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main.py
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main.py
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#==========================================================
import urllib
from PIL import Image
import os
import matplotlib.pyplot as plt
import cv2
import matplotlib.image as mpimg
import numpy as np
import pandas as pd
import math
import re
os.chdir('/home/linsam/kaggle/dl_test')
#============================function============================
# 對其一個點的周圍 2x2 的範圍進行掃描，並設定 threshold
# 若高於 threshold 則視為非雜點
def del_mis_pt_by_threshold(im2):
for col in range(3):
count = 0
for i in xrange(len(im2)):
for j in xrange( len(im2[i]) ):
if( im2[i,j,col] == 255):
count = 0
for k in range(-2,3):
#print(k)
for l in range(-2,3):
try:
if im2[i + k,j + l,col] == 255:
count += 1
except IndexError:
pass
# 這裡 threshold 設 4，當周遭小於 4 個點的話視為雜點
if count <= 4:
im2[i,j,col] = 0
return im2
def tran_gray(jpg_path):
#jpg_path = 't1.jpg'
image = Image.open(jpg_path)
#灰度化处理
#有很多种算法，这里选择rgb加权平均值算法
gray_image = Image.new('L', image.size)
#获得rgb数据序列，每一个都是(r,g,b)三元组
raw_data = image.getdata()
gray_data = []
for rgb in raw_data:
value = 0.299 * rgb[0] + 0.587 * rgb[1] + 0.114 * rgb[2]
#value就是灰度值，这里使用127作为阀值，
#小于127的就认为是黑色也就是0 大于等于127的就是白色，也就是255
if value < 127:
gray_data.append(0)
else:
gray_data.append(255)
gray_image.putdata(gray_data)
return gray_image
def xrange(x):
return iter(range(x))
def input_image_pix_and_plot(jpg_path):
#jpg_path = 't1.jpg'
image = cv2.imread(jpg_path, cv2.IMREAD_COLOR)
plt.imshow(image)
plt.show()
return image
def catch_axis_start_and_end(im4,axis='x'):
if(axis=='x'):
split = []
for i in range(im4.shape[1]):
if( sum( im4[:,i,0] ) != 0 ):
split.append(i)
split_num_start = [split[0]]
split_num_end = []
for i in range(2,len(split),1):
#print(i)
if(split[i]-split[i-1]>3): #and split[i-1]-split[i-2]!=1):
split_num_end.append(split[i-1])
split_num_start.append(split[i])
#print(split[i])
split_num_end.append(split[len(split)-1])
elif(axis=='y'):
split = []
for i in range(im4.shape[0]):
if( sum( im4[i,:,0] ) != 0 ):
split.append(i)
split_num_start = min(split)
split_num_end = max(split)
return split_num_start,split_num_end
def my_plt_fun(split_num_start,split_num_end,k):
start = split_num_start[k]-3
end = split_num_end[k]+3
img = im4[:,start:end,:]
plt.imshow(img)
y_start,y_end = catch_axis_start_and_end(img,axis='y')
y_start = y_start-3
y_end = y_end+3
img2 = img[y_start:y_end,:,:]
plt.imshow(img2)
plt.savefig('split_image_' + str(k) + '.png')
return img2
#============================main============================
# input image
im = cv2.imread('t3.jpg', cv2.IMREAD_COLOR)
plt.imshow(im)
# transform gray scale
retval, im2 = cv2.threshold(im, 115, 255, cv2.THRESH_BINARY_INV)
plt.imshow(im2)
# del miscellaneous points
im3 = del_mis_pt_by_threshold(im2)
plt.imshow(im3)
# image enhancement
# 雜點去除後，我們必須對剩下的影像做強化
im4 = cv2.dilate(im3, (2, 2), iterations=1)
plt.imshow(im4)
# save figure
plt.savefig('del_mix_pt.png')
#-------------------------------------------------
# split number
x_split_start,x_split_end = catch_axis_start_and_end(im4,axis='x')
# for example
img1 = my_plt_fun(x_split_start,x_split_end,0)
plt.imshow(img1)
# split number and save figure
for i in range(len(x_split_start)):
my_plt_fun(x_split_start,x_split_end,i)