Merge pull request 'Model generated + tests screenshots' (#31) from neural into master

Reviewed-on: #31

main.py

@@ -135,15 +135,15 @@ def recognize_plants(plants_array, fields_for_astar, fields_for_movement, agent)
         if b == 0:
             for j in range(11):
                 if plants_array[j][i] == 'carrot':
-                    img = 'assets/learning/test/carrot/' + str(random.randint(1, 25)) + '.jpg'
+                    img = 'assets/learning/test/carrot/' + str(random.randint(1, 200)) + '.jpg'
                     pred = neural_network.prediction(img, model)
                     show_plant_img(img)
                 elif plants_array[j][i] == 'potato':
-                    img = 'assets/learning/test/potato/' + str(random.randint(1, 25)) + '.jpg'
+                    img = 'assets/learning/test/potato/' + str(random.randint(1, 200)) + '.jpg'
                     pred = neural_network.prediction(img, model)
                     show_plant_img(img)
                 elif plants_array[j][i] == 'wheat':
-                    img = 'assets/learning/test/wheat/' + str(random.randint(1, 25)) + '.jpg'
+                    img = 'assets/learning/test/wheat/' + str(random.randint(1, 200)) + '.jpg'
                     pred = neural_network.prediction(img, model)
                     show_plant_img(img)
                 else:
@@ -156,15 +156,15 @@ def recognize_plants(plants_array, fields_for_astar, fields_for_movement, agent)
         else:
             for j in range(10,-1,-1):
                 if plants_array[j][i] == 'carrot':
-                    img = 'assets/learning/test/carrot/' + str(random.randint(1, 25)) + '.jpg'
+                    img = 'assets/learning/test/carrot/' + str(random.randint(1, 200)) + '.jpg'
                     pred = neural_network.prediction(img, model)
                     show_plant_img(img)
                 elif plants_array[j][i] == 'potato':
-                    img = 'assets/learning/test/potato/' + str(random.randint(1, 25)) + '.jpg'
+                    img = 'assets/learning/test/potato/' + str(random.randint(1, 200)) + '.jpg'
                     pred = neural_network.prediction(img, model)
                     show_plant_img(img)
                 elif plants_array[j][i] == 'wheat':
-                    img = 'assets/learning/test/wheat/' + str(random.randint(1, 25)) + '.jpg'
+                    img = 'assets/learning/test/wheat/' + str(random.randint(1, 200)) + '.jpg'
                     pred = neural_network.prediction(img, model)
                     show_plant_img(img)
                 else: