def main():
filename = "../data/img/fingerprint.png"
img = np.empty((2, 2))
if cv2.os.path.exists(filename):
img = image_processing.load_image_grayscale(filename)
else:
print("file does not exist")
exit(1)
result_img = Optimal_pattern(img)
image_processing.image_show(result_img)
young_pattern_hist(result_img)
def main():
# sample code
filename = "../data/img/fingerprint.png"
img = np.empty((2, 2))
if cv2.os.path.exists(filename):
img = image_processing.load_image_grayscale(filename)
else:
print("file does not exist")
exit(1)
YP = Young_Finger(3, 6, 16.0, -5.0)
YP.load_ndarray(img)
YP.far_generation(10)
YP.show()
def main():
filename = "../data/img/fingerprint.png"
img = np.empty((2, 2))
if cv2.os.path.exists(filename):
img = image_processing.load_image_grayscale(filename)
else:
print("file does not exist")
exit(1)
trimed = trim(img, 0.05, 0.9, 0.1, 0.9, True)
magnify = 10 / 8
resize_img = cv2.resize(trimed, dsize=None, fx=magnify, fy=magnify)
young = Young_Finger(3, 6, 15, -5)
young.load_ndarray(resize_img)
gif_maker(young, generation=5, duration=1000)
# get_radius_listをscan_line_pairs回行って、幅の最頻値を求める
# thined_img: 2次元配列(細線化済み)
# scan_line_pairs: 整数(この値の2倍の線について幅を確認する)
# 返り値: 数値(幅)
def get_width(thined_img, scan_line_pairs=1, *, debug=False):
height, width = thined_img.shape
radius_list = []
for i in range(scan_line_pairs):
radius_list += get_radius_list(thined_img, [
int(width / (scan_line_pairs + 1) * (i + 1)),
int(height / (scan_line_pairs + 1) * (i + 1))
],
debug=debug)
if debug:
# histogram
make_hist(radius_list)
return collections.Counter(radius_list).most_common()[0][0]
if __name__ == '__main__':
img = image_processing.load_image_grayscale("../data/img/fingerprint.png")
img = image_processing.blur(img)
img = image_processing.threshold(img)
img = image_processing.bitwise_not(img)
img = image_processing.morphological_transformations(img)
img = image_processing.thinning(img)
image_processing.image_save(img, title="sample")
width = get_width(img, debug=True)
print("最頻値(幅): " + str(width))
import cv2
import numpy as np
from edit_img import image_processing
from optimise.Optimal_pattern import Optimal_pattern
import sys
print("animal")
print(sys.path)
for i in range(1, 5):
filename = "../data/img_animal/animal0{0}.jpg".format(i)
print(filename)
img = np.empty((2, 2))
if cv2.os.path.exists(filename):
img = image_processing.load_image_grayscale(filename)
else:
print("file does not exist")
exit(1)
result_img = Optimal_pattern(img)
image_processing.image_show(result_img)