server.py

from flask import Flask, flash, request, redirect, url_for, render_template, send_file
from markupsafe import escape
from werkzeug.utils import secure_filename
from PIL import Image
import numpy as np
import os
import io
import cv2

PEOPLE_FOLDER = os.path.join('static','tmp')
PEOPLE_FOLDER1 = os.path.join('static','styles')
app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = PEOPLE_FOLDER


#Mammography config
class Config:

	def __init__(self):

		# Print the process or not
		self.verbose = True

		# Name of base network
		self.network = 'vgg'

		# Setting for data augmentation
		self.use_horizontal_flips = False
		self.use_vertical_flips = False
		self.rot_90 = False

		# Anchor box scales
    # Note that if im_size is smaller, anchor_box_scales should be scaled
    # Original anchor_box_scales in the paper is [128, 256, 512]
		#self.anchor_box_scales = [16,32,64]
		self.anchor_box_scales = [32,64,128] 

		# Anchor box ratios
		self.anchor_box_ratios = [[1, 1], [1./math.sqrt(2), 2./math.sqrt(2)], [2./math.sqrt(2), 1./math.sqrt(2)]]
		#self.anchor_box_ratios = [[1, 1]]

		# Size to resize the smallest side of the image
		# Original setting in paper is 600. Set to 300 in here to save training time
		self.im_size = 600

		# image channel-wise mean to subtract
		self.img_channel_mean = [103.939, 116.779, 123.68]
		self.img_scaling_factor = 1.0

		# number of ROIs at once
		self.num_rois = 4

		# stride at the RPN (this depends on the network configuration)
		self.rpn_stride = 16

		self.balanced_classes = False

		# scaling the stdev
		self.std_scaling = 4.0
		self.classifier_regr_std = [8.0, 8.0, 4.0, 4.0]

		# overlaps for RPN
		self.rpn_min_overlap = 0.08
		self.rpn_max_overlap = 0.3

		# overlaps for classifier ROIs
		self.classifier_min_overlap = 0.1
		self.classifier_max_overlap = 0.5

		# placeholder for the class mapping, automatically generated by the parser
		self.class_mapping = None

		self.model_path = None
        
#disable image caching
@app.after_request
def add_header(r):
    """
    Add headers to both force latest IE rendering engine or Chrome Frame,
    and also to cache the rendered page for 10 minutes.
    """
    r.headers["Cache-Control"] = "no-cache, no-store, must-revalidate"
    r.headers["Pragma"] = "no-cache"
    r.headers["Expires"] = "0"
    r.headers['Cache-Control'] = 'public, max-age=0'
    return r

@app.route('/')
def home():
    return render_template('home.html')

#renders the main webpage of your task
@app.route('/tasks/<task>', methods=['GET'])
def show_task(task):
    task = escape(task)
    return render_template('_'.join(task.split('-'))+'.html')

ALLOWED_IMAGE_EXTENSIONS = ['png', 'jpg', 'jpeg', 'tif', 'tiff']

#checks whether file in in the allowed image extensions
def allowed_file(filename):
    return '.' in filename and \
           filename.rsplit('.', 1)[1].lower() in ALLOWED_IMAGE_EXTENSIONS

#this route processes the input image and returns the output image as a file
@app.route('/process-image', methods=['POST'])
def process_image():
    if request.form['task'] == 'epithelium_segmentation':
        orig, mask = upload_files(['orig', 'mask'])
        lib = getattr(__import__('pipelines.'+request.form['task']), request.form['task'])
        display_image = lib.get_display_image(orig, mask)
        os.remove(orig)
        os.remove(mask)
    if request.form['task'] == 'mammography':
        image = upload_files(['image'])[0]
        lib = getattr(__import__('pipelines.'+request.form['task']), request.form['task'])
        display_image = lib.get_display_image(image)
        os.remove(image)
    img = Image.fromarray(display_image.astype('uint8'))
    file_object = io.BytesIO()
    img.save(file_object, 'PNG')
    file_object.seek(0)
    return send_file(file_object, mimetype='image/PNG')

#this route processes the lymph node input image and returns the output image as a file
@app.route('/process-lymph-image', methods=['POST'])
def process_lymph_image():
    if request.form['task'] == 'lymph_node':
        orig = upload_files(['orig'])
        lib = getattr(__import__('pipelines.'+request.form['task']), request.form['task'])
        display_ans= lib.get_display_image(orig)
        png_image=cv2.imread(orig[0])
        file_dir=orig[0].split(".")
        file_name=file_dir[0].split("/")
        cv2.imwrite(PEOPLE_FOLDER+"/"+file_name[1]+".png",png_image)
        #cv2.imshow("abcd.png",file_dir[0]+".png")
        lst=[]
        full_name=os.path.join(app.config['UPLOAD_FOLDER'], file_name[1]+".png")
        lst.append(full_name)
    return render_template('results.html',filename=lst,predicted=display_ans,location=PEOPLE_FOLDER1)

#send in a list of names (as coded in the task's html page) of the expected files
#and get the paths to which they are uploaded
def upload_files(fnames):
    if request.method == 'POST':
        ret_names = []
        for fname in fnames:
            if fname not in request.files:
                flash(f'No {fname} part')
                return redirect(request.url)
            file = request.files[fname]
            if file.filename == '':
                flash('No selected file')
                return redirect(request.url)
            if file and allowed_file(file.filename):
                filename = secure_filename(file.filename)
                if not os.path.exists('tmp'):
                    os.mkdir('tmp')
                fname = os.path.join('tmp', filename)
                file.save(fname)
                ret_names.append(fname)
        return ret_names

#JUST DO IT!!!
if __name__=="__main__":
    app.run(host="0.0.0.0", port="5010",threaded=False)