GFPGAN | GFPGAN aims at developing Practical Algorithms | Computer Vision library
kandi X-RAY | GFPGAN Summary
Support
Quality
Security
License
Reuse
- Parse command line arguments .
- Modify a checkpoint .
- Forward a list of styles .
- Enhance an image .
- return the SHA1 hash of the git repo
- Write the version python file .
- Initialize equal convolution .
- 3x3d Conv2d Conv2d Conv2d .
- Get the hash of the current working directory .
- Read requirements file .
GFPGAN Key Features
GFPGAN Examples and Code Snippets
wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P experiments/pretrained_models
python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.3 -s 2
Usage: python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.3 -s 2 [options]... -h show this help -i input Input image or folder. Default: inputs/whole_imgs -o output Output folder. Default: results -v version GFPGAN model version. Option: 1 | 1.2 | 1.3. Default: 1.3 -s upscale The final upsampling scale of the image. Default: 2 -bg_upsampler background upsampler. Default: realesrgan -bg_tile Tile size for background sampler, 0 for no tile during testing. Default: 400 -suffix Suffix of the restored faces -only_center_face Only restore the center face -aligned Input are aligned faces -ext Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto
@InProceedings{wang2021gfpgan, author = {Xintao Wang and Yu Li and Honglun Zhang and Ying Shan}, title = {Towards Real-World Blind Face Restoration with Generative Facial Prior}, booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2021} }
git clone https://github.com/TencentARC/GFPGAN.git cd GFPGAN
# Install basicsr - https://github.com/xinntao/BasicSR # We use BasicSR for both training and inference pip install basicsr # Install facexlib - https://github.com/xinntao/facexlib # We use face detection and face restoration helper in the facexlib package pip install facexlib pip install -r requirements.txt python setup.py develop # If you want to enhance the background (non-face) regions with Real-ESRGAN, # you also need to install the realesrgan package pip install realesrgan
Trending Discussions on Computer Vision
Trending Discussions on Computer Vision
QUESTION
The swift vision similarity feature is able to assign a number to the variance between 2 images. Where 0 variance between the images, means the images are the same. As the number increases this that there is more and more variance between the images.
What I am trying to do is turn this into a percentage of similarity. So one image is for example 80% similar to the other image. Any ideas how I could arrange the logic to accomplish this:
import UIKit
import Vision
func featureprintObservationForImage(atURL url: URL) -> VNFeaturePrintObservation? {
let requestHandler = VNImageRequestHandler(url: url, options: [:])
let request = VNGenerateImageFeaturePrintRequest()
do {
try requestHandler.perform([request])
return request.results?.first as? VNFeaturePrintObservation
} catch {
print("Vision error: \(error)")
return nil
}
}
let apple1 = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"apple1", withExtension: "jpg")!)
let apple2 = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"apple2", withExtension: "jpg")!)
let pear = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"pear", withExtension: "jpg")!)
var distance = Float(0)
try apple1!.computeDistance(&distance, to: apple2!)
var distance2 = Float(0)
try apple1!.computeDistance(&distance2, to: pear!)
ANSWER
Answered 2022-Mar-25 at 10:26It depends on how you want to scale it. If you just want the percentage you could just use Float.greatestFiniteMagnitude as the maximum value.
1-(distance/Float.greatestFiniteMagnitude)*100
A better solution would probably be to set a lower ceiling and everything above that ceiling would just be 0% similarity.
1-(min(distance, 10)/10)*100
Here the artificial ceiling would be 10, but it can be any arbitrary number.
