Image Segmentation by Foreground Extraction using GrabCut Algorithm based on Graph Cuts
The syntax for grabCut() is:
cv2.grabCut(img, mask, rect, bgdModel, fgdModel, iterCount[, mode])
Here are the descriptions on the parameters (Miscellaneous Image Transformations):
- img : Input 8-bit 3-channel image.
- mask :
Input/output 8-bit single-channel mask. The mask is initialized by the function when mode is set to GC_INIT_WITH_RECT. Its elements may have one of following values:
- GC_BGD defines an obvious background pixels.
- GC_FGD defines an obvious foreground (object) pixel.
- GC_PR_BGD defines a possible background pixel.
- GC_PR_FGD defines a possible foreground pixel.
- rect : ROI containing a segmented object. The pixels outside of the ROI are marked as obvious background. The parameter is only used when mode==GC_INIT_WITH_RECT .
- bgdModel : Temporary array for the background model. Do not modify it while you are processing the same image.
- fgdModel : Temporary arrays for the foreground model. Do not modify it while you are processing the same image.
- iterCount : Number of iterations the algorithm should make before returning the result. Note that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or mode==GC_EVAL .
- mode :
Operation mode that could be one of the following:
- GC_INIT_WITH_RECT The function initializes the state and the mask using the provided rectangle. After that it runs iterCount iterations of the algorithm.
- GC_INIT_WITH_MASK The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are automatically initialized with GC_BGD .
- GC_EVAL The value means that the algorithm should just resume.
We create a mask image similar to the loaded image:
mask = np.zeros(img.shape[:2],np.uint8)
Then, we create fgdModel and bgdModel. Then, we run the grabcut algorithm for 5 iterations with cv2.GC_INIT_WITH_RECT mode since we are using rectangle. It modifies the mask image:
In this new mask image, pixels will be marked with four flags denoting background/foreground as specified above. So we modify the mask such that all 0-pixels and 2-pixels are put to 0 (ie background) and all 1-pixels and 3-pixels are put to 1(ie foreground pixels):
mask2 = np.where((mask==2)|(mask==0),0,1).astype('uint8')
Now our final mask is ready, and we can just multiply it with input image to get the segmented image:
img_cut = img*mask2[:,:,np.newaxis]
import numpy as np import cv2 from matplotlib import pyplot as plt img = cv2.imread('bolt.jpg') # img.shape : (413, 620, 3) mask = np.zeros(img.shape[:2],np.uint8) # img.shape[:2] = (413, 620) bgdModel = np.zeros((1,65),np.float64) fgdModel = np.zeros((1,65),np.float64) rect = (300,120,470,350) # this modifies mask cv2.grabCut(img,mask,rect,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT) # If mask==2 or mask== 1, mask2 get 0, other wise it gets 1 as 'uint8' type. mask2 = np.where((mask==2)|(mask==0),0,1).astype('uint8') # adding additional dimension for rgb to the mask, by default it gets 1 # multiply it with input image to get the segmented image img_cut = img*mask2[:,:,np.newaxis] plt.subplot(211),plt.imshow(img) plt.title('Input Image'), plt.xticks(), plt.yticks() plt.subplot(212),plt.imshow(img_cut) plt.title('Grab cut'), plt.xticks(), plt.yticks() plt.show()
Though the result was not satisfactory but I'll stop here and may be comeback later with a better solution. In the OpenCV's official tutorial (Interactive Foreground Extraction using GrabCut Algorithm), additional manual touch was needed to make it work.
OpenCV 3 Tutorial
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Image noise reduction : Non-local Means denoising algorithm
Image object detection : Face detection using Haar Cascade Classifiers
Image segmentation - Foreground extraction Grabcut algorithm based on graph cuts
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