# Calculate statistics for measuring the goodness of the prediction.
stats_temp = lib.calculate_goodness_of_classification(labels_true=labels_true_cropped,
labels_predicted=labels_predicted,
labels_dict=labels_dict)
# Cast dict objects 'stats_temp' to a pd.DataFrame object and append it
# to 'stats_all' that stores statistics for all images of this test round.
stats_all = stats_all.append(pd.DataFrame.from_dict(stats_temp, orient='index').T)
# Plot predicted class labels over aerial view image.
lib.plot_sat_image_and_labels(img=img_cropped,
lab=labels_predicted,
alpha=0.5,
plot_img=False,
store_img=True,
plot_with='Image',
dir_name=RESULTS_ROOT,
file_name='test_image_' + area_name + RUN_NAME + str(number_of_training_steps),
cmap=cmap1)
# Store classification statistics calculated from these test images.
with open(RESULTS_ROOT + 'stats_all_after_training_' + str(number_of_training_steps) + '.pickle', 'w') as f:
pickle.dump(stats_all, f)
f.close()
# Calculate average statistics for classifications based on
# all images that have been used in this test round.
stats_average = stats_all.T.mean(axis=1)
print "Statistics for all labels:"
lib.print_statistics_to_console(stats_average, 3)
for dir_content in dir_contents:
if 'top_potsdam' in dir_content:
area_names.append(dir_content)
area_names.sort()
for i in range(len(area_names)):
area_name = area_names[i].replace('_RGB', '')
print "Processing area " + str(i + 1) + ' (' + str(area_name) + ") of " + str(len(area_names)) + " areas ..."
directory_name = STORE_DATA_TO_ROOT + area_name + '_RGB' + '/'
if not os.path.exists(directory_name):
os.makedirs(directory_name)
# Load aerial image.
img = Image.open(LOAD_IMAGES_FROM_ROOT + area_name + '/' + area_name + '_wholeSatImage_cropped_rotated_small.png')
# Load labels.
labels = np.load(LOAD_LABELS_FROM_ROOT + area_name + '_RGB/' + area_name + '_labels.npy')
# Overlay aerial image and labels for control and save.
lib.plot_sat_image_and_labels(img=np.asarray(img),
lab=labels,
cmap=cmap1,
plot_img=False,
store_img=True,
plot_with='Image',
file_name= area_name + '_w_B+H',
dir_name=directory_name)
return_mean_iu=False,
return_frequency_weighted_iu=False)
# Cast dict objects 'stats_temp' to a pd.DataFrame object and append it
# to 'stats_all' that stores statistics for all images of this test round.
stats_temp_Data_Frame = pd.DataFrame(stats_temp.values()).T
stats_temp_Data_Frame.columns = stats_temp.keys()
stats_all = stats_all.append(stats_temp_Data_Frame)
# Plot predicted class labels over aerial view image.
lib.plot_sat_image_and_labels(img=img_cropped,
lab_1=labels_predicted,
lab_2=None,
alpha_1=0.5,
alpha_2=0.25,
alpha_3=0.25,
plot_img=False,
store_img=True,
plot_with='Image',
dir_name=RESULTS_ROOT,
file_name='test_image_' + area_name + '_fcn-8s_p2_' + str(number_of_training_steps))
# Store classification statistics calculated from these test images.
with open(RESULTS_ROOT + 'stats_all_after_training_' + str(number_of_training_steps) + '.pickle', 'w') as f:
pickle.dump(stats_all, f)
# Calculate average statistics for classifications based on
# all images that have been used in this test round.
stats_average = stats_all.T.mean(axis=1)
lib.print_statistics_to_console(stats_average, 3)
# Choose randomly some tile images and overlay them with their corresponding tile label images.
# We do this to check whether tile images and tile label images correspond to each other.
tile_number = '0000101'
for idx in range(len(names_tiles_img)):
if names_tiles_img[idx].startswith(tile_number):
name_tile_img = names_tiles_img[idx]
name_tile_labels = names_tiles_labels[idx]
# Load tile image and corresponding tile label image.
img = np.load(TILES_ROOT + 'img_npy/' + name_tile_img)
labels = np.load(TILES_ROOT + 'labels_npy/' + name_tile_labels)
# Overlay tile image and tile labels.
lib.plot_sat_image_and_labels(img, labels)