QUESTION
import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport
Whilst importing pandas profile (please see above command), I am getting the following error message:-
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_3396/1468051405.py in
1 import numpy as np
2 import pandas as pd
----> 3 from pandas_profiling import ProfileReport
~\Anaconda3\lib\site-packages\pandas_profiling\__init__.py in
5
6 from pandas_profiling.config import Config, config
----> 7 from pandas_profiling.controller import pandas_decorator
8 from pandas_profiling.profile_report import ProfileReport
9 from pandas_profiling.version import __version__
~\Anaconda3\lib\site-packages\pandas_profiling\controller\pandas_decorator.py in
2 from pandas import DataFrame
3
----> 4 from pandas_profiling.__init__ import ProfileReport
5
6
~\Anaconda3\lib\site-packages\pandas_profiling\__init__.py in
6 from pandas_profiling.config import Config, config
7 from pandas_profiling.controller import pandas_decorator
----> 8 from pandas_profiling.profile_report import ProfileReport
9 from pandas_profiling.version import __version__
10
~\Anaconda3\lib\site-packages\pandas_profiling\profile_report.py in
9
10 from pandas_profiling.config import config
---> 11 from pandas_profiling.model.describe import describe as describe_df
12 from pandas_profiling.model.messages import MessageType
13 from pandas_profiling.report import get_report_structure
~\Anaconda3\lib\site-packages\pandas_profiling\model\describe.py in
9 from pandas_profiling.model.base import Variable
10 from pandas_profiling.model.correlations import calculate_correlation
---> 11 from pandas_profiling.model.summary import (
12 get_duplicates,
13 get_messages,
~\Anaconda3\lib\site-packages\pandas_profiling\model\summary.py in
11 import pandas as pd
12 from scipy.stats.stats import chisquare
---> 13 from visions.application.summaries.series import (
14 file_summary,
15 image_summary,
ModuleNotFoundError: No module named 'visions.application'
I have made sure that the vision module version is 0.7.4 as 0.7.5 is not compatible with pandas-profiling.
Does anyone have an idea about how you resolve this issue?
ANSWER
Answered 2022-Mar-22 at 13:26It appears that the 'visions.application' module was available in v0.7.1
https://github.com/dylan-profiler/visions/tree/v0.7.1/src/visions
But it's no longer available in v0.7.2
https://github.com/dylan-profiler/visions/tree/v0.7.2/src/visions
It also appears that the pandas_profiling project has been updated, the file summary.py no longer tries to do this import.
In summary: use visions version v0.7.1 or upgrade pandas_profiling.
QUESTION
I'm exploring Google Cloud Vision to detect handwriting in text. I see that the model is quite accurate in read handwritten text.
I'm following this guide: https://cloud.google.com/vision/docs/handwriting
Here is my question: is there a way to discover in the responses if the text is handwritten or typed?
A parameter or something in the response useful to classify images?
Here is the request:
{
"requests": [
{
"features": [
{
"type": "DOCUMENT_TEXT_DETECTION"
}
],
"image": {
"source": {
"imageUri": "gs://cloud-samples-data/vision/handwriting_image.png"
}
}
}
]
}
Here is the response:
{
"responses": [
{
"textAnnotations": [
{
"locale": "en",
"description": "Google Cloud\nPlatform\n",
"boundingPoly": {
"vertices": [
{
"x": 380,
"y": 66
},
{
"x": 714,
"y": 66
},
{
"x": 714,
"y": 257
},
{
"x": 380,
"y": 257
}
]
}
},
{
"description": "Google",
"boundingPoly": {
"vertices": [
{
"x": 380,
"y": 69
},
{
"x": 544,
"y": 67
},
{
"x": 545,
"y": 185
},
{
"x": 381,
"y": 187
}
]
}
},
...
Thank you
ANSWER
Answered 2022-Mar-01 at 00:36It seems that there's already an open discussion with the Google team to get this Feature Request addressed:
https://issuetracker.google.com/154156890
I would recommend you to comment on the Public issue tracker and indicate that "you are affected to this issue" to gain visibility and push for get this change done.
Other that that I'm unsure is that can be implemented locally.
QUESTION
I want to try out this tutorial and therefore used the code from here in order to calibrate my camera. I use this image:
The only thing I adapted was chessboard_size = (14,9)
so that it matches the corners of my image. I don't know what I do wrong. I tried multiple chessboard pattern and cameras but still cv2.findChessboardCorners always fails detecting corners. Any help would be highly appreciated.
