from flask import Flask, render_template, request
import warnings
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning)
import tensorflow as tf
from model import load
import re, base64, cv2
import numpy as np
from crop import borders
app = Flask(__name__)
model = load.init()
@app.route('/')
def index():
return render_template("index.html")
def model_predict(img_path, model):
img = cv2.imread(img_path, 0)
_, img = cv2.threshold(img, 128, 255, cv2.THRESH_BINARY_INV)
img = cv2.resize(img, (28, 28))
img = cv2.GaussianBlur(img, (3, 3), -1)
top, bottom, b = borders(img, 0)
img = img[top:bottom]
cv2.imwrite('abc.png', img)
img = img.reshape(-1, 28, 28, 1) / 255
preds = model.predict(img)
return preds
import io
import tensorflow as tf
from tensorflow.python.keras import backend as K
tf.compat.v1.disable_v2_behavior()
sys.path.append(os.path.abspath("./model"))
app = Flask(__name__)
app.config['SECRET_KEY'] = 'the quick brown fox jumps over the lazy dog'
app.config['CORS_HEADERS'] = "Content-Type"
cors = CORS(app, resources={r"/foo": {"origins": "127.0.0.1:8648"}})
K.clear_session()
global graph, model_mobile, sess, class_names
model_mobile, graph, sess = init() # เรียกจาก load.py
# model_mobile = keras.models.load_model('model_moblienet_v2.h5', custom_objects={
# 'f1_m': f1_m, 'precision_m': precision_m, 'recall_m': recall_m})
graph = tf.compat.v1.get_default_graph()
class_names = [
'kanom bua loi', 'kanom chan', 'kanom dok jok', 'kanom kai tao',
'kanom krok', 'kanom phoi tong', 'kanom salim', 'kanom sangkhaya faktong',
'kanom tong yib', 'kanom tong yod'
]
@app.route('/', methods=['POST', 'GET'])
@cross_origin(origin='*', headers=['Content-Type', 'Authorization'])
#for regular expressions, saves time dealing with string data
import re
#system level operations (like loading files)
import sys
#for reading operating system data
import os
#tell our app where our saved model is
sys.path.append(os.path.abspath("./model"))
from model.load import init
#initalize our flask app
app = Flask(__name__)
#global vars for easy reusability
global model, graph
#initialize these variables
model, graph = init()
#decoding an image from base64 into raw representation
def convertImage(imgData1):
imgstr = re.search(r'base64,(.*)', imgData1).group(1)
#print(imgstr)
with open('output.png', 'wb') as output:
output.write(imgstr.decode('base64'))
@app.route('/')
def index():
#initModel()
#render out pre-built HTML file right on the index page
return render_template("index.html")
from tifffile import imwrite
import os
from os import listdir
from tqdm import tqdm
from os.path import isfile, join
from ae import load_ae, util, load_cae
import anomaly
# tell our app where our saved model is
sys.path.append(os.path.abspath("./model"))
from model import load, process
# global vars for easy reusability
global model, graph, ae, ae_graph, cae, cae_graph
# initialize these variables
model, graph = load.init()
ae, ae_graph = load_ae.init()
cae, cae_graph = load_cae.init()
def fill_dict(d):
'''
Auxiliary function to fill dictionary with key values from 2 to 9.
:param dict d: Dictionary to be filled
:return dict d: Filled dictionary
'''
for i in range(2, 9):
if i not in d:
d[i] = 0