def test_gendataset(self, tmpdir, ringwidth):
amt = 10
zeropad = 2
outhead = str(tmpdir.join('gentest'))
igen.GenDataset(self.img, self.craters, outhead,
rawlen_range=[300, 1000], rawlen_dist='log',
ilen=self.imlen, tglen=self.imlen, cdim=self.cdim,
minpix=1, ringwidth=ringwidth, amt=amt, istart=0,
seed=self.seed)
imgs_h5 = h5py.File(outhead + '_images.hdf5', 'r')
craters_h5 = pd.HDFStore(outhead + '_craters.hdf5', 'r')
for i in range(amt):
# Find image number.
img_number = "img_{i:0{zp}d}".format(i=i, zp=zeropad)
# Load box.
box = np.array(imgs_h5['pix_bounds'][img_number][...])
im = self.img.crop(box)
im.load()
# Obtain long/lat bounds for coordinate transform.
ix = box[::2]
iy = box[1::2]
llong, llat = trf.pix2coord(ix, iy, self.cdim, list(self.img.size),
origin='upper')
llbd = np.r_[llong, llat[::-1]]
# Downsample image.
im = im.resize([self.imlen, self.imlen], resample=Image.NEAREST)
# Remove all craters that are too small to be seen in image.
ctr_sub = igen.ResampleCraters(self.craters, llbd, im.size[1],
minpix=1)
# Convert Plate Carree to Orthographic.
[imgo, ctr_xy, distortion_coefficient, clonglat_xy] = (
igen.PlateCarree_to_Orthographic(
im, llbd, ctr_sub, iglobe=self.iglobe, ctr_sub=True,
slivercut=0.5))
imgo_arr = np.asanyarray(imgo)
# Make target mask.
tgt = np.asanyarray(imgo.resize((self.imlen, self.imlen),
resample=Image.BILINEAR))
mask = igen.make_mask(ctr_xy, tgt, binary=True, rings=True,
ringwidth=ringwidth, truncate=True)
assert np.all(imgo_arr == imgs_h5['input_images'][i, ...])
assert np.all(mask == imgs_h5['target_masks'][i, ...])
assert np.all(llbd == imgs_h5['longlat_bounds'][img_number][...])
assert (distortion_coefficient ==
imgs_h5['pix_distortion_coefficient'][img_number][0])
assert np.all(clonglat_xy[["x", "y"]] ==
imgs_h5['cll_xy'][img_number][...])
assert np.all(ctr_xy == craters_h5[img_number])
imgs_h5.close()
craters_h5.close()
# 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,
cdim=sub_cdim,
arad=R_km,
minpix=minpix,
tglen=tglen,
binary=True,
rings=True,
ringwidth=ringwidth,
truncate=truncate,
amt=amt,
istart=istart,
verbose=verbose)
elapsed_time = time.time() - start_time
if verbose:
print("Time elapsed: {0:.1f} min".format(elapsed_time / 60.))
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")