main.py

@@ -13,6 +13,77 @@ from src.ID3 import make_decision
 import torch
 import cnn_model
 
+def recognize_plants(plants_array):
+    checkpoint = torch.load(f'plants.model')
+    model = cnn_model.Net(num_classes=3)
+    model.load_state_dict(checkpoint)
+    model.eval()
+    img = ''
+    b=0
+    j=0
+    field_array_small = []
+    field_array_big = []
+    for i in range(11):
+        field_array_small = []
+        if b == 0:
+            for j in range(11):
+                if plants_array[j][i] == 'carrot':
+                    img = 'assets/learning/test/carrot/' + str(random.randint(1, 200)) + '.jpg'
+                    pred = cnn_model.prediction(img, model)
+                    #show_plant_img(img)
+                elif plants_array[j][i] == 'potato':
+                    img = 'assets/learning/test/potato/' + str(random.randint(1, 200)) + '.jpg'
+                    pred = cnn_model.prediction(img, model)
+                    # show_plant_img(img)
+                elif plants_array[j][i] == 'wheat':
+                    img = 'assets/learning/test/wheat/' + str(random.randint(1, 200)) + '.jpg'
+                    pred = cnn_model.prediction(img, model)
+                    # show_plant_img(img)
+                else:
+                    pred = 'none'
+                field_array_small.append(pred)
+                print(i,',', j,'-',pred)
+                # agent_movement(['f'], agent, fields_for_movement, fields_for_astar)
+            # agent_movement(['r','f','r'], agent, fields_for_movement, fields_for_astar)
+            field_array_big.append(field_array_small)
+        else:
+            for j in range(10,-1,-1):
+                if plants_array[j][i] == 'carrot':
+                    img = 'assets/learning/test/carrot/' + str(random.randint(1, 200)) + '.jpg'
+                    pred = cnn_model.prediction(img, model)
+                    # show_plant_img(img)
+                elif plants_array[j][i] == 'potato':
+                    img = 'assets/learning/test/potato/' + str(random.randint(1, 200)) + '.jpg'
+                    pred = cnn_model.prediction(img, model)
+                    # show_plant_img(img)
+                elif plants_array[j][i] == 'wheat':
+                    img = 'assets/learning/test/wheat/' + str(random.randint(1, 200)) + '.jpg'
+                    pred = cnn_model.prediction(img, model)
+                    # show_plant_img(img)
+                else:
+                    pred = 'none'
+                field_array_small.append(pred)
+                print(i,',', j,'-',pred)
+                # agent_movement(['f'], agent, fields_for_movement, fields_for_astar)
+            field_array_small = field_array_small[::-1]
+            field_array_big.append(field_array_small)
+            # agent_movement(['l','f','l'], agent, fields_for_movement, fields_for_astar)
+        if b==0:
+            b=1
+        else:
+            b=0
+    correct = 0
+    incorrect = 0
+    for i in range(11):
+        for j in range(11):
+            if plants_array[i][j]=='none':
+                continue
+            else:
+                if plants_array[i][j]==field_array_big[j][i]:
+                    correct+=1
+                else:
+                    incorrect+=1
+    print("Accuracy: ",correct/(correct+incorrect)*100,'%')