One shape param experiment
| | |
| | | img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext)) |
| | | img = img.convert(self.image_mode) |
| | | mat_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext) |
| | | shape_path = os.path.join(self.data_dir, self.y_train[index] + '_shape.npy') |
| | | |
| | | # Crop the face |
| | | pt2d = utils.get_pt2d_from_mat(mat_path) |
| | |
| | | roll = pose[2] * 180 / np.pi |
| | | # Bin values |
| | | bins = np.array(range(-99, 102, 3)) |
| | | labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1) |
| | | binned_pose = np.digitize([yaw, pitch, roll], bins) - 1 |
| | | |
| | | # Get shape |
| | | shape = np.load(shape_path) |
| | | |
| | | labels = torch.LongTensor(np.concatenate((binned_pose, shape), axis = 0)) |
| | | |
| | | if self.transform is not None: |
| | | img = self.transform(img) |
| | |
| | | roll = self.fc_roll(x) |
| | | |
| | | return yaw, pitch, roll |
| | | |
| | | class Hopenet_shape(nn.Module): |
| | | # This is just Hopenet with 3 output layers for yaw, pitch and roll. |
| | | def __init__(self, block, layers, num_bins, shape_bins): |
| | | self.inplanes = 64 |
| | | super(Hopenet, self).__init__() |
| | | self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, |
| | | bias=False) |
| | | self.bn1 = nn.BatchNorm2d(64) |
| | | self.relu = nn.ReLU(inplace=True) |
| | | self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
| | | self.layer1 = self._make_layer(block, 64, layers[0]) |
| | | self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
| | | self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
| | | self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
| | | self.avgpool = nn.AvgPool2d(7) |
| | | self.fc_yaw = nn.Linear(512 * block.expansion, num_bins) |
| | | self.fc_pitch = nn.Linear(512 * block.expansion, num_bins) |
| | | self.fc_roll = nn.Linear(512 * block.expansion, num_bins) |
| | | self.fc_shape_1 = nn.Linear(512 * block.expansion, shape_bins) |
| | | |
| | | for m in self.modules(): |
| | | if isinstance(m, nn.Conv2d): |
| | | n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
| | | m.weight.data.normal_(0, math.sqrt(2. / n)) |
| | | elif isinstance(m, nn.BatchNorm2d): |
| | | m.weight.data.fill_(1) |
| | | m.bias.data.zero_() |
| | | |
| | | def _make_layer(self, block, planes, blocks, stride=1): |
| | | downsample = None |
| | | if stride != 1 or self.inplanes != planes * block.expansion: |
| | | downsample = nn.Sequential( |
| | | nn.Conv2d(self.inplanes, planes * block.expansion, |
| | | kernel_size=1, stride=stride, bias=False), |
| | | nn.BatchNorm2d(planes * block.expansion), |
| | | ) |
| | | |
| | | layers = [] |
| | | layers.append(block(self.inplanes, planes, stride, downsample)) |
| | | self.inplanes = planes * block.expansion |
| | | for i in range(1, blocks): |
| | | layers.append(block(self.inplanes, planes)) |
| | | |
| | | return nn.Sequential(*layers) |
| | | |
| | | def forward(self, x): |
| | | x = self.conv1(x) |
| | | x = self.bn1(x) |
| | | x = self.relu(x) |
| | | x = self.maxpool(x) |
| | | |
| | | x = self.layer1(x) |
| | | x = self.layer2(x) |
| | | x = self.layer3(x) |
| | | x = self.layer4(x) |
| | | |
| | | x = self.avgpool(x) |
| | | x = x.view(x.size(0), -1) |
| | | yaw = self.fc_yaw(x) |
| | | pitch = self.fc_pitch(x) |
| | | roll = self.fc_roll(x) |
| | | shape_1 = self.fc_shape_1(x) |
| | | |
| | | return yaw, pitch, roll, shape_1 |
New file |
| | |
| | | import numpy as np |
| | | import torch |
| | | import torch.nn as nn |
| | | from torch.autograd import Variable |
| | | from torch.utils.data import DataLoader |
| | | from torchvision import transforms |
| | | import torchvision |
| | | import torch.backends.cudnn as cudnn |
