argv) >= 3:
gt_pose = np.load(sys.argv[2])['pose']
# add scale param?
gt_pose[:, :3, 3] *= 50
W = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
H = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
F = float(os.getenv("F", "525"))
CNT = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
K = np.array([[F, 0, W // 2], [0, F, H // 2], [0, 0, 1]])
Kinv = np.linalg.inv(K)
if os.getenv("SEEK") is not None:
video.set(cv2.CAP_PROP_POS_FRAMES, int(os.getenv("SEEK")))
if W > 1024:
downscale = 1024.0 / W
F *= downscale
H = int(H * downscale)
W = 1024
print("using camera %dx%d with F %f" % (W, H, F))
disp = Display2D("Display Window", W, H) # 2d display window
i = 0
while (video.isOpened()):
ret, frame = video.read()
if ret == True:
process_frame(frame)
else:
break
i += 1
video.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
if len(sys.argv) < 2:
print('%s <video.mp4>' % sys.argv[0])
exit(-1)
cap = cv2.VideoCapture(sys.argv[1])
# frame props
W = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))//2
H = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))//2
# focal length
F = 1
# camera matrix
# adjust mapping: img coords have origin at center, digital have origin bottom-left
K = np.array([[F, 0, W//2], [0, F, H//2], [0, 0, 1]])
slam = SLAM(W, H, K)
disp = Display2D(W, H)
while cap.isOpened():
ret, frame = cap.read()
frame = cv2.resize(frame, (W, H))
if ret == True:
img = slam.process_frame(frame)
disp.paint(img)
else:
break
path = os.path.expanduser(path)
self.left = ImageReader(self.listdir(os.path.join(path, 'image_0')))
self.right = ImageReader(self.listdir(os.path.join(path, 'image_1')))
assert len(self.left) == len(self.right)
def listdir(self, dir):
files = [_ for _ in os.listdir(dir) if _.endswith('.png')]
return [os.path.join(dir, _) for _ in self.sort(files)]
def sort(self, xs):
return sorted(xs, key=lambda x: float(x[:-4]))
def __getitem__(self, idx):
return self.left[idx]
def __len__(self):
return len(self.left)
if __name__ == '__main__':
display = Display2D(1241, 376)
t = KittiDataReader('dataset/sequences/00')
for i in range(len(t.left)):
path = t.left.ids[i]
img = np.array(cv2.imread(path))
display.paint(img)
if args.type == 'KITTI':
# Kitti configuration
config = KittiConfig()
# Kitti dataset loader
dataset = KittiLoader(args.path)
# camera object
camera = Camera(dataset.cam.fx, dataset.cam.fy, dataset.cam.cx,
dataset.cam.cy, dataset.cam.width, dataset.cam.height)
# global SLAM object
slam = PYSLAM(config)
# display
viewer = Display2D(slam, config)
frames = []
for i in range(len(dataset)):
img = dataset[i]
# extract features
feature = Feature(img, config, camera)
feature.extract()
# init frame
frame = Frame(i, np.eye(4), feature, camera)
if slam.is_initialized():
img = slam.observe(frame)
else: