pod_Mikolaj

data.csv

@@ -0,0 +1,209 @@
+PLANT,FEED,GROWTH,DRYNESS,DEC
+BEETROOT,43,94,4,YES
+CABBAGE,17,99,9,NO
+CABBAGE,25,106,11,NO
+CABBAGE,47,118,17,NO
+CABBAGE,22,103,19,NO
+CABBAGE,55,112,20,NO
+CABBAGE,13,110,22,NO
+CABBAGE,20,100,22,NO
+CABBAGE,11,113,25,NO
+CABBAGE,55,95,26,NO
+CABBAGE,12,109,27,NO
+CABBAGE,18,119,28,NO
+CABBAGE,29,90,29,NO
+BEETROOT,23,99,30,NO
+CARROT,53,94,30,NO
+BEETROOT,28,120,31,NO
+CARROT,29,91,34,NO
+BEETROOT,34,121,34,NO
+BEETROOT,33,110,36,NO
+CARROT,52,102,36,NO
+BEETROOT,52,116,36,NO
+BEETROOT,26,118,37,NO
+CARROT,59,112,41,NO
+CARROT,51,90,42,NO
+CARROT,22,91,43,NO
+BEETROOT,62,114,43,NO
+BEETROOT,78,97,43,NO
+CARROT,60,92,44,NO
+BEETROOT,88,112,44,NO
+BEETROOT,12,111,45,NO
+BEETROOT,53,113,48,NO
+BEETROOT,68,104,51,NO
+CARROT,7,117,52,NO
+BEETROOT,51,113,54,NO
+PUMPKIN,20,89,69,YES
+BEETROOT,16,102,55,NO
+PUMPKIN,21,63,23,YES
+CARROT,13,111,56,NO
+BEETROOT,22,95,57,NO
+CARROT,8,104,58,NO
+PUMPKIN,23,26,19,YES
+BEETROOT,58,106,62,NO
+PUMPKIN,24,68,7,YES
+PUMPKIN,25,66,96,NO
+BEETROOT,55,99,63,NO
+CARROT,74,104,67,NO
+PUMPKIN,28,34,68,YES
+BEETROOT,57,102,68,NO
+PUMPKIN,29,29,61,YES
+PUMPKIN,46,90,70,NO
+CABBAGE,46,93,70,NO
+BEETROOT,29,105,71,NO
+PUMPKIN,30,28,31,YES
+CARROT,30,67,24,YES
+PUMPKIN,30,96,30,NO
+CABBAGE,30,89,30,YES
+CABBAGE,31,62,40,YES
+PUMPKIN,31,65,34,YES
+CARROT,31,81,17,YES
+CARROT,31,89,29,YES
+CARROT,32,20,19,YES
+CABBAGE,32,25,50,YES
+CARROT,32,90,22,NO
+CARROT,32,56,7,YES
+CABBAGE,33,15,50,YES
+CARROT,33,32,14,YES
+PUMPKIN,33,47,59,YES
+PUMPKIN,19,99,72,NO
+CARROT,33,63,30,NO
+CABBAGE,33,65,64,YES
+CABBAGE,34,27,44,YES
+CABBAGE,34,44,93,NO
+CARROT,34,44,22,YES
+PUMPKIN,51,99,72,NO
+CABBAGE,34,56,70,NO
+CARROT,34,56,18,YES
+CABBAGE,35,21,63,YES
+PUMPKIN,35,42,48,YES
+BEETROOT,35,74,12,YES
+PUMPKIN,35,76,71,NO
+BEETROOT,36,58,28,YES
+CABBAGE,36,57,54,YES
+BEETROOT,36,72,84,NO
+BEETROOT,36,83,71,NO
+CABBAGE,36,67,64,YES
+PUMPKIN,36,82,53,YES
+CABBAGE,37,47,57,YES
+CARROT,37,59,13,YES
+BEETROOT,37,40,15,YES
+BEETROOT,37,69,16,YES
+CABBAGE,37,63,60,YES
+PUMPKIN,37,72,46,YES
+CABBAGE,38,62,79,NO
+BEETROOT,38,98,12,NO
+BEETROOT,38,59,22,YES
+PUMPKIN,38,73,56,YES
+CABBAGE,38,74,52,YES
+PUMPKIN,38,90,39,NO
+CARROT,38,78,9,YES
+CABBAGE,39,86,64,NO
+PUMPKIN,39,31,45,YES
+PUMPKIN,39,102,53,NO
