I'm writing software for a solar panel inspection system and need to stitch camera images taken in a electroluminescence machine together. These images have only very few features and low contrast (as shown below) and the OpenCV image stitcher does not work by itself.
The approximate overlapping area of both images is known but I need the result as accurate as possible. I tried shifting one image over the other and computed different distance measures over the ROI, but without satisfying results. The SSD distance does not work due to vignetting and differences in the pixel intensity. Normalized Gradients or Cross Correlation were not robust either.
Any idea how to preprocess the images for the stitcher to work? Or is there another way to tackle this? Dark cells are not always present, making them not reliable as a feature.
There are vertical cell edges (nearly invisible due to bad contrast), but I have no method of detecting them either. If these could be detected, I could align the images with the cell edges.
Any help is much appreciated.
Related
I've been processing some image frames in videos and I discovered that sometimes one or two frames of the video will have artifacts or noise like the images below:
The artifacts look like abrasions of paint with noisy colors that covers only a small region (less than 100x100 in a 1000x2000 frame) of the image. I wonder if there are ways to detect the noisy frames? I've tried to use the difference of frames with SSIM, NMSE or PSNR but found limited effectiveness. Saliency map (left) or sobel/scharr filtering (right) providing more obvious view but regular borders are also included and I'm not sure how to form a classifier.
Scharr saliency map:
Since they are only a few frames in videos it's not quite necessary to denoising and I can just remove the frames one detected. The main problem here is that it's difficult to distinguish those frames in playing videos.
Can anybody offer some help here?
Detailing the comment as an answer with a few more details:
The Scharr and saliency map looks good.
Thresholding will result in a binary image which can be cleaned up with morphological filters (erode to enhance artefacts, dilate to 'erase' gradient contours).
Finding contours will result in lists of points which can be further processed/filtered using contour features.
If the gradients are always bigger than the artefacts, contour features, such as the bounding box dimensions and aspect ratio should help segment artefact contours from gradient contours (if any: hopefully dilation would've cleaned up the thresholded/binary image).
Another idea could be looking into oriented gradients:
either computer the oriented gradients (see visualisations): with the right cell size you might strike a balance where the artefacts have a high magnitude while gradient edges don't
you could try a full histogram of oriented gradients (HoG) classifier setup (using an SVM trained on histograms (as features))
The above options do rely on hand crafted features/making assumptions about the size of artefacts.
ML could be an interesting route too, hopefully it can generalise well enough.
Depending how many example images you have available, you could test a basic prototype using Teachable Machine (which behind the scenes would apply KNN to a transfer learning layer on top of MobileNet (or similar net)) fairly fast.
(Note: I've posted OpenCV Python links, but there are libraries that can help (e.g. scikit-image, scikit-learn, kornia, etc. in Python, cvv in c++, BoofCV in java, etc. (and there might be toolboxes for Matlab/Octave with similar features))
I want to know what are the types of image standardizations techniques available. For example, I'm working on hemoglobin concentration detection using photographs in which the patient pull lower conjunctiva downwards with one hand while holding a color calibration card in the other hand. All images are standardized to enable comparison using a previously established method. First, each image was split into its component 8-bit red, green and blue channels. Each channel’s brightness was adjusted by multiplying its brightness by 200/MB where MB is the mean brightness of the color calibration card’s white square. At this point, the channels were duplicated, with one set merged to produce a 24-bit white-balanced image.
This image standardization technique is not perfect and many times give wrong results. Is there any better image standardization technique available. Any idea or right direction will be helpful.
Edit
As i said above I'm working on the hemoglobin detection from a digital photograph. To reduce the effect of ambient lightning a image standardization technique is to be implemented. Current image standardization technique does not reduce the effect of ambient light to a great extent. The color calibration card in the photograph above could be used for the standardization process. I have heard of "white balance algorithm in mars probe" was used to overcome the same problem but was unable to find a proper source that could give enough information to implement it in OpenCV. Another approach or appropriate reference is helpful.
Is there a robust way to detect the water line, like the edge of a river in this image, in OpenCV?
(source: pequannockriver.org)
This task is challenging because a combination of techniques must be used. Furthermore, for each technique, the numerical parameters may only work correctly for a very narrow range. This means either a human expert must tune them by trial-and-error for each image, or that the technique must be executed many times with many different parameters, in order for the correct result to be selected.
