I'm trying to implement a python program to remove the background and extract the object/objects in the foreground from any given static image (as shown in the attached images). This is similar to "iPhoneX Portrait Effect" or "Bokeh Effect". However, instead of blurring the background one needs to completely remove it.
In short, I want to extract objects from any given image and also create a mask. Below are the examples of both respectively:
Object Extraction:
Image Mask:
I have somewhere listened to Google's DeepLab, but I don't know how to start with it.
Can someone help me, please!
Any step by step tutorial will be very appreciated.
Thanks in advance!
This is a really hard task, there is a reason there are basically no good software to do this already, even in photoshop this is a struggle. I can advice you to start with open Cv and their implemented facial tracking which you may need to configure to work with animals if thats your goal
resources
facial detection:
https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_objdetect/py_face_detection/py_face_detection.html#face-detection
object detection:
https://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html
Firstly you should collect data(image) for which object you want to detect or extract the object/objects in the foreground and also create a mask.
Then using tensorflow you can train an instance segmentation model Using your own dataset. (Ref : instance_segmentation)
After getting mask you can extract the foreground.
Related
I want to ask for some advice about the procedure that I should implement for image segmentation working with opencv in python.
I have this kind of image and my purpose is to detect the white fiber like here
Does anyone have a proposition of the steps of image processing that I should do?
Since I can notice that object's color is different than the background, I found this guide helpful. The concept is the following :
1.apply RGB filters to your image,
2.grab contours using OpenCV, then
3.apply some handcraft conditions to them so as to fit your desired output, and finally
4.produce the box.
If all of your images share the same color patterns, this should work.. If not, it will prove noisy ..
I'm trying to learn computer vision and more specifically open-cv in python.
I want to make a program that would track my barbell in a video and show me its path. (I know apps like this exists but I want to make it myself). I tried using the Canny edge detection and the HoughCircles functions but I seem to get everything but a good result.
I have been using this code to find the edges of my image:
gray = cv.cvtColor(src=img, code=cv.COLOR_BGR2GRAY)
blur = cv.blur(gray, (2,2))
canny = cv.Canny(blur, 60, 60)
And then this code to find the circle:
circles = cv.HoughCircles(canny, cv.HOUGH_GRADIENT, dp=2, minDist=1000, circles=None,maxRadius=50)
This is the result:
Result
left = original image with detected circle // right = canny image
Is this the right way to go or should I use another method?
Train the YOLO model for the barbell to detect barbel object is better than anything you tried with OpenCV. You need at least 500 images. Those images can be found on the internet easily. This tutorial is kick start tutorial on YOLO. Let's give a try.
If you tweak the parameters of HoughCircles it may recognize the barbell [EDIT: but with more preprocessing, gamma correction, blurring etc., so better not], however OpenCV has many algorithms for such object tracking - only a region from the image has to be specified first (if that's OK).
In your case the object is always visible and is not changing much, so I guess many of the available algorithms would work fine.
OpenCV has a built-in function for selection:
initBB = cv2.selectROI("Frame", frame, fromCenter=False, showCrosshair=True)
See this tutorial for tracking: https://www.pyimagesearch.com/2018/07/30/opencv-object-tracking/
The summary from the author suggestion is:
CSRT Tracker: Discriminative Correlation Filter (with Channel and Spatial Reliability). Tends to be more accurate than KCF but slightly slower. (minimum OpenCV 3.4.2)
Use CSRT when you need higher object tracking accuracy and can tolerate slower FPS throughput
I guess accuracy is what you want, if it is for offline usage.
Can you share a sample video?
What's your problem exactly? Why do you track the barbell? Do you need semantic segmentation or normal detection? These are important questions. Canny is a very basic approach It' needs a very stable background to use it. That's why there is deep learning to handle that kind of problem If we need to talk about deep learning you can use MaskRCNN, yolvoV4, etc. there are many available solutions out there.
I am trying to extract Image from a pan card with is ID card in India. Trying to extract the same with OpenCV and python. I have tried out the same using connected components background is interfering with it too much.
This is the sample input
This is the required output
I have followed this project as initial starting pointing, but not able to extract the same properly
because of the gradient in the background.
Thanks in advance for your time – if I’ve missed out anything, over- or under-emphasized a specific point let me know in the comments.
Adaptive thresholding on the blue component seems to do the job easily.
I have this picture. I need to identify the animal in this picture as shown using an image processing algorithm. I'm thinking of using Python for this. But I don't know which algorithm to use and I don't know where to start. Where should I start?
image
The best place to start is fast.ai. You will find the videos you need and the code. You can do it on any computer, even a cheap laptop. You will also want to look into LIME model explanations for image classifiers.
I am trying to detect the objects in an image which look similar to the reference image. Here is how i'm trying to accomplish it:
Here is the sample Image:
and here is the image with SURF keypoints:
The rectangle is drawn based on Clustering method like "Hierarchial Clustering".
The main problem is, in this case it doesnt detect the objects individually, it detects everything as one object.
Is there a way to seperate these keypoints, so as to detect each vehicle seperately?
Is this a good way to detect objects or if there is a better way please suggest.
SURF keypoints are useful in detecting similar images, or images taken of the same place from different perspectives. Although you can use Haar classifiers for the purpose of object detection. It is also a part of OpenCV library.
Here is another great tutorial regarding object detection using OpenCV.