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read_input.py
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read_input.py
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from skimage.io import imread
from skimage.filters import threshold_otsu
import numpy as np
#
def kd_reduce(func,seq):
res = seq[0]
for item in seq[1:]:
res = func(res,item)
return res
def flatten_image(image_array):
def flatten(a,b): return a + b
return kd_reduce(flatten, image_array.tolist())
#
#print(flatten_image(imread("chars74k-lite/chars74k-lite/"+"a"+"/"+"a"+"_"+"0"+".jpg")))
def read_input_otsu():
print("Otsu")
alphabet=["a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z"]
#alphabet=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,17,19,20,21,2]
n=70
train_data=[]
train_solutions=[]
for i in range(n):
for letter in alphabet:
image = imread("chars74k-lite/chars74k-lite/"+letter+"/"+letter+"_"+str(i)+".jpg")
img = np.asarray(image)
img.setflags(write=True)
thresh = threshold_otsu(img)
binary = image > thresh
binary = np.reshape(binary,(1,400))
train_data.append(np.asarray(binary[0]).astype(float))
#
t=[0]*26#init all vectors to zero
t[ord(letter)-97] = 1
t = np.asarray(t)
train_solutions.append(t)
#print(train_data[0])
#train_data = threshold_otsu(train_data)
#print(train_data[0])
#
m=88
test_data=[]
test_solutions=[]
for j in range(n,m):
for letter in alphabet:
image = imread("chars74k-lite/chars74k-lite/"+letter+"/"+letter+"_"+str(j)+".jpg")
img = np.asarray(image)
img.setflags(write=True)
thresh = threshold_otsu(img)
binary = image > thresh
binary = np.reshape(binary,(1,400))
test_data.append(np.asarray(binary[0]).astype(float))
#
t=[0]*26#init all vectors to zero
t[ord(letter)-97] = 1
t = np.asarray(t)
test_solutions.append(t)
#
'''print(len(train_data))
print(len(train_solutions))
print(len(test_data))
print(len(test_solutions))'''
return train_data, train_solutions, test_data, test_solutions
####
def read_input_otsu_OLE():
alphabet=["a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z"]
#alphabet=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,17,19,20,21,2]
n=70
train_data=[]
train_solutions=[]
for i in range(n):
for letter in alphabet:
image = imread("chars74k-lite/chars74k-lite/"+letter+"/"+letter+"_"+str(i)+".jpg")
img = np.asarray(image)
img.setflags(write=True)
thresh = threshold_otsu(img)
binary = image > thresh
binary = np.reshape(binary,(1,400))
train_data.append(np.asarray(binary[0]).astype(float))
#
train_solutions.append(ord(letter))
#print(train_data[0])
#train_data = threshold_otsu(train_data)
#print(train_data[0])
#
m=88
test_data=[]
test_solutions=[]
for j in range(n,m):
for letter in alphabet:
image = imread("chars74k-lite/chars74k-lite/"+letter+"/"+letter+"_"+str(j)+".jpg")
img = np.asarray(image)
img.setflags(write=True)
thresh = threshold_otsu(img)
binary = image > thresh
binary = np.reshape(binary,(1,400))
test_data.append(np.asarray(binary[0]).astype(float))
#
test_solutions.append(ord(letter))
#
print(len(train_data))
print(len(train_solutions))
print(len(test_data))
print(len(test_solutions))
return train_data, train_solutions, test_data, test_solutions
####
def read_input():
<<<<<<< HEAD
alphabet=["a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y"]
=======
print("Scaling")
alphabet=["a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z"]
>>>>>>> 056a49c09203093a2d2e8b6d093c3c0a16fb5453
#alphabet=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,17,19,20,21,2]
n=35
train_data=[]
train_solutions=[]
for i in range(n):
for letter in alphabet:
train_data.append(np.asarray(flatten_image(imread("chars74k-lite/chars74k-lite/"+letter+"/"+letter+"_"+str(i)+".jpg"))))
#train_data[-1] = np.divide(train_data[-1],255)
t=[0]*26#init all vectors to zero
t[ord(letter)-97] = 1
t = np.asarray(t)
train_solutions.append(t)
#print(train_data[0])
#train_data = threshold_otsu(train_data)
#print(train_data[0])
#
m=44
test_data=[]
test_solutions=[]
for j in range(n,m):
for letter in alphabet:
test_data.append(np.asarray(flatten_image(imread("chars74k-lite/chars74k-lite/"+letter+"/"+letter+"_"+str(j)+".jpg"))))
#test_data[-1] = np.divide(test_data[-1],255)
t=[0]*26#init all vectors to zero
t[ord(letter)-97] = 1
t = np.asarray(t)
test_solutions.append(t)
#
<<<<<<< HEAD
=======
'''print(len(train_data))
print(len(train_solutions))
print(len(test_data))
print(len(test_solutions))'''
>>>>>>> 056a49c09203093a2d2e8b6d093c3c0a16fb5453
return train_data, train_solutions, test_data, test_solutions
####
def read_input_OLE():
alphabet=["a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z"]
#alphabet=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,17,19,20,21,2]
n=70
train_data=[]
train_solutions=[]
for i in range(n):
for letter in alphabet:
train_data.append(np.asarray(flatten_image(imread("chars74k-lite/chars74k-lite/"+letter+"/"+letter+"_"+str(i)+".jpg"))))
train_data[-1] = np.divide(train_data[-1],255)
train_solutions.append(ord(letter))
#
m=88
test_data=[]
test_solutions=[]
for j in range(n,m):
for letter in alphabet:
test_data.append(np.asarray(flatten_image(imread("chars74k-lite/chars74k-lite/"+letter+"/"+letter+"_"+str(j)+".jpg"))))
test_data[-1] = np.divide(test_data[-1],255)
test_solutions.append(ord(letter))
#
print(len(train_data))
print(len(train_solutions))
print(len(test_data))
print(test_solutions)
return train_data, train_solutions, test_data, test_solutions
if __name__ == '__main__':
a,b,c,d=read_input()