# -*- coding: utf-8 -*-

"""

Created on Tue Aug 23 18:54:47 2016

@author: Boris Knyazev

This code is an attempt to reproduce results similar to

[1] Xie, D., Liang, L., Jin, L., Xu, J., & Li, M. (2015, October).

SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception.

In Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on (pp. 1821-1826). IEEE.

Disclaimer:

Measuring attractiveness or beauty is highly subjective, so this is just an experiment

In this dataset, I actually find many faces with low rating quite cute

"""

from __future__ import print_function

import numpy as np

import matplotlib.pyplot as plt

import os

import theano

from openpyxl import load_workbook

from PIL import Image

from sklearn.decomposition import PCA

from sklearn import svm

from sklearn.metrics import mean_absolute_error

from timeit import default_timer as timer

from theano import tensor as T

from theano.tensor.nnet import conv2d

from theano.tensor.signal import pool

from theano.tensor.nnet import relu

start = timer()

# options

SVR_kernel='linear'

PCA_dim = 0 # the number of PCA components (0 - no PCA)

# load data

# Dataset can be downloaded from http://www.hcii-lab.net/data/SCUT-FBP/EN/download.html

data_dir = '/home/boris/Project/data/images/SCUT-FBP/';

print('loading data')

wb = load_workbook(filename=data_dir+'Rating_Collection/Attractiveness label.xlsx', read_only=True)

ws = wb['Sheet1']

image_files = os.listdir(data_dir+'Data_Collection/')

image_list = []

labels=[]

# we want to resize images, because it is not reasonable to work with very large images

face_sz = (224,294)

for r in range(ws.min_row+1, ws.max_row+1):

labels.append((ws.cell('B%d' % r).value))

image_path = data_dir+'Data_Collection' + '/SCUT-FBP-%d.jpg' % int(str(r-1))

img = Image.open(open(image_path)).resize(face_sz)

img = np.asarray(img, dtype='float64')[35:259,:,:] / 256. # crop [40:296,:,:]

print(image_path + ', size: ' + str(img.shape) + ', attractiveness: %1.2f' % labels[r-2])

image_list.append(img)

labels = np.asarray(labels)

print('%d images and %d labels read' % (len(image_list), len(labels)))

# plot distribution of ratings

hist, bin_edges = np.histogram(labels, density=True, bins=50)

fig = plt.figure()

ax = fig.add_subplot(111)

plt.scatter(bin_edges[0:len(hist)], hist)

ax.set_xlabel('Attractiveness rating')

ax.set_ylabel('Frequency')

ax.set_title('Distribution of ratings')

plt.show()

# show less, average and more beautiful faces (just to check if data makes sense)

#least_beautiful = np.argmin(labels)

#avg_beautiful = np.argmin(abs(labels-np.mean(labels)))

#most_beautiful = np.argmax(labels)

less_beautiful = np.random.permutation(np.nonzero(labels<2)[0])[0]

avg_beautiful = np.random.permutation(np.nonzero((labels>2) & (labels<3))[0])[0]

more_beautiful = np.random.permutation(np.nonzero(labels>4.5)[0])[0]

fig = plt.figure()

ax = fig.add_subplot(111)

ax.set_title('Attractiveness (low): %1.2f' % labels[less_beautiful])

plt.imshow(image_list[less_beautiful])

fig = plt.figure()

ax = fig.add_subplot(111)

ax.set_title('Attractiveness (avg): %1.2f' % labels[avg_beautiful])

plt.imshow(image_list[avg_beautiful])

fig = plt.figure()

ax = fig.add_subplot(111)

ax.set_title('Attractiveness (high): %1.2f' % labels[more_beautiful])

plt.imshow(image_list[more_beautiful])

plt.show()

print('computing features using random filters')

# Visualization of filters

# http://sklearn-theano.github.io/auto_examples/plot_overfeat_layer1_filters.html

def make_visual(layer_weights):

