AI_brain/image_recognition.py

@@ -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)]))

main.py

@@ -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)