utils.py

# -*- coding: UTF-8 -*-
import numpy as np

def loadFromPts(filename):
    """从.pts文件中加载t=特征点数据到landmark中返回.

    Args:
        filename: 特征点数据所在文件路径
    """
    pass
    landmarks = np.genfromtxt(filename, skip_header=3, skip_footer=1)
    landmarks = landmarks - 1

    return landmarks

def saveToPts(filename, landmarks):
    """把当前特征点存储到.pts文件中.

    Args:
        filename: 特征点数据将存储的文件路径
        landmarks: 特征点数据
    """
    pass
    pts = landmarks + 1

    header = 'version: 1\nn_points: {}\n{{'.format(pts.shape[0])
    np.savetxt(filename, pts, delimiter=' ', header=header, footer='}', fmt='%.3f', comments='')

def bestFitRect(points, meanS, box=None):
    """根据points所标注的landmark位置或bounding box位置，将meanshape的坐标整体缩放，
    得到新的landmark_meanshape为s0返回.

    Args:
        points: 参照的landmark坐标数组
        meanS: meanshape的landmark坐标数组
        box: 为none的时候根据points计算最大值和最小值，非none时即直接传入boudingbox坐标了
    Return:
        S0: 根据meanshape放大的landmark坐标数组
    """
    pass
    if box is None:
        box = np.array([points[:, 0].min(), points[:, 1].min(), points[:, 0].max(), points[:, 1].max()])
    boxCenter = np.array([(box[0] + box[2]) / 2, (box[1] + box[3]) / 2 ])

    boxWidth = box[2] - box[0]
    boxHeight = box[3] - box[1]

    meanShapeWidth = meanS[:, 0].max() - meanS[:, 0].min()
    meanShapeHeight = meanS[:, 1].max() - meanS[:, 1].min()

    scaleWidth = boxWidth / meanShapeWidth
    scaleHeight = boxHeight / meanShapeHeight
    scale = (scaleWidth + scaleHeight) / 2

    # 把meanshape的坐标点坐标缩放到landmark所标注的坐标范围。
    S0 = meanS * scale

    S0Center = [(S0[:, 0].min() + S0[:, 0].max()) / 2, (S0[:, 1].min() + S0[:, 1].max()) / 2]
    S0 += boxCenter - S0Center

    return S0

def bestFit(destination, source, returnTransform=False):
    destMean = np.mean(destination, axis=0)
    srcMean = np.mean(source, axis=0)

    srcVec = (source - srcMean).flatten()
    destVec = (destination - destMean).flatten()

    # np.linalg.norm 范数，默认为2范数
    a = np.dot(srcVec, destVec) / np.linalg.norm(srcVec)**2
    b = 0
    for i in range(destination.shape[0]):
        b += srcVec[2*i] * destVec[2*i+1] - srcVec[2*i+1] * destVec[2*i]
    b = b / np.linalg.norm(srcVec)**2

    T = np.array([[a, b], [-b, a]])
    srcMean = np.dot(srcMean, T)

    if returnTransform:
        return T, destMean - srcMean
    else:
        return np.dot(srcVec.reshape((-1, 2)), T) + destMean

def mirrorShape(shape, imgShape=None):
    """调用mirrorShapes函数对特征点做镜像变换.

    Args:
        shape: 特征点
        imgShape: 当前img的维度特征
    """
    pass
    imgShapeTemp = np.array(imgShape)
    # numpy.reshape ： give a new shape to an array without changeing its data
    # 如果转换的形状中存在-1即意思是根据其他值来计算此处的维数值。
    shape2 = mirrorShapes(shape.reshape((1, -1, 2)), imgShapeTemp.reshape((1, -1)))[0]

    return shape2

def mirrorShapes(shapes, imgShapes=None):
    """对特征点做镜像变换，具体.

    Args:
        shape: 特征点
        imgShape: 当前img的维度特征
    """
    pass
    shapes2 = shapes.copy()

    for i in range(shapes.shape[0]):
        if imgShapes is None:
            shapes2[i, :, 0] = -shapes2[i, :, 0]
        else:
            shapes2[i, :, 0] = -shapes2[i, :, 0] + imgShapes[i][1]

        lEyeIndU = range(36, 40)
        lEyeIndD = [40, 41]
        rEyeIndU = range(42, 46)
        rEyeIndD = [46, 47]
        lBrowInd = range(17, 22)
        rBrowInd = range(22, 27)

        uMouthInd = range(48, 55)
        dMouthInd = range(55, 60)
        uInnMouthInd = range(60, 65)
        dInnMouthInd = range(65, 68)
        noseInd = range(31, 36)
        beardInd = range(17)

        lEyeU = shapes2[i, lEyeIndU].copy()
        lEyeD = shapes2[i, lEyeIndD].copy()
        rEyeU = shapes2[i, rEyeIndU].copy()
        rEyeD = shapes2[i, rEyeIndD].copy()
        lBrow = shapes2[i, lBrowInd].copy()
        rBrow = shapes2[i, rBrowInd].copy()

        uMouth = shapes2[i, uMouthInd].copy()
        dMouth = shapes2[i, dMouthInd].copy()
        uInnMouth = shapes2[i, uInnMouthInd].copy()
        dInnMouth = shapes2[i, dInnMouthInd].copy()
        nose = shapes2[i, noseInd].copy()
        beard = shapes2[i, beardInd].copy()

        lEyeIndU.reverse()
        lEyeIndD.reverse()
        rEyeIndU.reverse()
        rEyeIndD.reverse()
        lBrowInd.reverse()
        rBrowInd.reverse()

        uMouthInd.reverse()
        dMouthInd.reverse()
        uInnMouthInd.reverse()
        dInnMouthInd.reverse()
        beardInd.reverse()
        noseInd.reverse()

        shapes2[i, rEyeIndU] = lEyeU
        shapes2[i, rEyeIndD] = lEyeD
        shapes2[i, lEyeIndU] = rEyeU
        shapes2[i, lEyeIndD] = rEyeD
        shapes2[i, rBrowInd] = lBrow
        shapes2[i, lBrowInd] = rBrow

        shapes2[i, uMouthInd] = uMouth
        shapes2[i, dMouthInd] = dMouth
        shapes2[i, uInnMouthInd] = uInnMouth
        shapes2[i, dInnMouthInd] = dInnMouth
        shapes2[i, noseInd] = nose
        shapes2[i, beardInd] = beard

    return shapes2