def verify():
if request.method == 'POST':
file = request.files.get('signature')
id = request.form.get('customer_id')
X = Image.open(file.stream)
X = convert_to_image_tensor(invert_image(X)).view(1, 1, 220, 155)
anchor_img_file = get_file_from_db(id)
A = Image.open(BytesIO(anchor_img_file))
A = convert_to_image_tensor(invert_image(A)).view(1, 1, 220, 155)
model = load_model()
f_A, f_X = model.forward(A, X)
dist = distance_metric(f_A, f_X).detach().numpy()
print('Dist={}'.format(dist))
if dist <= 0.145139: # Threshold obtained using Test.py
return render_template('verify.html', result="<h6>Signatures are the same</h6>")
else:
return render_template('verify.html', result="<h6>Signatures are different</h6>")
else:
return render_template('verify.html', result='')
def verify():
try:
customer_id = request.form['customerID']
input_image = Image.open(request.files['newSignature'])
input_image_tensor = convert_to_image_tensor(
invert_image(input_image)).view(1, 1, 220, 155)
customer_sample_images = get_file_from_db(customer_id)
if not customer_sample_images:
return jsonify({'error': True})
anchor_images = [
Image.open(BytesIO(x)) for x in customer_sample_images
]
anchor_image_tensors = [
convert_to_image_tensor(invert_image(x)).view(-1, 1, 220, 155)
for x in anchor_images
]
model = load_model()
mindist = math.inf
for anci in anchor_image_tensors:
f_A, f_X = model.forward(anci, input_image_tensor)
dist = float(distance_metric(f_A, f_X).detach().numpy())
mindist = min(mindist, dist)
if dist <= 0.145139: # Threshold obtained using Test.py
return jsonify({
"match": True,
"error": False,
"threshold": "%.6f" % (0.145139),
"distance": "%.6f" % (mindist)
})
return jsonify({
"match": False,
"error": False,
"threshold": 0.145139,
"distance": round(mindist, 6)
})
except Exception as e:
print(e)
return jsonify({"error": True})
def verify():
try:
customer_id = request.form['customerID']
file = request.files['newSignature']
path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
file.save(path)
input_image = Image.open(request.files['newSignature'])
input_image_tensor = convert_to_image_tensor(
invert_image(input_image)).view(1, 1, 220, 155)
anchor_images = [
Image.open(BytesIO(x)) for x in get_file_from_db(customer_id)
]
anchor_image_tensors = [
convert_to_image_tensor(invert_image(x)).view(-1, 1, 220, 155)
for x in anchor_images
]
model = load_model()
mindist = math.inf
for anci in anchor_image_tensors:
f_A, f_X = model.forward(anci, input_image_tensor)
dist = float(distance_metric(f_A, f_X).detach().numpy())
mindist = min(mindist, dist)
if dist <= 0.145139: # Threshold obtained using Test.py
return render_template("result.html",
match="Yes",
threshold=0.145139,
distance=mindist,
file=path)
return render_template("result.html",
match="No",
threshold=0.145139,
distance=mindist,
file=path)
except Exception as e:
print(e)
def __getitem__(self, index):
item = self.pairs[index]
X = convert_to_image_tensor(invert_image_path(item[0]))
Y = convert_to_image_tensor(invert_image_path(item[1]))
return [X, Y, item[2]]
def verify():
try:
CREATE_TABLE = """CREATE TABLE IF NOT EXISTS signcout9 (id INTEGER PRIMARY KEY)"""
cursor = connect_to_db().cursor()
cursor.execute(CREATE_TABLE)
cursor.connection.commit()
cid = """SELECT id FROM signcout9 ORDER BY id DESC """
item = cursor.execute(cid)
mainid = 1
print("q1")
if item:
print("q2")
item1 = cursor.fetchone()
# cursor.connection.commit()
if item1 == None:
mainid = 1
conn = connect_to_db()
cursor = conn.cursor()
query = """INSERT INTO signcout9 VALUES(?)"""
cursor.execute(query, (1, ))
conn.commit()
cursor.close()
else:
print("adadad")
print(item1[0])
mainid = int(item1[0]) + 1
print("added", mainid)
cursor.close()
conn = connect_to_db()
cursor = conn.cursor()
query = """INSERT INTO signcout9 VALUES(?)"""
cursor.execute(query, (mainid, ))
conn.commit()
cursor.close()
# test = request.json
# print(test)
# return test['title']
# customer_id = request.form['customerID']
customer_id = mainid
# file1 = request.files['uploadedImage1']
# file2 = request.files['uploadedImage2']
# file3 = request.files['uploadedImage3']
# customer_id = request.form['customerID']
print(request.form)
# print("sad")
# s1=request.files['newSignature']
# print(type(s1))
# npimg = np.frombuffer(s1, np.uint8)
# result=cv2.fastNlMeansDenoisingColored(s1, None, 10, 10, 7, 15)
# print(type(result))
file1 = request.files['newSignature']
file2 = request.files['questioned']
input_image = Image.open(request.files['newSignature'])
print("Asdsa")
questioned = request.files['questioned']
input_image1 = Image.open(request.files['questioned'])
# conn = connect_to_db()
# cursor = conn.cursor()
print("1")
