API Analysis

Analysis

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Namespace: acl::analysis (CPP) / acl::neon::analysis (NEON)

integral

Integral image (Summed Area Table): I(x, y) = ∑ src(i, j), 0 ≤ i ≤ x, 0 ≤ j ≤ y. Used for O(1) rectangular region sums (boxFilter, Haar features, etc.).

Tier: Starter+
Channels: 1ch
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
`SrcType`	`uint8_t`, `uint16_t`, `float`	—
`IntegralType`	`int32_t`, `int64_t`, `double`	`sizeof(IntegralType) ≥ sizeof(SrcType)`

CPP Version

template<class SrcType, class IntegralType>
int integral(
    const SrcType* srcImage, IntegralType* integral,
    int width, int height,
    int srcStride = 0);

Parameter	Type	Meaning	Default
`srcImage`	`const SrcType*`	Input image (single channel)	non-null
`integral`	`IntegralType*`	Output integral image, size `(width+1) × (height+1)`	non-null
`width`, `height`	`int`	Input image size	> 0
`srcStride`	`int`	Bytes per row	`0` = auto

Row 0 and column 0 of integral are always 0 (implementation sentinel row and column to simplify boundary queries).

NEON Version

template<class SrcType, class IntegralType>
int integral(
    const SrcType* srcImage, IntegralType* integralImage,
    int width, int height,
    int srcStride = 0);

Example

uint8_t srcImage[1920*1080];
int32_t integ[(1920+1)*(1080+1)];

acl::neon::analysis::integral<uint8_t, int32_t>(
    srcImage, integ, 1920, 1080);

// O(1) rectangle (x0,y0)-(x1,y1) sum
auto rectSum = [&](int x0, int y0, int x1, int y1) {
    int W = 1921;
    return integ[(y1+1)*W + (x1+1)]
         - integ[(y1+1)*W + x0]
         - integ[y0*W + (x1+1)]
         + integ[y0*W + x0];
};

histogram

Compute the pixel-value histogram.

Tier: Starter+
Channels: 1ch (packed via runtime hcn × vcn parameters when needed)
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
`ST`	`{uint8_t, uint16_t}`	—
`HT`	`{int, long}` (histogram bin count type)	—

CPP Version

template<class ST, class HT>
int histogram(
    const ST* srcImage, HT* hist,
    int width, int height,
    int srcStride, int histLen,
    int hcn = 1, int vcn = 1);

Parameter	Type	Meaning	Default
`srcImage`	`const ST*`	Input image	non-null
`hist`	`HT*`	Output histogram (length `histLen`, zeroed by the caller)	non-null
`srcStride`	`int`	Bytes per row	`0` = auto
`histLen`	`int`	Number of histogram bins (u8 → `256`, u16 → `65536`)	—
`hcn`, `vcn`	`int`	Horizontal / vertical channel packing	`1`, `1`

NEON Version (`uint8_t` only, fixed `histLen = 256`)

int histogram(
    const uint8_t* srcImage, int* hist,
    int width, int height,
    int srcStride = 0);

hist has a fixed length of 256 int bins; for non-uint8_t or non-256 bins, use the CPP version.

histMatch

Histogram matching (normalization) — adjusts the pixel distribution of src so that it matches the histogram of ref.

Tier: Pro+
Channels: 1ch
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
`ST`	`uint8_t, uint16_t`	all params must be the same type
`DT`	`uint8_t, uint16_t`	all params must be the same type
`RT`	`uint8_t, uint16_t`	all params must be the same type

CPP Signature

template<class ST, class DT, class RT>
int histMatch(
    const ST* srcImage, DT* dstImage, const RT* refImage,
    int width, int height,
    int srcStride, int dstStride, int refStride,
    int srcHistLen, int refHistLen,
    double MATCH_TH = 0.0,
    int hcn = 1, int vcn = 1);

Parameter	Type	Meaning	Default
`srcImage`, `dstImage`	`const ST` / `DT`	input / output	non-null
`refImage`	`const RT*`	Reference image (its histogram is used as the target distribution)	non-null
`srcHistLen`, `refHistLen`	`int`	Source / reference bin counts	u8: `256`
`MATCH_TH`	`double`	Match tolerance threshold `[0, 1]`	`0.0`
`hcn`, `vcn`	`int`	Horizontal / vertical channel packing	`1`, `1`

equalizeHist

Histogram equalization.

