def infer_one_img(file):
input_img = imread(file)
input_batch_list, coordinate_batch_list, height, width = create_patch_batch_list(filename=file, batch_size=128, data_dir=data_dir)
logits_map = batch_logits_map_inference(input_tensor, logits, keep_probability, sess, is_training, input_batch_list, coordinate_batch_list, height, width)
# Inferring
pred_annotation_map = np.array(np.argmax(logits_map, axis=2), dtype=np.uint8)
imwrite('mask_1_' + basename(file), np.transpose(np.transpose(input_img, (2, 0, 1)) * pred_annotation_map, (1, 2, 0)))
_, thresh = threshold(pred_annotation_map, 0, 255, 0)
_, contours, _ = findContours(thresh, RETR_EXTERNAL, 2)
for cnt in contours:
x,y,w,h = boundingRect(cnt)
for i in range(w):
count_white = 0
for j in range(h):
if thresh[y + j, x + i] == 255:
count_white += 1
if count_white / h <= CHOPPING_RATIO:
for j in range(h):
pred_annotation_map[y + j, x + i] = 0
_, thresh = threshold(pred_annotation_map, 0, 255, 0)
imwrite('mask_2_' + basename(file), np.transpose(np.transpose(input_img, (2, 0, 1)) * pred_annotation_map, (1, 2, 0)))
boxes_map = np.zeros_like(pred_annotation_map, dtype=np.uint8)
_, thresh = threshold(pred_annotation_map, 0, 255, 0)
_, contours, _ = findContours(thresh, RETR_EXTERNAL, 2)
name_boxes = []
for cnt in contours:
if contourArea(cnt) > 200:
x,y,w,h = boundingRect(cnt)
input_height, _, _ = input_img.shape
# x = max(0, x - pad)
y = max(0, y - pad)
# end_x = min(x + w + pad, input_width)
end_x = x + w
end_y = min(y + h + pad, input_height)
# w = end_x - x
h = end_y - y
if w > h / 1.75:
name_boxes.append([x, y, end_x, end_y])
for i in range(w):
for j in range(h):
boxes_map[y + j, x + i] = 1
height = pred_annotation_map.shape[0]
width = pred_annotation_map.shape[1]
output_words = np.transpose(np.transpose(input_img, (2, 0, 1)) * boxes_map, (1, 2, 0))
imwrite(basename(file), output_words)
top_img[i] = imread("../ISPRS_semantic_labeling_Vaihingen/top/" +
filename[i] + ".tif")
logits_maps[i] = np.zeros(
(top_img[i].shape[0], top_img[i].shape[1], 6), dtype=np.float32)
# gt_annotation_maps[i] = imread("../ISPRS_semantic_labeling_Vaihingen/annotations/" + filename[i] + ".png", -1) # Comment if for submission
# num_pixels += gt_annotation_maps[i].shape[0] * gt_annotation_maps[i].shape[1] # Comment if for submission
# Accumulate logits maps
ckpt = tf.train.get_checkpoint_state(argv[1]) # checkpoint directory
for ckpt_path in ckpt.all_model_checkpoint_paths:
# for ckpt_path in [tf.train.get_checkpoint_state(argv[1]).model_checkpoint_path]:
saver.restore(sess, ckpt_path)
print(">> Restored:", ckpt_path)
for i in range(num_img_files):
print(ckpt_path, "inferring", filename[i])
input_batch_list, coordinate_batch_list, height, width = create_patch_batch_list(
filename=filename[i], batch_size=128, num_channels=5)
current_logits_map = batch_logits_map_inference(
input_tensor, logits, keep_probability, sess, is_training,
input_batch_list, coordinate_batch_list, height, width)
logits_maps[i] += current_logits_map
# Inferring
for i in range(num_img_files):
pred_annotation_maps[i] = np.argmax(logits_maps[i], axis=2)
# num_matches += np.sum(pred_annotation_maps[i] == gt_annotation_maps[i]) # Comment if for submission
height = pred_annotation_maps[i].shape[0]
width = pred_annotation_maps[i].shape[1]
output_image = np.zeros((height, width, 3), dtype=np.uint8)
print("Generating", filename[i] + '.tif......')
for y in range(height):
def infer_img(file, model_output_dir=argv[1]):
global num_matches
global num_pixels
if not exists(model_output_dir):
mkdir(model_output_dir)
gt_annotation_map = np.array(imread(join(data_dir, annotation_dir, file), -1), dtype=np.uint8)
if gt_annotation_map.shape[0] >= 32 and gt_annotation_map.shape[1] >= 32:
input_img = imread(join(data_dir, input_dir, file))
input_batch_list, coordinate_batch_list, height, width = create_patch_batch_list(filename=file, batch_size=128, data_dir=data_dir)
logits_map = batch_logits_map_inference(input_tensor, logits, keep_probability, sess, is_training, input_batch_list, coordinate_batch_list, height, width)
# Inferring
pred_annotation_map = np.array(np.argmax(logits_map, axis=2), dtype=np.uint8)
_, thresh = threshold(pred_annotation_map, 0, 255, 0)
_, contours, _ = findContours(thresh, RETR_EXTERNAL, 2)
for cnt in contours:
x,y,w,h = boundingRect(cnt)
for i in range(w):
count_white = 0
for j in range(h):
if thresh[y + j, x + i] == 255:
count_white += 1
if count_white / h <= CHOPPING_RATIO:
for j in range(h):
pred_annotation_map[y + j, x + i] = 0
if not exists(join(model_output_dir, pred_masks_dir)):
mkdir(join(model_output_dir, pred_masks_dir))
_, thresh = threshold(pred_annotation_map, 0, 255, 0)
imwrite(join(model_output_dir, pred_masks_dir, file), thresh)
boxes_map = np.zeros_like(pred_annotation_map, dtype=np.uint8)
_, thresh = threshold(pred_annotation_map, 0, 255, 0)
_, contours, _ = findContours(thresh, RETR_EXTERNAL, 2)
json_name = file.replace('png', 'json')
json_dict = {}
name_boxes = []
for cnt in contours:
if contourArea(cnt) > 200:
x,y,w,h = boundingRect(cnt)
input_height, input_width, _ = input_img.shape
# x = max(0, x - pad)
y = max(0, y - pad)
# end_x = min(x + w + pad, input_width)
end_x = x + w
end_y = min(y + h + pad, input_height)
# w = end_x - x
h = end_y - y
if w > h / 1.75:
name_boxes.append([x, y, end_x, end_y])
for i in range(w):
for j in range(h):
boxes_map[y + j, x + i] = 1
_, thresh_boxes = threshold(boxes_map, 0, 255, 0)
if not exists(join(model_output_dir, pred_boxed_masks_dir)):
mkdir(join(model_output_dir, pred_boxed_masks_dir))
imwrite(join(model_output_dir, pred_boxed_masks_dir, file), thresh_boxes)
json_dict['name_boxes'] = name_boxes
print(json_dict)
if not exists(output_json_dir):
mkdir(output_json_dir)
with open(join(output_json_dir, json_name), 'w') as outfile:
json.dump(json_dict, outfile)
height = pred_annotation_map.shape[0]
width = pred_annotation_map.shape[1]
# _, contours, _ = findContours(thresh, RETR_EXTERNAL, 2)
num_pixels += height * width
num_matches += np.sum(pred_annotation_map == gt_annotation_map)
output_words = np.transpose(np.transpose(input_img, (2, 0, 1)) * boxes_map, (1, 2, 0))
if not exists(join(model_output_dir, words_dir)):
mkdir(join(model_output_dir, words_dir))
imwrite(join(model_output_dir, words_dir, file), output_words)