def test_pix2coord(self, origin):
x, y = mkin.coord2pix(self.cx,
self.cy,
self.cdim,
self.imgdim,
origin=origin)
cx, cy = mkin.pix2coord(x, y, self.cdim, self.imgdim, origin=origin)
cxy = np.r_[cx, cy]
cxy_gt = np.r_[self.cx, self.cy]
assert np.all(np.isclose(cxy, cxy_gt, rtol=1e-7, atol=1e-10))
def setUp(self):
self.img = Image.open("moonmap_tiny.png").convert("L")
imgsize = list(self.img.size)
self.img = self.img.crop([0, 0, 300, 300])
self.img = self.img.resize([200, 200])
# Take top edge of long-lat bounds
ix = np.array([0, 300])
iy = np.array([0, 300])
cdim = [-180, 180, -90, 90]
llong, llat = mkin.pix2coord(ix, iy, cdim, imgsize, origin="upper")
self.llbd = np.r_[llong, llat[::-1]]
self.iglobe = ccrs.Globe(semimajor_axis=1737400,
semiminor_axis=1737400,
ellipse=None)
self.geoproj = ccrs.Geodetic(globe=self.iglobe)
self.iproj = ccrs.PlateCarree(globe=self.iglobe)
self.oproj = ccrs.Orthographic(central_longitude=np.mean(
self.llbd[:2]),
central_latitude=np.mean(self.llbd[2:]),
globe=self.iglobe)
xllr = np.array([self.llbd[0], np.mean(self.llbd[:2]), self.llbd[1]])
yllr = np.array([self.llbd[2], np.mean(self.llbd[2:]), self.llbd[3]])
xll, yll = np.meshgrid(xllr, yllr)
xll = xll.ravel()
yll = yll.ravel()
# [:,:2] becaus we don't need elevation data
res = self.iproj.transform_points(x=xll, y=yll,
src_crs=self.geoproj)[:, :2]
self.iextent = [
min(res[:, 0]),
max(res[:, 0]),
min(res[:, 1]),
max(res[:, 1])
]
res = self.oproj.transform_points(x=xll, y=yll,
src_crs=self.geoproj)[:, :2]
self.oextent = [
min(res[:, 0]),
max(res[:, 0]),
min(res[:, 1]),
max(res[:, 1])
]
self.craters = pd.DataFrame(np.vstack([xllr, yllr]).T,
columns=["Long", "Lat"])
self.craters["Diameter (km)"] = [10, 10, 10]
def test_pix2coord(self):
for origin in ["lower", "upper"]:
with self.subTest(origin=origin):
x, y = mkin.coord2pix(self.cx,
self.cy,
self.cdim,
self.imgdim,
origin=origin)
cx, cy = mkin.pix2coord(x,
y,
self.cdim,
self.imgdim,
origin=origin)
cxy = np.r_[cx, cy]
cxy_gt = np.r_[self.cx, self.cy]
self.assertTrue(
np.all(np.isclose(cxy, cxy_gt, rtol=1e-7, atol=1e-10)))
def ProjectionSpeedTest():
"""Sample speed test; modify to heart's content!"""
imgname = "out/out_01_01.png"
cdim = [-180, 180, -90, 90]
csvname = "out/out_01_01.csv"
imgdim = np.array([4292, 2145])
resolutions = np.array([32, 64, 128, 256, 512, 1024])
np.random.shuffle(resolutions)
resolutions = np.r_[np.array([32]), resolutions]
# Obtain long/lat bounds
pos = np.array([0, 0])
size = np.array([429, 214])
ix = np.array([pos[0], pos[0] + size[0]])
iy = np.array([pos[1], pos[1] + size[1]])
# Using origin="upper" means our latitude coordinates are reversed
llong, llat = mkin.pix2coord(ix, iy, cdim, imgdim, origin="upper")
llbd = np.r_[llong, llat[::-1]]
oname = "out/DELETELATER.png"
imgloadtime = np.mean(
timeit.repeat('img = Image.open(imgname).convert("L")',
repeat=10,
number=1,
setup="from __main__ import Image, imgname"))
raw_image = Image.open(imgname).convert("L")
restimes = np.zeros(resolutions.size)
for i, resolution in enumerate(resolutions):
img = raw_image.resize([
resolutions[i],
int(
np.round(resolutions[i] * raw_image.size[1] /
raw_image.size[0]))
])
ct = timeit.repeat("MakePCOTransform(img, oname, llbd, csvname)", \
setup="from __main__ import MakePCOTransform, img, oname, llbd, csvname", \
number=1, repeat=10)
restimes[i] = np.mean(ct)
return imgloadtime, zip(resolutions, restimes)
def setup(self):
self.img = Image.open(
'LunarLROLrocKaguya_1180mperpix_downsamp.png').convert("L")
self.craters = mkin.ReadLROCHeadCombinedCraterCSV(
filelroc="../LROCCraters.csv",
filehead="../HeadCraters.csv",
sortlat=True)
self.rawlen_range = [256, 512]
self.npseed = 1337
np.random.seed(self.npseed)
rawlen_min = np.log10(self.rawlen_range[0])
rawlen_max = np.log10(self.rawlen_range[1])
rawlen = int(10**np.random.uniform(rawlen_min, rawlen_max))
xc = np.random.randint(0, self.img.size[0] - rawlen)
yc = np.random.randint(0, self.img.size[1] - rawlen)
box = np.array([xc, yc, xc + rawlen, yc + rawlen], dtype='int32')
# THIS WILL BECOME WRONG!
assert np.all(box == np.array([860, 1256, 1166, 1562]))
# Take top edge of long-lat bounds.
ix = np.array([0, 300])
iy = np.array([0, 300])
cdim = [-180, 180, -60, 60]
llong, llat = mkin.pix2coord(ix, iy, cdim, imgsize, origin="upper")
self.llbd = np.r_[llong, llat[::-1]]
self.iglobe = ccrs.Globe(semimajor_axis=1737400,
semiminor_axis=1737400,
ellipse=None)
self.geoproj = ccrs.Geodetic(globe=self.iglobe)
self.iproj = ccrs.PlateCarree(globe=self.iglobe)
self.oproj = ccrs.Orthographic(central_longitude=np.mean(
self.llbd[:2]),
central_latitude=np.mean(self.llbd[2:]),
globe=self.iglobe)
xllr = np.array([self.llbd[0], np.mean(self.llbd[:2]), self.llbd[1]])
yllr = np.array([self.llbd[2], np.mean(self.llbd[2:]), self.llbd[3]])
xll, yll = np.meshgrid(xllr, yllr)
xll = xll.ravel()
yll = yll.ravel()
# [:,:2] becaus we don't need elevation data
res = self.iproj.transform_points(x=xll, y=yll,
src_crs=self.geoproj)[:, :2]
self.iextent = [
min(res[:, 0]),
max(res[:, 0]),
min(res[:, 1]),
max(res[:, 1])
]
res = self.oproj.transform_points(x=xll, y=yll,
src_crs=self.geoproj)[:, :2]
self.oextent = [
min(res[:, 0]),
max(res[:, 0]),
min(res[:, 1]),
max(res[:, 1])
]
self.craters = pd.DataFrame(np.vstack([xllr, yllr]).T,
columns=["Long", "Lat"])
self.craters["Diameter (km)"] = [10, 10, 10]