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import os
from typing import List
import metrics
import numpy as np
import torch
import torchvision.transforms.functional as TF
from PIL import Image
from sklearn.metrics import balanced_accuracy_score
from torchvision.ops import masks_to_boxes
from tqdm import tqdm
def compute_size_boundry_centroid(binary_mask):
is_empty = not np.any(binary_mask)
H, W = binary_mask.shape
if is_empty:
return (0, 0), (H // 2, W // 2), (W // 2, W // 2, H // 2, H // 2)
else:
y, x = np.where(binary_mask == True)
left_boundary = np.min(x)
right_boundary = np.max(x)
top_boundary = np.min(y)
bottom_boundary = np.max(y)
centroid_x = int(left_boundary + right_boundary) // 2
centroid_y = int(top_boundary + bottom_boundary) // 2
width, height = right_boundary - left_boundary + 1, bottom_boundary - top_boundary + 1
return (
(width, height),
(centroid_x, centroid_y),
(left_boundary, right_boundary, top_boundary, bottom_boundary),
)
def crop_mask(mask1, mask2):
"""
crop a pair of masks according to the size of the larger mask
"""
assert (
mask1.shape == mask2.shape
), f"Annotation({mask1.shape}) and segmentation:{mask2.shape} dimensions do not match."
mask1 = np.pad(
mask1,
((mask1.shape[0], mask1.shape[0]), (mask1.shape[0], mask1.shape[0])),
mode="constant",
constant_values=False,
)
mask2 = np.pad(
mask2,
((mask2.shape[0], mask2.shape[0]), (mask2.shape[0], mask2.shape[0])),
mode="constant",
constant_values=False,
)
size_1, centroid_1, boundary_1 = compute_size_boundry_centroid(mask1)
size_2, centroid_2, boundary_2 = compute_size_boundry_centroid(mask2)
width, height = max(size_1[0], size_2[0]), max(size_1[1], size_2[1])
# print(f"Crop Width: {width}, Crop Height: {height}")
compact_mask_1 = mask1[
centroid_1[1] - height // 2 : centroid_1[1] + height // 2 + 1,
centroid_1[0] - width // 2 : centroid_1[0] + width // 2 + 1,
]
compact_mask_2 = mask2[
centroid_2[1] - height // 2 : centroid_2[1] + height // 2 + 1,
centroid_2[0] - width // 2 : centroid_2[0] + width // 2 + 1,
]
return (size_1, size_2), (centroid_1, centroid_2), (compact_mask_1, compact_mask_2)
def crop_resize_using_mask(img: torch.Tensor, mask: torch.Tensor, target_size: List[int]):
"""TODO: Docstring for crop_resize_using_mask.
Args:
img (torch.Tensor): shape: [C,H,W].
mask (torch.Tensor): shape: [H,W].
target_size (List[int])
Returns: TODO
"""
if mask.sum() == 0:
return torch.ones([img.shape[0]] + list(target_size))
bbox = masks_to_boxes(mask.unsqueeze(0)).squeeze(0)
x1, y1, x2, y2 = bbox
h = y2 - y1
w = x2 - x1
if h > 0 and w > 0:
img = TF.crop(img, int(y1), int(x1), int(h), int(w)) # type: ignore
img = TF.resize(img, target_size)
return img
def load_mask(mask_path: str, mode="L"):
"""load mask to numpy array
Args:
img_path (str): path to image.
Returns: np.ndarray | None. dtype: np.bool.
return None if file not exist or occurred some errors during loading.
"""
try:
if not os.path.isfile(mask_path):
print(f"file not existed for image: {mask_path}")
return None
mask_pil = Image.open(mask_path).convert(mode)
mask_pil = mask_pil.resize(IMG_SIZE, resample=Image.NEAREST)
# foreground: >127 -> True
return np.array(mask_pil) > 127
except Exception as e:
print(f"Exception while loading image: {mask_path}: {e}")
return None
IMG_SIZE = (256, 256) # resize image to this size for evaluation.
DIR_GT = "ground-truths"
DIR_PRED = "predictions"
def evaluate(results_dir):
dir_gt = os.path.join(results_dir, DIR_GT)
dir_pred = os.path.join(results_dir, DIR_PRED)
img_names = os.listdir(dir_gt)
print(f"number of images: {len(img_names)}")
# metrics
# mask existence or not
mask_existence_gt_list = []
mask_existence_pred_list = []
# iou and boundary are calculated after register the prediction with the ground truth.
riou_score_list = []
rboundary_score_list = []
location_error_list = []
for _, img_name in enumerate(tqdm(img_names)):
if not img_name.endswith(".png"):
continue
mask_gt = load_mask(os.path.join(dir_gt, img_name))
if mask_gt is None: # skip if erros with GT image.
continue
# mask exist if any value is not False.
mask_existence_gt = np.any(mask_gt)
mask_pred = load_mask(os.path.join(dir_pred, img_name))
if mask_pred is None:
mask_existence_pred = False
else:
mask_existence_pred = np.any(mask_pred)
if not mask_existence_gt:
mask_existence_gt_list.append(mask_existence_gt)
mask_existence_pred_list.append(mask_existence_pred)
# skip other metrics if gt_mask is empty (no object)
continue
# object exists and prediction exists
if mask_pred is not None:
(
(gt_size, fake_size),
(centroid_gt, centroid_pred),
(gt_compact_mask, pred_compact_mask),
) = crop_mask(mask_gt, mask_pred)
centroid_distance = np.sqrt(
(centroid_gt[0] - centroid_pred[0]) ** 2
+ (centroid_gt[1] - centroid_pred[1]) ** 2
)
riou = metrics.db_eval_iou(gt_compact_mask, pred_compact_mask)
rboundary = metrics.db_eval_boundary(gt_compact_mask, pred_compact_mask)
location_error = centroid_distance / np.sqrt(IMG_SIZE[0] ** 2 + IMG_SIZE[1] ** 2)
else:
riou, rboundary, location_error = 0.0, 0.0, 1.0
mask_existence_gt_list.append(mask_existence_gt)
mask_existence_pred_list.append(mask_existence_pred)
riou_score_list.append(riou)
rboundary_score_list.append(rboundary)
location_error_list.append(location_error)
# balanced mask existence accuracy
balanced_mask_existence_acc = balanced_accuracy_score(
mask_existence_gt_list, mask_existence_pred_list
)
riou_score = np.mean(riou_score_list)
rboundary_score = np.mean(rboundary_score_list)
location_error = np.mean(location_error_list)
print(f"mask_existence balanced accuracy={balanced_mask_existence_acc}")
print(f"Registered IOU score={riou_score}")
print(f"Registered boundary score={rboundary_score}")
print(f"Location Error={location_error}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--results_dir", type=str, help="The directory of the results")
args = parser.parse_args()
evaluate(args.results_dir)
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