Python cv2.bitwise_xor() Examples
The following are 3
code examples of cv2.bitwise_xor().
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Example #1
Source File: picam.py From PiCamNN with MIT License | 7 votes |
def movement(mat_1,mat_2): mat_1_gray = cv2.cvtColor(mat_1.copy(),cv2.COLOR_BGR2GRAY) mat_1_gray = cv2.blur(mat_1_gray,(blur1,blur1)) _,mat_1_gray = cv2.threshold(mat_1_gray,100,255,0) mat_2_gray = cv2.cvtColor(mat_2.copy(),cv2.COLOR_BGR2GRAY) mat_2_gray = cv2.blur(mat_2_gray,(blur1,blur1)) _,mat_2_gray = cv2.threshold(mat_2_gray,100,255,0) mat_2_gray = cv2.bitwise_xor(mat_1_gray,mat_2_gray) mat_2_gray = cv2.blur(mat_2_gray,(blur2,blur2)) _,mat_2_gray = cv2.threshold(mat_2_gray,70,255,0) mat_2_gray = cv2.erode(mat_2_gray,np.ones((erodeval,erodeval))) mat_2_gray = cv2.dilate(mat_2_gray,np.ones((4,4))) _, contours,__ = cv2.findContours(mat_2_gray,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) if len(contours) > 0:return True #If there were any movements return False #if not #Pedestrian Recognition Thread
Example #2
Source File: logical_xor.py From plantcv with MIT License | 7 votes |
def logical_xor(bin_img1, bin_img2): """Join two images using the bitwise XOR operator. Inputs: bin_img1 = Binary image data to be compared to bin_img2 bin_img2 = Binary image data to be compared to bin_img1 Returns: merged = joined binary image :param bin_img1: numpy.ndarray :param bin_img2: numpy.ndarray :return merged: numpy.ndarray """ params.device += 1 merged = cv2.bitwise_xor(bin_img1, bin_img2) if params.debug == 'print': print_image(merged, os.path.join(params.debug_outdir, str(params.device) + '_xor_joined.png')) elif params.debug == 'plot': plot_image(merged, cmap='gray') return merged
Example #3
Source File: LogicalOperation.py From Finger-Detection-and-Tracking with BSD 2-Clause "Simplified" License | 5 votes |
def main(): basePath = "../data/" imageFileOne = basePath + "4.1.04.tiff" imageFileTwo = basePath + "4.1.05.tiff" imageOne = cv2.imread(imageFileOne, 1) imageTwo = cv2.imread(imageFileTwo, 1) imageOneRGB = cv2.cvtColor(imageOne, cv2.COLOR_BGR2RGB) imageTwoRGB = cv2.cvtColor(imageTwo, cv2.COLOR_BGR2RGB) negativeImage = cv2.bitwise_not(imageOneRGB) andImage = cv2.bitwise_and(imageOneRGB, imageTwoRGB) orImage = cv2.bitwise_or(imageOneRGB, imageTwoRGB) xorImage = cv2.bitwise_xor(imageOneRGB, imageTwoRGB) imageNames = [imageOneRGB, imageTwoRGB, negativeImage, andImage, orImage, xorImage] imageTitles = ["Image One", "Image Two", "Negative", "AND", "OR", "XOR"] for i in range(6): plt.subplot(2, 3, i + 1) plt.imshow(imageNames[i]) plt.title(imageTitles[i]) plt.xticks([]) plt.yticks([]) plt.show()