def accuracy_calculate():
# load mappings.txt
dict1 = read_CSV(conf.MAPPINGS)
dict2 = read_CSV(conf.TEST_MAPPINGS)
# Calculate the accuracy.
correct = 0
for i in range(conf.TEST_NUMBER):
if dict1[i] == dict2[i]:
correct += 1
else:
print("Number:", i, "False:", dict1[i], "True:", dict2[i])
print("Accuracy:", correct / conf.TEST_NUMBER)
return correct / conf.TEST_NUMBER
def cut_batch(start, end):
num2char = read_CSV(conf.TRAIN_MAPPINGS)
for i in range(start, end):
name = str(i).zfill(4)
path = conf.TRAIN_IMAGE_PATH+"/"+name+".jpg"
image = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
cnt = 0
cut_list = [image[:, 2:46], image[:, 37:81], image[:, 77:121], image[:, 117:161], image[:, 154:198]]
for j in range(len(cut_list)):
if not os.path.exists(conf.CUT_PATH+"/"+num2char[i][j]+"/"):
os.makedirs(conf.CUT_PATH+"/"+num2char[i][j]+"/")
path = conf.CUT_PATH+"/"+num2char[i][j]+"/"+name+"_"+str(j)+"_"+str(cnt)+".png"
image = denoise_opencv.remove_noise(cut_list[j])
cv2.imwrite(path, image)
print(">> Cutting pictures:"+str(i+1)+"/"+str(end))
def cut_batch(start, end):
""" Batch processing. """
mappings = read_CSV(conf.TRAIN_MAPPINGS)
for i in range(start, end):
name = str(i).zfill(4)
image = cv2.imread(conf.DENOISE_PATH + "/" + name + ".png")
horizontal_sum = np.sum(image, axis=0)
sum = [item[0] for item in horizontal_sum]
cut_list = image_cut(sum, image)
for j in range(len(cut_list)):
if not os.path.exists(conf.CUT_PATH + "/" +
name_dic[mappings[i][j]] + "/"):
os.makedirs(conf.CUT_PATH + "/" + name_dic[mappings[i][j]] +
"/")
path = conf.CUT_PATH + "/" + name_dic[
mappings[i][j]] + "/" + name + "_" + str(j) + ".png"
cv2.imwrite(path, cut_list[j])
print("Cutting pictures:" + str(i) + "/" + str(end - 1))
from util import read_CSV
import conf
# load mappings.txt
dict1 = read_CSV(conf.MAPPINGS)
dict2 = read_CSV(conf.TEST_MAPPINGS)
# Calculate the accuracy.
correct = 0
for i in range(conf.TEST_NUMBER):
if dict1[i] == dict2[i]:
correct += 1
else:
print("Number:", i, "False:", dict1[i], "True:", dict2[i])
print("Accuracy:", correct / conf.TEST_NUMBER)
import random
import cv2
import tensorflow as tf
from util import read_CSV
import numpy as np
import conf
from multiprocessing import Process
import time
import os
num2idx = read_CSV(conf.TRAIN_MAPPINGS)
def get_positive_and_negative_batch(number):
time1 = time.time()
for i in range(60000):
image1, image2, label = get_positive_pair()
image_1 = np.concatenate([image1, image2], axis=1)
save_path = conf.CUT_PATH + "/1/" + str(i) + "_" + str(number) + ".jpg"
cv2.imwrite(save_path, image_1)
image1, image2, label = get_negative_pair()
image_2 = np.concatenate([image1, image2], axis=1)
save_path = conf.CUT_PATH + "/0/" + str(i) + "_" + str(number) + ".jpg"
cv2.imwrite(save_path, image_2)
print("Cutting pictures:" + str(i + 1) + "/" + str(60000))
else:
time2 = time.time()
print(str(time2 - time1) + "s")
if not os.path.exists(conf.CUT_PATH + "/0/"):
os.makedirs(conf.CUT_PATH + "/0/")
import cv2
from util import read_CSV
import conf
import os
num2char = read_CSV(conf.TRAIN_MAPPINGS)
def cut_batch(start, end):
for i in range(start, end):
name = str(i).zfill(4)
path = conf.TRAIN_IMAGE_PATH + "/" + name + ".jpg"
image = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
cut_list = [
image[:, 6:62], image[:, 53:109], image[:, 96:152], image[:,
138:194]
]
for j in range(4):
if not os.path.exists(conf.CUT_PATH + "/" + num2char[i][j] + "/"):
os.makedirs(conf.CUT_PATH + "/" + num2char[i][j] + "/")
path = conf.CUT_PATH + "/" + num2char[i][
j] + "/" + name + "_" + str(j) + ".png"
if cut_list[j].shape == (80, 56):
_, image = cv2.threshold(cut_list[j], 180, 255,
cv2.THRESH_BINARY)
cv2.imwrite(path, image)
print("Cutting pictures:" + str(i) + "/" + str(end - 1))
if __name__ == '__main__':
cut_batch(0, 10000)
