def test_dataframes_equal(self):
lrochead = igen.ReadLROCHeadCombinedCraterCSV(
filelroc="../catalogues/LROCCraters.csv",
filehead="../catalogues/HeadCraters.csv",
sortlat=True)
lrochead_nosort = igen.ReadLROCHeadCombinedCraterCSV(
filelroc="../catalogues/LROCCraters.csv",
filehead="../catalogues/HeadCraters.csv",
sortlat=False)
assert np.all(lrochead == self.lrochead_t)
assert not np.all(lrochead == lrochead_nosort)
def get_craters(lroc_csv_path, head_csv_path, sub_cdim, R_km):
sys.path.append('../../DeepMoon/')
import input_data_gen as igen
craters = igen.ReadLROCHeadCombinedCraterCSV(filelroc=lroc_csv_path,
filehead=head_csv_path)
craters = igen.ResampleCraters(craters, sub_cdim, None, arad=R_km)
return craters
def setup(self):
# Seed.
self.seed = 1337
# Image length.
self.imlen = 256
# Image.
self.img = Image.open(
"LunarLROLrocKaguya_1180mperpix_downsamp.png").convert("L")
self.imgsize = self.img.size
# Crater catalogue.
self.craters = igen.ReadLROCHeadCombinedCraterCSV(
filelroc="../catalogues/LROCCraters.csv",
filehead="../catalogues/HeadCraters.csv",
sortlat=True)
# Long/lat limits
self.cdim = [-180., 180., -60., 60.]
# Coordinate systems.
self.iglobe = ccrs.Globe(semimajor_axis=1737400,
semiminor_axis=1737400,
ellipse=None)
start_time = time.time()
# Utilize mpi4py for multithreaded processing.
if use_mpi4py:
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
print("Thread {0} of {1}".format(rank, size))
istart = rank * amt
else:
istart = 0
# Read source image and crater catalogs.
img = Image.open(source_image_path).convert("L")
craters = igen.ReadLROCHeadCombinedCraterCSV(filehead=head_csv_path)
# Sample subset of image. Co-opt igen.ResampleCraters to remove all
# craters beyond cdim (either sub or source).
if sub_cdim != source_cdim:
img = igen.InitialImageCut(img, source_cdim, sub_cdim)
# This always works, since sub_cdim < source_cdim.
craters = igen.ResampleCraters(craters, sub_cdim, None, arad=R_km)
# Generate input images.
igen.GenDataset(img,
craters,
outhead,
rawlen_range=rawlen_range,
rawlen_dist=rawlen_dist,
ilen=ilen,
def main():
pdb.set_trace()
zenodo_path = './data/'
deepmoon_path = os.path.dirname(os.getcwd())
train_imgs = h5py.File(zenodo_path + 'train_images.hdf5', 'r')
fig = plt.figure(figsize=[12, 6])
[ax1, ax2] = fig.subplots(1, 2)
ax1.imshow(train_imgs['input_images'][3][...],
origin='upper',
cmap='Greys_r',
vmin=120,
vmax=200)
ax2.imshow(train_imgs['target_masks'][3][...],
origin='upper',
cmap='Greys_r')
plt.show()
ctrs = pd.HDFStore(zenodo_path + 'train_craters.hdf5', 'r')
# pdb.set_trace()
Image.MAX_IMAGE_PIXELS = None
img = Image.open(zenodo_path +
"/LunarLROLrocKaguya_118mperpix.png").convert("L")
# Read and combine the LROC and Head datasets (stored under ../catalogues)
craters = igen.ReadLROCHeadCombinedCraterCSV(
filelroc="catalogues/LROCCraters.csv",
filehead="catalogues/HeadCraters.csv")
# Generate 100 image/target sets, and corresponding crater dataframes. np.random.seed is set for consistency.
igen.GenDataset(img,
craters,
zenodo_path + '/test_zenodo',
amt=25,
seed=1337)
gen_imgs = h5py.File(zenodo_path + '/test_zenodo_images.hdf5', 'r')
sample_data = {
'imgs': [
gen_imgs['input_images'][...].astype('float32'),
gen_imgs['target_masks'][...].astype('float32')
]
}
proc.preprocess(
sample_data
) #now, input images is shape 25 x 256 x 256 x 1, target_masks is shape 25 x 256 x 256