ANSWER
Answered 2022-Jan-29 at 23:59Finally I could do it. I had to set chessboard_size = (12,7)
then it worked. I had to count the internal number of horizontal and vertical corners.
QUESTION
I am trying to get the RGB average inside of a non-rectangular multi-edge (closed) contour generated over a face landmark region in the frame (think of it as a face contour) from AVCaptureVideoDataOutput. I currently have the following code,
let landmarkPath = CGMutablePath()
let landmarkPathPoints = landmark.normalizedPoints
.map({ landmarkPoint in
CGPoint(
x: landmarkPoint.y * faceBoundingBox.height + faceBoundingBox.origin.x,
y: landmarkPoint.x * faceBoundingBox.width + faceBoundingBox.origin.y)
})
landmarkPath.addLines(between: landmarkPathPoints)
landmarkPath.closeSubpath()
let averageFilter = CIFilter(name: "CIAreaAverage", parameters: [kCIInputImageKey: frame, kCIInputExtentKey: landmarkPath])!
let outputImage = averageFilter.outputImage!
However, it currently throws *** Terminating app due to uncaught exception 'NSInvalidArgumentException', reason: '-[__NSCFType CGRectValue]: unrecognized selector sent to instance 0x283a57a80' terminating with uncaught exception of type NSException. I suspect this is as the kCIInputExtentKey is not a proper CIVector rectangular object. Is there anyway to fix this? How can I define a non-rectangular region for the CIAreaAverage filter? If not possible, what's the most efficient way of getting the average RGB across the region of interest?
Thanks a lot in advance!
ANSWER
Answered 2022-Jan-26 at 02:12If you could make all pixels outside of the contour transparent then you could use CIKmeans
filter with inputCount
equal 1
and the inputExtent
set to the extent of the frame to get the average color of the area inside the contour (the output of the filter will contain 1-pixel image and the color of the pixel is what you are looking for).
Now, to make all pixels transparent outside of the contour, you could do something like this:
- Create a mask image but setting all pixels inside the contour white and black outside (set background to black and fill the path with white).
- Use
CIBlendWithMask
filter where:inputBackgroundImage
is a fully transparent (clear) imageinputImage
is the original frameinputMaskImage
is the mask you created above
The output of that filter will give you the image with all pixels outside the contour fully transparent. And now you can use the CIKMeans
filter with it as described at the beginning.
BTW, if you want to play with every single of the 230 filters out there check this app out: https://apps.apple.com/us/app/filter-magic/id1594986951
UPDATE:CIFilters can only work with CIImages. So the mask image has to be a CIImage as well. One way to do that is to create a CGImage from CAShapeLayer containing the mask and then create CIImage out of it. Here is how the code could look like:
// Create the closed contour path from points
let path = CGMutablePath()
path.addLines(between: points)
path.closeSubpath()
// Create CAShapeLayer matching the dimensions of the input frame
let layer = CAShapeLayer()
layer.frame = frame.extent // Assuming frame is the input CIImage with the face
// Set background and fill color and set the path
layer.fillColor = UIColor.white.cgColor
layer.backgroundColor = UIColor.black.cgColor
layer.path = path
// Render the contents of the CAShapeLayer to CGImage
let width = Int(layer.bounds.width)
let height = Int(layer.bounds.height)
let context = CGContext(data: nil,
width: width,
height: height,
bitsPerComponent: 8,
bytesPerRow: 4 * width,
space: CGColorSpaceCreateDeviceRGB(),
bitmapInfo: CGImageAlphaInfo.premultipliedLast.rawValue)!
layer.render(in: context)
let cgImage = context.makeImage()!
