Update tractor.py
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15
tractor.py
15
tractor.py
@ -1,10 +1,11 @@
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import pygame
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from constant import size, rows, cols
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from decisiontree import predict
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from neuralnetwork import predict_image
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class Tractor:
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def __init__(self, row, col, model, feature_columns):
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def __init__(self, row, col, model, feature_columns, neuralnetwork):
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self.row = row
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self.col = col
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self.images = {
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@ -16,6 +17,7 @@ class Tractor:
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self.direction = "down"
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self.model = model
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self.feature_columns = feature_columns
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self.neuralnetwork = neuralnetwork
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def draw(self, win):
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tractor_image = self.images[self.direction]
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@ -41,6 +43,7 @@ class Tractor:
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elif self.direction == "left":
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self.direction = "up"
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def move_forward(self, board):
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if self.direction == "up" and self.row > 0:
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next_row, next_col = self.row - 1, self.col
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@ -53,6 +56,14 @@ class Tractor:
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else:
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return # Nie możemy się ruszyć poza planszę
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image_path = board.vegetables[next_row][next_col] # Ścieżka do obrazu warzywa
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# Predykcja warzywa po obrazie
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if image_path is not None:
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print(image_path, "mialo byc")
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vegetable_name = predict_image(image_path, self.neuralnetwork)
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print(vegetable_name, "jest")
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if board.is_dirt(next_row, next_col) and board.board[next_row][next_col] != 10:
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soil_features = board.soil_features
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sample = {
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@ -63,7 +74,7 @@ class Tractor:
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"proc_ekspo_na_swiatlo": soil_features["proc_ekspo_na_swiatlo"],
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"pora_dnia": soil_features["pora_dnia"],
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"pora_roku": soil_features["pora_roku"],
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"roslina": board.vegetable_types[board.vegetables[next_row][next_col]]
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"roslina": "warzywa/" + str(vegetable_name) + ".png"
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}
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should_water = predict(self.model, self.feature_columns, sample)
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