get_average_eye_coordinates(): In the EyeTrackerTests unit test, two distinct scenarios are assessed to ensure the robustness of the get_average_eye_coordinates method within an EyeTracker class. Test Case 1: Even Coordinate Values This test involves coordinates with even values, specifically using a MockLandmarks instance initialized with left eye coordinates at (2, 4) and right eye coordinates at (6, 8). The method under examination calculates the average eye coordinates, which should theoretically yield a midpoint at (4, 6). The test asserts that the method's output precisely matches this expected result, validating the method's accuracy in handling even-valued coordinates. Test Case 2: Odd Coordinate Values Contrasting the first, this scenario tests the method's performance with odd-valued coordinates. A new MockLandmarks instance is created with the left eye at (3, 5) and the right eye at (7, 9). The expected midpoint, in this case, would be (5, 7). Similar to the first test, the outcome from the get_average_eye_coordinates method is compared against this expected result. The test confirms that the method accurately computes the midpoint even with odd-numbered coordinates. These two test cases collectively aim to verify the get_average_eye_coordinates method's reliability across different types of input, ensuring it performs accurately regardless of whether the input coordinates are even or odd. This meticulous testing approach underscores the method's capability to handle varied data, crucial for its application in eye-tracking technologies where precision is paramount.

get_eye_coordinates():. The EyeTrackerTests class conducts a focused evaluation of the EyeTracker class's get_eye_coordinates function. This test begins by creating an EyeTracker instance to simulate real-world usage. A test image, 'test_frame.jpg', is loaded to assess the function's capability to accurately detect eye coordinates. The core of the test consists of several assertions: it checks that the function returns a result (not None), ensuring the method identifies eye positions. It verifies the output is a tuple of two Point objects, representing the left and right eye positions, which confirms the function's ability to differentiate and accurately locate each eye. Lastly, it asserts that these coordinates fall within the expected range of the camera's field of view, indicating the method's precision in real-world scenarios. Through these validations, the test aims to ensure the get_eye_coordinates function is reliable and accurate in detecting eye positions within an image frame, crucial for the effectiveness of the EyeTracker's applications

track_eyes(): Focuses on evaluating the track_eyes method of the EyeTracker class through two distinct scenarios: processing frames with valid eye coordinates and those without. In the first test, test_track_eyes_with_valid_coordinates, a hypothetical Transformer object and a test frame purportedly containing eye coordinates are prepared. The track_eyes method is then executed with these inputs. The expected outcome is a specific set of coordinates, here represented as [590.0172, 1229.2874], which the test asserts to be the result, confirming the method's ability to accurately track eyes when given valid input. Conversely, the second test, test_track_eyes_with_invalid_coordinates, follows a similar setup but utilizes a test frame that lacks eye coordinates. Upon invoking the track_eyes method under these conditions, the expected result is None, indicating the method's correct handling of cases where eyes cannot be tracked due to the absence of identifiable eye features in the input frame. Both tests emphasize the EyeTracker method's capability to discern and appropriately respond to different types of input, showcasing its reliability in eye tracking under varied conditions

ApplicationOverlay.py is responsible for handling the interactive portion of the application's overlay through methods like:

• on_press():
• on_move():
• run():This script tests the functionality of a TransparentOverlay class, responsible for managing a transparent overlay on a screen. It starts by initializing the overlay and creating a message queue. A specific message is added to the queue, instructing the overlay to draw a diamond-shaped marker at a defined position and size. The script then simulates the processing of queued messages by setting the overlay's queue attribute. Finally, it runs the overlay to observe its behavior. Assertions are used to verify that the marker is displayed on the transparent window and animates as expected, ensuring the correct rendering of instructions by the overlay component.

This utility module ensures the user is at the correct distance for eye tracking to function properly: __init__(): check_distance(): The test_check_distance() function serves as a unit test for the check_distance() function, which is responsible for calculating the area of a triangle formed by landmarks on a face. This test suite is crucial for ensuring the correctness of the distance calculation algorithm and verifying that the function behaves as expected under different scenarios. In the first test case, the function is invoked with a mock frame and a None face_mesh object, simulating a scenario where no landmarks are detected on the face. The expected outcome of this test is None, indicating that the function appropriately handles situations where no landmarks are available for distance calculation. Conversely, the second test case evaluates the function's behavior when landmarks are detected. Here, the function is called with a mock frame and a face_mesh object containing landmarks. The function computes the area of the triangle formed by these landmarks and returns False to indicate that the distance check is not triggered. This test ensures that the function correctly processes detected landmarks and performs the necessary distance calculations. Overall, these test cases provide comprehensive coverage of the check_distance() function, verifying its functionality under different conditions and ensuring robustness in handling both scenarios with and without detected landmarks. Additional test cases can be incorporated to further validate the function's behavior and enhance test coverage