assert observation is not None AssertionError when creating observation space for custom environment

Question

EDIT: Fixed it eventually. Solution in bottom of question

I want to create a custom environment to play a game. It plays by using a screengrab of the game as an input and using a DQN outputs either jump, or don't. I have tried a few ways of creating an observation space but none seems to work. The all give me the error: assert observation is not None AssertionError.

I create the spaces like this:

def __init__(self):
    # Get screen size of monitor 1
    user32 = ctypes.windll.user32
    screensize = user32.GetSystemMetrics(0), user32.GetSystemMetrics(1)
    self.screen = (0, 0) + screensize
    
    # Create spaces
    self.action_space = spaces.Discrete(2)
    self.observation_space = spaces.Box(np.array([screensize[0], screensize[0], 0]), np.array([screensize[0], screensize[0], 255]), dtype=np.int)

My step function looks like this:

def step(self, action):
    # Take action
    self.do_action(action)

    # Check if done
    if self.done:
        return np.array(self.getScreen()), self.reward, self.done, {}

    if self.reward <= -1000:
        self.done = True
        return np.array(self.getScreen()), self.reward, self.done, {}

    # Get reward
    self.reward = self.calculate_reward()

    return np.array(self.getScreen()), self.reward, self.done, {}

Is there a good way of making an observation space for a 1920x1080 image? It does not change size. Sorry if this question was badly formulated its my first time asking on stackoverflow and im open to criticism/tips.

EDIT: I managed to get the box into the same shape as the numpy array representing my image with:

self.observation_space = spaces.Box(np.array([[[0, 0, 0] for j in range(1920)] for i in range(1080)]), np.array([[[1, 1, 1] for j in range(1920)] for i in range(1080)]), dtype=np.float)

But i still get the error: assert observation is not None AssertionError.

EDIT 2: Apparently it wasn't a problem with the observation space at all, but the reset function. I forgot to return an observation after resetting the game. Now i return a screengrab of the game as a normalized numpy array.

    observation = np.array(self.getScreen())/255
    return observation