# Specify which large aerial images should be used for training
# and which should be used for testing.
areas_all_numbers = []
for i in range(2, 8):
for j in range(10, 13):
areas_all_numbers.append(str(i) + '_' + str(j))
for i in range(6, 8):
for j in range(7, 10):
areas_all_numbers.append(str(i) + '_' + str(j))
areas_testing_numbers = np.asarray(['2_12', '6_10', '7_11'])
areas_training_numbers = np.setdiff1d(areas_all_numbers, areas_testing_numbers)
areas_training_names = []
for area in areas_training_numbers:
# Calculate statistics for measuring the goodness of the prediction.
stats_temp = lib.calculate_goodness_of_classification(
labels_true=labels_true_cropped, labels_predicted=labels_predicted, labels_dict=labels_dict
)
# Cast dict objects 'stats_temp' to a pd.DataFrame object and append it
# to 'stats_all' that stores statistics for all images of this test round.
stats_all = stats_all.append(pd.DataFrame.from_dict(stats_temp, orient="index").T)
# Plot predicted class labels over aerial view image.
lib.plot_sat_image_and_labels(
img=img_cropped,
lab=labels_predicted,
alpha=0.5,
plot_img=False,
store_img=True,
plot_with="Image",
dir_name=RESULTS_ROOT,
file_name="test_image_" + area_name + RUN_NAME + ADDITIONAL_INFO + str(number_of_training_steps),
cmap=cmap1,
)
# Store classification statistics calculated from these test images.
with open(
RESULTS_ROOT + "stats_all_after_training_" + RUN_NAME + ADDITIONAL_INFO + str(number_of_training_steps) + ".pickle",
"w",
) as f:
pickle.dump(stats_all, f)
f.close()
# Calculate average statistics for classifications based on
labels_out = np.copy(isprs_labels)
# Add additional road labels.
if os.path.exists(road_add_labels_name):
labels_out[road_add_labels[:,:,0] != 255] = 2
# Subract road labels that should be removed. Note that we only remove road labels
# from pixels that were previously labeled as roads.
road_labels = (isprs_labels == 2)
if os.path.exists(road_subtract_labels_name):
road_labels_to_subtract_1 = (road_subtract_labels[:,:,0] != 255)
road_labels_to_subtract_2 = (road_labels_to_subtract_1 & road_labels)
labels_out[road_labels_to_subtract_2] = 0
# Store 'labels_out'
out_name_1 = area_name + '_label_ETH_transformed_3.npy'
np.save(STORE_DATA_ROOT + out_name_1, labels_out)
# Overlay 'labels_out' with aerial image.
out_name_2 = area_name + '_w_B+H_mod_2'
cmap = colors.ListedColormap(['white', 'red', 'blue'])
lib.plot_sat_image_and_labels(img=img,
lab=labels_out,
cmap=cmap,
plot_img=False,
store_img=True,
plot_with='Image',
dir_name=STORE_DATA_ROOT,
file_name=out_name_2
)
LOAD_LABELS_FROM = LOAD_IMAGE_FROM
STORE_DATA_ROOT = LOAD_IMAGE_FROM
# Set color map.
cmap1 = colors.ListedColormap(['white', 'red', 'blue'])
# Specify names of areas.
area_names = os.listdir(LOAD_IMAGE_FROM)
for i in range(len(area_names)):
print "Processing area " + str(i + 1) + " of " + str(len(area_names)) + " areas ..."
area_name = area_names[i]
image = np.asarray(Image.open(LOAD_IMAGE_FROM + area_name + '/' + area_name +
"_RGB.png"))
labels = np.load(LOAD_LABELS_FROM + area_name + '/' + area_name + "_labels.npy")
labels_3d = np.full(shape=labels.shape + (3,), fill_value=255).astype(np.uint8)
labels_3d[(labels == 1), :] = (255, 0, 0)
labels_3d[(labels == 2), :] = (0, 0, 255)
labels_out = Image.fromarray(labels_3d, 'RGB')
labels_out.save(STORE_DATA_ROOT + area_name + '/' + area_name + "_RGB_wholeImage_o_B+H.png")
lib.plot_sat_image_and_labels(img=image,
lab=labels,
cmap=cmap1,
plot_with='Image',
plot_img=False,
store_img=True,
dir_name=STORE_DATA_ROOT + area_name + '/',
file_name=area_name + '_wholeSatImage_cropped_rotated_small_w_B+H')
img_google_np = np.asarray(img_google)
img_google_np_cropped = img_google_np[crop_ind_left:crop_ind_right, crop_ind_top:crop_ind_bottom]
labels_isprs_new_rotated_reshaped_np_2d_cropped = \
labels_isprs_new_rotated_reshaped_np_2d[crop_ind_left:crop_ind_right, crop_ind_top:crop_ind_bottom]
