What is the format of the mask for OpenCV's Python ORB module?

user_3107858 0
Viewed 1460

I've been trying to use ORB to find keypoint/descriptors and I need to mask out part of the image because many features are very similar in two parts of my image. However, I can't determine the correct format of the mask parameter to the detectandcompute function, and the documentation is ambiguous to me. I tried looking at the source code but I am not familiar enough with C++ to understand it. I thought it was just a binary array where 1 = use and 0 = ignore, but every mask I've tried doesn't return any keypoints. Here is some example code:

img1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
#ignore the left half of the first image
mask1 = np.ones(img1_gray.shape)
mask1[:,:mask1.shape[1]/2] = 0
#ignore the right half of the second image
mask2 = np.ones(img2_gray.shape)
mask2[:,mask2.shape[1]/2:] = 0
kp1, des1 =orb.detectAndCompute(img1_gray,mask1)
kp2, des2 =orb.detectAndCompute(img2_gray,mask2)

The documentation is here:http://docs.opencv.org/3.0-beta/modules/features2d/doc/feature_detection_and_description.html

img1 img2

opencv python c++
3 Answers

The bug in the above code was that the masks had to be changed to Uint8. Thus, this would change the masks to be of CV_8UC1 type with values 0 to 255. Here is the fully tested working Python code (Version 3) using SIFT features rather than ORB features:

import cv2
import numpy as np 
import matplotlib.pyplot as plt

def showing_features(img1, key_points):
   plt.imshow(cv2.drawKeypoints(img1, key_points, None))
   plt.show() 

img1 = cv2.imread('img1.jpg')
img2 = cv2.imread('img2.jpg')

img1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

sift= cv2.xfeatures2d.SIFT_create(nfeatures=0,
                            nOctaveLayers=3,
                            contrastThreshold=0.05,
                            edgeThreshold=10.0,
                            sigma=1.6)

"*-------------- Create Masks --------------*"
mask1 = np.ones(img1_gray.shape)
#ignore the left half of the first image
mask1[:,:int(mask1.shape[1]/2)] = 0
#ignore the right half of the second image
mask2 = np.ones(img2_gray.shape)
mask2[:,int(mask2.shape[1]/2):] = 0

"*-------------- Change Masks to Uint8 --------------*"
mask1 = mask1.astype(np.uint8)
mask2 = mask2.astype(np.uint8)

"*-------------- Extract SIFT Features --------------*"
kp1m, des1m =sift.detectAndCompute(img1_gray,mask1)
kp2m, des2m =sift.detectAndCompute(img2_gray,mask2)

"*-------------- Display SIFT features after using MASK --------------*"
showing_features(img1, kp1m)
showing_features(img2, kp2m)
user_60733220
Related