From dbb637af6c11df9580d243d32c495c9dee2e457b Mon Sep 17 00:00:00 2001
From: dardwo <dardwo@st.amu.edu.pl>
Date: Sat, 3 Jun 2023 11:24:47 +0200
Subject: [PATCH] test photos

---
 main.py | 71 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 71 insertions(+)

diff --git a/main.py b/main.py
index 7e85fc2..09cbbf5 100644
--- a/main.py
+++ b/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,'%')