def test_topk():
assert topk(2, [4, 1, 5, 2]) == (5, 4)
assert topk(2, [4, 1, 5, 2], key=lambda x: -x) == (1, 2)
assert topk(2, iter([5, 1, 4, 2]), key=lambda x: -x) == (1, 2)
assert topk(2, [{'a': 1, 'b': 10}, {'a': 2, 'b': 9},
{'a': 10, 'b': 1}, {'a': 9, 'b': 2}], key='a') == \
({'a': 10, 'b': 1}, {'a': 9, 'b': 2})
assert topk(2, [{'a': 1, 'b': 10}, {'a': 2, 'b': 9},
{'a': 10, 'b': 1}, {'a': 9, 'b': 2}], key='b') == \
({'a': 1, 'b': 10}, {'a': 2, 'b': 9})
def test_topk():
assert topk(2, [4, 1, 5, 2]) == (5, 4)
assert topk(2, [4, 1, 5, 2], key=lambda x: -x) == (1, 2)
assert topk(2, iter([5, 1, 4, 2]), key=lambda x: -x) == (1, 2)
assert topk(2, [{'a': 1, 'b': 10}, {'a': 2, 'b': 9},
{'a': 10, 'b': 1}, {'a': 9, 'b': 2}], key='a') == \
({'a': 10, 'b': 1}, {'a': 9, 'b': 2})
assert topk(2, [{'a': 1, 'b': 10}, {'a': 2, 'b': 9},
{'a': 10, 'b': 1}, {'a': 9, 'b': 2}], key='b') == \
({'a': 1, 'b': 10}, {'a': 2, 'b': 9})
def segment_batch_of_images(model, list_of_images, list_of_filenames):
results = model.detect(list(list_of_images), verbose=1)
def area(box):
top, left, bottom, right = box
return abs(bottom - top) * abs(right - left)
def max_bound(item):
if set(max_bounded_box) == set(item):
return 1
else:
return 0
for counter, output in enumerate(results):
x = topk(1, output['rois'], key=area)
if not x:
print("No bounding Box detected")
else:
max_bounded_box = list(x[0])
max_mask_id = 0
for index, item in enumerate(map(max_bound, output['rois'])):
if item == 1:
max_mask_id = index
break
mask_img = output['masks'][:, :, max_mask_id]
mask_rgb = cv2.cvtColor(mask_img, cv2.COLOR_GRAY2RGB)
img = list_of_images[counter]
new_Image = np.asarray(img) * (mask_rgb != 0)
# Try and run with even black background by commenting the below one line
new_Image = np.where(new_Image == 0, 255, new_Image)
new_Image = Image.fromarray(new_Image.astype('uint8'), 'RGB')
if not list_of_filenames:
new_Image.save(
os.path.join(args.output_image_path,
"filename_zero" + str(counter) + '.jpg'))
else:
new_Image.save(
os.path.join(args.output_image_path,
list_of_filenames[counter]))
def segment_images(model, image):
new_image_path = os.path.join(args.output_image_path,
image['image_id']) + '.jpg'
if os.path.isfile(new_image_path):
print(f'Skipping previously segmened image {image["image_id"]}.')
return image['image_id']
results = model.detect([image['image']], verbose=1)
output = results[0]
def area(box):
top, left, bottom, right = box
return abs(bottom - top) * abs(right - left)
def max_bound(item):
if set(max_bounded_box) == set(item):
return 1
else:
return 0
x = topk(1, output['rois'], key=area)
if not x:
print("No bounding Box detected")
else:
max_bounded_box = list(x[0])
max_mask_id = 0
for index, item in enumerate(map(max_bound, output['rois'])):
if item == 1:
max_mask_id = index
break
mask_img = output['masks'][:, :, max_mask_id]
mask_rgb = cv2.cvtColor(mask_img, cv2.COLOR_GRAY2RGB)
new_image = image['image'] * (mask_rgb != 0)
new_image = np.where(mask_rgb == 0, 255, new_image)
new_image = Image.fromarray(new_image, 'RGB')
new_image.save(new_image_path)
return image['image_id']
def test_topk_is_stable():
assert topk(4, [5, 9, 2, 1, 5, 3], key=lambda x: 1) == (5, 9, 2, 1)
def test_topk_is_stable():
assert topk(4, [5, 9, 2, 1, 5, 3], key=lambda x: 1) == (5, 9, 2, 1)
def predict_discrete(limit, distance_fn, fm):
nearest_neighbors = topk(limit,
trainset_fms,
key=lambda x: -distance_fn(x[1], fm))
return map(op.itemgetter(0), nearest_neighbors)