| | | import torch.nn.functional as F |
| | | |
| | | import cv2 |
| | | import matplotlib.pyplot as plt |
| | | import sys |
| | | import os |
| | | import argparse |
| | | |
| | | import datasets |
| | | import hopenet |
| | | import torch.utils.model_zoo as model_zoo |
| | | |
| | | model_urls = { |
| | | 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', |
| | | 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', |
| | | 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', |
| | | 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', |
| | | 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', |
| | | } |
| | | |
| | | def parse_args(): |
| | | """Parse input arguments.""" |
| | | parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.') |
| | | parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]', |
| | | default=0, type=int) |
| | | parser.add_argument('--num_epochs', dest='num_epochs', help='Maximum number of training epochs.', |
| | | default=5, type=int) |
| | | parser.add_argument('--batch_size', dest='batch_size', help='Batch size.', |
| | | default=16, type=int) |
| | | parser.add_argument('--lr', dest='lr', help='Base learning rate.', |
| | | default=1e-5, type=float) |
| | | parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.', |
| | | default='', type=str) |
| | | parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.', |
| | | default='', type=str) |
| | | |
| | | args = parser.parse_args() |
| | | |
| | | return args |
| | | |
| | | def get_ignored_params(model): |
| | | # Generator function that yields ignored params. |
| | | b = [] |
| | | b.append(model.conv1) |
| | | b.append(model.bn1) |
| | | b.append(model.layer1) |
| | | b.append(model.layer2) |
| | | b.append(model.layer3) |
| | | b.append(model.layer4) |
| | | for i in range(len(b)): |
| | | for j in b[i].modules(): |
| | | for k in j.parameters(): |
| | | yield k |
| | | |
| | | def get_non_ignored_params(model): |
| | | # Generator function that yields params that will be optimized. |
| | | b = [] |
| | | b.append(model.fc_yaw) |
| | | b.append(model.fc_pitch) |
| | | b.append(model.fc_roll) |
| | | b.append(model.fc_shape_1) |
| | | for i in range(len(b)): |
| | | for j in b[i].modules(): |
| | | for k in j.parameters(): |
| | | yield k |
| | | |
| | | def load_filtered_state_dict(model, snapshot): |
| | | # By user apaszke from discuss.pytorch.org |
| | | model_dict = model.state_dict() |
| | | # 1. filter out unnecessary keys |
| | | snapshot = {k: v for k, v in snapshot.items() if k in model_dict} |
| | | # 2. overwrite entries in the existing state dict |
| | | model_dict.update(snapshot) |
| | | # 3. load the new state dict |
| | | model.load_state_dict(model_dict) |
| | | |
| | | if __name__ == '__main__': |
| | | args = parse_args() |
| | | |
| | | cudnn.enabled = True |
| | | num_epochs = args.num_epochs |
| | | batch_size = args.batch_size |
| | | gpu = args.gpu_id |
| | | |
| | | if not os.path.exists('output/snapshots'): |
| | | os.makedirs('output/snapshots') |
| | | |
| | | # ResNet101 with 3 outputs |
| | | # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], 66) |
| | | # ResNet50 |
| | | model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66) |
| | | # ResNet18 |
| | | # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66) |
| | | load_filtered_state_dict(model, model_zoo.load_url(model_urls['resnet50'])) |
| | | |
| | | print 'Loading data.' |
| | | |
| | | transformations = transforms.Compose([transforms.Scale(224),transforms.RandomCrop(224), |
| | | transforms.ToTensor()]) |
| | | |
| | | pose_dataset = datasets.Pose_300W_LP_binned(args.data_dir, args.filename_list, |
| | | transformations) |