// Create CIImage out of it
let ciImage = CIImage(cgImage: cgImage)
// To create clear background CIImage just do this:
let bgImage = CIImage.clear.cropped(to: frame.extent)
QUESTION
I am actually experimenting with the Vision Framework. I have simply an UIImageView in my Storyboard and my class is from type UIViewController. But when I try to override viewDidAppear(_ animated: Bool) I get the error message: Method does not override any method from its superclass Do anyone know what the issue is? Couldn't find anything that works for me...
ANSWER
Answered 2022-Jan-21 at 19:37This is my complete code:
import UIKit
import Vision
class ViewController: UIViewController {
@IBOutlet weak var imageView: UIImageView!
var imageOrientation = CGImagePropertyOrientation(.up)
override func viewDidAppear(_ animated: Bool) {
super.viewDidAppear(animated)
if let image = UIImage(named: "group") {
imageView.image = image
imageView.contentMode = .scaleAspectFit
imageOrientation = CGImagePropertyOrientation(image.imageOrientation)
guard let cgImage = image.cgImage else {return}
setupVision(image: cgImage)
}
}
private func setupVision (image: CGImage) {
let faceDetectionRequest = VNDetectFaceRectanglesRequest(completionHandler: self.handelFaceDetectionRequest)
let imageRequestHandler = VNImageRequestHandler(cgImage: image, orientation: imageOrientation, options: [:])
do {
try imageRequestHandler.perform([faceDetectionRequest])
}catch let error as NSError {
print(error)
return
}
}
private func handelFaceDetectionRequest (request: VNRequest?, error: Error?) {
if let requestError = error as NSError? {
print(requestError)
return
}
guard let image = imageView.image else {return}
guard let cgImage = image.cgImage else {return}
let imageRect = self.determineScale(cgImage: cgImage, imageViewFrame: imageView.frame)
self.imageView.layer.sublayers = nil
if let results = request?.results as? [VNFaceObservation] {
for observation in results {
let faceRect = convertUnitToPoint(originalImageRect: imageRect, targetRect: observation.boundingBox)
let emojiRect = CGRect(x: faceRect.origin.x, y: faceRect.origin.y - 5, width: faceRect.size.width + 5, height: faceRect.size.height + 5)
let textLayer = CATextLayer()
textLayer.string = "🦸♂️"
textLayer.fontSize = faceRect.width
textLayer.frame = emojiRect
textLayer.contentsScale = UIScreen.main.scale
self.imageView.layer.addSublayer(textLayer)
}
}
}
}
and:
import UIKit
class UIViewController {
public func convertUnitToPoint (originalImageRect: CGRect, targetRect: CGRect) -> CGRect {
var pointRect = targetRect
pointRect.origin.x = originalImageRect.origin.x + (targetRect.origin.x * originalImageRect.size.width)
pointRect.origin.y = originalImageRect.origin.y + (1 - targetRect.origin.y - targetRect.height)
pointRect.size.width *= originalImageRect.size.width
pointRect.size.height *= originalImageRect.size.height
return pointRect
}
public func determineScale (cgImage: CGImage, imageViewFrame: CGRect) -> CGRect {
let originalWidth = CGFloat(cgImage.width)
let originalHeigth = CGFloat(cgImage.height)
let imageFrame = imageViewFrame
let widthRatio = originalWidth / imageFrame.width
let heigthRatio = originalHeigth / imageFrame.height
let scaleRatio = max(widthRatio, heigthRatio)
let scaledImageWidth = originalWidth / scaleRatio
let scaledImageHeigth = originalHeigth / scaleRatio
let scaledImageX = (imageFrame.width - scaledImageWidth) / 2
let scaledImageY = (imageFrame.height - scaledImageHeigth) / 2
return CGRect(x: scaledImageX, y: scaledImageY, width: scaledImageWidth, height: scaledImageHeigth)
}
}
extension CGImagePropertyOrientation {
init(_ orientation: UIImage.Orientation) {
switch orientation {
case .up: self = .up
case .upMirrored: self = .upMirrored
case .down: self = .down