Tier: Starter+
Channels: 1ch
Inplace: supported
Types:

Template parameter	Allowed types	Constraint
`T` (CPP)	`uint8_t, uint16_t`	—
`T` (NEON)	`uint8_t`	NEON-only

CPP Version

template<class T>
int equalizeHist(
    const T* srcImage, T* dstImage,
    int width, int height,
    int srcStride = 0, int dstStride = 0);

NEON Version (`uint8_t` only)

int equalizeHist(
    const uint8_t* srcImage, uint8_t* dstImage,
    int width, int height,
    int srcStride = 0, int dstStride = 0);

clahe

Contrast Limited Adaptive Histogram Equalization — divides the image into tilesX × tilesY tiles, performs histogram equalization in each tile and clips the contrast upper bound, then bilinearly interpolates (blends) the results, avoiding the over-contrast from global equalization.

Tier: Pro+
Channels: 1ch
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
`T`	`uint8_t`	—

CPP / NEON Signature (identical)

int clahe(
    const uint8_t* srcImage, uint8_t* dstImage,
    int width, int height,
    int srcStride = 0, int dstStride = 0,
    double clipLimit = 40.0,
    int tilesX = 8, int tilesY = 8);

Parameter	Type	Meaning	Default
`srcImage`, `dstImage`	`const uint8_t` / `uint8_t`	input / output	non-null
`width`, `height`	`int`	Image size	must satisfy `width ≥ tilesX`, `height ≥ tilesY`
`srcStride`, `dstStride`	`int`	Bytes per row	`0` = auto
`clipLimit`	`double`	Contrast upper bound (higher = stronger contrast)	`40.0` (OpenCV default)
`tilesX`, `tilesY`	`int`	Horizontal / vertical tile count	`8` × `8`

minMaxLoc

Find the minimum / maximum value in the image and their locations.

Tier: Starter+
Channels: 1ch
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
`T`	`uint8_t, uint16_t, float`	—

CPP / NEON Signature (identical)

template<class T>
int minMaxLoc(
    const T* srcImage, int width, int height, int srcStride,
    T* minVal, T* maxVal,
    int* minLocX, int* minLocY,
    int* maxLocX, int* maxLocY);

Parameter	Type	Meaning	Default
`srcImage`	`const T*`	Input image	non-null
`srcStride`	`int`	Bytes per row	`0` = auto
`minVal`, `maxVal`	`T*`	Output min / max values (may be `nullptr`)	—
`minLocX`, `minLocY`, `maxLocX`, `maxLocY`	`int*`	Output corresponding coordinates (may be `nullptr`)	—

When any output pointer is nullptr, the corresponding result is skipped.

moments

Spatial moments (orders 0~3). Single-channel image; outputs a Moments struct containing 10 double raw moments: m00, m10, m01, m20, m11, m02, m30, m21, m12, m03.

Tier: Pro+
Channels: 1ch
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
`T` (CPP)	`uint8_t, uint16_t, float`	—

CPP Signature

struct Moments {
    double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03;
};

template<class T>
int moments(
    const T* srcImage, int width, int height, int srcStride,
    Moments& m,
    bool binaryImage = false);

Parameter	Type	Meaning	Default
`srcImage`	`const T*`	Input image	non-null
`m`	`Moments&`	Output moments struct	filled by the function
`binaryImage`	`bool`	`true` = treat all non-zero pixels as 1 (binary evaluation)	`false`

copyMakeBorder

Add a border around the image, supporting multiple border modes. Typical use: padding before convolution.

Tier: Starter+
Channels: 1ch / 3ch / 4ch
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
`T`	`uint8_t, uint16_t, float`	—
BorderType: `BORDER_REPLICATE` / `BORDER_REFLECT` / `BORDER_REFLECT_101` / `BORDER_WRAP` / `BORDER_CONSTANT`, etc.

