def from_logits(path):
inp = tf.keras.layers.Input((3145728 / 6, 6))
#x = tf.keras.layers.Reshape((-1,6))(inp)
x = tf.keras.layers.Activation('softmax')(inp)
m = tf.keras.models.Model(inputs=[inp], outputs=[x])
pbar = tqdm.tqdm(total=933)
for (pre, post) in flow.get_test_files():
filename = os.path.basename(pre)
logits1 = np.fromfile("logits/1-{}".format(filename)).astype(
np.int32).reshape((-1, 6))
logits2 = np.fromfile("logits/2-{}".format(filename)).astype(
np.int32).reshape((-1, 6))
logits = (logits1 + logits2) / 2
pred = m.predict(np.expand_dims(logits, axis=0))
mask = infer.convert_prediction(pred).astype(np.uint8)
filename = filename.replace("pre", "localization").replace(
".png", "_prediction.png")
write_solution(names=[filename], images=[mask], path=path)
filename = filename.replace("localization", "damage")
write_solution(names=[filename], images=[mask], path=path)
pbar.update(1)
def damage_random(path):
"""
Generate solution .png files using random damage.
"""
model = train.build_model(
train=False) #, save_path="motokimura-stacked-2.hdf5")
model = train.load_weights(model, S.MODELSTRING_BEST)
df = flow.Dataflow(files=flow.get_test_files(),
transform=False,
batch_size=1,
buildings_only=False,
shuffle=False,
return_postmask=False,
return_stacked=True,
return_average=False)
pbar = tqdm.tqdm(total=len(df))
for image, filename in df:
filename = os.path.basename(filename)
filename = filename.replace("pre", "localization").replace(
".png", "_prediction.png")
#if os.path.exists(os.path.join("solution", filename)):
# continue
# localization (segmentation)
pred = model.predict([image])
mask = infer.convert_prediction(pred)
write_solution(names=[filename], images=[mask], path=path)
mask = randomize_damage(mask)
filename = filename.replace("localization", "damage")
write_solution(names=[filename], images=[mask], path=path)
pbar.update(1)
def damage_by_segmentation(path):
"""
Generate solution .png files, using a single multiclass segmentation
model to do so.
"""
model = train.build_model(classes=6, damage=True)
model = train.load_weights(model,
"damage-motokimura-mobilenetv2-best.hdf5")
#model.load_individual_weights()# = train.load_weights(model, S.DMG_MODELSTRING_BEST)
df = flow.Dataflow(files=flow.get_test_files(),
transform=False,
batch_size=1,
buildings_only=False,
shuffle=False,
return_postmask=False,
return_stacked=True,
return_average=False)
pbar = tqdm.tqdm(total=len(df))
for image, filename in df:
filename = os.path.basename(filename)
filename = filename.replace("pre", "localization").replace(
".png", "_prediction.png")
#if os.path.exists(os.path.join("solution", filename)):
# continue
# localization (segmentation)
pred = model.predict([image])
mask = infer.convert_prediction(pred)
write_solution(names=[filename], images=[mask], path=path)
filename = filename.replace("localization", "damage")
write_solution(names=[filename], images=[mask], path=path)
pbar.update(1)
def damage_by_building_classification(path):
"""
Generate solution .png files, classifying damage using contiguous
regions in the segmentation model's predicted masks in order to extract
individual building polygons from pre-disaster and post-disaster images.
"""
# load the localization (segmentation) model
S.BATCH_SIZE = 1
model = train.build_model(architecture=S.ARCHITECTURE, train=True)
model = train.load_weights(
model, S.MODELSTRING_BEST) #.replace(".hdf5", "-best.hdf5"))
# load the damage classification model
dmg_model = damage.build_model()
dmg_model = damage.load_weights(dmg_model, S.DMG_MODELSTRING_BEST)
# get a dataflow for the test files
df = flow.Dataflow(files=flow.get_test_files(),
transform=False,
shuffle=False,
buildings_only=False,
batch_size=1,
return_stacked=True)
i = 0
pbar = tqdm.tqdm(total=len(df))
# x = pre-disaster image, y = post-disaster image
for stacked, filename in df:
filename = os.path.basename(filename)
x = stacked
#filename = os.path.basename(df.samples[i][0].img_name)
filename = filename.replace("pre", "localization").replace(
".png", "_prediction.png")
#if os.path.exists(os.path.join("solution", filename)):
# continue
# localization (segmentation)
pred = model.predict(x)
mask = infer.convert_prediction(pred)
write_solution(names=[filename], images=[mask], path=path)
# damage classification
filename = filename.replace("localization", "damage")
pre, post = stacked[..., :3], stacked[
..., 3:] #df.samples[i][0].image(), df.samples[i][1].image()
boxes, coords = flow.Building.get_all_in(pre, post, mask)
if len(boxes) > 0:
labels = dmg_model.predict(boxes)
for k, c in enumerate(coords):
x, y, w, h = c
mask[y:y + h, x:x + w] = np.argmax(labels[k]) + 1
write_solution(names=[filename], images=[mask], path=path)
pbar.update(1)
i += 1
def predict_and_show(df, argmax=True):
model = train.build_model()
model = train.load_weights(model, S.MODELSTRING_BEST)
for imgs, mask in df:
pre = imgs[...,:3]
post = imgs[...,3:]
pred = model.predict(imgs)
mask = infer.convert_prediction(mask)
maxed = infer.convert_prediction(pred, argmax=True)
pred, _ = infer.convert_prediction(pred, argmax=False)
pred1 = pred[...,0]
pred2 = pred[...,1]
try:
display_images([pre, post, maxed, pred1, pred2, mask], ["Pre", "Post", "Argmax", "Pred1", "Pred2", "Ground Truth"])
except Exception as exc:
[print(x.shape) for x in [pre,post,maxed,pred1,pred2,mask]]
raise exc
def show(df):
i = 0
for imgs, masks in df:
pre = imgs[...,:3]
post = imgs[...,3:]
mask = infer.convert_prediction(masks)
prename = df.samples[i][0].img_name
postname = df.samples[i][1].img_name
display_images([pre, post, mask], [prename, postname, "Mask"])
i += 1