# Cast google aerial image back from numpy to RGB.
img_google_cropped = Image.fromarray(img_google_np_cropped)
img_google_np_cropped = None
# Save cropped google aerial image as PNG.
img_google_cropped.save(STORE_DATA_TO_DIR + area_name + '/transform_out/' + area_name + '_wholeSatImage_cropped.png')
# Overlay the Google Maps image and the ISPRS pixel-wise object labels.
lib.plot_sat_image_and_labels(img=np.asarray(img_google_cropped),
lab=labels_isprs_new_rotated_reshaped_np_2d_cropped,
cmap=color_map,
plot_img=False,
store_img=True,
plot_with='Image',
dir_name=STORE_DATA_TO_DIR + area_name + '/transform_out/',
file_name=area_name + '_overlay_google_img_isprs_labels')
# Overlay the Google Maps image and the ISPRS image.
img_isprs_rotated_reshaped = img_isprs_rotated.copy()
img_isprs_rotated_reshaped = img_isprs_rotated_reshaped.resize(google_img_shape, Image.ANTIALIAS)
img_google_isprs_overlay = Image.blend(img_google, img_isprs_rotated_reshaped, 0.5)
img_google_isprs_overlay.save(STORE_DATA_TO_DIR + area_name + '/transform_out/' + area_name + '_overlay_google_isprs_images.png')
# Store the rotated and cropped, pixel-wise ISPRS labels as numpy file
np.save(STORE_DATA_TO_DIR + area_name + '/transform_out/' + area_name + '_ground_truth_labels.npy', labels_isprs_new_rotated_reshaped_np_2d_cropped)
)
# Cast dict objects 'stats_temp' to a pd.DataFrame object and append it
# to 'stats_all' that stores statistics for all images of this test round.
stats_temp_Data_Frame = pd.DataFrame(stats_temp.values()).T
stats_temp_Data_Frame.columns = stats_temp.keys()
stats_all = stats_all.append(stats_temp_Data_Frame)
# Plot predicted class labels over aerial view image.
lib.plot_sat_image_and_labels(
img=img_cropped,
lab_1=labels_predicted,
lab_2=None,
alpha_1=0.5,
alpha_2=0.25,
alpha_3=0.25,
plot_img=False,
store_img=True,
plot_with="Image",
dir_name=RESULTS_ROOT,
file_name="test_image_" + area_name + "_" + str(number_of_training_steps),
)
# Store classification statistics calculated from these test images.
with open(RESULTS_ROOT + "stats_all_after_training_" + str(number_of_training_steps) + ".pickle", "w") as f:
pickle.dump(stats_all, f)
# Calculate average statistics for classifications based on
# all images that have been used in this test round.
stats_average = stats_all.T.mean(axis=1)
lib.print_statistics_to_console(stats_average, 3)
print "Processing area " + str(i + 1) + ' (' + str(area_name) + ") of " + str(len(area_names)) + " areas ..."
directory_name = STORE_DATA_TO_ROOT + area_name + '/'
if not os.path.exists(directory_name):
os.makedirs(directory_name)
# Load aerial image.
img = Image.open(LOAD_IMAGES_FROM_ROOT + area_name + '.png')
# Load label.
labels = Image.fromarray(np.load(LOAD_LABELS_FROM_ROOT + area_name.replace('RGB', 'label_ETH_transformed_3.npy')))
# Shrink aerial image and labels.
img_shrunken = img.resize((wdt_new, hgh_new), PIL.Image.ANTIALIAS)
labels_shrunken = np.asarray(labels.resize((wdt_new, hgh_new), resample=Image.NEAREST)).astype(np.uint8)
# Store aerial image and labels.
img_shrunken.save(STORE_DATA_TO_ROOT + area_name + '/' + area_name + '.png')
np.save(STORE_DATA_TO_ROOT + area_name + '/' + area_name.replace('RGB', 'labels') , labels_shrunken)
# Overlay aerial image and labels for control and store.
lib.plot_sat_image_and_labels(img=np.asarray(img_shrunken),
lab=labels_shrunken,
cmap=cmap1,
plot_img=False,
store_img=True,
plot_with='Image',
file_name= area_name + '_w_B+H',
dir_name=STORE_DATA_TO_ROOT + area_name + '/')
