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preparing recognnition file to implimintation

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Pavel 2023-06-04 18:52:06 +02:00
parent 82b964ac2a
commit f98855d93c
2 changed files with 19 additions and 18 deletions
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31
AI_brain/image_recognition.py
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@ -7,36 +7,37 @@ import random
class VacuumRecognizer: 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: def recognize(self, image_path) -> str:
class_names = ['Banana', 'Cat', 'Earings', 'Plant'] class_names = ['Banana', 'Cat', 'Earings', 'Plant']
img = cv2.imread(image_path, flags=cv2.IMREAD_GRAYSCALE) img = cv2.imread(image_path, flags=cv2.IMREAD_GRAYSCALE)
# print(img.shape)
cv2.imshow("lala", img)
cv2.waitKey(0) cv2.waitKey(0)
img = (np.expand_dims(img, 0)) img = (np.expand_dims(img, 0))
predictions = self.model.predict(img)[0].tolist() predictions = self.model.predict(img)[0].tolist()
print(class_names) # print(img.shape)
print(predictions) # cv2.imshow("test_show", img)
print(max(predictions)) # print(class_names)
print(predictions.index(max(predictions))) # print(predictions)
# print(max(predictions))
# print(predictions.index(max(predictions)))
return class_names[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: for image_path in image_paths:
dirs = os.listdir(image_path) dirs = os.listdir(image_path)
for i in range(10): for i in range(3):
print(uio.recognize(image_path + dirs[random.randint(0, len(dirs)-1)])) print(uio.recognize(image_path + '\\' + dirs[random.randint(0, len(dirs)-1)]))'''

6
domain/world.py
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@ -15,14 +15,14 @@ class World:
self.doc_station = None self.doc_station = None
def add_entity(self, entity: Entity): 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) self.dust[entity.x][entity.y].append(entity)
elif entity.type == "EARRING": elif entity.type == "EARRING":
self.dust[entity.x][entity.y].append(entity) self.dust[entity.x][entity.y].append(entity)
elif entity.type == "VACUUM": elif entity.type == "VACUUM":
self.vacuum = entity self.vacuum = entity
elif entity.type == "DOC_STATION":
self.doc_station = entity
elif entity.type == "CAT": elif entity.type == "CAT":
self.cat = entity self.cat = entity
self.obstacles[entity.x][entity.y].append(entity) self.obstacles[entity.x][entity.y].append(entity)