Facial keypoints detection Kaggle 竞赛系列

3.2# Facial keypoints detection

作者：Stu. Rui
QQ: 1026163725
原文链接：http://blog.csdn.net/i_love_home/article/details/51051888

该题主要任务是检測面部关键点位置

问题表述

在本问题中。要求计算面部关键点的位置，即关键点在图片中的百分比坐标。

因此该问题的机理就是 [0, 1] 范围内的数值拟合，当然了，这也是一个多输出的拟合的问题。

给定图片与其相应的 30 个标签的百分比位置，标签信息例如以下：

left_eye_center_x	left_eye_center_y	right_eye_center_x
right_eye_center_y	left_eye_inner_corner_x	left_eye_inner_corner_y
left_eye_outer_corner_x	left_eye_outer_corner_y	right_eye_inner_corner_x
right_eye_inner_corner_y	right_eye_outer_corner_x	right_eye_outer_corner_y
left_eyebrow_inner_end_x	left_eyebrow_inner_end_y	left_eyebrow_outer_end_x
left_eyebrow_outer_end_y	right_eyebrow_inner_end_x	right_eyebrow_inner_end_y
right_eyebrow_outer_end_x	right_eyebrow_outer_end_y	nose_tip_x
nose_tip_y	mouth_left_corner_x	mouth_left_corner_y
mouth_right_corner_x	mouth_right_corner_y	mouth_center_top_lip_x
mouth_center_top_lip_y	mouth_center_bottom_lip_x	mouth_center_bottom_lip_y

求解方案

求解过程例如以下：: Step 1. 选择拟合器 SVR/KernelRidge 以及相应的 kernel; Step 2. 交叉验证实验选择超參数，超參数的选择通过枚举的方法; Step 3. 选定超參数后，用全部训练集训练拟合器; Step 4. 对測试集做预測。并输出结果

实验结果

结果
First idea:: Using 30 fitter to fit 30 labels, then I got 3.48060 RMSE
Second idea: Using 1 fitter to fit 30 labels, then I got 3.43998 RMSE[Better]
Third idea: Adding symmetrical training data, then resulting in abnormal result, such as position was greater then 96.

So, I can see that the result of fitting is only cover [0,96](or [0,1])

备注

超參数选择 gamma

for G in G_para:

       scores = list()

       for i in range(3):

           X1, X2, y1, y2 = train_test_split(train_X, train_y, test_size=0.3, random_state=42)

           clf = KernelRidge(kernel='rbf', gamma=G, alpha=1e-2)

           pred = clf.fit(X1, y1).predict(X2)

           sco = calbais(pred, y2)

           scores.append(sco)

       print('G:', G, 'Score:', scores)

30 个拟合器超參数调试的方法与结果例如以下：

拟合器 KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2)

0.7:0.3 训练集划分拟合误差：

[0] 0.7792    [10] 0.9744    [20] 1.0985

[1] 0.6383    [11] 0.7451    [21] 1.2300

[2] 0.7714    [12] 0.9513    [22] 1.2636

[3] 0.6482    [13] 0.9299    [23] 1.1784

[4] 0.7355    [14] 1.0870    [24] 1.2469

[5] 0.6005    [15] 1.1898    [25] 1.2440

[6] 0.9636    [16] 0.9012    [26] 0.9444

[7] 0.7063    [17] 0.9462    [27] 1.3718

[8] 0.7214    [18] 1.1349    [28] 0.9961

[9] 0.6089    [19] 1.1669    [29] 1.5076

pandas usage:

数据统计：DataFrame.count()

数据去缺失项：DataFrame.dropna()

字符串切割：Series = Series.apply(lambda im: numpy.fromstring(im, sep=' '))

值得注意的地方：

镜像图片，似乎对本问题採用 kernel ridge 拟合器 的求解没有帮助。

Conclusion

The 30 fitter is replaced by the only 1 fitter. The grade is better.