CPP Version

template<class T>
int copyMakeBorder(
    const T* srcImage, T* dstImage,
    int srcWidth, int srcHeight, int channelNum,
    int srcStride, int dstStride,
    int top, int bottom, int left, int right,
    const T* constant = nullptr,
    acl::BorderType bt = acl::BorderType::BORDER_REFLECT_101);

Parameter	Type	Meaning	Default
`srcImage`	`const T*`	Input image	non-null
`dstImage`	`T*`	Output image (size `(srcWidth + left + right) × (srcHeight + top + bottom)`)	non-null
`channelNum`	`int`	Channel count	—
`top`, `bottom`, `left`, `right`	`int`	Padding width in each of the four directions	≥ 0
`constant`	`const T*`	`BORDER_CONSTANT` fill-value array (length `channelNum`)	`nullptr`
`bt`	`acl::BorderType`	Border-handling mode	`BORDER_REFLECT_101`

NEON Version

template<class T>
int copyMakeBorder(
    const T* srcImage, T* dstImage,
    int srcWidth, int srcHeight, int channelNum,
    int srcStride = 0, int dstStride = 0,
    int top = 0, int bottom = 0, int left = 0, int right = 0,
    const T* constant = nullptr,
    acl::BorderType bt = acl::BorderType::BORDER_REFLECT_101);

count

Count the number of pixels satisfying a condition.

Tier: Starter+
Channels: 1ch
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
`T`	`uint8_t, uint16_t, float`	—

CPP Signature (3 entry points)

// == threshold
template<class T>
int countEQ(const T* srcImage, int width, int height, int stride, const T& threshold);

// <= threshold
template<class T>
int countLET(const T* srcImage, int width, int height, int stride, const T& threshold);

// < threshold
template<class T>
int countLT(const T* srcImage, int width, int height, int stride, const T& threshold);

The return value is the count (not an error code); srcImage == nullptr returns 0.

mean

Per-pixel mean of two or N images: dst[i] = (A[i] + B[i] + …) / N.

Tier: Starter+
Channels: 1ch / 3ch / 4ch
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
`AT`	`uint8_t, uint16_t, float`	—
`BT`	`uint8_t, uint16_t, float`	—
`DT`	`uint8_t, uint16_t, float`	—

CPP Signature (two overloads: 2-image + N-image)

// 2-image
template<class AT, class BT, class DT>
int mean(
    const AT* src1Image, const BT* src2Image, DT* dstImage,
    int width, int height, int cn = 1,
    int src1Stride = 0, int src2Stride = 0, int dstStride = 0);

// N-image
template<class ST, class DT>
int mean(
    const ST* const* srcImages, int srcNum, DT* dstImage,
    int width, int height, int cn = 1,
    int srcStride = 0, int dstStride = 0);

matchTemplate

Template matching (6 similarity metrics). FFT-accelerated; suitable for large image × small template.

Tier: Pro+
Channels: 1ch
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
`T` (CPP)	`{uint8_t, float}`	—
`T` (NEON)	`uint8_t`	NEON-only

TemplateMatchMethod:

TM_SQDIFF — sum of squared differences
TM_SQDIFF_NORMED — normalized squared differences
TM_CCORR — cross-correlation
TM_CCORR_NORMED — normalized cross-correlation
TM_CCOEFF — correlation coefficient
TM_CCOEFF_NORMED — normalized correlation coefficient

CPP Version

template<class T>
int matchTemplate(
    const T* srcImage, int srcW, int srcH, int srcStride,
    const T* templ, int templW, int templH, int templStride,
    float* result, int resultStride,
    acl::TemplateMatchMethod tm = acl::TemplateMatchMethod::TM_SQDIFF);

NEON Version (`uint8_t` only)

template<class T>
int matchTemplate(
    const T* srcImage, int srcW, int srcH, int srcStride,
    const T* templ, int templW, int templH, int templStride,
    float* result, int resultStride,
    acl::TemplateMatchMethod tm = acl::TemplateMatchMethod::TM_SQDIFF);