case .downMirrored: self = .downMirrored
case .right: self = .right
case .rightMirrored: self = .rightMirrored
default: self = .up
}
}
}
The first code snipped is from the ViewController file
QUESTION
I'm using Vision Framework to detecting faces with iPhone's front camera. My code looks like
func detect(_ cmSampleBuffer: CMSampleBuffer) {
guard let pixelBuffer = CMSampleBufferGetImageBuffer(cmSampleBuffer) else {return}
var requests: [VNRequest] = []
let requestLandmarks = VNDetectFaceLandmarksRequest { request, _ in
DispatchQueue.main.async {
guard let results = request.results as? [VNFaceObservation],
print(results)
}
}
requests.append(requestLandmarks)
let handler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer, orientation: .leftMirrored)
do {
try handler.perform(requests)
} catch {
print(error)
}
}
ANSWER
Answered 2021-Dec-23 at 14:33For some reason, remove
let connectionVideo = videoDataOutput.connection(with: AVMediaType.video)
connectionVideo?.videoOrientation = AVCaptureVideoOrientation.portrait
from my AVCaptureVideoDataOutput
solved the problem 🤡
QUESTION
I would like to read Japanese characters from a scanned image using swift's Vision framework. However, when I attempt to set the recognition language of VNRecognizeTextRequest
to Japanese using
request.recognitionLanguages = ["ja", "en"]
the output of my program becomes nonsensical roman letters. For each image of japanese text there is unexpected recognized text output. However, when set to other languages such as Chinese or German the text output is as expected. What could be causing the unexpected output seemingly peculiar to Japanese?
I am building from the github project here.
ANSWER
Answered 2021-Oct-12 at 23:37As they said in WWDC 2019 video, Text Recognition in Vision Framework:
First, a prerequisite, you need to check the languages that are supported by language-based correction...
Look at supportedRecognitionLanguages
for VNRecognizeTextRequestRevision2
for “accurate” recognition, and it would appear that the supported languages are:
["en-US", "fr-FR", "it-IT", "de-DE", "es-ES", "pt-BR", "zh-Hans", "zh-Hant"]
If you use “fast” recognition, the list is shorter:
["en-US", "fr-FR", "it-IT", "de-DE", "es-ES", "pt-BR"]
And if you fall back to VNRecognizeTextRequestRevision1
, it is even shorter (lol):
["en-US"]
It would appear that Japanese is not a supported language at this point.
QUESTION
For my research project I'm trying to distinguish between hydra plant (the larger amoeba looking oranges things) and their brine shrimp feed (the smaller orange specks) so that we can automate the cleaning of petri dishes using a pipetting machine. An example of a snap image from the machine of the petri dish looks like so:
I have so far applied a circle mask and an orange color space mask to create a cleaned up image so that it's mostly just the shrimp and hydra.
There is some residual light artifacts left in the filtered image, but I have to bite the cost or else I lose the resolution of the very thin hydra such as in the top left of the original image.
I was hoping to box and label the larger hydra plants but couldn't find much applicable literature for differentiating between large and small objects of similar attributes in an image, to achieve my goal.
I don't want to approach this using ML because I don't have the manpower or a large enough dataset to make a good training set, so I would truly appreciate some easier vision processing tools. I can afford to lose out on the skinny hydra, just if I can know of a simpler way to identify the more turgid, healthy hydra from the already cleaned up image that would be great.
I have seen some content about using openCV findCountours
? Am I on the right track?
