def fig2(plt,days,values):
fig, axes = plt.subplots(4,1,figsize=(8,4))
plt.subplots_adjust(hspace=0.5)
## plot each index
for i in range(4):
## display MM/DD/YYYY
axes[i].xaxis.set_major_formatter(dates.DateFormatter('%m/%d/%y'))
## label each new day/month/year
axes[i].xaxis.set_major_locator(dates.MonthLocator())
## break up the i-th time series into quartiles
horizon(axes[i],days[i],values[i],4)
axes[i].grid()
axes[i].set(title=filenames[i],ylabel='Value',xlabel='Time')
pass
plt.gcf().autofmt_xdate()
## display and save
#plt.show()
plt.savefig('stockMarket-Horizon.png')
pass
def create_isopach(hor1, hor2, extent='intersection'):
"""Create new horizon with the difference between hor1 and hor2."""
if isinstance(extent, basestring):
if extent.lower() == 'union':
extent = bbox_union(hor1.grid_extents, hor2.grid_extents)
elif extent.lower() == 'intersection':
extent = bbox_intersection(hor1.grid_extents, hor2.grid_extents)
if extent is None:
raise ValueError('Horizons do not overlap!')
else:
raise ValueError('Invalid extent type specified')
xmin, xmax, ymin, ymax = extent
x, y = np.mgrid[xmin:xmax+1, ymin:ymax+1]
hor1.grid_extents = extent
hor2.grid_extents = extent
grid = hor1.grid - hor2.grid
mask = ~grid.mask * np.ones_like(grid, dtype=np.bool)
x, y, z, mask = x.ravel(), y.ravel(), grid.ravel(), mask.ravel()
iso = horizon.horizon(x=x[mask], y=y[mask], z=z[mask])
iso._grid = grid
return iso
def create_isopach(hor1, hor2, extent='intersection'):
"""Create new horizon with the difference between hor1 and hor2."""
if isinstance(extent, basestring):
if extent.lower() == 'union':
extent = bbox_union(hor1.grid_extents, hor2.grid_extents)
elif extent.lower() == 'intersection':
extent = bbox_intersection(hor1.grid_extents, hor2.grid_extents)
if extent is None:
raise ValueError('Horizons do not overlap!')
else:
raise ValueError('Invalid extent type specified')
xmin, xmax, ymin, ymax = extent
x, y = np.mgrid[xmin:xmax+1, ymin:ymax+1]
hor1.grid_extents = extent
hor2.grid_extents = extent
grid = hor1.grid - hor2.grid
mask = ~grid.mask * np.ones_like(grid, dtype=np.bool)
x, y, z, mask = x.ravel(), y.ravel(), grid.ravel(), mask.ravel()
iso = horizon.horizon(x=x[mask], y=y[mask], z=z[mask])
iso._grid = grid
return iso
print("Skipping %d frames..." % counter)
else:
continue
print("Frame %d" % counter)
method = cv2.INTER_AREA
frame_scale = cv2.resize(frame, (0, 0),
fx=scale,
fy=scale,
interpolation=method)
cv2.imshow('scaled orig', frame_scale)
frame_undist = cv2.undistort(frame_scale, K, np.array(dist))
# test horizon detection
lines = horizon.horizon(frame_undist)
if not lines is None:
#best_line = horizon.track_best(lines)
best_line = lines[0]
roll, pitch = horizon.get_camera_attitude(best_line, IK, cu, cv)
if last_roll is None or last_pitch is None:
roll_rate = 0
pitch_rate = 0
else:
roll_rate = (roll - last_roll) * fps * d2r
pitch_rate = (pitch - last_pitch) * fps * d2r
last_roll = roll
last_pitch = pitch
horizon.draw(frame_undist, best_line, IK, cu, cv)
row = {
'frame': counter,
def extractWindow(hor, vol, upper=0, lower=None, offset=0, region=None,
masked=False):
"""Extracts a window around a horizion out of a geoprobe volume
Input:
hor: a geoprobe horizion object or horizion filename
vol: a geoprobe volume object or volume filename
upper: (default, 0) upper window interval around horizion in voxels
lower: (default, upper) lower window interval around horizion in voxels
offset: (default, 0) amount (in voxels) to offset the horizion by along
the Z-axis
region: (default, overlap between horizion and volume) sub-region to
use instead of full extent. Must be a 4-tuple of (xmin, xmax,
ymin, ymax)
masked: (default, False) if True, return a masked array where nodata
values in the horizon are masked. Otherwise, return an array
where the nodata values are filled with 0.
