Update flask server

Server/.gitignore

+uploads
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Server/Aptfile

+libsm6
+libxrender1
+libfontconfig1
+libice6
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Server/Procfile

+web: gunicorn app:app
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Server/app.py

+import os
+import cv2
+import mahotas as mt
+from pyasn1.type.univ import Null
+from werkzeug.utils import secure_filename
+import numpy as np
+import pandas as pd
+from flask import Flask, request, redirect, url_for, send_from_directory, render_template
+import pyrebase
+import joblib
+
+#
+ALLOWED_EXTENSIONS = set(['jpg', 'jpeg', 'png'])
+UPLOAD_FOLDER = 'uploads'
+
+outFileFolder = './model/'
+fileP = outFileFolder + 'org_Cu_Model.joblib'
+stdp = outFileFolder + 'std_scaler.bin'
+    #open file
+mfile = open(fileP, "rb")
+sfile = open(stdp, "rb")
+#load the trained model
+model = joblib.load(mfile) 
+sc_X = joblib.load(sfile)
+
+def allowed_file(filename):
+    return '.' in filename and \
+           filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
+
+
+def bg_sub(file):
+    main_img  = cv2.imread(file)
+    img = cv2.cvtColor(main_img, cv2.COLOR_BGR2RGB)
+    resized_image = cv2.resize(img, (1400, 1000))
+    size_y,size_x,_ = img.shape
+    gr_scale = cv2.cvtColor(resized_image,cv2.COLOR_RGB2GRAY)
+    blur = cv2.GaussianBlur(gr_scale, (5,5),0)
+    ret_otsu,im_bw_otsu = cv2.threshold(blur,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
+    kernel = np.ones((50,50),np.uint8)
+    closing = cv2.morphologyEx(im_bw_otsu, cv2.MORPH_CLOSE, kernel)
+    
+    contours, hierarchy = cv2.findContours(closing,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+    
+    contains = []
+    y_ri,x_ri, _ = resized_image.shape
+    for cc in contours:
+        yn = cv2.pointPolygonTest(cc,(x_ri//2,y_ri//2),False)
+        contains.append(yn)
+
+    val = [contains.index(temp) for temp in contains if temp>0]
+    index = val[0]
+    
+    black_img = np.empty([1000,1400,3],dtype=np.uint8)
+    black_img.fill(0)
+    
+    cnt = contours[index]
+    mask = cv2.drawContours(black_img, [cnt] , 0, (255,255,255), -1)
+    
+    maskedImg = cv2.bitwise_and(resized_image, mask)
+    white_pix = [255,255,255]
+    black_pix = [0,0,0]
+    
+    final_img = maskedImg
+    h,w,channels = final_img.shape
+    for x in range(0,w):
+        for y in range(0,h):
+            channels_xy = final_img[y,x]
+            if all(channels_xy == black_pix):
+                final_img[y,x] = white_pix
+    return final_img
+
+def feature_extract(img):  
+
+    #Preprocessing
+    gs = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
+    blur = cv2.GaussianBlur(gs, (25,25),0)
+    ret_otsu,im_bw_otsu = cv2.threshold(blur,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
+    kernel = np.ones((50,50),np.uint8)
+    closing = cv2.morphologyEx(im_bw_otsu, cv2.MORPH_CLOSE, kernel)
+
+    #Shape features
+    contours, _ = cv2.findContours(closing,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+    cnt = contours[0]
+    M = cv2.moments(cnt)
+    area = cv2.contourArea(cnt)
+    perimeter = cv2.arcLength(cnt,True)
+    x,y,w,h = cv2.boundingRect(cnt)
+    aspect_ratio = float(w)/h
+    rectangularity = w*h/area
+    circularity = ((perimeter)**2)/area
+
+    #Color features
+    red_channel = img[:,:,0]
+    green_channel = img[:,:,1]
+    blue_channel = img[:,:,2]
+    blue_channel[blue_channel == 255] = 0
+    green_channel[green_channel == 255] = 0
+    red_channel[red_channel == 255] = 0
+
+    red_mean = np.mean(red_channel)
+    green_mean = np.mean(green_channel)
+    blue_mean = np.mean(blue_channel)
+
+    red_std = np.std(red_channel)
+    green_std = np.std(green_channel)
+    blue_std = np.std(blue_channel)
+
+    #Texture features
+    textures = mt.features.haralick(gs)
+    ht_mean = textures.mean(axis=0)