| | | train_loader = torch.utils.data.DataLoader(dataset=pose_dataset, |
| | | batch_size=batch_size, |
| | | shuffle=True, |
| | | num_workers=2) |
| | | |
| | | model.cuda(gpu) |
| | | criterion = nn.CrossEntropyLoss() |
| | | reg_criterion = nn.MSELoss() |
| | | # Regression loss coefficient |
| | | alpha = 0.1 |
| | | lsm = nn.Softmax() |
| | | |
| | | idx_tensor = [idx for idx in xrange(66)] |
| | | idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu) |
| | | |
| | | optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': args.lr}, |
| | | {'params': get_non_ignored_params(model), 'lr': args.lr * 10}], |
| | | lr = args.lr) |
| | | |
| | | print 'Ready to train network.' |
| | | |
| | | for epoch in range(num_epochs): |
| | | for i, (images, labels, name) in enumerate(train_loader): |
| | | images = Variable(images).cuda(gpu) |
| | | label_yaw = Variable(labels[:,0]).cuda(gpu) |
| | | label_pitch = Variable(labels[:,1]).cuda(gpu) |
| | | label_roll = Variable(labels[:,2]).cuda(gpu) |
| | | label_shape_1 = Variable(labels[:,3]).cuda(gpu) |
| | | |
| | | optimizer.zero_grad() |
| | | yaw, pitch, roll, shape_1 = model(images) |
| | | |
| | | # Cross entropy loss |
| | | loss_yaw = criterion(yaw, label_yaw) |
| | | loss_pitch = criterion(pitch, label_pitch) |
| | | loss_roll = criterion(roll, label_roll) |
| | | |
| | | # MSE loss |
| | | yaw_predicted = F.softmax(yaw) |
| | | pitch_predicted = F.softmax(pitch) |
| | | roll_predicted = F.softmax(roll) |
| | | |
| | | yaw_predicted = torch.sum(yaw_predicted.data * idx_tensor, 1) |
| | | pitch_predicted = torch.sum(pitch_predicted.data * idx_tensor, 1) |
| | | roll_predicted = torch.sum(roll_predicted.data * idx_tensor, 1) |
| | | |
| | | loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw.float()) |
| | | loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float()) |
| | | loss_reg_roll = reg_criterion(roll_predicted, label_roll.float()) |
| | | |
| | | # Shape space loss |
| | | loss_shape_1 = criterion(shape_1, label_shape_1) |
| | | |
| | | # Total loss |
| | | loss_yaw += alpha * loss_reg_yaw |
| | | loss_pitch += alpha * loss_reg_pitch |
| | | loss_roll += alpha * loss_reg_roll |
| | | |
| | | loss_seq = [loss_yaw, loss_pitch, loss_roll, loss_shape_1] |
| | | grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))] |
| | | model.zero_grad() |
| | | torch.autograd.backward(loss_seq, grad_seq) |
| | | optimizer.step() |
| | | |
| | | # print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f' |
| | | # %(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0])) |
| | | |
| | | if (i+1) % 100 == 0: |
| | | print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f' |
| | | %(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0])) |
| | | if epoch == 0: |
| | | torch.save(model.state_dict(), |
| | | 'output/snapshots/resnet50_iter_'+ str(i+1) + '.pkl') |
| | | |
| | | # Save models at numbered epochs. |
| | | if epoch % 1 == 0 and epoch < num_epochs - 1: |
| | | print 'Taking snapshot...' |
| | | torch.save(model.state_dict(), |
| | | 'output/snapshots/resnet50_epoch_'+ str(epoch+1) + '.pkl') |
| | | |
| | | # Save the final Trained Model |
| | | torch.save(model.state_dict(), 'output/snapshots/resnet50_epoch_' + str(epoch+1) + '.pkl') |
New file |
| | |
| | | { |
| | | "cells": [ |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 77, |
| | | "metadata": { |
| | | "collapsed": true |
| | | }, |
| | | "outputs": [], |
| | | "source": [ |
| | | "%matplotlib inline\n", |
| | | "import numpy as np\n", |
| | | "import torch\n", |
| | | "from torch.utils.serialization import load_lua\n", |
| | | "import os\n", |
| | | "import scipy.io as sio\n", |
| | | "import cv2\n", |
| | | "import math\n", |
