max_orientation, max_magnitude, max_frequency = gabor_feature_real(arr, sigmax=sigmax, sigmay=sigmay)
from src.utils.gabor import normalize
normal_gabor = normalize(max_magnitude)
fn = '/Users/ruhansa/Desktop/4.jpg'
saveimage_pil(normal_gabor, fn)
arr = filename2arr(fn)
from src.utils.canny import decide_sigma
sigma = 0.0
for i in range(5):
print "sigma: " + str(sigma)
ret = my_canny(arr, sigma=sigma, save=False, show=True)
s = decide_sigma(ret, ret.size)
if s is False:
sigma += 0.5
else:
break
raw = Image.fromarray(arr)
w, h = raw.size
from src.utils.hough import my_hough_2
import math
v_hough_end = math.pi / 9
v_hough_start = - math.pi / 9
# horizontal
h_hough_end = math.pi / 2 + math.pi / 9
h_hough_start = math.pi / 2 - math.pi / 9
cur_arr = cur_arr_3[:, :, 0]
from src.utils import preprocessing
import cv2
from src.utils.canny import decide_sigma
for sigma in [5]:
for neighbor in [30]:
blur = cv2.bilateralFilter(cur_arr.copy(), neighbor, sigma, sigma)
showimage_pil(blur)
cur_arr = blur
sigma = 0.0
r = 1
threshold = 0.025
while r > threshold:
print "sigma: " + str(sigma)
ret = my_canny(cur_arr, sigma=sigma, save=False, show=False)
s, r = decide_sigma(ret, ret.size, threshold=threshold,show=True)
if s is False:
sigma += 0.5
else:
if sigma == 0.0 and r < 0.01:
sharpen = preprocessing.sharpen(blur, sigma=2)
cur_arr= sharpen
showimage_pil(sharpen)
r = 1
continue
break
showimage_pil(ret)
from src.utils.hough import hough_horizontal, hough_vertical
from src.utils.canny import my_canny
from src.utils.preprocessing import normalize
box = boxes[2]
window = arr[box[1]+5: box[3]-5, box[0]+5: box[2]-5] # to eliminate the edge scenario
width = window.shape[1]
height = window.shape[0]
window = normalize(window)
ret = my_canny(window, sigma=0.0, save=False, show=DEBUG)
raw = Image.fromarray(np.zeros((height, width)))
lineh, raw = hough_horizontal(ret, hough_line_len=30,line_gap=50, save=False, show=True, raw=raw)
linev, raw = hough_vertical(ret,hough_line_len=30,line_gap=50, save=False, show=True, raw=raw )
raw.show()
f = np.array(raw.convert('L').getdata()).reshape(height, width)/255.
from src.utils.canny import decide_sigma
decide_sigma(f, area=width*height)
from src.utils.GVF import GVF, normalize_GVF_external_force
u, v = GVF(f, 0.2, 80)
px,py = normalize_GVF_external_force(u, v)
from src.utils.io import show_vector
line, plt = show_vector(px, 0-py, skip=6, holdon=True)
from src.utils.snake import snake_disp
from src.utils.snake import init_rect
x, y = init_rect(px.shape[0], px.shape[1])
snake_disp(line, plt, x, y)
from src.utils.snake import snake_deformation
snake_deformation(x, y, px, py, 30, show=True, line=line, plt=plt)
plt.show()