源代码

import pandas as pd

import numpy as np

import csv as csv

import matplotlib.pyplot as plt

from sklearn.utils import shuffle

from sklearn.svm import SVR

from sklearn.kernel_ridge import KernelRidge

from sklearn.cross_validation import cross_val_score, train_test_split

train_file = 'training.csv'         # 训练集数据

test_file = 'test.csv'              # 測试集数据 1783 张图片

test_type = 'IdLookupTable.csv'     # 測试集样表 行号, 图编号, 标签名

pd.set_option('chained_assignment',None)

# csv 数据读取，返回 df (pandas)

def csvFileRead(filename):

    print('Loading', filename)

    df = pd.read_csv(filename, header=0, encoding='GBK')

    print('Loaded')

    # 缺失项数据删除

    if 'train' in filename:

        df = df.dropna()

    ''' 数据查看

    print('\n数据表尺寸: ', df.values.shape)

    print('类别统计：\n')

    print(df.count(), '\n')

    '''

    return df

# 结果存储

def csvSave(filename, ids, predicted):

    with open(filename, 'w') as mycsv:

        mywriter = csv.writer(mycsv)

        mywriter.writerow(['RowId','Location'])

        mywriter.writerows(zip(ids, predicted))

# 训练集数据预处理

def preTrain():

      print('-----------------Training reading...-----------------')

      df = csvFileRead(train_file)

    print('Image: str -> narray')

    df.Image = df.Image.apply(lambda im: np.fromstring(im, sep=' '))

    print('Image transfered.\n')

    # problem: 7049*9046 MemoryError -> df.dropna()

    X = np.vstack(df.Image.values) / 255.

    X.astype(np.float32)

    y = df[df.columns[:-1]].values

    y = (y-48)/48.

    y = y.astype(np.float32)

    '''

    # 增加人工镜像图片

    print('增加人工镜像图片...')

    X, y = imageSym(X, y)

    '''

    X, y = shuffle(X, y, random_state=42)

    yd = dict()

    for i in range(len(df.columns[:-1].values)):

        yd[df.columns[i]] = i

    return X, y, yd

# 预測集数据预处理

def preTest():

    print('-----------------Test reading...-----------------')

    df = csvFileRead(test_file)

    print('Image: str -> narray')

    df.Image = df.Image.apply(lambda im: np.fromstring(im, sep=' '))

    print('Image transfered.\n')

    # 測试集图像

    X = np.vstack(df.Image.values) / 255.

    X.astype(np.float32)

    # 预測内容：行号, 图编号, 标签名

    df = csvFileRead(test_type)

    RowId = df.RowId.values

    ImageId = df.ImageId.values - 1

    FeatureName = df.FeatureName.values

    return RowId, ImageId, FeatureName, X

# 人工特征：镜像图片

def imageSym(X, y):

    nX = np.zeros(X.shape)

    ny = np.zeros(y.shape)

    for i in range(X.shape[0]):

        temp = X[i,:].reshape(96, 96)

        temp = temp[:,::-1]

        nX[i,:] = temp.reshape(-1)

        ny[i,0::2] = -y[i,0::2]

        ny[i,1::2] = y[i,1::2]

    X = np.vstack((X, nX))

    y = np.vstack((y, ny))

    return X, y

# 30 个拟合器进行拟合

def modelfit(train_X, train_y, test_X, yd, ImageId, FeatureName):

    #There are fitting codes.

