fix random moving tractor
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4c3f216c03
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@ -2,7 +2,7 @@
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### 1. Requirements
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python version 3.9 or higher
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```bash
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python3 -v
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python3 --version
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```
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### 2. Create virtual environments and install libs
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```bash
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@ -35,14 +35,14 @@ VERTICAL_NUM_OF_FIELDS = 3
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HORIZONTAL_NUM_OF_FIELDS = 3
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```
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\
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####4.1 Save generated map:
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#### 4.1 Save generated map:
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```bash
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python main.py --save-map
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```
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Map will be saved in maps directory.
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Generated filename: map-uuid
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####4.2 Load map
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#### 4.2 Load map
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```bash
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python main.py --load-map=name_of_map
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```
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@ -28,9 +28,9 @@ class NeuralNetwork:
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self.input_shape = (self.img_width, self.img_height, self.img_num_channels)
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# labels
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self.labels = ["cabbage", "carrot", "corn", "lettuce", "paprika", "potato", "sunflower" , "tomato"]
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self.labels = ["cabbage", "carrot", "corn", "lettuce", "paprika", "potato", "sunflower", "tomato"]
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def init_model(self):
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def init_model(self) -> None:
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if not self.model_dir_is_empty():
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# Load the model
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self.model = load_model(
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@ -60,9 +60,9 @@ class NeuralNetwork:
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shuffle=False)
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# Display a model summary
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#self.model.summary()
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# self.model.summary()
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def load_images(self):
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def load_images(self) -> None:
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# Create a generator
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self.train_datagen = ImageDataGenerator(
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rescale=1. / 255
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@ -210,7 +210,7 @@ class Tractor(BaseField):
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time.sleep(TIME_OF_MOVING)
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is_running.clear()
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def move_or_rotate(self, movement: str):
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def move_or_rotate(self, movement: str) -> None:
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print(f"Move {movement}")
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if movement == M_GO_FORWARD:
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self.move()
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@ -267,14 +267,25 @@ class Tractor(BaseField):
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board.get_fields()[x][y] = obj()
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return obj()
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def harvest_checked_fields_handler(self, is_running: threading.Event):
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def harvest_checked_fields_handler(self, is_running: threading.Event) -> None:
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thread = threading.Thread(target=self.harvest_checked_fields, args=(is_running,), daemon=True)
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thread.start()
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def harvest_checked_fields(self, is_running: threading.Event):
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moves = [M_GO_FORWARD, M_ROTATE_LEFT, M_ROTATE_RIGHT]
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distribution=[0.6,0.2,0.2]
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def go_forward_is_legal_move(self) -> bool:
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flag = False
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if (self.__direction == D_EAST and self.__pos_y - self.__move >= 0) or \
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(self.__direction == D_NORTH and self.__pos_y + self.__move + FIELD_SIZE <= HEIGHT) or \
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(self.__direction == D_WEST and self.__pos_x - self.__move >= 0) or \
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(self.__direction == D_SOUTH and self.__pos_x + self.__move + FIELD_SIZE <= WIDTH):
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flag = True
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return flag
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def harvest_checked_fields(self, is_running: threading.Event) -> None:
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while True:
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moves = [M_GO_FORWARD, M_ROTATE_LEFT, M_ROTATE_RIGHT]
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distribution = [0.6, 0.2, 0.2]
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field = self.get_field_from_board()
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self.__neural_network = NeuralNetwork()
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@ -283,8 +294,12 @@ class Tractor(BaseField):
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if prediction.capitalize() in CROPS:
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self.harvest()
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break
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chosen_move = random.choices(moves,distribution)
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if not self.go_forward_is_legal_move():
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moves = moves[1:]
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distribution = distribution[1:]
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chosen_move = random.choices(moves, distribution)
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self.move_or_rotate(chosen_move[0])
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time.sleep(1)
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