def cache_train_vehicle():
image_set = extra_functions.get_class_image(classes=[9, 10])
num_train = len(image_set)
print('num_train_images =', num_train)
train_shapes = shapes[shapes['image_id'].isin(image_set)]
image_rows = train_shapes['height'].min()
image_cols = train_shapes['width'].min()
num_channels = 22
num_mask_channels = 2
f = h5py.File(os.path.join(data_path, 'train_vehicle.h5'), 'w')
imgs = f.create_dataset('train',
(num_train, num_channels, image_rows, image_cols),
dtype=np.float32,
compression='gzip',
compression_opts=9)
imgs_mask = f.create_dataset(
'train_mask', (num_train, num_mask_channels, image_rows, image_cols),
dtype=np.uint8,
compression='gzip',
compression_opts=9)
ids = []
i = 0
for image_id in image_set:
print(image_id)
image = extra_functions.read_image_22(image_id)
height, width, _ = image.shape
imgs[i] = np.transpose(
cv2.resize(image, (image_cols, image_rows),
interpolation=cv2.INTER_CUBIC), (2, 0, 1))
imgs_mask[i] = np.transpose(
cv2.resize(np.transpose(
extra_functions.generate_mask(
image_id,
height,
width,
start=0,
num_mask_channels=num_mask_channels,
train=train_wkt), (1, 2, 0)), (image_cols, image_rows),
interpolation=cv2.INTER_CUBIC), (2, 0, 1))
ids += [image_id]
i += 1
# fix from there: https://github.com/h5py/h5py/issues/441
f['train_ids'] = np.array(ids).astype('|S9')
f.close()
x_scaler, y_scaler = extra_functions.get_scalers(H, W, x_max, y_min)
mask_channel = first_class
result += [(image_id, mask_channel + 1,
mask2poly(new_mask[:, :, 0], threashold1, x_scaler, y_scaler))]
mask_channel = first_class + 1
result += [(image_id, mask_channel + 1,
mask2poly(new_mask[:, :, 1], threashold2, x_scaler, y_scaler))]
return result
for image_id in tqdm(test_ids):
#for image_id in test_ids:
print(image_id)
image = extra_functions.read_image_22(image_id)
H = image.shape[0]
W = image.shape[1]
x_max, y_min = extra_functions._get_xmax_ymin(image_id)
model = read_model('b_s.h5')
result = predict_poly(model, 0.1, 0.1, result, 0)
model = read_model('r_t.h5')
result = predict_poly(model, 0.1, 0.1, result, 2)
model = read_model('t_c.h5')
result = predict_poly(model, 0.9, 0.9, result, 4)
model = read_model('vehicle.h5')
result = predict_poly(model, 0.9, 0.05, result, 8)