image recognition

AI_brain/image_recognition.py

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+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)]))