The following outline is highly-specific to this sample image. It might not work with any other images.
One bit of advice: As usual, any multi-step image analysis should always begin with the most reliable step, and then proceed down to the less reliable steps. Whenever possible, the less reliable step should make use of the result of more-reliable steps to augment its own accuracy.
Detection of sky
Convert image to HSV colorspace, and find the cyan located at the upper-half of the image.
Keep this HSV image, becuase it could be handy for the next few steps as well.
Detection of shrubs
Run Canny edge detection on the grayscale version of image, with suitably chosen sigma and thresholds. This will pick up the branches on the shrubs, which would look like a bunch of noise. Meanwhile, the water surface would be relatively smooth.
Grayscale is used in this technique in order to reduce the influence of reflections on the water surface (the green and yellow reflections from the shrubs). There might be other colorspaces (or preprocessing techniques) more capable of removing that reflection.
Detection of water ripples from a lower elevation angle viewpoint
Firstly, mark off any image parts that are already classified as shrubs or sky. Since shrub detection would be more reliable than water detection, shrub detection's result should be used to inform the less-reliable water detection.
Observation
Because of the low elevation angle viewpoint, the water ripples appear horizontally elongated. In fact, every image feature appears stretched horizontally. This is called Anisotropy. We could make use of this tendency to detect them.
Note: I am not experienced in anisotropy detection. Perhaps you can get better ideas from other people.
Idea 1:
Use maximally-stable extremal regions (MSER) as a blob detector.
The Wikipedia introduction appears intimidating, but it is really related to connected-component algorithms. A naive implementation can be done similar to Dijkstra's algorithm.
Idea 2:
Notice that the image features are horizontally stretched, a simpler approach is to just sum up the absolute values of horizontal gradients and compare that to the sum of absolute values of vertical gradients.
I had asked this on photo stackexchange but thought it might be relevant here as well, since I want to implement this programatically in my implementation.
I am trying to implement a blur detection algorithm for my imaging pipeline. The blur that I want to detect is both -
1) Camera Shake: Pictures captured using hand which moves/shakes when shutter speed is less.
2) Lens focussing errors - (Depth of Field) issues, like focussing on a incorrect object causing some blur.
3) Motion blur: Fast moving objects in the scene, captured using a not high enough shutter speed. E.g. A moving car a night might show a trail of its headlight/tail light in the image as a blur.
How can one detect this blur and quantify it in some way to make some decision based on that computed 'blur metric'?
What is the theory behind blur detection?
I am looking of good reading material using which I can implement some algorithm for this in C/Matlab.
thank you.
-AD.
Motion blur and camera shake are kind of the same thing when you think about the cause: relative motion of the camera and the object. You mention slow shutter speed -- it is a culprit in both cases.
Focus misses are subjective as they depend on the intent on the photographer. Without knowing what the photographer wanted to focus on, it's impossible to achieve this. And even if you do know what you wanted to focus on, it still wouldn't be trivial.
With that dose of realism aside, let me reassure you that blur detection is actually a very active research field, and there are already a few metrics that you can try out on your images. Here are some that I've used recently:
Edge width. Basically, perform edge detection on your image (using Canny or otherwise) and then measure the width of the edges. Blurry images will have wider edges that are more spread out. Sharper images will have thinner edges. Google for "A no-reference perceptual blur metric" by Marziliano -- it's a famous paper that describes this approach well enough for a full implementation. If you're dealing with motion blur, then the edges will be blurred (wide) in the direction of the motion.
Presence of fine detail. Have a look at my answer to this question (the edited part).
Frequency domain approaches. Taking the histogram of the DCT coefficients of the image (assuming you're working with JPEG) would give you an idea of how much fine detail the image has. This is how you grab the DCT coefficients from a JPEG file directly. If the count for the non-DC terms is low, it is likely that the image is blurry. This is the simplest way -- there are more sophisticated approaches in the frequency domain.
There are more, but I feel that that should be enough to get you started. If you require further info on either of those points, fire up Google Scholar and look around. In particular, check out the references of Marziliano's paper to get an idea about what has been tried in the past.