+BEETROOT,39,49,14,YES
+BEETROOT,39,54,81,NO
+CABBAGE,39,87,57,NO
+CABBAGE,40,8,32,YES
+PUMPKIN,40,57,63,YES
+CABBAGE,40,92,61,NO
+BEETROOT,40,52,27,YES
+PUMPKIN,41,8,29,YES
+CABBAGE,41,25,48,YES
+CABBAGE,41,43,51,YES
+CABBAGE,41,52,60,YES
+BEETROOT,41,20,24,YES
+CARROT,41,83,25,YES
+CABBAGE,41,85,37,YES
+CABBAGE,42,64,41,YES
+CARROT,42,66,26,YES
+CABBAGE,42,69,59,YES
+BEETROOT,42,38,9,YES
+CARROT,43,15,9,YES
+CARROT,43,93,22,NO
+CABBAGE,43,60,38,YES
+PUMPKIN,43,78,43,YES
+BEETROOT,43,29,18,YES
+BEETROOT,43,96,20,NO
+BEETROOT,43,84,4,YES
+CABBAGE,61,130,72,NO
+CABBAGE,44,59,31,YES
+PUMPKIN,44,64,44,YES
+BEETROOT,44,62,19,YES
+CABBAGE,45,35,69,YES
+CARROT,45,65,27,YES
+CARROT,45,71,2,YES
+PUMPKIN,45,83,57,YES
+BEETROOT,45,31,26,YES
+BEETROOT,45,61,10,YES
+BEETROOT,45,76,85,NO
+CARROT,46,48,24,YES
+CARROT,10,96,73,NO
+CABBAGE,62,123,74,NO
+CARROT,47,37,16,YES
+CARROT,47,42,41,NO
+BEETROOT,47,50,11,YES
+PUMPKIN,11,99,75,NO
+CABBAGE,16,98,75,NO
+CARROT,48,77,12,YES
+CABBAGE,48,106,75,NO
+BEETROOT,48,32,23,YES
+BEETROOT,48,44,19,YES
+BEETROOT,48,98,14,NO
+CABBAGE,8,124,76,NO
+CARROT,49,64,4,YES
+PUMPKIN,59,114,76,NO
+PUMPKIN,50,119,77,NO
+CABBAGE,55,116,77,NO
+CARROT,50,74,21,YES
+BEETROOT,50,47,25,YES
+CABBAGE,53,117,78,NO
+PUMPKIN,55,91,78,NO
+PUMPKIN,49,91,79,NO
+CABBAGE,56,125,79,NO
+CARROT,57,95,79,NO
+PUMPKIN,8,123,80,NO
+CARROT,16,99,80,NO
+PUMPKIN,55,93,80,NO
+CABBAGE,60,99,80,NO
+CARROT,13,114,81,NO
+BEETROOT,20,100,81,NO
+CABBAGE,49,100,81,NO
+PUMPKIN,54,92,81,NO
+CABBAGE,10,94,83,NO
+PUMPKIN,17,91,83,NO
+BEETROOT,17,95,84,NO
+PUMPKIN,53,92,84,NO
+CARROT,65,110,85,NO
+PUMPKIN,73,101,85,NO
+PUMPKIN,18,102,88,NO
+PUMPKIN,47,120,88,NO
+PUMPKIN,48,102,89,NO
+CARROT,60,98,89,NO
+CABBAGE,68,120,89,NO
+CABBAGE,58,16,26,NO
+CARROT,53,107,90,NO
+CABBAGE,8,92,92,NO
+CARROT,11,97,92,NO
+PUMPKIN,14,98,92,NO
+BEETROOT,58,103,92,NO
+CABBAGE,59,131,92,NO
+PUMPKIN,57,104,93,NO
+CARROT,67,97,93,NO
+CABBAGE,3,107,94,NO
+PUMPKIN,17,104,94,NO
+PUMPKIN,17,94,96,NO
+CABBAGE,56,101,96,NO
+PUMPKIN,58,95,97,NO
+CARROT,80,93,97,NO
+PUMPKIN,10,105,99,NO
+PUMPKIN,50,121,99,NO
+PUMPKIN,9,97,100,NO
+PUMPKIN,12,95,102,NO
+PUMPKIN,16,97,103,NO
+PUMPKIN,60,107,107,NO
+PUMPKIN,58,107,109,NO
+PUMPKIN,13,96,111,NO
+PUMPKIN,61,111,113,NO
+PUMPKIN,49,110,116,NO
+PUMPKIN,64,112,120,NO

