def fill_person_info(cls, person_id):
if person_id in cls.standards:
return
signatures = FileAccess.get_files_of_person(person_id)
normalized = [
Normalizer.crop_make_binary_image(signature)
for signature in signatures
]
average_height = round(mean([image.shape[0] for image in normalized]))
average_width = round(mean([image.shape[1] for image in normalized]))
scaled_to_standard_dimensions_signatures =\
[Normalizer.resize(average_width, average_height, img) for img in normalized]
def get_black_pixel_no(pic):
count = 0
for i in range(pic.shape[0]):
for j in range(pic.shape[1]):
if pic[i, j] == 0:
count += 1
return count
number_of_black_pixels = \
[get_black_pixel_no(pic) for pic in scaled_to_standard_dimensions_signatures]
max_no_black_pixels = max(number_of_black_pixels)
min_no_black_pixels = min(number_of_black_pixels)
cls.standards[person_id] = \
SignatureStandard(average_height, average_width, min_no_black_pixels, max_no_black_pixels)
def test_normalizer_crop(self):
pic = FileAccess.get_files_of_person("002")[0]
cropped = Normalizer.crop_make_binary_image(pic)
cv2.imshow("original", pic)
cv2.imshow("cropped", cropped)
cv2.waitKey(0)
def __get_normalized_image(cls,
signature_image_name: str) -> numpy.ndarray:
raw_pic = FileAccess.get_file(signature_image_name)
normalized = Normalizer.crop_make_binary_image(raw_pic)
return normalized
