Frame helpers¶

This module contains a helper class for frame operations.

class mio.process.frame_helper.BaseSingleFrameHelper¶

Base class for single frame operations.

abstractmethod find_invalid_area(frame: ndarray) → Tuple[bool, ndarray]¶

Find the invalid area in a single frame.

Parameters:: frame (np.ndarray) – The frame to process.
Returns:: A boolean indicating if the frame is invalid and the processed frame.
Return type:: Tuple[bool, np.ndarray]

abstractmethod process_frame(frame: ndarray) → ndarray¶

Process a single frame.

Parameters:: frame (np.ndarray) – The frame to process.
Returns:: The processed frame.
Return type:: np.ndarray

class mio.process.frame_helper.BlackAreaDetector(config: BlackAreaDetectorConfig)¶

Helper class for black area detection.

_detect_black_pixels(current_frame: ndarray) → Tuple[bool, ndarray]¶

Detect black-out noise by checking for black pixels (value 0) over rows of pixels.

Returns:: A boolean indicating if the frame is corrupted and noise mask.
Return type:: Tuple[bool, np.ndarray]

find_invalid_area(frame: ndarray) → Tuple[bool, ndarray]¶

Process a single frame and verify if it is valid.

Parameters:: frame (np.ndarray) – The frame to process.
Returns:: A boolean indicating if the frame is valid and the processed frame.
Return type:: Tuple[bool, np.ndarray]

class mio.process.frame_helper.FrameSplitter¶

Helper class for splitting frames into buffers. Currently only for getting the buffer shape from pixel count.

get_buffer_shape(frame_height: int, px_per_buffer: int) → list[int]¶

Get the shape of each buffer in a frame.

Parameters:

frame_width (int) – The width of the frame.
frame_height (int) – The height of the frame.
px_per_buffer (int) – The number of pixels per buffer.

Returns:

The shape of each buffer in the frame.

Return type:

list[int]

class mio.process.frame_helper.FrequencyMaskHelper(height: int, width: int, freq_mask_config: FreqencyMaskingConfig)¶

Helper class for frequency masking operations.

_gen_freq_mask() → ndarray¶: Generate a mask to filter out horizontal and vertical frequencies. A central circular region can be removed to allow low frequencies to pass.

freq_domain(img: ndarray) → ndarray¶

Compute the frequency spectrum of an image.

Parameters:: img (np.ndarray) – The image to process.
Returns:: The frequency spectrum of the image.
Return type:: np.ndarray

property freq_mask: ndarray¶

Get the frequency mask.

Returns:: The frequency mask.
Return type:: np.ndarray

process_frame(img: ndarray) → ndarray¶

Perform FFT/IFFT to remove horizontal stripes from a single frame.

Parameters:

img (np.ndarray) – The image to process.
cast_f32 (bool) – Cast the image to float32 before processing.

Returns:

The filtered image

Return type:

np.ndarray

Todo

Confirm if the option for casting to float32 is necessary. See issue #104.

class mio.process.frame_helper.GradientNoiseDetector(config: GradientDetectorConfig)¶

Helper class for gradient noise detection.

_detect_with_gradient(current_frame: ndarray) → Tuple[bool, ndarray]¶

Detect noise using local contrast (second derivative) in the x-dimension (along rows, across columns)

Returns:: A boolean indicating if the frame is noisy and the noise mask.
Return type:: Tuple[bool, np.ndarray]

find_invalid_area(frame: ndarray) → Tuple[bool, ndarray]¶

Process a single frame and verify if it is valid.

Parameters:: frame (np.ndarray) – The frame to process.
Returns:: A boolean indicating if the frame is valid and the processed frame.
Return type:: Tuple[bool, np.ndarray]

class mio.process.frame_helper.InvalidFrameDetector(noise_patch_config: NoisePatchConfig)¶

Helper class for combined invalid frame detection.

find_invalid_area(frame: ndarray) → Tuple[bool, ndarray]¶

Process a single frame and verify if it is valid.

Parameters:: frame (np.ndarray) – The frame to process.
Returns:: A boolean indicating invalid frames and the invalid area.
Return type:: Tuple[bool, np.ndarray]

class mio.process.frame_helper.MSENoiseDetector(config: MSEDetectorConfig)¶

Helper class for mean squared error noise detection.

_detect_with_mean_error(current_frame: ndarray) → Tuple[bool, ndarray]¶

Detect noise using mean error between current and previous frames.

Returns:: A boolean indicating if the frame is noisy and the noise mask.
Return type:: Tuple[bool, np.ndarray]

find_invalid_area(frame: ndarray) → Tuple[bool, ndarray]¶

Process a single frame and verify if it is valid.

Parameters:: frame (np.ndarray) – The frame to process.
Returns:: A boolean indicating if the frame is valid and the processed frame.
Return type:: Tuple[bool, np.ndarray]

register_previous_frame(previous_frame: ndarray) → None¶

Parameters:: previous_frame (np.ndarray) – The previous frame to compare against.

mio.process.frame_helper.mean_intensity(frame: ndarray) → float¶

Calculate the mean pixel intensity for a frame.

Parameters:: frame (np.ndarray) – The frame whose mean intensity is to be calculated on filtered data (broken frames replaced, spatial mask filter).
Returns:: The mean intensity of the frame.
Return type:: float