/
main.py
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/
main.py
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from flask import Flask, render_template, request, flash, abort, send_file
from flask_cors import CORS
import base64
import random
import os
import glob
import cv2
import keras_segmentation as seg
from keras import backend as K
import time
import numpy as np
app = Flask(__name__)
app.secret_key = '208f2hdd029duq0isjc0jqwij0d2r0fj02'
CORS(app)
models_list = []
image_count = 0
t = 0
image1 = 0
image2 = 0
def getModels():
global models_list
m = os.listdir('model')
models_list+=m
def getcount():
return len(os.listdir("data/train/"))
@app.route("/")
def index():
return render_template("index.html")
@app.route("/getimages", methods=["GET", "POST"])
def getData():
getModels()
if request.method == "POST":
image1 = request.form.get("image1").split(",")[1]
image2 = request.form.get("image2").split(",")[1]
img1data = base64.b64decode(image1)
img2data = base64.b64decode(image2)
file_name = str(image_count)+".png"
with open("data/train/"+file_name, 'wb') as f:
f.write(img1data)
with open("data/ann/"+file_name, 'wb') as f:
f.write(img2data)
img = cv2.imread("data/ann/"+file_name, 0)
_,t_img = cv2.threshold(img, 100, 1, cv2.THRESH_BINARY)
t_img = cv2.resize(t_img, (224,224))
cv2.imwrite("data/ann/"+file_name, t_img)
img = cv2.imread("data/train/"+file_name, 0)
img = cv2.resize(img, (224,224))
cv2.imwrite("data/train/"+file_name, img)
flash(format("Got the image"),'success')
global image_count
image_count += 1
return render_template("index.html", models = models_list)
@app.route("/getcount")
def test():
real_count = len(os.listdir("data/train"))
return render_template("test.html", count = image_count, real = real_count)
def delt(path):
files = glob.glob(path)
for f in files:
os.remove(f)
@app.route("/delete")
def delete():
delt('data/train/*')
delt('data/ann/*')
return render_template("delete.html")
@app.route("/infer", methods=["GET", "POST"])
def infer():
global image1, image2
if request.method == "POST":
image = request.form.get("image").split(",")[1]
model_name = request.form['model']
imgdata = base64.b64decode(image)
with open("static/input.png", 'wb') as f:
f.write(imgdata)
if model_name in os.listdir('model'):
K.clear_session()
model = seg.models.segnet.mobilenet_segnet(n_classes=2, input_height=224, input_width=224)
model.load_weights("model/"+model_name)
out = model.predict_segmentation(inp = "static/input.png", out_fname= "static/out.png")
original = cv2.imread("static/input.png",0)
#original = cv2.resize(original, (224,224))
out = cv2.imread("static/out.png",0)
_,out2 = cv2.threshold(out, 155, 255, cv2.THRESH_BINARY)
edges = cv2.Canny(out2, 100, 200)
edges = cv2.bitwise_not(edges)
out3 = cv2.bitwise_and(original, original, mask=edges)
out3 = np.hstack((original, out, out3))
cv2.imwrite("static/out2.png", out3)
return render_template("infer.html", models = os.listdir('model'),im1= image1, im2 = image2)
@app.route("/result")
def result():
try:
return send_file("static/out2.png")
except:
abort(404)
@app.route("/train")
def training():
K.clear_session()
model = seg.models.segnet.mobilenet_segnet(n_classes=2, input_height=224, input_width=224)
model.train( train_images = "data/train", train_annotations = "data/ann", checkpoints_path = "model/" , epochs=1 )
model.save_weights('model/mobilent_segnet'+str(time.time()).split('.')[0]+'.h5')
return "training has finished, now you should be able to select new model"
if __name__ == '__main__':
app.run(debug = True, host = '0.0.0.0')