Parameter	Type	Meaning	Default
`srcImage`, `templ`	`const T*`	Search image, template image	non-null, `templW ≤ srcW`, `templH ≤ srcH`
`result`	`float*`	Output score map, size `(srcW - templW + 1) × (srcH - templH + 1)`	non-null
`*Stride`	`int`	Bytes per row (result counted as `float`)	`0` = auto
`tm`	`acl::TemplateMatchMethod`	Similarity metric	`TM_SQDIFF`

connectedComponent_8n_dfs

8-connected component labeling (DFS). Takes a binary image as input; outputs the pixel coordinate list for each connected component.

Tier: Business
Channels: 1ch
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
`T`	`uint8_t, uint16_t`	—
`LabelType`	`int`	—

CPP Signature

template<class T>
int connectedComponent_8n_dfs(
    T* binaryImage, int width, int height, int stride,
    std::vector<std::vector<std::pair<int, int>>>& regions,
    int minArea, int maxArea,
    int frontFlag = 255, int backFlag = 0);

Parameter	Type	Meaning
`binaryImage`	`T*`	Input binary image (the algorithm may overwrite pixels as markers)
`regions`	`vector<vector<pair<int, int>>>&`	Output list of `(x, y)` pixel coordinates for each connected component
`minArea`, `maxArea`	`int`	Filter: only retain components with `area ∈ [minArea, maxArea]`
`frontFlag`, `backFlag`	`int`	Foreground / background pixel value used as DFS markers (defaults `255` / `0`)

connectedComponentLabeling

Connected component labeling with a label image (union-find). Outputs a label image (per-pixel label) + a label list sorted by area.

Tier: Business
Channels: 1ch
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
`DataInType`	`uint8_t, uint16_t`	—
`LabelType`	`int`	—

CPP Signature

template<class DataInType, class LabelType>
int connectedComponentLabeling(
    DataInType* dataIn, LabelType* label,
    std::vector<std::pair<LabelType, int>>& sortLabelHist,
    DataInType threshold,
    int topAreaCnt, int minArea,
    int width, int height,
    int inStride = 0, int labelStride = 0);

Parameter	Type	Meaning
`dataIn`	`DataInType*`	Input image (binarized via `threshold`)
`label`	`LabelType*`	Output label image
`sortLabelHist`	`vector<pair<LabelType, int>>&`	Output `(label, area)` list sorted by descending area
`threshold`	`DataInType`	Input binarization threshold
`topAreaCnt`	`int`	Only retain the `topAreaCnt` components with the largest area

findContours

Find contours in a binary image (Suzuki-Abe algorithm, OpenCV compatible).

Tier: Pro+
Channels: 1ch
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
`T`	`uint8_t`	—

CPP Signature

enum ContourRetrMode {
    CONTOUR_RETR_EXTERNAL = 0,  // only the outermost layer
    CONTOUR_RETR_LIST     = 1,  // all contours, no hierarchy
    CONTOUR_RETR_CCOMP    = 2,  // two layers (outer + inner holes)
    CONTOUR_RETR_TREE     = 3   // full hierarchy tree
};

enum ContourApproxMethod {
    CONTOUR_CHAIN_APPROX_NONE   = 1,  // retain all contour points
    CONTOUR_CHAIN_APPROX_SIMPLE = 2   // compress intermediate points on horizontal / vertical / diagonal segments
};

struct Point2i { int x, y; };
struct HierarchyEntry { int next, prev, first_child, parent; };

int findContours(
    const uint8_t* srcImage, int width, int height, int srcStride,
    std::vector<std::vector<Point2i>>& contours,
    std::vector<HierarchyEntry>* hierarchy = nullptr,
    ContourRetrMode mode = CONTOUR_RETR_LIST,
    ContourApproxMethod method = CONTOUR_CHAIN_APPROX_SIMPLE,
    int offsetX = 0, int offsetY = 0);

Parameter	Type	Meaning
`contours`	`vector<vector<Point2i>>&`	Output contours; each contour is an array of `Point2i`
`hierarchy`	`vector<HierarchyEntry>*`	(optional) hierarchy info `[next, prev, first_child, parent]`
`offsetX`, `offsetY`	`int`	Offset added to all contour point coordinates

distanceTransform

Distance transform — each pixel outputs the distance to its nearest 0 pixel (L1 / L2 / L∞).