(max_scale - min_scale)).astype('uint8')

def make_mosaic(layer_weights, sz):

return lw

def plot_filters(layer_weights, sz=(8,9), title=None, show=False):

plt.show()

# normalize images

for i, img in enumerate(image_list):

image_list[i] = (img-img.mean())#/img.std()

# instantiate 4D tensor for input

# from http://deeplearning.net/tutorial/lenet.html

input = T.tensor4(name='input')

# initialize shared variable for weights.

# 16 random filters

rng = np.random.RandomState(23455)

w_shp = (24, 3, 9, 9)

w_bound = np.sqrt(3 * 9 * 9)

W_real = np.asarray(rng.uniform(low=-1.0 / w_bound,

high=1.0 / w_bound,

size=w_shp),

dtype=input.dtype)

plot_filters(W_real, sz=(6,4), title='Random filters', show=True)

W = theano.shared(W_real, name ='W')

# convolution, max-pooling and ReLU

f1 = theano.function([input], conv2d(input, W))

f2 = theano.function([input], pool.pool_2d(input, (16, 16), ignore_border=True))

feat_maps = relu(f2(f1(np.asarray(image_list, dtype='float32').transpose(0,3,1,2))))

feat_maps = feat_maps.reshape(feat_maps.shape[0],np.prod(feat_maps.shape[1:]))

if PCA_dim > 0:

pca = PCA(n_components=PCA_dim)

# 10-fold cross-validation

print('performing cross-validation using SVM regression')

n_folds = 10

# shuffle samples

ids = np.random.permutation(feat_maps.shape[0])

feat_maps = feat_maps[ids,:]

labels_ = labels[ids]

n = len(labels_)/n_folds

PC = []

MAE = []

for fold_id in range(n_folds):

test_ids = np.arange(fold_id*n,(fold_id+1)*n)

train_ids = np.concatenate((np.arange(0,fold_id*n),

np.arange((fold_id+1)*n,len(labels_))))

assert(len(train_ids) == n*(n_folds-1))

assert(len(test_ids) == n)

feat_maps_train = feat_maps[train_ids,:]

feat_maps_test = feat_maps[test_ids,:]

if PCA_dim > 0:

feat_maps_train = pca.fit_transform(feat_maps_train)

feat_maps_test = pca.transform(feat_maps_test)

# feature scaling

feat_maps_train = (feat_maps_train - np.mean(feat_maps_train,0))#/np.std(feat_maps_train,0)

feat_maps_test = (feat_maps_test - np.mean(feat_maps_test,0))#/np.std(feat_maps_test,0)

clf = svm.SVR(kernel=SVR_kernel, C=1)

clf.fit(feat_maps_train, labels_[train_ids])

pred = clf.predict(feat_maps_test)

PC.append(np.corrcoef(labels_[test_ids],pred)[0,1])

MAE.append(mean_absolute_error(labels_[test_ids],pred))

fig = plt.figure()

plt.plot(np.arange(n),labels_[test_ids], label='true')

plt.plot(np.arange(n),pred, label='predicted')

plt.legend(loc='upper left')

plt.title('fold %d, PC = %1.2f, MAE = %1.2f' %

(fold_id,np.corrcoef(labels_[test_ids],pred)[0,1],mean_absolute_error(labels_[test_ids],pred)))

plt.show()

print('PC (Pearson correlation) mean = %1.2f (random guess = %1.2f)' % (np.mean(PC),np.corrcoef(labels_[test_ids],2.5*np.ones((len(test_ids),))+0.1*np.random.rand(len(test_ids),)

)[0,1]))

print('MAE (Mean absolute error) mean = %1.2f (random guess = %1.2f)' % (np.mean(MAE),mean_absolute_error(labels_[test_ids],2.5*np.ones((len(test_ids),)))))

end = timer()

print('Test took %1.2f sec' % (end - start))