Output:
returns a numpy volume "flattened" along the horizion
"""
# If the lower window isn't specified, assume it's equal to the
# upper window
if lower == None: lower=upper
# If filenames are input instead of volume/horizion objects, create
# the objects
if type(hor) == type('String'):
hor = horizon.horizon(hor)
if type(vol) == type('String'):
vol = volume.volume(vol)
#Gah, changed the way hor.grid works... Should probably change it back
depth = hor.grid.T
# Find the overlap between the horizon and volume
vol_extents = [vol.xmin, vol.xmax, vol.ymin, vol.ymax]
hor_extents = [hor.xmin, hor.xmax, hor.ymin, hor.ymax]
extents = bbox_intersection(hor_extents, vol_extents)
# Raise ValueError if horizon and volume do not intersect
if extents is None:
raise ValueError('Input horizon and volume do not intersect!')
# Find the overlap between the (optional) subregion current extent
if region is not None:
extents = bbox_intersection(extents, region)
if extents is None:
raise ValueError('Specified region does not overlap with'\
' horizon and volume')
elif len(extents) != 4:
raise ValueError('"extents" must be a 4-tuple of'\
' (xmin, xmax, ymin, ymax)')
xmin, xmax, ymin, ymax = extents
# Convert extents to volume indicies and select subset of the volume
xstart, ystart = xmin - vol.xmin, ymin - vol.ymin
xstop, ystop = xmax - vol.xmin, ymax - vol.ymin
data = vol.data[xstart:xstop, ystart:ystop, :]
# Convert extents to horizion grid indicies and select
# subset of the horizion
xstart, ystart = xmin - hor.xmin, ymin - hor.ymin
xstop, ystop = xmax - hor.xmin, ymax - hor.ymin
depth = depth[xstart:xstop, ystart:ystop]
nx,ny,nz = data.shape
# convert z coords of horizion to volume indexes
depth -= vol.zmin
depth /= abs(vol.dz)
depth = depth.astype(np.int)
# Initalize the output array
window_size = upper + lower + 1
# Not creating a masked array here due to speed problems when
# iterating through ma's
subVolume = np.zeros((nx,ny,window_size), dtype=np.uint8)
# Using fancy indexing to do this uses tons of memory...
# As it turns out, simple iteration is much, much more memory
# efficient, and almost as fast
mask = depth.mask # Need to preserve the mask for later
depth = depth.filled() # Iterating through masked arrays is much slower...
for i in xrange(nx):
for j in xrange(ny):
if depth[i,j] != hor.nodata:
# Where are we in data indicies
z = depth[i,j] + offset
top = z - upper
bottom = z + lower + 1
# Be careful to extract the right vertical region in cases
# where the window goes outside the data bounds (find region of
# overlap)
data_top = max([top, 0])
data_bottom = min([bottom, nz])
window_top = max([0, window_size - bottom])
window_bottom = min([window_size, nz - top])
# Extract the window out of data and store it in subVolume
subVolume[i, j, window_top : window_bottom] \
= data[i, j, data_top : data_bottom]
# If masked is True (input option), return a masked array
if masked:
nx,ny,nz = subVolume.shape
mask = mask.reshape((nx,ny,1))
mask = np.tile(mask, (1,1,nz))
subVolume = np.ma.array(subVolume, mask=mask)
# If upper==lower==0, (default) subVolume will be (nx,ny,1), so
# return 2D array instead
subVolume = subVolume.squeeze()
return subVolume
def extractWindow(hor, vol, upper=0, lower=None, offset=0, region=None,
masked=False):
"""Extracts a window around a horizion out of a geoprobe volume
Input:
hor: a geoprobe horizion object or horizion filename
vol: a geoprobe volume object or volume filename
upper: (default, 0) upper window interval around horizion in voxels
lower: (default, upper) lower window interval around horizion in voxels
offset: (default, 0) amount (in voxels) to offset the horizion by along
the Z-axis
region: (default, overlap between horizion and volume) sub-region to
use instead of full extent. Must be a 4-tuple of (xmin, xmax,
ymin, ymax)
masked: (default, False) if True, return a masked array where nodata
values in the horizon are masked. Otherwise, return an array
where the nodata values are filled with 0.
Output:
returns a numpy volume "flattened" along the horizion
"""
# If the lower window isn't specified, assume it's equal to the
# upper window
if lower == None: lower=upper
# If filenames are input instead of volume/horizion objects, create
# the objects
if type(hor) == type('String'):
hor = horizon.horizon(hor)
if type(vol) == type('String'):
vol = volume.volume(vol)
#Gah, changed the way hor.grid works... Should probably change it back
depth = hor.grid.T
# Find the overlap between the horizon and volume
vol_extents = [vol.xmin, vol.xmax, vol.ymin, vol.ymax]
hor_extents = [hor.xmin, hor.xmax, hor.ymin, hor.ymax]
extents = bbox_intersection(hor_extents, vol_extents)
# Raise ValueError if horizon and volume do not intersect
if extents is None:
raise ValueError('Input horizon and volume do not intersect!')
