com.googlecode.leptonica.android

Class AdaptiveMap

com.googlecode.leptonica.android.AdaptiveMap

public class AdaptiveMap

Image adaptive mapping methods.

Field Detail

DEFAULT_TILE_WIDTH

public static int DEFAULT_TILE_WIDTH

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Constant Field Values

DEFAULT_TILE_HEIGHT

public static int DEFAULT_TILE_HEIGHT

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Constant Field Values

DEFAULT_MIN_COUNT

public static int DEFAULT_MIN_COUNT

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Constant Field Values

DEFAULT_X_SMOOTH_SIZE

public static int DEFAULT_X_SMOOTH_SIZE

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Constant Field Values

DEFAULT_Y_SMOOTH_SIZE

public static int DEFAULT_Y_SMOOTH_SIZE

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Constant Field Values

Method Detail

backgroundNormMorph

public static Pix backgroundNormMorph(Pix pixs)

Normalizes an image's background using default parameters.

Parameters:

pixs - A source pix image.

Returns:

org.jetbrains.dokka.NodeRenderContent@263547b7: the source pix image with a normalized background

backgroundNormMorph

public static Pix backgroundNormMorph(Pix pixs,
                                      int normReduction,
                                      int normSize,
                                      int normBgValue)

Normalizes an image's background to a specified value.

Notes:

1. This is a top-level interface for normalizing the image intensity by mapping the image so that the background is near the input value 'bgval'.
2. The input image is either grayscale or rgb.
3. For each component in the input image, the background value is estimated using a grayscale closing; hence the 'Morph' in the function name.
4. An optional binary mask can be specified, with the foreground pixels typically over image regions. The resulting background map values will be determined by surrounding pixels that are not under the mask foreground. The origin (0,0) of this mask is assumed to be aligned with the origin of the input image. This binary mask must not fully cover pixs, because then there will be no pixels in the input image available to compute the background.
5. The map is computed at reduced size (given by 'reduction') from the input pixs and optional pixim. At this scale, pixs is closed to remove the background, using a square Sel of odd dimension. The product of reduction * size should be large enough to remove most of the text foreground.
6. No convolutional smoothing needs to be done on the map before inverting it.
7. A 'bgval' target background value for the normalized image. This should be at least 128. If set too close to 255, some clipping will occur in the result.

Parameters:

pixs - A source pix image.

normReduction - Reduction at which morphological closings are done.

normSize - Size of square Sel for the closing.

normBgValue - Target background value.

Returns:

org.jetbrains.dokka.NodeRenderContent@1c94c65: the source pix image with a normalized background

pixContrastNorm

public static Pix pixContrastNorm(Pix pixs)

Adaptively attempts to expand the contrast to the full dynamic range in each tile using default parameters.

Parameters:

pixs - A source pix image

Returns:

org.jetbrains.dokka.NodeRenderContent@7c94df1e: a new image with expanded contrast range

See Also:

#pixContrastNorm(Pix, int, int, int, int, int)

pixContrastNorm

public static Pix pixContrastNorm(Pix pixs,
                                  int sizeX,
                                  int sizeY,
                                  int minDiff,
                                  int smoothX,
                                  int smoothY)

Adaptively attempts to expand the contrast to the full dynamic range in each tile.

Notes:

1. If the contrast in a tile is smaller than minDiff, it uses the min and max pixel values from neighboring tiles. It also can use convolution to smooth the min and max values from neighboring tiles. After all that processing, it is possible that the actual pixel values in the tile are outside the computed [min ... max] range for local contrast normalization. Such pixels are taken to be at either 0 (if below the min) or 255 (if above the max).
2. sizeX and sizeY give the tile size; they are typically at least 20.
3. minDiff is used to eliminate results for tiles where it is likely that either fg or bg is missing. A value around 50 or more is reasonable.
4. The full width and height of the convolution kernel are (2 * smoothx + 1) and (2 * smoothy + 1). Some smoothing is typically useful, and we limit the smoothing half-widths to the range from 0 to 8. Use 0 for no smoothing.
5. A linear TRC (gamma = 1.0) is applied to increase the contrast in each tile. The result can subsequently be globally corrected, by applying pixGammaTRC() with arbitrary values of gamma and the 0 and 255 points of the mapping.

Parameters:

pixs - A source pix image

sizeX - Tile width

sizeY - Tile height

minDiff - Minimum difference to accept as valid

smoothX - Half-width of convolution kernel applied to min and max arrays

smoothY - Half-height of convolution kernel applied to min and max arrays

Returns:

org.jetbrains.dokka.NodeRenderContent@47d6dec2: a new image with expanded contrast range