decisiontree.py

@@ -0,0 +1,59 @@
+import pandas as pd
+#NIE AKTULAIZOWAĆ scikit / sklearn
+from sklearn import tree
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.tree.export import export_text
+from sklearn.tree import export_graphviz
+
+
+
+def decision(plant,feed,growth,dryness):
+
+    # czytanie pliku csv 
+    df = pd.read_csv("C:\DATA\#Mike\#Studia\#Rok II- Semestr II\#Sztuczna_inteligencja\Projekt\AIProjekt\data.csv")
+    #print(df)
+    
+    #zmiana słów na cyfry, ponieważ drzewo decyzyjne opiera się na cyfrach
+    z = {'CABBAGE': 2, 'PUMPKIN':4, 'CARROT':3, 'BEETROOT': 1}
+    df['PLANT'] = df['PLANT'].map(z)
+
+    d = {'NO': 0, 'YES': 1}
+    df['DEC'] = df['DEC'].map(d)
+
+    #print(df)
+    
+    features_rest = ['PLANT','FEED','GROWTH','DRYNESS'] #dane, na których opiera się decyzja
+    features_dec = ['DEC'] #kolumna z decyją
+
+    X = df[features_rest]
+    y = df[features_dec]
+
+    #wyświetlkanie kolumn
+    #print(X)
+    #print(y)
+
+    #tworzenie drzewa
+    dtree = DecisionTreeClassifier()
+    #przypisanie danych
+    dtree = dtree.fit(X, y)
+    #eksport drzewa do tekstu
+    r = export_text(dtree, feature_names=features_rest)
+    #print("\nDrzewo decyzyjne\n")
+    #print(r)
+
+    a = dtree.predict([[plant,feed,growth,dryness]])
+    #return a
+    #print("\n[1] means FEED THE PLANT")
+    #print("[0] means NOT FEED THE PLANT\n")
+    print ("Decision for: ",plant,", feed: ", feed,", growth: ", growth,", dryness:", dryness," is ", a,"")
+    
+"""    
+plant = 1
+feed = 35
+growth = 20
+#vermins = 0
+dryness = 12
+decision(plant,feed,growth,dryness)
+"""
+
+

main.py

@@ -1,6 +1,7 @@
 import pygame, sys
 from traktor import Traktor
 import dijkstra as di
+import decisiontree as dt 
 
 import matplotlib.pyplot as plt
 from tensorflow.keras.preprocessing import image
@@ -45,8 +46,10 @@ class Game(object):
 
         #ładowanie tablicy ze zdjeciami
         imgs = []
+        data_plant = []
         img_dir = './imgs'
         for _ in lokalizacje:
+            data_plant.append([random.randrange(5, 90),random.randrange(0, 120),random.randrange(5, 90)])
             imgs.append(img_dir + '/' + random.choice(os.listdir(img_dir)))
 
         #model do rozpoznawania
@@ -139,6 +142,20 @@ class Game(object):
                 #detektor roslin, 5 rezultatow
                 plt.imshow(img)
                 preds = model.predict(x)
+                list_of_preds = preds.tolist()[0]
+                index = list_of_preds.index(max(list_of_preds))
+                #print(index)
+                
+                
+                if index == 0:
+                    dt.decision(4, data_plant[pt][0],data_plant[pt][1],data_plant[pt][2])
+                if index == 1:
+                    dt.decision(1, data_plant[pt][0],data_plant[pt][1],data_plant[pt][2])
+                if index == 2:
+                    dt.decision(2, data_plant[pt][0],data_plant[pt][1],data_plant[pt][2])
+                if index == 3:
+                    dt.decision(3, data_plant[pt][0],data_plant[pt][1],data_plant[pt][2])
+                
                 print("kapusta, kalafior, grzyb, dynia\n {}\n".format(preds) )
                 plt.show()