script.py 文件源码

def convertToLogPolar(img, centerTrans, angleStep, logBase, mode = "nearest"):
    if mode == "nearest":
        # Step 1 - Initialize transformed image
        transformedImage = np.zeros(img.shape, dtype = img.dtype)
        # Step 2 - Apply reverse log polar transformation
        for radius in range(img.shape[COLS_AXIS]): # start with radius, because calculating exponential power is time consuming
            actRadius = logBase ** radius
            for angle in range(img.shape[ROWS_AXIS]):
                anglePi =  angle * angleStep
                # calculate euclidian coordinates (source: https://en.wikipedia.org/wiki/Log-polar_coordinates)
                row = int(centerTrans[ROWS_AXIS] + actRadius * math.sin(anglePi))
                col = int(centerTrans[COLS_AXIS] + actRadius * math.cos(anglePi))
                # copy pixel from the location to log polar image
                if 0 <= row < img.shape[ROWS_AXIS] and 0 <= col < img.shape[COLS_AXIS]:
                    transformedImage[angle, radius] = img[row, col]

        return transformedImage
    else:
        print("Base: " + str(logBase))
        # create matrix with angles
        anglesMap = np.zeros(img.shape, dtype=np.float64)
        # each column has 0 in its first row and -pi in its last row
        anglesVector = -np.linspace(0, np.pi, img.shape[0], endpoint=False)
        # initialize it by columns using the same vector
        anglesMap.T[:] = anglesVector
        # create matrix with radii
        radiusMap = np.zeros(img.shape, dtype=np.float64)
        # each line contains a vector with numbers from  in (0, cols) to power logBase
        radiusVector = np.power(logBase, np.arange(img.shape[1], dtype=np.float64)) - 1.0
        # initialize it by rows using the same vector
        radiusMap[:] = radiusVector
        # calculate x coordinates (source: https://en.wikipedia.org/wiki/Log-polar_coordinates)
        x = radiusMap * np.sin(anglesMap) + centerTrans[1]
        # calculate y coordinates (source: https://en.wikipedia.org/wiki/Log-polar_coordinates)
        y = radiusMap * np.cos(anglesMap) + centerTrans[0]
        # initialize final image
        outputImg = np.zeros(img.shape)
        # use spline interpolation to map pixels from original image to calculated coordinates
        ndii.map_coordinates(img, [x, y], output=outputImg)
        return outputImg


# computes phase correlation and returns position of pixel with highest value (row, column)