forked from s444420/AL-2020
agent can put one product.py
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d9c2092551
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@ -1,7 +1,7 @@
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import cv2
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import cv2
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import torch
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import torch
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from nn_model import Net
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from torchvision.transforms import transforms
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from torchvision.transforms import transforms
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from codes_recognizer.network_model import Net
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def recognizer(path):
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def recognizer(path):
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@ -14,9 +14,8 @@ def recognizer(path):
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# load nn model
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# load nn model
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model = Net()
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model = Net()
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model.load_state_dict(torch.load('model.pt'))
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model.load_state_dict(torch.load('codes_recognizer/model.pt'))
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model.eval()
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model.eval()
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img = cv2.imread(path)
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img = cv2.imread(path)
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img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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5
main.py
5
main.py
@ -76,11 +76,14 @@ def run():
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'''
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'''
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# list [x, y]
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# list [x, y]
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dest_shelf = new_product.shelf()
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dest_shelf = new_product.shelf()
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dest_field = board[dest_shelf[0], dest_shelf[1]]
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dest_field = board[dest_shelf[0]][dest_shelf[1]]
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path = functions.a_star(board[agent.y][agent.x], dest_field, board)
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path = functions.a_star(board[agent.y][agent.x], dest_field, board)
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''''''
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''''''
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agent.item = new_product
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agent.item = new_product
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path.pop(len(path) - 1)
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next_step = path.pop(len(path) - 1)
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agent.is_busy = True
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if next_step is not None:
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if next_step is not None:
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@ -32,4 +32,5 @@ class FinalProduct:
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self.img = create_image(self)
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self.img = create_image(self)
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def shelf(self):
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def shelf(self):
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print(self.img)
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return assigning(self.img);
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return assigning(self.img);
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