Attached is the code I have so you know what datatypes I'm working with.
import cv2
import os
import numpy as np
import PIL
#abspath = "/Users/johannpally/Documents/GitHub/HydraBot/vis_processing/hydra_sample_imgs/00049.jpg"
#note we are in the vis_processing folder already
#PIL.Image.open(path)
path = os.getcwd() + "/hydra_sample_imgs/00054.jpg"
img = cv2.imread(path)
c_img = cv2.imread(path)
#==============GEOMETRY MASKS===================
# start result mask with circle mask
ww, hh = img.shape[:2]
r = 173
xc = hh // 2
yc = ww // 2
cv2.circle(c_img, (xc - 10, yc + 2), r, (255, 255, 255), -1)
hsv_cir = cv2.cvtColor(c_img, cv2.COLOR_BGR2HSV)
l_w = np.array([0,0,0])
h_w = np.array([0,0,255])
result_mask = cv2.inRange(hsv_cir, l_w, h_w)
#===============COLOR MASKS====================
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
#(hMin = 7 , sMin = 66, vMin = 124), (hMax = 19 , sMax = 255, vMax = 237)
# Threshold of orange in HSV space output from the HSV picker tool
l_orange = np.array([7, 66, 125])
h_orange = np.array([19, 255, 240])
orange_mask = cv2.inRange(hsv_img, l_orange, h_orange)
orange_res = cv2.bitwise_and(img, img, mask = orange_mask)
#===============COMBINE MASKS====================
for i in range(len(result_mask)):
for j in range(len(result_mask[i])):
if result_mask[i][j] == 255 & orange_mask[i][j] == 255:
result_mask[i][j] = 255
else:
result_mask[i][j] = 0
c_o_res = cv2.bitwise_and(img, img, mask=result_mask)
cv2.imshow('res', c_o_res)
cv2.waitKey(0)
cv2.destroyAllWindows()
ANSWER
Answered 2021-Oct-12 at 10:58You are on the right track, but I have to be honest. Without DeepLearning you will get good results but not perfect.
That's what I managed to get using contours:
Code:
import cv2
import os
import numpy as np
import PIL
#abspath = "/Users/johannpally/Documents/GitHub/HydraBot/vis_processing/hydra_sample_imgs/00049.jpg"
#note we are in the vis_processing folder already
#PIL.Image.open(path)
path = os.getcwd() + "/hydra_sample_imgs/00054.jpg"
img = cv2.imread(path)
c_img = cv2.imread(path)
#==============GEOMETRY MASKS===================
# start result mask with circle mask
ww, hh = img.shape[:2]
r = 173
xc = hh // 2
yc = ww // 2
cv2.circle(c_img, (xc - 10, yc + 2), r, (255, 255, 255), -1)
hsv_cir = cv2.cvtColor(c_img, cv2.COLOR_BGR2HSV)
l_w = np.array([0,0,0])
h_w = np.array([0,0,255])
result_mask = cv2.inRange(hsv_cir, l_w, h_w)
#===============COLOR MASKS====================
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
#(hMin = 7 , sMin = 66, vMin = 124), (hMax = 19 , sMax = 255, vMax = 237)
# Threshold of orange in HSV space output from the HSV picker tool
l_orange = np.array([7, 66, 125])
h_orange = np.array([19, 255, 240])
orange_mask = cv2.inRange(hsv_img, l_orange, h_orange)
orange_res = cv2.bitwise_and(img, img, mask = orange_mask)
#===============COMBINE MASKS====================
for i in range(len(result_mask)):
for j in range(len(result_mask[i])):
if result_mask[i][j] == 255 & orange_mask[i][j] == 255:
result_mask[i][j] = 255
else:
result_mask[i][j] = 0
c_o_res = cv2.bitwise_and(img, img, mask=result_mask)
# We have to use gray image (1 Channel) to use cv2.findContours
gray = cv2.cvtColor(c_o_res, cv2.COLOR_RGB2GRAY)
contours, _ = cv2.findContours(gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
minAreaSize = 150
for contour in contours:
if cv2.contourArea(contour) > minAreaSize:
# -------- UPDATE 1 CODE --------
# Rectangle Bounding box Drawing Option
# rect = cv2.boundingRect(contour)
# x, y, w, h = rect
# cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
# FINDING CONTOURS CENTERS
M = cv2.moments(contour)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
# DRAW CENTERS
cv2.circle(img, (cX, cY), radius=0, color=(255, 0, 255), thickness=5)
# -------- END OF UPDATE 1 CODE --------
# DRAW
cv2.drawContours(img, contour, -1, (0, 255, 0), 1)
cv2.imshow('FinallyResult', img)
cv2.imshow('res', c_o_res)
cv2.waitKey(0)
cv2.destroyAllWindows()
Update 1:
To find the center of the contours we can use cv2.moments. The code was edited with # -------- UPDATE 1 CODE --------
comment inside the for loop. As I mentioned before, this is not perfect approach and maybe there is a way to improve my answer to find the centers of the hydras without DeepLearning.
