name = fname.split("/")[-1]
#arr = wv.get_image(fname)
with rasterio.open(fname, "r") as r:
arr = r.read()
arr = np.rollaxis(arr, 0, 3)
im, box, classes_final = wv.chip_image(arr, coords[chips == name],
classes[chips == name], it)
#Shuffle images & boxes all at once. Comment out the line below if you don't want to shuffle images
im, box, classes_final = shuffle_images_and_boxes_classes(
im, box, classes_final)
split_ind = int(im.shape[0] * args.test_percent)
for idx, image in enumerate(im):
tf_example = tfr.to_tf_example(image, box[idx],
classes_final[idx])
#Check to make sure that the TF_Example has valid bounding boxes.
#If there are no valid bounding boxes, then don't save the image to the TFRecord.
float_list_value = tf_example.features.feature[
'image/object/bbox/xmin'].float_list.value
if (ind_chips < max_chips_per_res
and np.array(float_list_value).any()):
tot_box += np.array(float_list_value).shape[0]
if idx < split_ind:
test_writer.write(tf_example.SerializeToString())
test_chips += 1
else:
train_writer.write(tf_example.SerializeToString())
# debug
# get statistics about number of damaged buildings and non-damaged buildings
#print("type of new_classes: ", type(new_classes))
# print('new_classes', new_classes)
#print('new_classes[new_classes==1]: ', new_classes.count(1))
# number of class 1 bbox in the small chip
local_class1 = new_classes[new_classes == 1].shape[0]
local_class2 = new_classes[new_classes == 2].shape[0]
# debug
# here only write into TF RECORD classes == 1
#tf_example = tfr.to_tf_example(image, new_coords[new_classes ==1], new_classes[new_classes == 1])
tf_example = tfr.to_tf_example(image, new_coords, new_classes)
#Check to make sure that the TF_Example has valid bounding boxes.
#If there are no valid bounding boxes, then don't save the image to the TFRecord.
float_list_value_xmin = tf_example.features.feature[
'image/object/bbox/xmin'].float_list.value
float_list_value_ymin = tf_example.features.feature[
'image/object/bbox/ymin'].float_list.value
float_list_value_xmax = tf_example.features.feature[
'image/object/bbox/xmax'].float_list.value
float_list_value_ymax = tf_example.features.feature[
'image/object/bbox/ymax'].float_list.value
if (ind_chips < max_chips_per_res
and np.array(float_list_value_xmin).any()
and np.array(float_list_value_xmax).any()
num_cloud_rm += 1
# debug: changed image,box[idx],classes_final[idx] to newly constructed img and box
#tf_example = tfr.to_tf_example(image,box[idx],classes_final[idx])
local_class1 = new_classes[new_classes == 1].shape[0]
local_class2 = new_classes[new_classes == 2].shape[0]
if len(new_coords) == 1 and np.all(new_coords == 0):
print('This chip contains no bboxes, removing...')
continue
# debug
# here only write into TF RECORD classes == 1
tf_example = tfr.to_tf_example(image,
new_coords[new_classes == 1],
new_classes[new_classes == 1])
#tf_example = tfr.to_tf_example(image, new_coords, new_classes)
#Check to make sure that the TF_Example has valid bounding boxes.
#If there are no valid bounding boxes, then don't save the image to the TFRecord.
float_list_value_xmin = tf_example.features.feature[
'image/object/bbox/xmin'].float_list.value
# float_list_value_ymin = tf_example.features.feature['image/object/bbox/ymin'].float_list.value
# float_list_value_xmax = tf_example.features.feature['image/object/bbox/xmax'].float_list.value
# float_list_value_ymax = tf_example.features.feature['image/object/bbox/ymax'].float_list.value
# if (ind_chips < max_chips_per_res and np.array(float_list_value_xmin).any() and np.array(float_list_value_xmax).any() and np.array(float_list_value_ymin).any() and np.array(float_list_value_ymax).any()):
tot_box += np.array(float_list_value_xmin).shape[0]
