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predict.py
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import numpy as np
import os
import sys
from tensorflow.keras import models
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import load_img
import cv2 as cv
import tensorflow as tf
os.chdir('/Users/evan/Desktop/BIML_project')
img_rows = None
img_cols = None
digits_in_img=4
model = None
np.set_printoptions(suppress=True, linewidth=150, precision=9, formatter={'float': '{: 0.9f}'.format})
def split_digits_in_img(img_array):
x_list = list()
img1 = np.zeros((80,215,1), np.uint8)
img = np.zeros((80,215,1), np.uint8)
gray1 = img_array
gray2 = img_array
img=gray2
for t in range(3):
for x in range(80):
for y in range(215):
if x>=6 and x<=74 and y<= 213:
if img[x][y]==112:
if (img[x+1][y]==140 and img[x+2][y]==140 ) or (img[x-1][y]==140 and img[x-2][y]==140) and img[x+1][y]!=255 and img[x-1][y]!=255 and img[x+4][y]!=255:
img[x][y]=(140)
for t in range(5):
for x in range(80):
for y in range(215):
if x<=76 and y>=3 and y<=200:
if img[x][y]==112:
if (img[x][y-1]==140 and (img[x][y-2]==140 or img[x][y+5]==140 ) and img[x-1][y]!=255 and img[x-1][y]!=112 and img[x +3][y]!=255):
img[x][y]=(140)
##for t in range(8):
for x in range(80):
for y in range(215):
img1[x][y]=(img[x][y])
if x>=3 and x<=75 and y>=2 and y<=212:
if img[x][y]==112 or img[x][y]==117:
countt=0
if img[x+1][y]==140:
countt+=1
if img[x-1][y]==140:
countt+=1
if img[x][y+1]==140:
countt+=1
if img[x][y-1]==140:
countt+=1
if img[x+1][y+1]==140:
countt+=1
if img[x-1][y-1]==140:
countt+=1
if img[x+1][y-1]==140:
countt+=1
if img[x-1][y+1]==140:
countt+=1
if countt>=5:
img1[x][y]=(140)
a=np.array(140)
mask=cv.inRange(img1,a,a)
cv.bitwise_not(mask, mask)
ret, binary = cv.threshold(mask, 0, 255, cv.THRESH_BINARY_INV or cv.THRESH_OTSU)
kernel = cv.getStructuringElement(cv.MORPH_RECT, (1, 2))
binl = cv.morphologyEx(binary, cv.MORPH_OPEN, kernel)
cv.bitwise_not(binl, binl)
##切
xrec=0
yrec=0
for x in range(80):
county = 0
for y in range(215):
if binl[x][y]==0:
county+=1
if county>=20:
yrec=x
break
for y in range(215):
countx=0
for x in range(80):
if binl[x][y]==0:
countx+=1
if countx>=8:
xrec=y
break
binl = binl[yrec-5:yrec+32, xrec-4:xrec+96]
for t in range(2):
for x in range(35):
for y in range(92):
if x>=1 and x<=32:
if binl[x][y]==255:
if (binl[x+1][y]==0 or binl[x+2][y]==0) and binl[x-1][y]==0:
binl[x][y]=(0)
for x in range(35):
for y in range(92):
if y>=1 and y<=94:
if binl[x][y]==0:
if (binl[x][y+1]==255 and binl[x][y-1]==255):
binl[x][y]=(255)
gray=binl
li=[]
j=5
while j<97 :
count=0
countleft=0
countright=0
countright1=0
count1=0
countup=0
countmid=0
countlow=0
for i in range(36):
if gray[i][j]==255:
count+=1
if gray[i][j+1]==255:
countright+=1
if gray[i][j+3]==255:
countright1+=1
if i>=0 and i<=12:
if gray[i][j]==255:
countup+=1
if i>12 and i<=24:
if gray[i][j]==0:
countmid+=1
if i>24 and i<=37:
if gray[i][j]==255:
countlow+=1
if count>=35 and countright1>35:
li.append(j+2)
j+=18
count1+=1
elif count>25 and countright>35 and countup>8 and countlow<3:
li.append(j+1)
j+=18
count1+=1
elif countup>8 and countmid>6 and countlow>8 and count1>0:
li.append(j+1)
j+=18
count1+=1
elif count>=33:
li.append(j+1)
j+=18
count1+=1
elif count==36:
li.append(j+2)
j+=18
count1+=1
else:
j+=1
if count1==2:
break
tmp=0
ttmp=li[0]
cc=0
ccc=0
for w in range(4):
a=gray[0:36,tmp:ttmp+1]
if len(a[0])>=38:
mid=int(len(a[0])/2)
a1=a[0:36,0:mid]
a2=a[0:36,mid:(mid*2)-1]
size=a1.shape
wet=size[1]
wet1=int(int(40-wet)/2)
wett=wet1
if wet+wet1*2<40:
wett=wett+1
img_patch = cv.copyMakeBorder(a1, 0, 0, wet1, wett, cv.BORDER_CONSTANT, value=255)
x_list.append(tf.expand_dims(img_patch/255.0,2))
if(w>=3): break
size1=a2.shape
wet2=size1[1]
wet3=int(int(40-wet2)/2)
wettt=wet3
if wet2+wet3*2<40:
wettt=wettt+1
img_patch2 = cv.copyMakeBorder(a2, 0, 0, wet3, wettt, cv.BORDER_CONSTANT, value=255)
x_list.append(tf.expand_dims(img_patch2/255.0,2))
if(cc>=2):break
tmp=li[cc]
if(cc<=2):
ttmp=li[cc+1]-2
else:
ttmp=94
cc+=1
else:
size=a.shape
wet0=size[1]
wet11=int(int(40-wet0)/2)
wett1=wet11
if wet0+wet11*2<40:
wett1=wett1+1
img_patch3 = cv.copyMakeBorder(a, 0, 0, wet11, wett1, cv.BORDER_CONSTANT, value=255)
x_list.append(tf.expand_dims(img_patch3/255.0,2))
if(cc>2):break
tmp=li[cc]
if(cc<2):
ttmp=li[cc+1]-2
else:
ttmp=95
cc+=1
return x_list
if os.path.isfile('cnn_model.h5'):
print("Model found.")
model = models.load_model('cnn_model.h5')
else:
print('No trained model found.')
exit(-1)
img_filename = input('Varification code img filename: ')
img_array=cv.imread(img_filename,cv.IMREAD_GRAYSCALE)
x_list = split_digits_in_img(img_array)
varification_code = list()
for i in range(digits_in_img):
confidences = model.predict(np.array([x_list[i]]), verbose=0)
result_class = model.predict_classes(np.array([x_list[i]]), verbose=0)
varification_code.append(result_class[0])
print('Digit {0}: Confidence=> {1} Predict=> {2}'.format(i + 1, np.squeeze(confidences), np.squeeze(result_class)))
print('Predicted varification code:', varification_code)