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
return shapely.wkt.dumps(polygons.buffer(2.6e-5))
#%%
for image_id in tqdm(test_ids):
image = extra_functions.read_image_16(image_id)
file_name = '{}.tif'.format(image_id)
image_3 = tiff.imread(os.path.join(three_band_path, file_name))
image_3 = np.transpose(image_3, (1, 2, 0))
image_3 = image_3 / 2047 * 255
image_3 = np.array(image_3, dtype=np.uint8)
H = image.shape[1]
W = image.shape[2]
x_max, y_min = extra_functions._get_xmax_ymin(image_id)
predicted_mask = extra_functions.make_prediction_cropped(
model,
image,
initial_size=(128, 128),
final_size=(128 - 32, 128 - 32),
num_masks=num_mask_channels,
num_channels=num_channels)
mask_to_draw = np.zeros((H, W, 3), np.uint8)
for i in range(num_mask_channels):
mask_to_draw[predicted_mask[i] >= threshold] = color[i + 1]
#mask_to_draw[predicted_mask[i]<threshold] = (255,188,64)
image_mask = cv2.addWeighted(image_3, 0.6, mask_to_draw, 0.4, 0)
