def test_save_pred_vs_label_7tuple_short_side_60_img():
"""
When the image is too low in resolution (e.g. short side 60),
we cannot use cv2's text rendering code. Instead, we will also save
the upsampled version.
"""
short_side = 60 # pixels
data_dir = f'{TEST_DATA_ROOT}/Camvid_test_data'
img_fpath = f'{data_dir}/images/0016E5_08159.png'
label_fpath = f'{data_dir}/preds/0016E5_08159.png'
img_rgb = imageio.imread(img_fpath)
label_img = imageio.imread(label_fpath)
img_rgb = resize_img_by_short_side(img_rgb, short_side, img_type='rgb')
label_img = resize_img_by_short_side(label_img,
short_side,
img_type='label')
img_h, img_w = label_img.shape
pred_img = np.random.randint(0, 200, (img_h, img_w)).astype(np.uint16)
id_to_class_name_map = get_dataloader_id_to_classname_map(
'pascal-context-460')
save_fpath = f'{TEST_DATA_ROOT}/rand_549_temp_small.png'
save_pred_vs_label_7tuple(img_rgb, pred_img, label_img,
id_to_class_name_map, save_fpath)
os.remove(
f'{TEST_DATA_ROOT}/rand_549_temp_small_upsample_pred_labels_palette.png'
)
os.remove(f'{TEST_DATA_ROOT}/rand_549_temp_small_pred_labels_palette.png')
def test_save_pred_vs_label_7tuple_uniform_random_preds():
"""
"""
data_dir = f'{TEST_DATA_ROOT}/Camvid_test_data'
img_fpath = f'{data_dir}/images/0016E5_08159.png'
label_fpath = f'{data_dir}/preds/0016E5_08159.png'
img_rgb = imageio.imread(img_fpath)
label_img = imageio.imread(label_fpath)
img_h, img_w = label_img.shape
pred_img = np.random.randint(0, 459, (img_h, img_w)).astype(np.uint16)
id_to_class_name_map = get_dataloader_id_to_classname_map(
'pascal-context-460')
save_fpath = f'{TEST_DATA_ROOT}/rand_549_temp.png'
save_pred_vs_label_7tuple(img_rgb, pred_img, label_img,
id_to_class_name_map, save_fpath)
os.remove(f'{TEST_DATA_ROOT}/rand_549_temp_pred_labels_palette.png')
def test_save_pred_vs_label_7tuple_shifted():
""" """
data_dir = f'{TEST_DATA_ROOT}/Camvid_test_data'
img_fpath = f'{data_dir}/images/0016E5_08159.png'
label_fpath = f'{data_dir}/preds/0016E5_08159.png'
img_rgb = imageio.imread(img_fpath)
label_img = imageio.imread(label_fpath)
pred_img = np.zeros_like(label_img)
pred_img[:, 150:] = label_img[:, :-150]
id_to_class_name_map = get_dataloader_id_to_classname_map(
'pascal-context-460')
save_fpath = f'{TEST_DATA_ROOT}/shifted_temp.png'
save_pred_vs_label_7tuple(img_rgb, pred_img, label_img,
id_to_class_name_map, save_fpath)
os.remove(f'{TEST_DATA_ROOT}/shifted_temp_pred_labels_palette.png')
def evaluate_predictions(self, save_vis: bool = True) -> None:
""" Calculate accuracy.
Args:
- data_list:
- pred_folder:
Returns:
- None
"""
pred_folder = self.gray_folder
for i, (image_path, target_path) in enumerate(self.data_list):
if self.args.img_name_unique:
image_name = Path(image_path).stem
else:
image_name = get_unique_stem_from_last_k_strs(image_path)
pred = cv2.imread(os.path.join(pred_folder, image_name + '.png'),
cv2.IMREAD_GRAYSCALE)
target_img = imageio.imread(target_path)
target_img = target_img.astype(np.int64)
target_img = self.convert_label_to_pred_taxonomy(target_img)
self.sam.update_metrics_cpu(pred, target_img,
self.num_eval_classes)
if (i + 1) % self.args.vis_freq == 0:
print_str = f'Evaluating {i + 1}/{len(self.data_list)} on image {image_name+".png"},' + \
f' accuracy {self.sam.accuracy:.4f}.'
logger.info(print_str)
if save_vis:
if (i + 1) % self.args.vis_freq == 0:
mask_save_dir = pred_folder.replace(
'gray', 'rgb_mask_predictions')
grid_save_fpath = f'{mask_save_dir}/{image_name}.png'
rgb_img = cv2_imread_rgb(image_path)
save_pred_vs_label_7tuple(rgb_img, pred, target_img,
self.id_to_class_name_map,
grid_save_fpath)
def test_save_pred_vs_label_7tuple_100_strided_preds():
"""
"""
data_dir = f'{TEST_DATA_ROOT}/Camvid_test_data'
img_fpath = f'{data_dir}/images/0016E5_08159.png'
label_fpath = f'{data_dir}/preds/0016E5_08159.png'
img_rgb = imageio.imread(img_fpath)
label_img = imageio.imread(label_fpath)
img_h, img_w = label_img.shape
class_ids_to_sample = np.array([0, 100, 200, 300])
pred_img = np.random.choice(a=class_ids_to_sample,
size=(img_h, img_w)).astype(np.uint16)
id_to_class_name_map = get_dataloader_id_to_classname_map(
'pascal-context-460')
save_fpath = f'{TEST_DATA_ROOT}/100_strided_temp.png'
save_pred_vs_label_7tuple(img_rgb, pred_img, label_img,
id_to_class_name_map, save_fpath)
os.remove(f'{TEST_DATA_ROOT}/100_strided_temp_pred_labels_palette.png')
