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Slicer.py
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Slicer.py
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import cv2
from math import floor
from os import listdir
import numpy as np
import random
import math
from sklearn.utils import shuffle
from PIL import Image
class Slicer():
def __init__(self):
pass
def split(self,img_array,bw,bh,stridew,strideh):
total_width = floor(img_array.shape[0] / strideh)
total_height = floor(img_array.shape[1] / stridew)
sub_images = []
coords1 = []
for x in range(total_width):
x_ini = x*strideh
x_fim = x*strideh+bw
if(x_fim>img_array.shape[0]):
diff = x_fim-img_array.shape[0]
x_ini -= diff
x_fim -= diff
for y in range(total_height):
y_ini = y*stridew
y_fim = y*stridew+bh
if(y_fim>img_array.shape[1]):
diff = y_fim-img_array.shape[1]
y_ini -= diff
y_fim -= diff
sub_images.append(img_array[x_ini:x_fim,y_ini:y_fim])
coords1.append([y_ini,y_fim,x_ini,x_fim])
return np.array(sub_images), np.array(coords1)
def isOverlapping(self,r1,r2):
if(len(r2)==0):
return False
if ( r1[0] > r2[1] or r1[1] < r2[0]):
return False
if ( r1[2] > r2[3] or r1[3] < r2[2]):
return False
return True
def OverlapArea(self,r1,r2):
if(not self.isOverlapping(r1,r2)):
return 0
rs = r1
if(r1[1]>r2[1]):
r = r1
r1 = r2
r2 = r
x_overlap = max(0, min(r1[1], r2[1]) - max(r1[0], r2[0]))
y_overlap = max(0, min(r1[3], r2[3]) - max(r1[2], r2[2]))
if((x_overlap * y_overlap)<=0):
return 0
v = (( (rs[1]-rs[0])*(rs[3]-rs[2]) )/(x_overlap * y_overlap))
return 1/v