def polygons2mask_layer(height, width, polygon, image_id, i):
"""
:param height:
:param width:
:param polygons:
:return:
"""
x_max, y_min = extra_functions._get_xmax_ymin(image_id)
x_scaler, y_scaler = extra_functions.get_scalers(height, width, x_max,
y_min)
polygons = shapely.affinity.scale(polygon,
xfact=x_scaler,
yfact=y_scaler,
origin=(0, 0, 0))
img_mask = np.zeros((height, width), np.uint8)
if not polygons:
return img_mask
int_coords = lambda x: np.array(x).round().astype(np.int32)
exteriors = [int_coords(poly.exterior.coords) for poly in polygons]
interiors = [
int_coords(pi.coords) for poly in polygons for pi in poly.interiors
]
cv2.fillPoly(img_mask, exteriors, 11 - i)
cv2.fillPoly(img_mask, interiors, 0)
return img_mask
def predict_poly(model, threashold1, threashold2, result, first_class):
predicted_mask = extra_functions.make_prediction_cropped(
model,
image,
initial_size=(112, 112),
final_size=(112 - 32, 112 - 32),
num_masks=num_mask_channels,
num_channels=num_channels)
image_v = np.flipud(image)
predicted_mask_v = extra_functions.make_prediction_cropped(
model,
image_v,
initial_size=(112, 112),
final_size=(112 - 32, 112 - 32),
num_masks=2,
num_channels=num_channels)
image_h = np.fliplr(image)
predicted_mask_h = extra_functions.make_prediction_cropped(
model,
image_h,
initial_size=(112, 112),
final_size=(112 - 32, 112 - 32),
num_masks=2,
num_channels=num_channels)
image_s = np.rot90(image)
predicted_mask_s = extra_functions.make_prediction_cropped(
model,
image_s,
initial_size=(112, 112),
final_size=(112 - 32, 112 - 32),
num_masks=2,
num_channels=num_channels)
new_mask = np.power(
predicted_mask * np.flipud(predicted_mask_v) *
np.fliplr(predicted_mask_h) * np.rot90(predicted_mask_s, 3), 0.25)
x_scaler, y_scaler = extra_functions.get_scalers(H, W, x_max, y_min)
mask_channel = first_class
result += [(image_id, mask_channel + 1,
mask2poly(new_mask[:, :, 0], threashold1, x_scaler, y_scaler))]
mask_channel = first_class + 1
result += [(image_id, mask_channel + 1,
mask2poly(new_mask[:, :, 1], threashold2, x_scaler, y_scaler))]
return result
num_masks=1,
num_channels=num_channels)
image_s = image.swapaxes(1, 2)
predicted_mask_s = extra_functions.make_prediction_cropped(
model,
image_s,
initial_size=(112, 112),
final_size=(112 - 32, 112 - 32),
num_masks=1,
num_channels=num_channels)
new_mask = np.power(
predicted_mask * flip_axis(predicted_mask_v, 1) *
flip_axis(predicted_mask_h, 2) * predicted_mask_s.swapaxes(1, 2), 0.25)
x_scaler, y_scaler = extra_functions.get_scalers(H, W, x_max, y_min)
mask_channel = 0
result += [(image_id, mask_channel + 1,
mask2poly(new_mask, threashold, x_scaler, y_scaler))]
submission = pd.DataFrame(result,
columns=['ImageId', 'ClassType', 'MultipolygonWKT'])
sample = sample.drop('MultipolygonWKT', 1)
submission = sample.merge(submission, on=['ImageId', 'ClassType'],
how='left').fillna('MULTIPOLYGON EMPTY')
submission.to_csv('temp_building_3.csv', index=False)
yfact=1.0 / y_scaler,
origin=(0, 0, 0))
return shapely.wkt.dumps(polygons)
result = []
for image_id in tqdm(test_ids):
rgb = tiff.imread('../data/three_band/{}.tif'.format(image_id))
_, height, width = rgb.shape
rgb = np.rollaxis(rgb, 0, 3)
m = tiff.imread('../data/sixteen_band/{}_M.tif'.format(image_id))
# get our index
CCCI = CCCI_index(m, rgb)
x_max, y_min = extra_functions._get_xmax_ymin(image_id)
x_scaler, y_scaler = extra_functions.get_scalers(height, width, x_max,
y_min)
# you can look on histogram and pick your favorite threshold value(0.11 is my best)
predicted_mask = (CCCI > 0.11).astype(np.float32)
if predicted_mask.sum() > 680000:
result += [(image_id, 8, 'MULTIPOLYGON EMPTY')]
else:
result += [(image_id, 8, mask2poly(predicted_mask, x_scaler,
y_scaler))]
submission = pd.DataFrame(result,
columns=['ImageId', 'ClassType', 'MultipolygonWKT'])
sample = sample.drop('MultipolygonWKT', 1)
submission = sample.merge(submission, on=['ImageId', 'ClassType'],
