42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
![]() |
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)]))
|