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2 changed files with 1 additions and 43 deletions

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

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@ -28,7 +28,7 @@ class Main:
tiles_x = 10
tiles_y = 10
self.renderer = Renderer(600, 600, tiles_x, tiles_y)
self.renderer = Renderer(800, 800, tiles_x, tiles_y)
self.world = generate_world(tiles_x, tiles_y)