dnn_blobFromImage

Deep neural networksfunctionOpenCV 5.0.0

import { dnn_blobFromImage } from '@banou/opencv-wasm'

Use after await initOpenCV(). See the initialization and named imports guide.

Explore visually: Neural-network inference →

Execute a trained model by passing tensors through its computation graph.

VISUAL WALKTHROUGHDeep learning

REFERENCE INPUTImage to classify

The sample drawing makes the data representation visible; it is not an actual model input/output pair.

→

RESULTInterpret the output tensor

These illustrative class scores show one possible output contract. Detection, segmentation and other networks require different interpretation.

Illustrative example. The stages explain the method; they are not a live OpenCV execution.

Creates 4-dimensional blob from image. Optionally resizes and crops image from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Note: The order and usage of scalefactor and mean are (input - mean) * scalefactor.

dnn_blobFromImage(image: Mat, scalefactor: number, size: Size, mean: Scalar, swapRB: boolean, crop: boolean, ddepth: number): Mat;

7 available overloads

dnn_blobFromImage(image: Mat): Mat;

dnn_blobFromImage(image: Mat, scalefactor: number): Mat;

dnn_blobFromImage(image: Mat, scalefactor: number, size: Size): Mat;

dnn_blobFromImage(image: Mat, scalefactor: number, size: Size, mean: Scalar): Mat;

dnn_blobFromImage(image: Mat, scalefactor: number, size: Size, mean: Scalar, swapRB: boolean): Mat;

dnn_blobFromImage(image: Mat, scalefactor: number, size: Size, mean: Scalar, swapRB: boolean, crop: boolean): Mat;

dnn_blobFromImage(image: Mat, scalefactor: number, size: Size, mean: Scalar, swapRB: boolean, crop: boolean, ddepth: number): Mat;

image

input image (with 1-, 3- or 4-channels).

scalefactor

multiplier for images values.

size

spatial size for output image

mean

scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.

swapRB

flag which indicates that swap first and last channels in 3-channel image is necessary.

crop

flag which indicates whether image will be cropped after resize or not

ddepth

Depth of output blob. Choose CV_32F or CV_8U.

Returns

4-dimensional Mat with NCHW dimensions order. Release returned native handles with using or delete(), including handles nested in results.

Upstream declaration ↗

These signatures describe this package. Upstream documentation can mention optional backends that are absent from this build. Check runtime compatibility before choosing a backend or file format.