My work consists of a detection of faults existing in the contour of flywheel. More precisely at surface of teeths existing on the contour of the steering wheel.
I want to capture a video of the contour of the steering wheel, I want to eventually extract photos from this video the result will be an image containing the tooth with some details that which i will not need it.
I only need to extract the tooth so I can compare it after with a non-defective tooth to conclude if there is or there isn't a defect, since i only need the saw tooth and the tooth shape is rectangular, so I need to make a rectangular shape recognition.
I work with Eclipse C++ and OpenCV.
Now I'm following this tutorial
Example image
Expected result
Related
Is there any method to create a polygon(not a rectangle) around an object in an image for object recognition.
Please refer the following images:
the result I am looking for
and
the original image
.
I am not looking for bounding rectangles like this.I know the concepts of transfer learning, using pre-trained models for object recognition and other object detection concepts.
The main aim is the object detection but not giving results using bounding box but a fitter polygon instead.Link to some resources or papers will be helpful.
Here is a very simple (and a bit hacky) idea, but it might help: take a per-pixel scene labeling algorithm, e.g. SegNet, and then turn the resulting segmented image into a binary image, where the white pixels are the class of interest (in your example, white for cars and black for the rest). Now compute edges. You can add those edges to the original image to obtain a result similar to what you want.
What you want is called image segmentation, which is different to object detection. The best performing methods for common object classes (e.g. cars, bikes, people, dogs,...) do this using trained CNNs, and are usually called semantic segmentation networks awesome links. This will, in theory, give you regions in your image corresponding to the object you want. After that you can fit an enclosing polygon using what is called the convex hull.
Here I say that I have tried many tutorials to implement face recognition in OpenCV 3.2 by using the FaceRecognizer class in face module. But I did not get the accepted result as I wish.
Here I want to ask and I want to know, that what is the best way or what are the conditions to be care off during training and recognizing?
What I have done to improve the accuracy:
Create (at least) 10 faces for training each person in the best quality, size, and angle.
Try to fit the face in the image.
Equalize the HIST of the images
And then I have tried all the three face recognizer (EigenFaceRecognizer, FisherFaceRecognizer, LBPHFaceRecognizer), the result all was the same, but the recognition rate was really very low, I have trained only for three persons, but also cannot recognize very well (the fist person was recognized as the second and so on problems).
Questions:
Do the training and the recognition images must be from the same
camera?
Do the training images cropped manually (photoshop -> read images then train) or this task
must be done programmatically (detect-> crop-> resize then train)?
And what are the best parameters for the each face recognizer (int num_components, double threshold)
And how to set training Algorithm to return -1 when it is an unknown
person.
Expanding on my comment, Chapter 8 in Mastering OpenCV provides really helpful tips for pre-processing faces to make aid the recognition process, such as:
taking a sample only when both eyes are detected (via haar cascade)
Geometrical transformation and cropping: This process would include scaling, rotating, and translating the images so that the eyes are aligned, followed by the removal of the forehead, chin, ears, and background from the face image.
Separate histogram equalization for left and right sides: This process standardizes the brightness and contrast on both the left- and right-hand sides of the face independently.
Smoothing: This process reduces the image noise using a bilateral filter.
Elliptical mask: The elliptical mask removes some remaining hair and background from the face image.
I've added a hacky load/save to my fork of the example code, feel free to try it/tweak it as you need it. Currently it's very limited, but it's a start.
Additionally, you should also check OpenFace and it's DNN face recognizer.
I haven't played with that yet so can't provide details, but it looks really cool.
I am using Opencv to detect shapes and size of material( like disc, washers, nuts and bolts of different size) on that will be held on running belt. what function would be best to distinguish between them.
I am planing to use cvFindContours( to find the shapes) and cvArcLength & cvContourArea to get their area.
Any better approach ?
This is a simple approach to shape matching:
Convert to grayscale
Smoothen the image.
Apply some morphological operations (if necessary).
Edge detect
Find contours (the same you mentioned). The contour function is hierarchical. Hence, segmenting the required (outer in most cases) contour(s) should be easy. Disc and washers can be distinguished by the hole in the contour hierarchy.
Use ApproxPolyDP to get your contour to a rough regular shape. You might be able to distinguish the shapes based on the vertex count in the contour.
Use moments to distinguish the shapes if ApproxPolyDP is not sufficient.
