Neural_network #4
@ -63,14 +63,34 @@ def main():
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for event in pygame.event.get():
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if event.type == pygame.QUIT:
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run = False
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#small test of work_on_field method:
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time.sleep(1)
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tile1 = tiles[0]
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# movement based on route-planning (test):
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tractor.draw_tractor(WIN)
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time.sleep(1)
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if moves != False:
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do_actions(tractor, WIN, moves)
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#guessing the image under the tile:
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goalTile = tiles[tile_index]
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goalTile.display_photo()
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image_path = goalTile.photo
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image_tensor = load_image(image_path)
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prediction = guess_image(load_model(), image_tensor)
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print(f"The predicted image is: {prediction}")
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p1 = Plant('wheat', 'cereal', random.randint(1,100), random.randint(1,100), random.randint(1,100))
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goalTile.plant = p1
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d1 = Dirt(random.randint(1, 100), random.randint(1,100))
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d1.pests_and_weeds()
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tile1.ground=d1
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goalTile.ground=d1
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#getting the name and type of the recognized plant:
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p1.update_name(prediction)
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#decission tree test:
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if d1.pest:
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pe = 1
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else:
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@ -117,25 +137,12 @@ def main():
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model = joblib.load('model.pkl')
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nowe_dane = pd.read_csv('model_data.csv')
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predykcje = model.predict(nowe_dane)
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# movement based on route-planning (test):
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tractor.draw_tractor(WIN)
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time.sleep(1)
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if moves != False:
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do_actions(tractor, WIN, moves)
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print(predykcje)
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if predykcje == 'work':
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tractor.work_on_field(tile1, d1, p1)
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#guessing the image under the tile:
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tiles[tile_index].display_photo()
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image_path = tiles[tile_index].photo
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image_tensor = load_image(image_path)
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prediction = guess_image(load_model(), image_tensor)
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print(f"The predicted image is: {prediction}")
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#work on field:
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if predykcje == 'work':
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tractor.work_on_field(goalTile, d1, p1)
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time.sleep(30)
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print("\n")
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@ -19,7 +19,23 @@ class Plant:
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else:
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print("Unable to grow due to bad condition of the ground")
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# more properties
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def update_name(self, predicted_class):
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if predicted_class == "Apple":
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self.name = "Apple"
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self.plant_type = "fruit"
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elif predicted_class == "Strawberry":
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self.name = "Strawberry"
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self.plant_type = "fruit"
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# add init, getters,setters
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elif predicted_class == "Cucumber":
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self.name = "Cucumber"
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self.plant_type = "vegetable"
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elif predicted_class == "Cauliflower":
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self.name = "Cauliflower"
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self.plant_type = "vegetable"
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elif predicted_class == "Wheat":
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self.name = "Wheat"
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self.plant_type = "cereal"
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