From f98855d93c94d49d0ab3f1de4dccb22d3624758a Mon Sep 17 00:00:00 2001 From: Pavel Date: Sun, 4 Jun 2023 18:52:06 +0200 Subject: [PATCH] preparing recognnition file to implimintation --- AI_brain/image_recognition.py | 31 ++++++++++++++++--------------- domain/world.py | 6 +++--- 2 files changed, 19 insertions(+), 18 deletions(-) diff --git a/AI_brain/image_recognition.py b/AI_brain/image_recognition.py index 58ba878..d5c097b 100644 --- a/AI_brain/image_recognition.py +++ b/AI_brain/image_recognition.py @@ -7,36 +7,37 @@ import random class VacuumRecognizer: - model = keras.models.load_model("D:/Image_dataset/model.h5") + model = keras.models.load_model('AI_brain\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))) + # print(img.shape) + # cv2.imshow("test_show", img) + # 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 testing the neuron model +'''image_paths = [] +image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Banana') +image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Cat') +image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Earings') +image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\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)])) \ No newline at end of file + for i in range(3): + print(uio.recognize(image_path + '\\' + dirs[random.randint(0, len(dirs)-1)]))''' \ No newline at end of file diff --git a/domain/world.py b/domain/world.py index d3a7e8a..caf17f8 100644 --- a/domain/world.py +++ b/domain/world.py @@ -15,14 +15,14 @@ class World: self.doc_station = None def add_entity(self, entity: Entity): - if entity.type == "PEEL": + if entity.type == "DOC_STATION": + self.doc_station = entity + elif entity.type == "PEEL": self.dust[entity.x][entity.y].append(entity) elif entity.type == "EARRING": self.dust[entity.x][entity.y].append(entity) elif entity.type == "VACUUM": self.vacuum = entity - elif entity.type == "DOC_STATION": - self.doc_station = entity elif entity.type == "CAT": self.cat = entity self.obstacles[entity.x][entity.y].append(entity)