There is a great paper called : "analysis of focus measure operators for shape-from-focus" (https://www.researchgate.net/publication/234073157_Analysis_of_focus_measure_operators_in_shape-from-focus) , which does a comparison about 30 different techniques.
Out of all the different techniques, the "Laplacian" based methods seem to have the best performance. Most image processing programs like : MATLAB or OPENCV have already implemented this method . Below is an example using OpenCV : http://www.pyimagesearch.com/2015/09/07/blur-detection-with-opencv/
One important point to note here is that an image can have some blurry areas and some sharp areas. For example, if an image contains portrait photography, the image in the foreground is sharp whereas the background is blurry. In sports photography, the object in focus is sharp and the background usually has motion blur. One way to detect such a spatially varying blur in an image is to run a frequency domain analysis at every location in the image. One of the papers which addresses this topic is "Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes" (cvpr2017).
the authors look at multi resolution DCT coefficients at every pixel. These DCT coefficients are divided into low, medium, and high frequency bands, out of which only the high frequency coefficients are selected.
The DCT coefficients are then fused together and sorted to form the multiscale-fused and sorted high-frequency transform coefficients
A subset of these coefficients are selected. the number of selected coefficients is a tunable parameter which is application specific.
The selected subset of coefficients are then sent through a max pooling block to retain the highest activation within all the scales. This gives the blur map as the output, which is then sent through a post processing step to refine the map.
This blur map can be used to quantify the sharpness in various regions of the image. In order to get a single global metric to quantify the bluriness of the entire image, the mean of this blur map or the histogram of this blur map can be used
Here are some examples results on how the algorithm performs:
The sharp regions in the image have a high intensity in the blur_map, whereas blurry regions have a low intensity.
The github link to the project is: https://github.com/Utkarsh-Deshmukh/Spatially-Varying-Blur-Detection-python
The python implementation of this algorithm can be found on pypi which can easily be installed as shown below:
pip install blur_detector
A sample code snippet to generate the blur map is as follows:
import blur_detector
import cv2
if __name__ == '__main__':
img = cv2.imread('image_name', 0)
blur_map = blur_detector.detectBlur(img, downsampling_factor=4, num_scales=4, scale_start=2, num_iterations_RF_filter=3)
cv2.imshow('ori_img', img)
cv2.imshow('blur_map', blur_map)
cv2.waitKey(0)
For detecting blurry images, you can tweak the approach and add "Region of Interest estimation".
In this github link: https://github.com/Utkarsh-Deshmukh/Blurry-Image-Detector , I have used local entropy filters to estimate a region of interest. In this ROI, I then use DCT coefficients as feature extractors and train a simple multi-layer perceptron. On testing this approach on 20000 images in the "BSD-B" dataset (http://cg.postech.ac.kr/research/realblur/) I got an average accuracy of 94%
Just to add on the focussing errors, these may be detected by comparing the psf of the captured blurry images (wider) with reference ones (sharper). Deconvolution techniques may help correcting them but leaving artificial errors (shadows, rippling, ...). A light field camera can help refocusing to any depth planes since it captures the angular information besides the traditional spatial ones of the scene.
I had asked this on photo stackexchange but thought it might be relevant here as well, since I want to implement this programatically in my implementation.
I am trying to implement a blur detection algorithm for my imaging pipeline. The blur that I want to detect is both -
1) Camera Shake: Pictures captured using hand which moves/shakes when shutter speed is less.
2) Lens focussing errors - (Depth of Field) issues, like focussing on a incorrect object causing some blur.
3) Motion blur: Fast moving objects in the scene, captured using a not high enough shutter speed. E.g. A moving car a night might show a trail of its headlight/tail light in the image as a blur.
How can one detect this blur and quantify it in some way to make some decision based on that computed 'blur metric'?
What is the theory behind blur detection?
I am looking of good reading material using which I can implement some algorithm for this in C/Matlab.
thank you.
-AD.
Motion blur and camera shake are kind of the same thing when you think about the cause: relative motion of the camera and the object. You mention slow shutter speed -- it is a culprit in both cases.
Focus misses are subjective as they depend on the intent on the photographer. Without knowing what the photographer wanted to focus on, it's impossible to achieve this. And even if you do know what you wanted to focus on, it still wouldn't be trivial.
