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Reads a single 1D/2D code from the image – supports formats such as UPC, EAN, Code 39, QR, and others. |
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Reads a Data Matrix code from the image – a two-dimensional code consisting of black and white squares arranged in a grid pattern. |
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Detects and reads text in the image – Optical Character Recognition (OCR) for text extraction. |
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Applies the Sobel operator – computes the gradient magnitude and emphasizes edges and intensity variations. |
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Applies the Canny edge detection algorithm – detects edges based on intensity gradients in the image. |
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Applies the Laplace operator – computes the second derivative and enhances areas with rapid intensity changes. |
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Detects and outlines contours (white areas) in a binary image – output is sorted by size (from largest to smallest). |
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Detects and outlines contours (white areas) in binary images – requires white objects; output is sorted from largest to smallest. |
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Displays contours on an image – facilitates the identification of object or region outlines. Prerequisite: prior contour detection using “Find Contours.” |
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Retrieves contours from a polyline array based on an index range – index is based on the distance from the image origin (0,0). Prerequisite: prior contour detection using “Find Contours.” |
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Filters contours by area – sorted from largest to smallest area. Prerequisite: prior contour detection using “Find Contours.” |
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Converts contours into a binary image (region) – can be used as a mask for further processing. Prerequisite: prior contour detection using “Find Contours.” |
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Encloses an outline in a single box. |
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Encloses a contour in a single circle. |
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Calculates the width and height of a text for a specific font as well as scaling and line thickness. |
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Determines deviations from the target value in the image – highlights areas that differ from the expected pattern. |
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Adjusts the contrast and brightness of an image – enhances the display and visibility of details. |
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Sharpens an image by increasing the contrast between neighboring pixels – enhances details and clarity. |
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Adjusts the gamma value of the image – controls brightness and contrast for optimized display. |
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Subtracts pixel values of one image from another – highlights differences between the two images. |
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Raises pixel values to a power – increases or decreases brightness to enhance contrast or emphasize details. |
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Applies an average filter – smooths the image and reduces noise or grainy structures. |
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Applies a bilateral filter – smooths the image while preserving edges by considering spatial distance and intensity differences. |
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Applies a Gaussian filter – smooths the image by averaging pixel values within a Gaussian kernel size. |
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Applies a median filter – reduces noise by replacing each pixel with the median of its neighborhood. |
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Scales the width and height of an image by different factors – allows for non-proportional resizing. |
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Scales an image uniformly in both directions – maintaining the aspect ratio. |
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Resizes an image to specified dimensions – width and height are adjusted precisely. |
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Shifts an image horizontally and/or vertically – freed areas are filled with black pixels. |
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Flips an image horizontally or vertically – creates a mirror image along the chosen axis. |
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Rotates an image by a specified angle – rotation is performed around the center point. |
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Removes the so-called fisheye effect from the image – corrects extreme wide-angle distortions. |
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It improves local contrast by dividing the image into smaller regions and calculating histograms for each. A contrast-limited histogram correction is then applied. |
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Measures similarities and differences between histograms. |
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Improves the contrast of an image by stretching the histogram and making better use of the brightness values. |
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Increases contrast without altering image properties. A low-contrast image is thus stretched across the entire area. |
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It checks the pixel distribution. It finds the target image in the region where the color distribution most closely matches the reference image. |
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Uniform distribution of pixel values over a defined area without changing the histogram. |
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Compares two images and highlights differences – deviations appear dark, similarities lighter. |
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Converts an RGB image to grayscale – based on brightness, simplifying color-independent image processing. |
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Crops an image by coordinates – defined by the top-left and bottom-right corners, extracting the desired area. |
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Crops an image based on a defined shape – selectively extracts the area within that shape. |
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Draws a line on the image – useful for marking, highlighting, or annotating in image processing tasks. |
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Enables drawing shapes on the image – ideal for highlighting or marking specific areas. |
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Draws an ellipse on the image – useful for annotation, highlighting, or marking specific image areas. |
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Limits the image area to a shape without cropping – pixels outside the shape are blacked out, coordinates remain unchanged. |
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Calculates the mean of all pixels – provides information about the average brightness or color intensity in the image. |
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Calculates the standard deviation of pixel values – indicates how much brightness or color varies or spreads within the image. |
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Determines the smallest pixel value in the image – indicates the darkest or lowest intensity area within the entire image. |
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Determines the highest pixel value in the image – indicates the brightest or most intense area within the entire image. |
