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| /server/models | ||
| /server/static | ||
| desktop.ini |
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| /*////////////////////////////////////////////////////////////////// | ||
| [ FONT ]*/ | ||
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| server_url = "http://127.0.0.1:5000/process_image"; | ||
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| function sendFile(type){ | ||
| $('#input_handle').click(); | ||
| $('#type').val(type); | ||
| console.log(type); | ||
| } | ||
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| $('document').ready(function(){ | ||
| $('#input_handle').change(function(){ | ||
| var data = new FormData($('#form')[0]); | ||
| $.ajax({ | ||
| url: server_url, | ||
| data: data, | ||
| cache: false, | ||
| contentType: false, | ||
| processData: false, | ||
| method: 'POST', | ||
| type: 'POST', | ||
| success: function (data) { | ||
| $('#output').show(); | ||
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| var reader = new FileReader(); | ||
| reader.onload = function (e) { | ||
| $('#input_image').attr('src', e.target.result); | ||
| } | ||
| reader.readAsDataURL($('#input_handle')[0].files[0]); | ||
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| $('#output_image').attr("src", 'http://127.0.0.1:5000/static/uploads/' + data); | ||
| } | ||
| }); | ||
| }); | ||
| }); |
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| from flask import Flask, request, send_file | ||
| import tensorflow as tf | ||
| from keras.models import load_model | ||
| from keras.preprocessing.image import load_img, img_to_array, save_img | ||
| from flask_cors import CORS | ||
| import os | ||
| import random | ||
| import numpy as np | ||
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| global graph | ||
| graph = tf.get_default_graph() | ||
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| image_to_map = load_model('models/image_to_map.h5') | ||
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| app = Flask(__name__) | ||
| CORS(app) | ||
| app.config['UPLOAD_FOLDER'] = 'static/uploads' | ||
| app.config['OUTPUT_FOLDER'] = 'static/outputs' | ||
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| def predict(filename, process_type): | ||
| input_path = os.path.join(app.config['UPLOAD_FOLDER'], filename) | ||
| output_path = os.path.join(app.config['OUTPUT_FOLDER'], filename) | ||
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| if process_type == 'object_removal': | ||
| pass | ||
| if process_type == 'fogg_removal': | ||
| pass | ||
| if process_type == 'image_to_map': | ||
| # size=(256,256) | ||
| # sat_img = load_img(input_path, target_size=size) | ||
| # sat_img = img_to_array(sat_img) | ||
| # # split into satellite and map | ||
| # sat_img = (sat_img - 127.5) / 127.5 | ||
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| # input_image=[] | ||
| # input_image.append(sat_img) | ||
| # input_image=np.array(input_image) | ||
| # gen_image=image_to_map.predict(input_image) | ||
| # gen_image=(gen_image+1)/2.0 | ||
| # save_img(output_path, gen_image) | ||
| pass | ||
| if process_type == 'blur_to_hd': | ||
| pass | ||
| if process_type == 'face_aging': | ||
| pass | ||
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| @app.route("/") | ||
| def index(): | ||
| return "helllo" | ||
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| chars = "abcdefghijklmnopqrstuvwxyz" | ||
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| @app.route("/process_image", methods = ['POST']) | ||
| def process_image(): | ||
| if 'image' in request.files: | ||
| file = request.files['image'] | ||
| filename = ''.join(random.choice(chars) for x in range(10)) + '.' + file.filename.split('.')[-1] | ||
| path = os.path.join(app.config['UPLOAD_FOLDER'], filename) | ||
| print(path) | ||
| file.save(path) | ||
| predict(filename, request.form['type']) | ||
| return filename | ||
| return "Hello" | ||
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| if __name__ == '__main__': | ||
| app.run(debug = True) |