With that dose of realism aside, let me reassure you that blur detection is actually a very active research field, and there are already a few metrics that you can try out on your images. Here are some that I've used recently:
Edge width. Basically, perform edge detection on your image (using Canny or otherwise) and then measure the width of the edges. Blurry images will have wider edges that are more spread out. Sharper images will have thinner edges. Google for "A no-reference perceptual blur metric" by Marziliano -- it's a famous paper that describes this approach well enough for a full implementation. If you're dealing with motion blur, then the edges will be blurred (wide) in the direction of the motion.
Presence of fine detail. Have a look at my answer to this question (the edited part).
Frequency domain approaches. Taking the histogram of the DCT coefficients of the image (assuming you're working with JPEG) would give you an idea of how much fine detail the image has. This is how you grab the DCT coefficients from a JPEG file directly. If the count for the non-DC terms is low, it is likely that the image is blurry. This is the simplest way -- there are more sophisticated approaches in the frequency domain.
There are more, but I feel that that should be enough to get you started. If you require further info on either of those points, fire up Google Scholar and look around. In particular, check out the references of Marziliano's paper to get an idea about what has been tried in the past.
There is a great paper called : "analysis of focus measure operators for shape-from-focus" (https://www.researchgate.net/publication/234073157_Analysis_of_focus_measure_operators_in_shape-from-focus) , which does a comparison about 30 different techniques.
Out of all the different techniques, the "Laplacian" based methods seem to have the best performance. Most image processing programs like : MATLAB or OPENCV have already implemented this method . Below is an example using OpenCV : http://www.pyimagesearch.com/2015/09/07/blur-detection-with-opencv/
One important point to note here is that an image can have some blurry areas and some sharp areas. For example, if an image contains portrait photography, the image in the foreground is sharp whereas the background is blurry. In sports photography, the object in focus is sharp and the background usually has motion blur. One way to detect such a spatially varying blur in an image is to run a frequency domain analysis at every location in the image. One of the papers which addresses this topic is "Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes" (cvpr2017).
the authors look at multi resolution DCT coefficients at every pixel. These DCT coefficients are divided into low, medium, and high frequency bands, out of which only the high frequency coefficients are selected.
The DCT coefficients are then fused together and sorted to form the multiscale-fused and sorted high-frequency transform coefficients
A subset of these coefficients are selected. the number of selected coefficients is a tunable parameter which is application specific.
The selected subset of coefficients are then sent through a max pooling block to retain the highest activation within all the scales. This gives the blur map as the output, which is then sent through a post processing step to refine the map.
This blur map can be used to quantify the sharpness in various regions of the image. In order to get a single global metric to quantify the bluriness of the entire image, the mean of this blur map or the histogram of this blur map can be used
Here are some examples results on how the algorithm performs:
The sharp regions in the image have a high intensity in the blur_map, whereas blurry regions have a low intensity.
The github link to the project is: https://github.com/Utkarsh-Deshmukh/Spatially-Varying-Blur-Detection-python
The python implementation of this algorithm can be found on pypi which can easily be installed as shown below:
pip install blur_detector
A sample code snippet to generate the blur map is as follows:
import blur_detector
import cv2
if __name__ == '__main__':
img = cv2.imread('image_name', 0)
blur_map = blur_detector.detectBlur(img, downsampling_factor=4, num_scales=4, scale_start=2, num_iterations_RF_filter=3)
cv2.imshow('ori_img', img)
cv2.imshow('blur_map', blur_map)
cv2.waitKey(0)
For detecting blurry images, you can tweak the approach and add "Region of Interest estimation".
In this github link: https://github.com/Utkarsh-Deshmukh/Blurry-Image-Detector , I have used local entropy filters to estimate a region of interest. In this ROI, I then use DCT coefficients as feature extractors and train a simple multi-layer perceptron. On testing this approach on 20000 images in the "BSD-B" dataset (http://cg.postech.ac.kr/research/realblur/) I got an average accuracy of 94%
Just to add on the focussing errors, these may be detected by comparing the psf of the captured blurry images (wider) with reference ones (sharper). Deconvolution techniques may help correcting them but leaving artificial errors (shadows, rippling, ...). A light field camera can help refocusing to any depth planes since it captures the angular information besides the traditional spatial ones of the scene.