thermalReg.py 文件源码

def translationalThermalReg(im1,im2):
    import cv2,numpy

    #get dimensions
    s1=im1.shape
    s2=im2.shape

    #check sizes agree as a sanity check for inputs

    if s1!=s2:
        raise TypeError('Array Inputs are of different sizes!')

    #Select translation model in CV
    warp_model = cv2.MOTION_AFFINE

    #Define 2x3 Warp Matrix
    warp_matrix = numpy.eye(2, 3, dtype=numpy.float32)

    #Number of iterations allowed to converge on solution
    num_it=10000

    #Terminal Threshold
    termTh = 1e-9

    #Define Stopping Criteria
    criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, num_it,  termTh)

    #Ensure images are of datatype float32 (for compatibility with transformation convergence)
    im1=im1.astype(numpy.float32)
    im2=im2.astype(numpy.float32)

    #Find Ideal Transform given input parameters
    (cc, warp_matrix) = cv2.findTransformECC(im1,im2,warp_matrix, warp_model, criteria)

    #Apply Transform
    aligned = cv2.warpAffine(im2, warp_matrix, (s1[1], s1[0]), flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP);
    print('Calculated Affine Warp Matrix:')
    print(warp_matrix)

    return aligned, warp_matrix


#Test Harness for debugging and testing of functions