from flask import Flask, render_template, request import numpy as np from ratio_pyrometry import ratio_pyrometry_pipeline import base64 import cv2 as cv import plotly.figure_factory as ff from scipy import stats app = Flask( __name__, static_folder='static', static_url_path='/s/' ) @app.route('/', methods=['GET']) def index(): return render_template('index.jinja2') @app.route('/ratio_pyro', methods=['POST']) def ratio_pyro(): f = request.files['file'] f_bytes = np.fromstring(f.read(), np.uint8) img_orig, img_res, key, ptemps = ratio_pyrometry_pipeline( f_bytes, ISO=float(request.form['iso']), I_Darkcurrent=float(request.form['i_darkcurrent']), exposure_time=float(request.form['exposure_time']), f_stop=float(request.form['f_stop']), MAX_TEMP=float(request.form['max_temp']), MIN_TEMP=float(request.form['min_temp']), smoothing_radius=int(request.form['smoothing_radius']), key_entries=int(request.form['legend_entries']), eqn_scaling_factor=float(request.form['equation_scaling_factor']) ) # get base64 encoded images img_orig_b64 = base64.b64encode(cv.imencode('.png', img_orig)[1]).decode(encoding='utf-8') img_res_b64 = base64.b64encode(cv.imencode('.png', img_res)[1]).decode(encoding='utf-8') # generate prob. distribution histogram & return embed fig = ff.create_distplot( [ptemps], group_labels=[f.filename], show_rug=False, show_hist=False, ) fig.update_layout( autosize=False, width=800, height=600, ) fig.update_xaxes( title_text="Temperature (°C)", ) fig.update_yaxes( title_text="Probability (1/°C)", ) freq_plot = fig.to_html() return render_template( 'results.jinja2', img_orig_b64=img_orig_b64, img_res_b64=img_res_b64, legend=key, freq_plot=freq_plot )