82 lines
3.4 KiB
Python
82 lines
3.4 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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# You can download model from https://uam-my.sharepoint.com/:f:/g/personal/pavbia_st_amu_edu_pl/EmBHjnETuk5LiCZS6xk7AnIBNsnffR3Sygf8EX2bhR1w4A
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# Change the path to model + to datasets (string 12 + strings 35,41,47,53)
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class VacuumRecognizer:
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model = keras.models.load_model("AI_brain\model.h5") # Neuron model path
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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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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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# More information about neuron decision
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# print(img.shape)
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# cv2.imshow("test_show", img)
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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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def get_random_dir(self, type) -> str:
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if type == "Plant":
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plant_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Plant" # Plant dataset path
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plant_dirs = os.listdir(plant_image_paths)
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full_path = (
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plant_image_paths
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+ "\\"
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+ plant_dirs[random.randint(0, len(plant_dirs) - 1)]
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)
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print(full_path)
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return full_path
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elif type == "Earings":
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earnings_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Earings" # Earings dataset path
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earning_dirs = os.listdir(earnings_image_paths)
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full_path = (
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earnings_image_paths
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+ "\\"
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+ earning_dirs[random.randint(0, len(earning_dirs) - 1)]
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)
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print(full_path)
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return full_path
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elif type == "Banana":
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banana_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Banana" # Banana dataset path
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banana_dirs = os.listdir(banana_image_paths)
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full_path = (
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banana_image_paths
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+ "\\"
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+ banana_dirs[random.randint(0, len(banana_dirs) - 1)]
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)
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print(full_path)
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return full_path
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elif type == "Cat":
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cat_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Cat" # Cat dataset path
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cat_dir = os.listdir(cat_image_paths)
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# For testing the neuron model
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"""image_paths = []
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image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Banana')
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image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Cat')
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image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Earings')
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image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\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(3):
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print(uio.recognize(image_path + '\\' + dirs[random.randint(0, len(dirs)-1)]))"""
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