67 lines
1.4 KiB
Python
67 lines
1.4 KiB
Python
|
# -*- coding: UTF-8 -*-
|
||
|
|
import numpy as np
|
||
|
|
from scipy import ndimage
|
||
|
|
|
||
|
|
def find_miny(mat,val = 1):
|
||
|
|
(n,m) = mat.shape
|
||
|
|
for i in range(0,n):
|
||
|
|
for j in range(0,m):
|
||
|
|
if mat[i,j] == val:
|
||
|
|
return i
|
||
|
|
|
||
|
|
return None
|
||
|
|
|
||
|
|
def find_minx(mat,val = 1):
|
||
|
|
(n,m) = mat.shape
|
||
|
|
for j in range(0,m):
|
||
|
|
for i in range(0,n):
|
||
|
|
if mat[i,j] == val:
|
||
|
|
return j
|
||
|
|
|
||
|
|
return None
|
||
|
|
|
||
|
|
|
||
|
|
def find_maxy(mat,val = 1):
|
||
|
|
(n,m) = mat.shape
|
||
|
|
for i in range(n-1,-1,-1):
|
||
|
|
for j in range(m-1,-1,-1):
|
||
|
|
if mat[i,j] == val:
|
||
|
|
return i
|
||
|
|
|
||
|
|
return None
|
||
|
|
|
||
|
|
def find_maxx(mat,val = 1):
|
||
|
|
(n,m) = mat.shape
|
||
|
|
for j in range(m-1,-1,-1):
|
||
|
|
for i in range(n-1,-1,-1):
|
||
|
|
if mat[i,j] == val:
|
||
|
|
return j
|
||
|
|
|
||
|
|
return None
|
||
|
|
|
||
|
|
|
||
|
|
def calc_image_crop(img, val = 255):
|
||
|
|
minx = find_minx(img,val)
|
||
|
|
maxx = find_maxx(img,val)
|
||
|
|
miny = find_miny(img,val)
|
||
|
|
maxy = find_maxy(img,val)
|
||
|
|
return (minx,maxx,miny,maxy)
|
||
|
|
|
||
|
|
def crop_image(img,val = 255):
|
||
|
|
(minx,maxx,miny,maxy) = calc_image_crop(img,val)
|
||
|
|
return img[miny:maxy,minx:maxx]
|
||
|
|
|
||
|
|
def crop_image_prefilled(img,val = 255):
|
||
|
|
(minx,maxx,miny,maxy) = calc_image_crop(img,val)
|
||
|
|
img_filled = ndimage.binary_fill_holes(img)
|
||
|
|
return img_filled[miny:maxy,minx:maxx]
|
||
|
|
|
||
|
|
def dimension_rate(img):
|
||
|
|
(n,m) = img.shape
|
||
|
|
return float(m)/float(n)
|
||
|
|
|
||
|
|
def image_to_mask(img,threshold = 128):
|
||
|
|
m = img >= threshold
|
||
|
|
return 1*m
|
||
|
|
|