def test_dice_coefficient(self):
# paths
image_train_path = "/Users/shiwakaga/Amodel_Data/train/*/images/*.jpg"
image_test_path = "/Users/shiwakaga/Amodel_Data/test/*/images/*.png"
x_path = "/Users/shiwakaga/Desktop/output/"
y_path = "/Users/shiwakaga/Amodel_Data/*/amodel/"
# read images
import glob
xs = glob.glob(x_path)
for img in xs:
x = imread(y_path + img.split('/')[-1].replace('raw','instrument_instances'))
y = imread(img)
# make images binary
x[x < 0.5] = 0
x[x >= 0.5] = 1
y[y < 0.5] = 0
y[y >= 0.5] = 1
# calculate dice
# dice = []
dice = compute_dice_coefficient(x,y)
print(dice)
with open('dice_sorce.txt','a+') as f:
f.write(img.split('/')[-1]+':'+str(dice)+'\n')
def detect_and_color_splash(model, image_path=None, video_path=None, out_path=None):
assert image_path or video_path
out_path = out_path
# Run model detection and generate the color splash effect
print("Running on {}".format(image_path))
# Read image
image = skimage.io.imread(image_path)
# Detect objects
r = model.detect([image], verbose=1)[0]
#--------- for show -----------#
splash,mask_bi = color_splash(image, r['masks'])
# get dice score
x_path = image_path.replace('raw', 'instrument_instances').replace('img', 'gt')
gt = imread(x_path)
gt[gt < 0.5] = 0
gt[gt >= 0.5] = 1
dice = compute_dice_coefficient(gt, mask_bi)
print(dice)
with open('60_flow_dice_sorce.txt', 'a+') as f:
f.write(x_path.split('/')[-1] + ':' + str(dice) + '\n')
# Save output
file_name = out_path+image_path.split('/')[-1]
skimage.io.imsave(file_name, splash)
def test_empty_mask_in_gt(self):
# test empty mask in ground truth
mask_gt = np.zeros((128, 128, 128), np.uint8)
mask_pred = np.zeros((128, 128, 128), np.uint8)
# mask_gt[50,60,70] = 1
mask_pred[50, 60, 72] = 1
surface_distances = compute_surface_distances(mask_gt, mask_pred, spacing_mm=(3, 2, 1))
average_surface_distance = compute_average_surface_distance(surface_distances)
hausdorf_100 = compute_robust_hausdorff(surface_distances, 100)
hausdorf_95 = compute_robust_hausdorff(surface_distances, 95)
surface_overlap_1_mm = compute_surface_overlap_at_tolerance(surface_distances, 1)
surface_dice_1mm = compute_surface_dice_at_tolerance(surface_distances, 1)
volumetric_dice = compute_dice_coefficient(mask_gt, mask_pred)
print("average surface distance: {} mm".format(average_surface_distance))
print("hausdorff (100%): {} mm".format(hausdorf_100))
print("hausdorff (95%): {} mm".format(hausdorf_95))
print("surface overlap at 1mm: {}".format(surface_overlap_1_mm))
print("surface dice at 1mm: {}".format(surface_dice_1mm))
print("volumetric dice: {}".format(volumetric_dice))
self.assertAlmostEqual(surface_dice_1mm, 0.0, delta=self.delta)
self.assertAlmostEqual(volumetric_dice, 0.0, delta=self.delta)
def test_bi_score(self,x_path,mask):
gt = imread(x_path)
gt[gt < 0.5] = 0
gt[gt >= 0.5] = 1
dice = compute_dice_coefficient(gt, mask)
print(dice)
with open('60_dice_sorce.txt', 'a+') as f:
f.write(x_path.split('/')[-1] + ':' + str(dice) + '\n')
def test_two_cubes(self):
# two cubes. cube 1 is 100x100x100 mm^3 and cube 2 is 102x100x100 mm^3
mask_gt = np.zeros((100, 100, 100), np.uint8)
mask_pred = np.zeros((100, 100, 100), np.uint8)
spacing_mm = (2, 1, 1)
mask_gt[0:50, :, :] = 1
mask_pred[0:51, :, :] = 1
surface_distances = compute_surface_distances(mask_gt, mask_pred, spacing_mm)
expected_average_distance_gt_to_pred = 0.836145008498
expected_volumetric_dice = 2. * 100 * 100 * 100 / (100 * 100 * 100 + 102 * 100 * 100)
surface_dice_1mm = compute_surface_dice_at_tolerance(surface_distances, 1)
volumetric_dice = compute_dice_coefficient(mask_gt, mask_pred)
print("surface dice at 1mm: {}".format(compute_surface_dice_at_tolerance(surface_distances, 1)))
print("volumetric dice: {}".format(compute_dice_coefficient(mask_gt, mask_pred)))
self.assertAlmostEqual(surface_dice_1mm, expected_average_distance_gt_to_pred, delta=self.delta)
self.assertAlmostEqual(volumetric_dice, expected_volumetric_dice, delta=self.delta)
def test_single_pixels_2mm_away(self):
mask_gt = np.zeros((128, 128, 128), np.uint8)
mask_pred = np.zeros((128, 128, 128), np.uint8)
mask_gt[50, 60, 70] = 1
mask_pred[50, 60, 72] = 1
surface_distances = compute_surface_distances(mask_gt, mask_pred, spacing_mm=(3, 2, 1))
surface_dice_1mm = compute_surface_dice_at_tolerance(surface_distances, 1)
volumetric_dice = compute_dice_coefficient(mask_gt, mask_pred)
print("surface dice at 1mm: {}".format(surface_dice_1mm))
print("volumetric dice: {}".format(volumetric_dice))
self.assertAlmostEqual(surface_dice_1mm, 0.5, delta=self.delta)
self.assertAlmostEqual(volumetric_dice, 0.0, delta=self.delta)