    # 30 个拟合器相应 1 个位置

    n_clf = 30

    clfs = [

        KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2), KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2),

        KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2), KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2),

        KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2), KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2),

        KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2), KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2),

        KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2), KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2),

        KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2), KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2),

        KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2), KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2),

        KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2), KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2),

        KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2), KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2),

        KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2), KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2),

        KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2), KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2),

        KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2), KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2),

        KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2), KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2),

        KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2), KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2),

        KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2), KernelRidge(kernel='rbf', gamma=2e-4, alpha=1e-2)]

    print('-----------------開始训练...------------------')

    # 超參数

    C_para = np.logspace(-2, 4, 7)      # SVR.C

    G_para = np.logspace(-4, -3, 6)     # kernel = 'rbf'.gamma

    A_para = np.logspace(-3, 1, 5)      # KernelRidge.alpha

    # 训练

    for i in range(n_clf):

        print('Training', i, 'clf...')

        clfs[i].fit(train_X, train_y[:,i])

    # 打印训练误差

    predict = np.zeros([train_y.shape[0], 30]).astype(np.float32)

    for i in range(n_clf):

        predict[:,i] = clfs[i].predict(train_X)

    print(calbais(predict, train_y))

    print()

    print('-----------------開始预測...------------------')

    # 预測

    pred = np.zeros([test_X.shape[0], 30]).astype(np.float32)

    for i in range(n_clf):

        pred[:,i] = clfs[i].predict(test_X)

    predicted = np.zeros(len(FeatureName))

    for i in range(len(FeatureName)):

        if i % 500 == 0:

            print('i =', i)

        else:

            pass

        imageID = ImageId[i]

        clfID = yd[FeatureName[i]]

        predicted[i] = pred[imageID, clfID]

    predicted = predicted*48.+48.

    return predicted

# 单一拟合器，同一时候对 30 个标签做拟合

def modelfitOne(train_X, train_y, test_X, yd, ImageId, FeatureName):

    n_clf = 1

    # 拟合器

    clf = KernelRidge(kernel='rbf', gamma=6e-4, alpha=2e-2)

    # 训练

    print('-----------------開始训练...------------------')

    clf.fit(train_X, train_y)

    # 预測

    print('-----------------開始预測...------------------')

    pred = clf.predict(test_X)

    predicted = np.zeros(len(FeatureName))

    for i in range(len(FeatureName)):

        if i % 500 == 0:

            print('i =', i)

        else:

            pass

        imageID = ImageId[i]

        clfID = yd[FeatureName[i]]

        predicted[i] = pred[imageID, clfID]

    predicted = predicted*48.+48.

    return predicted

# 均方根计算方法

def calbais(pred, y2):

    y_diff = pred - y2

    y_diff = y_diff.reshape(-1)

    sco = np.linalg.norm(y_diff)/(len(y2)**0.5)

    return sco

# 參数选择的调试函数

# 超參数调试 X-y

def testfit(clf, train_X, train_y):

    scores = list()

    for i in range(3):

        X1, X2, y1, y2 = train_test_split(train_X, train_y, test_size=0.3, random_state=42)

        pred = clf.fit(X1, y1).predict(X2)

        sco = calbais(pred, y2)

        scores.append(sco)

    print(scores)

# 測试图

def plotface(x, y):

    img = x.reshape(96, 96)

    plt.imshow(img, cmap='gray')

    y = y * 48 + 48

    plt.scatter(y[0::2], y[1::2], marker='x', s=20)

    plt.show()

# 训练集数据读取

df = csvFileRead(train_file)

train_X, train_y, yd = preTrain()

# 測试集数据读取

RowId, ImageId, FeatureName, test_X = preTest()

# 1) 数据拟合: 30 个拟合器

predicted = modelfit(train_X, train_y, test_X, yd, ImageId, FeatureName)

# 2) 数据拟合: 1 个拟合器

predicted = modelfitOne(train_X, train_y, test_X, yd, ImageId, FeatureName)

# 结果存储

csvSave('KernelRidge.csv', np.linspace(1, len(predicted), len(predicted)).astype(int), predicted)

rmse

Facial keypoints detection Kaggle 竞赛系列

问题表述

求解方案

实验结果

备注

超參数选择 gamma

30 个拟合器超參数调试的方法与结果例如以下：

pandas usage:

值得注意的地方：

Conclusion

源代码