+    contrast = ht_mean[1]
+    correlation = ht_mean[2]
+    inverse_diff_moments = ht_mean[4]
+    entropy = ht_mean[8]
+
+    vector = [area,perimeter,w,h,aspect_ratio,rectangularity,circularity,\
+              red_mean,green_mean,blue_mean,red_std,green_std,blue_std,\
+              contrast,correlation,inverse_diff_moments,entropy
+             ]
+
+    df_temp = pd.DataFrame([vector])
+
+    print(vector)
+    
+    print(df_temp)
+
+    return df_temp           
+#
+
+config = {
+	"apiKey": "AIzaSyDbGBvceqiGUB-iPJN7AXXS-AyUx80cg6s",
+    "authDomain": "esp32ndvi.firebaseapp.com",
+    "databaseURL": "https://esp32ndvi-default-rtdb.asia-southeast1.firebasedatabase.app",
+    "projectId": "esp32ndvi",
+    "storageBucket": "esp32ndvi.appspot.com",
+    "messagingSenderId": "960081609737",
+    "appId": "1:960081609737:web:bc2cc955d5b7f1737be4d8",
+    "measurementId": "G-9LZKTS2PPH"
+}
+
+firebase = pyrebase.initialize_app(config)
+
+db = firebase.database()
+
+app = Flask(__name__)
+#
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+#
+
+@app.route('/ndvi', methods=['GET','POST'])
+def basic():
+	if request.method == 'POST':
+		if request.form['submit'] == 'add':
+                    ndvis = db.child("Sensor").get()
+                    name = request.form['name']
+                    for ndvi in ndvis.each():
+                            while (ndvi.key() == name):
+                                ndvi = db.child("Sensor").child(name).get()
+                                dict_ndvi = dict(ndvi.val())
+                                val_ndvi = round(dict_ndvi['ndvi'], 2)
+                                val_vis = dict_ndvi['vis']
+                                val_nir = dict_ndvi['nir']
+                                return render_template('orangeNdvi.html', n = val_ndvi , v = val_vis, r = val_nir ,key = ndvi.key(), conn = " Connected" )
+
+	return render_template('orangeNdvi.html', mess = "Please enter right device ID")
+
+@app.route('/refresh/<device_name>')
+def refresh(device_name):
+    ndvi = db.child("Sensor").child(device_name).get()
+    dict_ndvi = dict(ndvi.val())
+    val_ndvi = round(dict_ndvi['ndvi'], 2)
+    val_vis = dict_ndvi['vis']
+    val_nir = dict_ndvi['nir']
+    return render_template('orangeNdvi.html', n = val_ndvi , v = val_vis, r = val_nir ,key = ndvi.key(), conn = " Connected" )
+    
+
+@app.route('/')
+def home():
+	    return render_template('client.html')
+
+@app.route("/cucumber")
+def template_test():
+    return render_template('cucumberOrg.html', label='', imagesource='')
+
+
+@app.route('/cucumber', methods=['GET','POST'])
+def upload_file():
+    if request.method == 'POST':
+        file = request.files['file']
+        
+        if file and allowed_file(file.filename):
+            filename = secure_filename(file.filename)
+            file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+            file.save(file_path)
+            bg_remove_img = bg_sub(file_path)
+            featuresOfimg = feature_extract(bg_remove_img)
+            scaled_feature = sc_X.transform(featuresOfimg)
+            print(scaled_feature)
+            output = model.predict_proba(scaled_feature)[0]
+            out = {'Organic:': output[0], 'Inorganic': output[1]}
+    return render_template("cucumberOrg.html", label=output, imagesource=file_path)
+
+#
+@app.route('/uploads/<filename>')
+def uploaded_file(filename):
+    return send_from_directory(app.config['UPLOAD_FOLDER'],
+                               filename)
+#
+
+
+if __name__ == '__main__':
+    app.run(debug=True)
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Server/requirements.txt

 argon2-cffi==21.1.0

Server/static/css/style.css

+body::before {
+  display: block;
+  content: '';
+  height: 60px;
+}
+
+#map {
+  width: 100%;
+  height: 100%;
+  border-radius: 10px;
+}
+
+@media (min-width: 768px) {
+  .news-input {
+    width: 50%;
+  }
+}