Tier: Business
Channels: 1ch
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
Input	`uint8_t`	—
Output	`float`	—

DistanceType: DIST_L1 (Manhattan) / DIST_L2 (Euclidean, exact algorithm) / DIST_LINF (chessboard)

CPP Signature (2 entry points: float or u8 output)

// float output (high precision)
int distanceTransform(
    const uint8_t* srcImage, float* dstImage,
    int width, int height,
    int srcStride = 0, int dstStride = 0,
    DistanceType distType = DIST_L2);

// u8 output (normalized to 0-255, suitable for visualization)
int distanceTransformU8(
    const uint8_t* srcImage, uint8_t* dstImage,
    int width, int height,
    int srcStride = 0, int dstStride = 0,
    DistanceType distType = DIST_L2);

DIST_L2 uses the Felzenszwalb-Huttenlocher exact Euclidean algorithm (not an approximation).

blockAverage

Take the mean over each U × V block as output (image downsampling). U = V = 2 corresponds to 2×2 averaging.

Tier: Starter+
Channels: 1ch / 3ch
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
`T`	`uint8_t, uint16_t, float`	—

Commercial package type availability:

Tier	Callable `T` in delivered `<acl/api.h>`
Starter	`uint8_t`
Pro	`uint8_t`, `uint16_t`
Business	`uint8_t`, `uint16_t`, `float`

CPP / NEON Signature (identical)

template<class T>
int blockAverage(
    const T* srcImage, T* dstImage,
    int srcWidth, int srcHeight,
    int U, int V,
    int srcStride = 0, int dstStride = 0,
    bool round = true,
    int hcn = 1, int vcn = 1);

Parameter	Type	Meaning	Default
`U`, `V`	`int`	Block horizontal / vertical size	—
`round`	`bool`	`true` = round to nearest, `false` = truncate	`true`
`hcn`, `vcn`	`int`	Horizontal / vertical channel packing	`1`, `1`

extractBlockPixels

Extract the pixel at (u, v) from every U × V block (i.e. downsample while specifying the sampling point).

Tier: Business
Channels: 1ch
Inplace: not supported
Types:

Template parameter	Allowed types	Constraint
`ST`	`uint8_t, uint16_t, float`	—
`DT`	`uint8_t, uint16_t, float`	—

CPP Signature

template<class ST, class DT>
int extractBlockPixels(
    const ST* srcImage, DT* dstImage,
    int srcWidth, int srcHeight,
    int srcStride, int dstStride,
    int U, int V, int u, int v);

Parameter	Type	Meaning
`U`, `V`	`int`	Block size
`u`, `v`	`int`	Pixel position sampled from each block (`0 ≤ u < U`, `0 ≤ v < V`); auto-clamped

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API Analysis

Analysis

integral

CPP Version

NEON Version

Example

histogram

CPP Version

NEON Version (uint8_t only, fixed histLen = 256)

histMatch

CPP Signature

equalizeHist

CPP Version

NEON Version (uint8_t only)

clahe

CPP / NEON Signature (identical)

minMaxLoc

CPP / NEON Signature (identical)

moments

CPP Signature

copyMakeBorder

CPP Version

NEON Version

count

CPP Signature (3 entry points)

mean

CPP Signature (two overloads: 2-image + N-image)

matchTemplate

CPP Version

NEON Version (uint8_t only)

connectedComponent_8n_dfs

CPP Signature

connectedComponentLabeling

CPP Signature

findContours

CPP Signature

distanceTransform

CPP Signature (2 entry points: float or u8 output)

blockAverage

CPP / NEON Signature (identical)

extractBlockPixels

CPP Signature

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NEON Version (`uint8_t` only, fixed `histLen = 256`)

NEON Version (`uint8_t` only)

NEON Version (`uint8_t` only)