# Find the overlap between the (optional) subregion current extent
if region is not None:
extents = bbox_intersection(extents, region)
if extents is None:
raise ValueError('Specified region does not overlap with'\
' horizon and volume')
elif len(extents) != 4:
raise ValueError('"extents" must be a 4-tuple of'\
' (xmin, xmax, ymin, ymax)')
xmin, xmax, ymin, ymax = extents
# Convert extents to volume indicies and select subset of the volume
i, j = vol.model2index([xmin, xmax], [ymin, ymax])
data = vol.data[i[0]:i[1], j[0]:j[1], :]
# Convert extents to horizion grid indicies and select
# subset of the horizion
hxmin, hxmax, hymin, hymax = hor.grid_extents
xstart, ystart = xmin - hxmin, ymin - hymin
xstop, ystop = xmax - hxmin, ymax - hymin
depth = depth[xstart:xstop, ystart:ystop]
nx,ny,nz = data.shape
# convert z coords of horizion to volume indexes
depth -= vol.zmin
depth /= abs(vol.dz)
depth = depth.astype(np.int)
# Initalize the output array
window_size = upper + lower + 1
# Not creating a masked array here due to speed problems when
# iterating through ma's
subVolume = np.zeros((nx,ny,window_size), dtype=np.uint8)
# Using fancy indexing to do this uses tons of memory...
# As it turns out, simple iteration is much, much more memory
# efficient, and almost as fast
mask = depth.mask.copy() # Need to preserve the mask for later
if not mask.shape:
mask = np.zeros(depth.shape, dtype=np.bool)
depth = depth.filled() # Iterating through masked arrays is much slower.
for i in xrange(nx):
for j in xrange(ny):
if depth[i,j] != hor.nodata:
# Where are we in data indicies
z = depth[i,j] + offset
if z < 0:
mask[i,j] = True
continue
top = z - upper
bottom = z + lower + 1
# Be careful to extract the right vertical region in cases
# where the window goes outside the data bounds (find region of
# overlap)
data_top = max([top, 0])
data_bottom = min([bottom, nz])
window_top = max([0, window_size - bottom])
window_bottom = min([window_size, nz - top])
# Extract the window out of data and store it in subVolume
subVolume[i, j, window_top : window_bottom] \
= data[i, j, data_top : data_bottom]
# If masked is True (input option), return a masked array
if masked:
nx,ny,nz = subVolume.shape
mask = mask.reshape((nx,ny,1))
mask = np.tile(mask, (1,1,nz))
subVolume = np.ma.array(subVolume, mask=mask)
# If upper==lower==0, (default) subVolume will be (nx,ny,1), so
# return 2D array instead
subVolume = subVolume.squeeze()
return subVolume
# Save the diamond square image
Image.fromarray(
(ds).astype('uint8')).convert('RGB').save('0-diamond-square.png')
# Binarize the diamond square
margin = 10
nb_pixels = ds.shape[0] * ds.shape[1]
mean = numpy.mean(ds)
binary = numpy.array((ds < mean - margin) + (ds > mean + margin),
dtype=numpy.int)
# take care of side borders
ds_h = binary.shape[0]
ds_w = binary.shape[1]
horizon_roughness = 0.65
b = horizon(binary.shape[0], horizon_roughness, ds_h // 20)
for i in range(binary.shape[0]):
binary[i, 0:b[i]] = 0
b = horizon(binary.shape[0], horizon_roughness, ds_h // 20)
for i in range(binary.shape[0]):
binary[i, (binary.shape[1] - b[-i]):binary.shape[1]] = 0
b = horizon(binary.shape[1], horizon_roughness, ds_w // 20)
for i in range(binary.shape[1]):
binary[0:b[i], i] = 0
b = horizon(binary.shape[1], horizon_roughness, ds_w // 20)
for i in range(binary.shape[1]):
binary[(binary.shape[0] - b[-i]):binary.shape[0], i] = 0
# Save it for fun
Image.fromarray(
(255 * binary).astype('uint8')).convert('RGB').save('1-binary.png')
else:
continue
#print("Frame %d" % counter)
method = cv2.INTER_AREA
frame_scale = cv2.resize(frame, (0, 0),
fx=scale,
fy=scale,
interpolation=method)
cv2.imshow('scaled orig', frame_scale)
frame_undist = cv2.undistort(frame_scale, K, np.array(dist))
# test horizon detection
if args.no_otsu:
lines = horizon.horizon(frame_undist, False)
else:
lines = horizon.horizon(frame_undist)
if not lines is None:
#best_line = horizon.track_best(lines)
best_line = lines[0]
roll, pitch = horizon.get_camera_attitude(best_line, IK, cu, cv)
if last_roll is None or last_pitch is None:
roll_rate = 0
pitch_rate = 0
else:
roll_rate = (roll - last_roll) * fps * d2r
pitch_rate = (pitch - last_pitch) * fps * d2r
last_roll = roll
last_pitch = pitch
horizon.draw(frame_undist, best_line, IK, cu, cv)
