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34 changes: 16 additions & 18 deletions src/adverse_weather_classification/weather_classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,50 +8,48 @@


class AdverseWeatherClassifier:
def __init__(self, model_path, model_input_size: Tuple[int, int] = (256, 256)) -> None:
def __init__(self, model_path: str, model_input_size: Tuple[int, int] = (256, 256)):
self.model = None
self.model_path = model_path
self.model_input_size = model_input_size
self.class_labels = ['day', 'night']

def load(self):
def load_model(self):
start_time = time.time()
self.model = keras.models.load_model(self.model_path)
print("Model loaded in {} seconds.".format(time.time() - start_time))
print("Model loaded in {:.2f} seconds.".format(time.time() - start_time))

def __preprocess_frame__(self, frame: np.ndarray) -> np.ndarray:
frame = cv2.resize(frame, self.model_input_size)
frame = frame.astype('float32') / 255.0
frame = cv2.resize(frame, self.model_input_size).astype('float32') / 255.0
frame = np.expand_dims(frame, axis=0)
return frame

def predict(self, preprocessed_frame: np.ndarray) -> str:
if self.model is not None:
predictions = self.model.predict(preprocessed_frame)
else:
raise ValueError("Model is not loaded. Please call load() method first.")
if self.model is None:
raise ValueError("Model is not loaded. Please call load_model() method first.")
predictions = self.model.predict(preprocessed_frame)
predicted_class = self.class_labels[np.argmax(predictions)]
return predicted_class

def exec(self, frame: np.ndarray) -> str:
preprocessed_frame = self.__preprocess_frame__(frame)
def execute(self, frame: np.ndarray) -> str:
preprocessed_frame = self.preprocess_frame(frame)
predicted_class = self.predict(preprocessed_frame)
return predicted_class


if __name__ == "__main__":
img_dir = "/home/ahv/PycharmProjects/Visual-Inertial-Odometry/simulation/CARLA/output/root_dir/testing_imgs"
model_path_ = "/src/adverse_weather_classification/output/checkpoints/best_model.h5"
adverse_weather_classifier = AdverseWeatherClassifier(model_path_)
adverse_weather_classifier.load()
img_dir = "/home/ahv/PycharmProjects/Visual-Inertial-Odometry/simulation/CARLA/output/testing_imgs"
model_path = "/home/ahv/PycharmProjects/Visual-Inertial-Odometry/simulation/CARLA/output/checkpoint_directory"
adverse_weather_classifier = AdverseWeatherClassifier(model_path)
adverse_weather_classifier.load_model()
for root, dirs, files in os.walk(img_dir):
# shuffle the files
# Shuffle the files
np.random.shuffle(files)
for file in files:
img = cv2.imread(os.path.join(root, file))
visualization_img = img.copy()
predicted_class_ = adverse_weather_classifier.exec(img)
cv2.putText(visualization_img, predicted_class_, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
predicted_class = adverse_weather_classifier.execute(img)
cv2.putText(visualization_img, predicted_class, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
cv2.imshow("Image", visualization_img)
cv2.waitKey(0)
cv2.destroyAllWindows()