42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
import os
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import numpy as np
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import tensorflow as tf
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from tensorflow import keras
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import cv2
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import random
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class VacuumRecognizer:
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model = keras.models.load_model("D:/Image_dataset/model.h5")
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def recognize(self, image_path) -> str:
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class_names = ['Banana', 'Cat', 'Earings', 'Plant']
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img = cv2.imread(image_path, flags=cv2.IMREAD_GRAYSCALE)
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# print(img.shape)
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cv2.imshow("lala", img)
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cv2.waitKey(0)
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img = (np.expand_dims(img, 0))
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predictions = self.model.predict(img)[0].tolist()
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print(class_names)
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print(predictions)
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print(max(predictions))
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print(predictions.index(max(predictions)))
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return class_names[predictions.index(max(predictions))]
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image_paths = []
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image_paths.append('D:/Image_dataset/Image_datasetJPGnewBnW/Image_datasetJPGnewBnW/test/Banana/')
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image_paths.append('D:/Image_dataset/Image_datasetJPGnewBnW/Image_datasetJPGnewBnW/test/Cat/')
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image_paths.append('D:/Image_dataset/Image_datasetJPGnewBnW/Image_datasetJPGnewBnW/test/Earings/')
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image_paths.append('D:/Image_dataset/Image_datasetJPGnewBnW/Image_datasetJPGnewBnW/test/Plant/')
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uio = VacuumRecognizer()
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for image_path in image_paths:
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dirs = os.listdir(image_path)
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for i in range(10):
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print(uio.recognize(image_path + dirs[random.randint(0, len(dirs)-1)])) |