sd_input_images = sample_data['imgs'][0] #25 x 256 x 256 x 1
sd_target_masks = sample_data['imgs'][1] #25 x 256 x 256p
# Plot the data for fun.
fig = plt.figure(figsize=[12, 6])
[ax1, ax2] = fig.subplots(1, 2)
ax1.imshow(sd_input_images[5].squeeze(),
origin='upper',
cmap='Greys_r',
vmin=0,
vmax=1.1)
ax2.imshow(sd_target_masks[5].squeeze(), origin='upper', cmap='Greys_r')
plt.savefig("plots/processed_image_and_mask1.png")
fig = plt.figure(figsize=[12, 6])
[ax1, ax2] = fig.subplots(1, 2)
ax1.imshow(sd_input_images[8].squeeze(),
origin='upper',
cmap='Greys_r',
vmin=0,
vmax=1.1)
ax2.imshow(sd_target_masks[8].squeeze(), origin='upper', cmap='Greys_r')
plt.savefig("plots/processed_image_and_mask2.png")
fig = plt.figure(figsize=[12, 6])
[ax1, ax2] = fig.subplots(1, 2)
ax1.imshow(sd_input_images[12].squeeze(),
origin='upper',
cmap='Greys_r',
vmin=0,
vmax=1.1)
ax2.imshow(sd_target_masks[12].squeeze(), origin='upper', cmap='Greys_r')
plt.savefig("plots/processed_image_and_mask3.png")
fig = plt.figure(figsize=[12, 6])
[ax1, ax2] = fig.subplots(1, 2)
ax1.imshow(sd_input_images[16].squeeze(),
origin='upper',
cmap='Greys_r',
vmin=0,
vmax=1.1)
ax2.imshow(sd_target_masks[16].squeeze(), origin='upper', cmap='Greys_r')
plt.savefig("plots/processed_image_and_mask4.png")
sample_images = train_imgs['input_images'][0:20]
sample_masks = train_imgs['target_masks'][0:20]
pdb.set_trace()
model = load_model('models/model_30k.h5')
#### TEST CRATERS
test_imgs = h5py.File(zenodo_path + '/test_images.hdf5', 'r')
test_data = {
'imgs': [
test_imgs['input_images'][...].astype('float32'),
test_imgs['target_masks'][...].astype('float32')
]
}
proc.preprocess(test_data)
sd_input_images = test_data['imgs'][0]
sd_target_masks = test_data['imgs'][1]
iwant = 3
pred = model.predict(sd_input_images[iwant:iwant + 1])
extracted_rings = tmt.template_match_t(
pred[0].copy(), minrad=2.) # x coord, y coord, radius
fig = plt.figure(figsize=[16, 16])
[[ax1, ax2], [ax3, ax4]] = fig.subplots(2, 2)
ax1.imshow(sd_input_images[iwant].squeeze(),
origin='upper',
cmap='Greys_r',
vmin=0,
vmax=1.1)
ax2.imshow(sd_target_masks[iwant].squeeze(),
origin='upper',
cmap='Greys_r')
ax3.imshow(pred[0], origin='upper', cmap='Greys_r', vmin=0, vmax=1)
ax4.imshow(sd_input_images[iwant].squeeze(),
origin='upper',
cmap="Greys_r")
for x, y, r in extracted_rings:
circle = plt.Circle((x, y),
r,
color='blue',
fill=False,
linewidth=2,
alpha=0.5)
ax4.add_artist(circle)
ax1.set_title('Moon DEM Image')
ax2.set_title('Ground-Truth Target Mask')
ax3.set_title('CNN Predictions')
ax4.set_title('Post-CNN Craters')
plt.savefig("plots/trained_model_results1.png")
iwant = 6
pred = model.predict(sd_input_images[iwant:iwant + 1])
extracted_rings = tmt.template_match_t(
pred[0].copy(), minrad=2.) # x coord, y coord, radius
fig = plt.figure(figsize=[16, 16])
[[ax1, ax2], [ax3, ax4]] = fig.subplots(2, 2)
ax1.imshow(sd_input_images[iwant].squeeze(),
origin='upper',
cmap='Greys_r',
vmin=0,
vmax=1.1)
ax2.imshow(sd_target_masks[iwant].squeeze(),
origin='upper',
cmap='Greys_r')
ax3.imshow(pred[0], origin='upper', cmap='Greys_r', vmin=0, vmax=1)
ax4.imshow(sd_input_images[iwant].squeeze(),
origin='upper',
cmap="Greys_r")
for x, y, r in extracted_rings:
circle = plt.Circle((x, y),
r,
color='blue',
fill=False,
linewidth=2,
alpha=0.5)
ax4.add_artist(circle)
ax1.set_title('Moon DEM Image')
ax2.set_title('Ground-Truth Target Mask')
ax3.set_title('CNN Predictions')
ax4.set_title('Post-CNN Craters')
plt.savefig("plots/trained_model_results2.png")
iwant = 13
pred = model.predict(sd_input_images[iwant:iwant + 1])
extracted_rings = tmt.template_match_t(
pred[0].copy(), minrad=2.) # x coord, y coord, radius
fig = plt.figure(figsize=[16, 16])
[[ax1, ax2], [ax3, ax4]] = fig.subplots(2, 2)
ax1.imshow(sd_input_images[iwant].squeeze(),
origin='upper',
cmap='Greys_r',
vmin=0,
vmax=1.1)
ax2.imshow(sd_target_masks[iwant].squeeze(),
origin='upper',
cmap='Greys_r')
ax3.imshow(pred[0], origin='upper', cmap='Greys_r', vmin=0, vmax=1)
ax4.imshow(sd_input_images[iwant].squeeze(),
origin='upper',
cmap="Greys_r")
for x, y, r in extracted_rings:
circle = plt.Circle((x, y),
r,
color='blue',
fill=False,
linewidth=2,
alpha=0.5)
ax4.add_artist(circle)
ax1.set_title('Moon DEM Image')
ax2.set_title('Ground-Truth Target Mask')
ax3.set_title('CNN Predictions')
ax4.set_title('Post-CNN Craters')
plt.savefig("plots/trained_model_results3.png")