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Draws text at a specified position on the image – non-displayable characters are replaced with question marks. |
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Provides metadata about the input image – determines image type, width, and height. |
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Determines the RGB color channels of an image – returns one value per channel. |
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Determines the hue, saturation, and brightness of an RGB image – by converting it to the HSV color space and splitting the channels. |
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Creates a color image by combining individual images for the red, green, and blue channels. |
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Calculates the histogram of an image. Supports grayscale (mono), RGB and HSV channels. |
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Determines the intersection point of two segments in the image – optionally displaying the measurement result. |
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Detects edges in the image – marks strong brightness or color changes, usually at object boundaries or prominent structures. |
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Detects a circle within a region. Smallest, Fitting, Mean, or Largest Circle can be selected. |
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Measures the shortest, average, and longest distance between two segments – useful for analyzing spatial relationships. |
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Measures the distance between a point and a line segment – selectable: shortest distance, or distance to the segment’s start or end point. |
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Measures the distance between a point and a line – useful for precise positioning relative to the line. |
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Measures the angle between two lines – measured counterclockwise from a reference line. |
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Measures the distance between two lines – useful for analyzing parallelism or spacing within the image. |
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Measures the angle between two lines – based on a reference line, measured counterclockwise. |
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Measures the angle between a line and a side of a non-rectangular rectangle counterclockwise. |
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Measures the angle between two vectors – measured counterclockwise starting from the reference vector. |
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Reduces white areas by removing edge pixels – useful for separating connected objects or removing small spots. |
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Expands white areas by turning edge pixels white – fills gaps and connects broken object parts in the image. |
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Applies erosion followed by dilation – smooths contours, removes noise, and reliably separates overlapping objects. |
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Applies dilation followed by erosion – closes gaps, connects object parts, and smooths white areas in the image. |
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Calculates the difference between dilation and erosion – highlights object contours and aids in edge detection. |
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Calculates the difference between the original image and the opening operation – highlights small bright details such as fine structures or noise. |
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Calculates the difference between the closing operation and the original image – highlights small dark details such as spots or objects against a bright background. |
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Detects specific pixel patterns using two structuring elements – ideal for precise shape and pattern recognition in the image. |
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Calculates the area of each white region in the binary image – provides quantitative size information about objects in the image. |
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Detects and quantifies connected regions – groups of connected pixels with the same value are considered as individual objects. |
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Counts black pixels in the image – useful for determining the size or extent of black areas. |
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Detects and counts individual regions – connected pixels with the same value are identified as separate objects. |
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Counts white pixels in the image – useful for determining the size or extent of white areas. |
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Merges two regions into one connected region – useful for object recognition, merging, or segmentation evaluation. |
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Combines two binary regions using a logical OR – white areas from both images are merged and preserved. |
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Subtracts one region from another (Region1 – Region2) – useful for isolating differences or overlaps. |
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Compares the intersection of two regions – useful for identifying common areas or features in segmentations. |
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Inverts the pixels of a region – typically white to black and vice versa – for highlighting or creating a negative image. |
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Converts a binary region into a grayscale image. |
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Selects regions based on area, index, height, or width within a specified value range. |
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Identifies the largest contiguous region – ideal for extracting dominant objects or relevant image areas. |
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Calculates the smallest circle that completely encloses a region – useful for shape analysis and measurement. |
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Calculates the smallest rectangle that completely encloses a region – useful for analysis and measurement. |
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Returns the width and height of a region based on the input. |
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Sorts regions by X, Y, width, height or area – with specific sorting direction depending on the feature. |
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Sorts contours by X, Y, length, or area; the number of returned contours can be limited. |
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Sorts an array of edges based on a specific feature. |
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Cuts a region into a specific shape. |
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Reduziert den Bereich einer Region auf eine bestimmte Form, jedoch ohne dabei die Region abzuschneiden. |
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Encloses an area within a single circle. |
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Encloses an area within a single rectangle. |
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Adaptive Thresholding adjusts the threshold locally – ideal for uneven lighting, significantly improves detection accuracy. |
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Sets pixels below the threshold to zero, and above to 255, i.e. white. |
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This node specifically isolates image areas whose pixels lie in a defined color range. |
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Sets pixels below the threshold to zero, leaving others unchanged to preserve detail. |
