/
signature_verification.py
executable file
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/
signature_verification.py
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#!/usr/bin/env python
# coding: utf-8
# ## Standard libraries
# In[1]:
import cv2
import numpy as np
import matplotlib.pyplot as plt
# ## Import keras
# In[2]:
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.models import model_from_json
from tensorflow.keras.optimizers import Adam, RMSprop
from tensorflow.keras import backend as K
# Loss function to find the distance score between two images
# In[3]:
def contrastive_loss(y_true, y_pred):
'''Contrastive loss from Hadsell-et-al.'06
http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
'''
margin = 1
return K.mean(y_true * K.square(y_pred) + (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))
# ## Load Siamese N/W architecture from saved model
# In[4]:
json_file = open('siamese_BaseNetwork.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
base_network = model_from_json(loaded_model_json)
#print("checking")
#base_network.summary()
# ## Root mean Square loss function
# In[5]:
# ## Load saved model architeture
# In[6]:
#def model_load():
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# model.summary()
#def weights_load():
rms = RMSprop(lr=1e-4, rho=0.9, epsilon=1e-08)
model.load_weights('./Weights/signet-Engsig260-003.h5')
model.compile(loss=contrastive_loss, optimizer=rms)
graph = tf.get_default_graph()
# In[22]:
def Show(img1,img2):
'''Function to preview the signatures'''
fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (10, 10))
ax1.imshow(np.squeeze(img1), cmap='gray')
ax2.imshow(np.squeeze(img2), cmap='gray')
ax1.axis('off')
ax1.set_title("on record")
ax2.axis('off')
ax2.set_title("to be verified")
plt.show()
# In[25]:
def Result(img1,img2):
tr_acc, threshold=(0.8836851256190756, 0.14)#0.15
global graph
with graph.as_default():
result=model.predict([img1, img2])
diff = result[0][0]
#print("Difference Score = ", diff)
resultt=''
if diff > threshold:
resultt += "Its a Forged Signature"
else:
resultt += "Its a Genuine Signature"
return threshold,diff,resultt
# In[21]:
img_h, img_w = 155, 220
def imageETL(img1,img2):
'''Function to extract transform and load the signature acc. to model trained.'''
img1 = cv2.resize(img1,(img_w,img_h))
img2 = cv2.resize(img2,(img_w,img_h))
img1 = np.array(img1, dtype = np.float64) #uint8
img2 = np.array(img2, dtype = np.float64)
img1 /= 255.0
img2 /= 255.0
img1 = img1[..., np.newaxis]
img2 = img2[..., np.newaxis]
img1 = img1[np.newaxis, ...]
img2 = img2[np.newaxis, ...]
L=[]
L.append(img1)
L.append(img2)
return L
def main(genuine,candidate):
print(genuine)
print(candidate)
img1 = cv2.imread(genuine,0)
img2 = cv2.imread(candidate,0)
ETL = imageETL(img1,img2)
threshold,diff,result = Result(ETL[0], ETL[1])
return threshold,diff,result
##def Rdjango(genuine,candidate):
## print(genuine)
## print(candidate)
## img1 = cv2.imread(genuine,0)
## img2 = cv2.imread(candidate,0)
## print(img1.shape,img2.shape)
## diff,result = 0.045,"Its a Genuine Signature"
## return diff,result