I am trying to determine when a food packaging have error or not error. Example
the logo " McDonald's " have error misprints or not, as the wrong label, wrong color..( i can not post picture )
What should I do, please help me!!
It's not a trivial task by any stretch of the imagination. Two images of the same identical object will always be different according to lightning conditions, perspective, shooting angle, etc.
Basically you need to:
1. Process the 2 images into "digested" data - dominant color, shapes, etcw
2. Design and run your own similarity algorithm between the 2 objects
You may want to look at Feature detectors in OpenCV: Surf, SIFT, etc.
Along a result I just found your question, so I think I come too late.
If not I think your problem car easily be resolved, it exists since years and is called Sikuli .
While it's for testing purposes, I have been using it in the same way as you need : compare a reference and a production image. Based on OpenCV it does it very well.
Related
As I am working on my project that is to detect FOD (Foreign Object Debirs) that is found on the runway. FOD include anything like nuts, bolts, screws, locking wires, plastic debris, stones etc. that has the potential to cause damage to the aircraft. Now I have searched on the Internet to find any image dataset but no dataset is available related to FOD. Now my question is kindly guide me that how can I make my own dataset of images that can then be used for training purpose.
Kindly guide me in making image dataset for both classification and detection purposes. And also the data pre-processing that will be required. Thanks and waiting for the reply!
Although the question is a bit vague regarding your requirements and the specs of your machine, I'll try to answer it. You'll need object detection to do your task. There are many models available which you can use like Yolo, SSD, etc..
To create your own dataset, you can follow these steps:
Take lots of images of your objects of interest in various conditions, viewpoints and backgrounds. (Around 2000 per class should be good enough).
Now annotate (or mark) where your object is in the image. If you're using Yolo, make use of Yolo-mark for annotating. There should be other similar tools for SSD and other models.
Now you can begin training.
These steps should get you started or at least point you in the right direction.
You can build your own dataset with this code. I wrote it, and it works correctly.
You need to import the libraries and add your DATADIR.
if __name__ == "__main__":
for category in CATEGORIES:
path = os.path.join(DATADIR, category)
class_num = CATEGORIES.index(category)
for img in os.listdir(path):
try:
img_array = cv2.imread(os.path.join(path,img))
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
training_data.append([new_array, class_num])
except Exception as e:
pass
for features, label in training_data:
x_train.append(features)
y_train.append(label)
#create pikle
pickle_out = open("x_train.pickle", "wb")
pickle.dump(x_train, pickle_out)
pickle_out.close()
pickle_out = open("y_train.pickle", "wb")
pickle.dump(y_train, pickle_out)
pickle_out.close()
In case if you're starting completely from scratch, you can use "Dataset Directory", available on Play store. The App helps you in creating custom datasets using your mobile. You'll have to sign in to your Google drive such that your dataset is stored in Drive rather on your mobile. Additionally, It also contains Labelling the entity for classification and Regression predictive models.
Currently, the App supports Binary Image Classification and Image Regression.
Hope this Helped!
Download Link :
https://play.google.com/store/apps/details?id=com.applaud.datasetdirectory
I am working on a project where my task is to identify machine part by its part number written on label attached to it or engraved on its surface. One such example of label and engraved part is shown in below figures.
My task is to recognise 9 or 10 alphanumerical number (03C 997 032 D in 1st image and 357 955 531 in 2nd image). This seems to be easy task however I am facing problem in distinguishing between useful information in the image and rest of the part i.e. there are many other numbers and characters in both image and I want to focus on only mentioned numbers. I tried many things but no success as of now. Does anyone know the image pre processing methods or any ML/DL model which I should apply to get desired result?
Thanks in advance!
JD
You can use OCR to the get all characters from the image and then use regular expressions to extract the desired patterns.
You can use OCR method, like Tesseract.
Maybe, you want to clean the images before running the text-recognition system, by performing some filtering to remove noise / remove extra information, such as:
Convert to gray scale (colors are not relevant, aren't them?)
Crop to region of interest
Canny Filter
A good start can be one of this tutorial:
OpenCV OCR with Tesseract (Python API)
Recognizing text/number with OpenCV (C++ API)
I have an image of a layer of carbon nanotubes taken with an electronic microscope and I would like to extract the "shape" of every nanotube.
I think that ImageJ could be very helpful since it has already led me to encouraging results, but I am sure that they could be improved.
Here is the image I start with :
I have seen that there are many tools to detect for example cells, or to divide an image in two areas (with the trainable Weka segmentation tool for instance), but I did not find anything to "follow" a tube from the beginning to the end. Does such a tool exist ?
What would you advise me to do to clean the image ? So far, I have tried 'auto local threshold' (loss of information), 'Remove outliers', 'analyse particles' (to remove the too-little-to-be-tubes-particles), and some skeleton tools + OrientationJ to extract information.
Thank you very much for your help !
EDIT : the "Tubeness" plugin was very helpful Tubeness documentation
I'm working with openCV and I'm a newbie in this field. I'm researching about Camshift. I want to extend this method by using multiple histograms. It means when tracking an object has many than one apperance (ex: rubik cube with six apperance), if we use only one histogram, Camshift will most likely fail.
I know calcHist function in openCV (http://docs.opencv.org/modules/imgproc/doc/histograms.html#calchist) has a parameter is "accumulate", but I don't know how to use and when to use (apply for camshiftdemo.cpp in opencv samples folder). This function can help me solve this problem? Or I have to use difference solution?
I have an idea, that is: create an array histogram for object, for every appearance condition that strongly varies in color, we pre-compute and store all to this array. But when we compute new histogram? It means that the pre-condition to start compute new histogram is what?
And what happend if I have to track multiple object has same color?
Everybody please help me. Thank you so much!
I'm trying to reduce the calculation time of my stitching algorithm. I got some images which I want to stitch in a defined order but it seems like cv::stitcher.stitch() function tries to stitch every image with every other image.
I feel like I might find the solution in the parameters of OpenCV Stitcher. If not maybe I have to modify the function or try something else to reduce calculation time. But since I'm pretty much a beginner, I don't know how. I know that using GPU might be a possibility but I just don't get CUDA running on Ubuntu at the moment.
It would be great if you could give me some advice!
Parameters for OpenCV Stitcher module:
Stitcher Stitcher::createDefault(bool try_use_gpu) {
Stitcher stitcher;
stitcher.setRegistrationResol(0.6);
stitcher.setSeamEstimationResol(0.1);
stitcher.setCompositingResol(ORIG_RESOL);
stitcher.setPanoConfidenceThresh(1);
stitcher.setWaveCorrection(true);
stitcher.setWaveCorrectKind(detail::WAVE_CORRECT_HORIZ);
stitcher.setFeaturesMatcher(new detail::BestOf2NearestMatcher(try_use_gpu));
stitcher.setBundleAdjuster(new detail::BundleAdjusterRay());
from stitcher.cpp:
https://code.ros.org/trac/opencv/browser/trunk/opencv/modules/stitching/src/stitcher.cpp?rev=7244
I want to stitch in a defined order but it seems like
cv::stitcher.stitch() function tries to stitch every image with every
other image.
cv::stitcher does not have a parameter to fulfil your requirement.
However, in the stitching_detailed.cpp sample you have the --rangewidth parameter. By setting it to 1, the algorithm will only consider adjacent image pairs (e.g. for pair 1-2 matches would be computed but not for pair 1-3)