def generate_submission(self):
test_datapath = '/data/Kaggle/test-png'
# Get list of files
img_files = natsorted(glob(join(test_datapath, '*.png')))
# load files into array
test_imgs = np.stack(
[self.LoadImgForTest(f, self.dims) for f in img_files])[...,
np.newaxis]
# Get predicted masks
tqdm.write('Getting mask predictions...')
masks = self.model.predict(test_imgs,
batch_size=self.batch_size,
verbose=1)
# data to write to csv
submission_data = []
# process mask
for ind, cur_file in enumerate(tqdm(img_files)):
cur_mask = masks[ind, ..., 0]
cur_im = test_imgs[ind, ..., 0]
cur_mask = (cur_mask > .5).astype(np.int)
cur_id = splitfile(cur_file)
processed_mask = CleanMask_v1(cur_mask)
lbl_mask, numObj = scipy_label(processed_mask)
if numObj > 0:
for label in range(1, numObj + 1):
temp_mask = np.zeros_like(cur_mask)
temp_mask[lbl_mask == label] = 1
temp_mask = cv2.resize(temp_mask.astype(np.float),
(1024, 1024))
temp_mask[temp_mask < .5] = 0
temp_mask[temp_mask > 0] = 255
temp_mask = np.transpose(temp_mask)
cur_rle = mask2rle(temp_mask, 1024, 1024)
submission_data.append([cur_id, cur_rle])
else:
cur_rle = -1
submission_data.append([cur_id, cur_rle])
# write to csv
# generate time-stamped filename
timestamp = datetime.datetime.now().strftime("%Y_%m_%d_%H%M")
csv_filename = 'TestSubmission_{}'.format(timestamp)
tqdm.write('Writing csv...')
with open(csv_filename, mode='w') as f:
writer = csv.writer(f, delimiter=',')
for row in tqdm(submission_data):
writer.writerow(row)
print('Done')
def GetSubData(file, label, mask):
mask = mask[..., 0]
mask = (mask > .5).astype(np.int)
fid = splitfile(file)
if label == 0:
return [fid, -1]
processed_mask = CleanMask_v1(mask)
lbl_mask, numObj = scipy_label(processed_mask)
if numObj > 0:
processed_mask[processed_mask > 0] = 255
processed_mask = np.transpose(processed_mask)
rle = mask2rle(processed_mask, 1024, 1024)
else:
rle = -1
return [fid, rle]
for ind, cur_file in enumerate(tqdm(img_files)):
cur_mask = masks[ind, ..., 0]
cur_im = test_imgs[ind, ..., 0]
cur_mask = (cur_mask > .5).astype(np.int)
cur_id = splitfile(cur_file)
processed_mask = CleanMask_v1(cur_mask)
lbl_mask, numObj = scipy_label(processed_mask)
if numObj > 0:
for label in range(1, numObj + 1):
temp_mask = np.zeros_like(cur_mask)
temp_mask[lbl_mask == label] = 1
temp_mask = cv2.resize(temp_mask.astype(np.float), (1024, 1024))
temp_mask[temp_mask < .5] = 0
temp_mask[temp_mask > 0] = 255
temp_mask = np.transpose(temp_mask)
cur_rle = mask2rle(temp_mask, 1024, 1024)
submission_data.append([cur_id, cur_rle])
else:
cur_rle = -1
submission_data.append([cur_id, cur_rle])
# write to csv
tqdm.write('Writing csv...')
with open(submission_filepath, mode='w') as f:
writer = csv.writer(f, delimiter=',')
for row in tqdm(submission_data):
writer.writerow(row)
print('Done')
# data to write to csv
submission_data = []
# process mask
tqdm.write('Processing masks...')
for ind, cur_file in enumerate(tqdm(img_files)):
cur_mask = masks[ind, ..., 0]
cur_im = test_imgs[ind, ..., 0]
cur_mask = (cur_mask > .5).astype(np.int)
cur_id = splitfile(cur_file)
processed_mask = CleanMask_v1(cur_mask)
lbl_mask, numObj = scipy_label(processed_mask)
if numObj > 0:
processed_mask[processed_mask > 0] = 255
processed_mask = np.transpose(processed_mask)
cur_rle = mask2rle(processed_mask, 1024, 1024)
else:
cur_rle = -1
submission_data.append([cur_id, cur_rle])
# write to csv
tqdm.write('Writing csv...')
with open(submission_filepath, mode='w', newline='') as f:
writer = csv.writer(f, delimiter=',')
writer.writerow(['ImageId', 'EncodedPixels'])
for data in tqdm(submission_data):
writer.writerow(data)
# display some images
from VisTools import mask_viewer0
mask_viewer0(test_imgs[:100,...,0],masks[:100,...,0])