# query = """INSERT INTO signatures5 VALUES(?,?)"""
# cursor.execute(query, (customer_id, questioned.read()
# ))
# conn.commit()
print(customer_id)
# path=os.path.join
print(os.path)
# path = r'D:\signature\signature69'
# directory = r'D:\signature\signature69'
# img = cv2.imread(path)
# os.chdir(directory)
# print("Before saving")
# print(os.listdir(directory))
# filename = 'cat.jpg'
# cv2.imwrite(filename, img)
# print("After saving")
# print(os.listdir(directory))
input_image.save('aaa' + str(customer_id) + '.png')
input_image1.save('bbb' + str(customer_id) + '.png')
# plt.imsave('abc'+str(customer_id)+'.png', input_image)
# plt.imsave('xyz'+str(customer_id)+'.png', input_image1)
Denoise.test('aaa' + str(customer_id) + '.png',
'bbb' + str(customer_id) + '.png')
input_image = Image.open('aaa' + str(customer_id) + '.png')
input_image1 = Image.open('bbb' + str(customer_id) + '.png')
# img1 = extract_signature(request.files['newSignature'])
# image = cv2.cvtColor(input_image, cv2.COLOR_BGR2HSV)
# lower = np.array([90, 38, 0])
# upper = np.array([145, 255, 255])
# mask = cv2.inRange(image, lower, upper)
# print(input_image)
# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
# opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)
# close = cv2.morphologyEx(
# opening, cv2.MORPH_CLOSE, kernel, iterations=2)
# cnts = cv2.findContours(close, cv2.RETR_EXTERNAL,
# cv2.CHAIN_APPROX_SIMPLE)
# cnts = cnts[0] if len(cnts) == 2 else cnts[1]
# boxes = []
# for c in cnts:
# (x, y, w, h) = cv2.boundingRect(c)
# boxes.append([x, y, x+w, y+h])
# boxes = np.asarray(boxes)
# left = np.min(boxes[:, 0])
# top = np.min(boxes[:, 1])
# right = np.max(boxes[:, 2])
# bottom = np.max(boxes[:, 3])
# result[close == 0] = (255, 255, 255)
# ROI = result[top:bottom, left:right].copy()
# cv2.rectangle(result, (left, top), (right, bottom), (36, 255, 12), 2)
# cv2.imshow('result', result)
# cv2.imshow('ROI', ROI)
# cv2.imshow('close', close)
# cv2.imwrite('result.png', result)
# cv2.imwrite('ROI.png', ROI)
print(type(input_image))
input_image_tensor = convert_to_image_tensor(
invert_image(input_image)).view(1, 1, 220, 155)
customer_sample_images = get_file_from_db(customer_id)
# print(customer_sample_images)
plt.imsave('don.png', input_image_tensor)
# if not customer_sample_images:
# return jsonify({'error': "uyugy"})
# anchor_images = [Image.open(BytesIO(x))
# for x in customer_sample_images]
anchor_image_tensors = convert_to_image_tensor(
invert_image(input_image1)).view(-1, 1, 220, 155)
# for x in anchor_images
model = load_model()
mindist = math.inf
# for anci in anchor_image_tensors:
f_A, f_X = model.forward(anchor_image_tensors, input_image_tensor)
f1 = model.forward_once(anchor_image_tensors).detach().numpy()
f2 = model.forward_once(input_image_tensor).detach().numpy()
# plt.imshow(f1)
# cv2.imshow('result.png', f1)
plt.imsave('file1.png', f1)
plt.imsave('file2.png', f1)
# pq=ContrastiveLoss().forward(f1,f2,0.145139)
# print("model1",f1)
# print("model2",f2)
out_arr = np.subtract(f1, f2)
plt.imsave('file3.png', out_arr)
print("Output array: ", out_arr)
# print("bla",pq)
dist = float(distance_metric(f_A, f_X).detach().numpy())
mindist = min(mindist, dist)
# return send_file(
# 'aaa'+str(customer_id)+'.png',
# as_attachment=True,
# attachment_filename='aaa'+str(customer_id)+'.png',
# mimetype='image/jpeg',
# # abcd=jsonify({"match": True, "error": False, "threshold": "%.6f" % (0.145139), "distance": "%.6f" % (mindist)})
# )
# os.remove( 'aaa'+str(customer_id)+'.png')
# os.remove( 'bbb'+str(customer_id)+'.png')
matcheddis = mindist * 100
percen = 100 - matcheddis
print(percen)
if dist <= 0.145139: # Threshold obtained using Test.py
return jsonify({
"match": True,
"error": False,
"threshold": "%.6f" % (0.145139),
"distance": "%.6f" % (mindist),
"difference": str(out_arr),
'fileid': mainid,
'percentage': percen
})
return jsonify({
"match": False,
"error": False,
"threshold": 0.145139,
"distance": round(mindist, 6),
"difference": str(out_arr),
'fileid': mainid,
'percentage': percen
})
except Exception as e:
print(e)
return jsonify({"error": True, "adad": str(e)})