"""template.py - Template matching
"""
import numpy as np
import _template

try:
    import cv
    opencv_available = True
except ImportError:
    opencv_available = False



#XXX add to opencv backend once backend system in place
def match_template_cv(image, template, out=None,  method="norm-coeff"):
    """Finds a template in an image using normalized correlation.

    Parameters
    ----------
    image : array_like, dtype=float
        Image to process.
    template : array_like, dtype=float
        Template to locate.
    out: array_like, dtype=float, optional
        Optional destination.
    Returns
    -------
    output : ndarray, dtype=float
        Correlation results between 0.0 and 1.0, maximum indicating the most
        probable match.
    """
    if not opencv_available:
        raise ImportError("Opencv 2.0+ required")
    if out == None:
        out = np.empty((image.shape[0] - template.shape[0] + 1,
                        image.shape[1] - template.shape[1] + 1),
                       dtype=image.dtype)
    if method == "norm-corr":
        cv.MatchTemplate(image, template, out, cv.CV_TM_CCORR_NORMED)
    elif method == "norm-coeff":
        cv.MatchTemplate(image, template, out, cv.CV_TM_CCOEFF_NORMED)
    else:
        raise ValueError("Unknown template method: %s" % method)
    return out


def match_template(image, template, method="norm-coeff"):
    """Finds a template in an image using normalized correlation.

    Parameters
    ----------
    image : array_like, dtype=float
        Image to process.
    template : array_like, dtype=float
        Template to locate.
    method: str (default 'norm-coeff')
        The correlation method used in scanning.
        T represents the template, I the image and R the result.
        The summation is done over X = 0..w-1 and Y = 0..h-1 of the template.
        'norm-coeff':
            R(x, y) = Sigma(X,Y)[T(X, Y).I(x + X, y + Y)] / N
            N = sqrt(Sigma(X,Y)[T(X, Y)**2].Sigma(X,Y)[I(x + X, y + Y)**2])
        'norm-corr':
            R(x,y) = Sigma(X,y)[T'(X, Y).I'(x + X, y + Y)] / N
            N = sqrt(Sigma(X,y)[T'(X, Y)**2].Sigma(X,Y)[I'(x + X, y + Y)**2])
            where:
            T'(x, y) = T(X, Y) - 1/(w.h).Sigma(X',Y')[T(X', Y')]
            I'(x + X, y + Y) = I(x + X, y + Y)
                               - 1/(w.h).Sigma(X',Y')[I(x + X', y + Y')]

    Returns
    -------
    output : ndarray, dtype=float
        Correlation results between 0.0 and 1.0, maximum indicating the most
        probable match.
    """
    if method == "norm-corr":
        method_num = 0
    elif method == "norm-coeff":
        method_num = 1
    else:
        raise ValueError("Unknown template method: %s" % method)
    return _template.match_template(image, template, method_num)