It works for most cases. Always provide sample images to help us assess the complexity of the problem :D.
Check for haar cascade object detection technique in opencv
here are some links....
http://coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.html
http://www.technolabsz.com/2011/08/how-to-do-opencv-haar-training.html
For working with haar cascade u need haar kit for traing purpose..
http://kineme.net/files/haar.zip
I've working on this some time now, and can't find a decent solution for this.
I use OpenCV for image processing and my workflow is something like this:
Took a picture of a tv.
Split image in to R, G, B planes - I'm starting to test using H, S, V too and seems a bit promising.
For each plane, threshold image for a range values in 0 to 255
Reduce noise, detect edges with canny, find the contours and approximate it.
Select contours that contains the center of the image (I can assume that the center of the image is inside the tv screen)
Use convexHull and HougLines to filter and refine invalid contours.
Select contours with certain area (area between 10%-90% of the image).
Keep only contours that have only 4 points.
But this is too slow (loop on each channel (RGB), then loop for the threshold, etc...) and is not good enought as it not detects many tv's.
My base code is the squares.cpp example of the OpenCV framework.
The main problems of TV Screen detection, are:
Images that are half dark and half bright or have many dark/bright items on screen.
Elements on the screen that have the same color of the tv frame.
Blurry tv edges (in some cases).
I also have searched many SO questions/answers on Rectangle detection, but all are about detecting a white page on a dark background or a fixed color object on a contrast background.
My final goal is to implement this on Android/iOS for near-real time tv screen detection. My code takes up to 4 seconds on a Galaxy Nexus.
Hope anyone could help . Thanks in advance!
Update 1: Just using canny and houghlines, does not work, because there can be many many lines, and selecting the correct ones can be very difficult. I think that some sort of "cleaning" on the image should be done first.
Update 2: This question is one of the most close to the problem, but for the tv screen, it didn't work.
Hopefully these points provide some insight:
1)
If you can properly segment the image via foreground and background, then you can easily set a bounding box around the foreground. Graph cuts are very powerful methods of segmenting images. It appears that OpenCV provides easy to use implementations for it. So, for example, you provide some brush strokes which cover "foreground" and "background" pixels, and your image is converted into a digraph which is sliced optimally to split the two. Here is a fun example:
http://docs.opencv.org/trunk/doc/py_tutorials/py_imgproc/py_grabcut/py_grabcut.html
This is a quick something I put together to illustrate its effectiveness:
2)
If you decide to continue down the edge detection route, then consider using Mathematical Morphology to "clean up" the lines you detect before trying to fit a bounding box or contour around the object.
http://en.wikipedia.org/wiki/Mathematical_morphology
3)
You could train across a dataset containing TVs and use the viola jones algorithm for object detection. Traditionally it is used for face detection but you can adapt it for TVs given enough data. For example you could script downloading images of living rooms with TVs as your positive class and living rooms without TVs as your negative class.
http://en.wikipedia.org/wiki/Viola%E2%80%93Jones_object_detection_framework
http://docs.opencv.org/trunk/doc/py_tutorials/py_objdetect/py_face_detection/py_face_detection.html
4)
You could perform image registration using cross correlation, like this nice MATLAB example demonstrates:
http://www.mathworks.com/help/images/examples/registering-an-image-using-normalized-cross-correlation.html
As for your template TV image which would be slid across the search image, you could obtain a bunch of pictures of TVs and create "Eigenscreens" similar to how Eigenfaces are used for facial recognition and generate an average TV image:
http://jeremykun.com/2011/07/27/eigenfaces/
5)
It appears OpenCV has plenty of fun tools for describing shape and structure features, which appears to be mainly what you're interested in. Worth a look if you haven't seen this already:
http://docs.opencv.org/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html
Best of luck.
For face detection I have used the haarcascade_frontalface_alt.xml.
The problem is that the this this algorithm gives me a roi a little bit larger so the rectangle catches some hair and some of the background. Is there a solution to change the dimension of this rectangle?
This what the haarcascade_frontalface_alt.xml detects:
And this what I want to detect:
You cannot reply on OpenCV to do this because its model is trained based on face images just like the first one. That is to say, it is supposed to give face detections like the first one.
Instead, consider to crop the detected rectangles a little bit, whatever size you want it be.
To be more accurate, you can crop the faces based on the facial features, as discussed in this thread.