QUESTION
Assume you have a binary buffer or file which represents a 2-dimensional image.
How can you convert the binary data into a IMAQ image for further processing using LabVIEW?
ANSWER
Answered 2021-Sep-30 at 13:54For LabVIEW users who have the NI vision library installed, there are VIs that allow for the image data of an IMAQ image to be copied from a 2D array.
For single-channel images (U8
, U16
, I16
, float
) the VI is
Vision and Motion >> Vision Utilites >> Pixel Manipulation >> IMAQ ArrayToImage.vi
For multichannel images (RGB
etc) the VI is
Vision and Motion >> Vision Utilites >> Color Utilities >> IMAQ ArrayColorToImage.vi
Example 1
An example of using the IMAQ ArrayToImage.vi
is shown in the snippet below where U16
data is read from a binary file and written to a Greyscale U16
type IMAQ image
. Please note, if the file has been created by other software than LabVIEW then it is likely that it will have to be read in little-endian format which is specified for the Read From Binary File.vi
Example 2
A similar process can be used when some driver DLL call is used to get the image data as a buffer. For example, if the driver has a function capture(unsigned short * buffer)
then the following technique could be employed where a correctly sized array is initialized before the function call using the initialize array
primitive.
// example function which fills a buffer with image data
#include
__declspec(dllexport) int capture(uint16_t * buffer)
{
int width,height;
width = 2500;
height = 3052;
// check pointer
if(!buffer){
return -1;
}
// fill buffer with some data for testing
// this should be a greyscale gradient
// black in the top left corner
// to white in the bottom left
for(int row = 0; row
Without NI Vision For LabVIEW users who do not have NI vision installed, we can use a VI called GetImagePixelPtr.vi
which is installed alongside the NI-IMAQ toolkit/library. This VI may not be visible in the palettes but should be on disk in \vi.lib\vision\Basics.llb
.
In addition, we will use the MoveBlock
shared-library call from LabVIEW's memory manager library
These VI/library calls can be used as shown in the snippet below where, as in the previous snippet, U16
data is read from a binary file and written to a Greyscale U16 type IMAQ image.
Once we have the image data as a 2D array we need to prepare the IMAQ image by setting its dimensions. A for-loop is then used to iterate over the rows of the image data; for each row, we obtain a pointer to the start of the corresponding IMAQ Image row and use the MoveBlock call to copy the data across. After each MoveBlock call, we unmap the IMAQ image pointer to tidy up.
Please note, this example used U16 data; For other data types ensure that the bytes per pixels
numeric constant (in the for-loop) is updated accordingly.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install GFPGAN
Clone repo git clone https://github.com/TencentARC/GFPGAN.git cd GFPGAN
Install dependent packages # Install basicsr - https://github.com/xinntao/BasicSR # We use BasicSR for both training and inference pip install basicsr # Install facexlib - https://github.com/xinntao/facexlib # We use face detection and face restoration helper in the facexlib package pip install facexlib pip install -r requirements.txt python setup.py develop # If you want to enhance the background (non-face) regions with Real-ESRGAN, # you also need to install the realesrgan package pip install realesrgan
Support
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesExplore Kits - Develop, implement, customize Projects, Custom Functions and Applications with kandi kits
Save this library and start creating your kit
Share this Page