| | | "from matplotlib import pyplot as plt\n", |
| | | "from torch.utils.data.dataset import Dataset\n", |
| | | "\n", |
| | | "from sklearn.decomposition import PCA" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 78, |
| | | "metadata": { |
| | | "collapsed": true |
| | | }, |
| | | "outputs": [], |
| | | "source": [ |
| | | "TRAIN_DATA_DIR = '/Data/nruiz9/data/facial_landmarks/300W_LP/'" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 79, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [], |
| | | "source": [ |
| | | "shape_params_dict = dict()\n", |
| | | "\n", |
| | | "with open(os.path.join(TRAIN_DATA_DIR, 'filename_list_filtered.txt')) as f:\n", |
| | | " for idx, line in enumerate(f):\n", |
| | | " original_line = line\n", |
| | | " line = line.strip('\\n')\n", |
| | | " mat_path = os.path.join(TRAIN_DATA_DIR, line + '.mat')\n", |
| | | " mat = sio.loadmat(mat_path)\n", |
| | | "\n", |
| | | " shape_params_dict[line] = np.array(mat['Shape_Para'][:,0])" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 80, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [ |
| | | { |
| | | "name": "stdout", |
| | | "output_type": "stream", |
| | | "text": [ |
| | | "122415\n" |
| | | ] |
| | | } |
| | | ], |
| | | "source": [ |
| | | "X = [shape_params_dict[datum] for datum in shape_params_dict]\n", |
| | | "print len(X)" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 81, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [ |
| | | { |
| | | "name": "stdout", |
| | | "output_type": "stream", |
| | | "text": [ |
| | | "[ 0.4954199 0.13912562 0.11082269 0.07658631 0.04858431 0.02813001\n", |
| | | " 0.01758898 0.01631346 0.01002331 0.00814171]\n", |
| | | "0.950736293253\n" |
| | | ] |
| | | } |
| | | ], |
| | | "source": [ |
| | | "pca = PCA(n_components=10)\n", |
| | | "pca.fit(X)\n", |
| | | "print(pca.explained_variance_ratio_)\n", |
| | | "print sum(pca.explained_variance_ratio_)" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 82, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [ |
| | | { |
| | | "name": "stdout", |
| | | "output_type": "stream", |
| | | "text": [ |
| | | "122415\n" |
| | | ] |
| | | } |
| | | ], |
| | | "source": [ |
| | | "new_X = pca.transform(X)\n", |
| | | "print len(new_X)" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 83, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [ |
| | | { |
| | | "name": "stdout", |
| | | "output_type": "stream", |
| | | "text": [ |
| | | "[-2556323.53165033 -1140679.77655202 -1371614.65446089 -1119583.33472875\n", |
| | | " -754535.15912458 -821857.44375049 -534676.82841282 -499987.22775606\n", |
| | | " -426309.70779568 -446477.723748 ]\n", |
| | | "[ 5002830.39467843 1820495.74291969 1441834.85901925 1429397.04589405\n", |
| | | " 1223356.93869825 924078.41304442 760271.63359562 805551.96296291\n", |
| | | " 466004.54589496 545186.01453756]\n", |
| | | "[-176340.23999371 440.18163592 -2284.73154146 -6407.94592961\n", |
| | | " -11806.29047245 -2078.74081487 -3059.95282595 -5356.39323994\n", |
| | | " -3081.66667384 2027.97251627]\n", |
| | | "[ 7559153.92632876 2961175.51947171 2813449.51348013 2548980.3806228\n", |
| | | " 1977892.09782283 1745935.8567949 1294948.46200844 1305539.19071897\n", |
| | | " 892314.25369063 991663.73828556]\n", |
| | | "[ 7559153.92632876 2961175.51947171 2813449.51348013 2548980.3806228\n", |
| | | " 1977892.09782283 1745935.8567949 1294948.46200844 1305539.19071897\n", |
| | | " 892314.25369063 991663.73828556]\n", |
| | | "(122415, 10)\n" |
| | | ] |
| | | } |
| | | ], |
| | | "source": [ |
| | | "print np.amin(new_X, 0)\n", |
| | | "print np.amax(new_X, 0)\n", |
| | | "print np.median(new_X, 0)\n", |
| | | "\n", |
| | | "print np.abs(np.amax(new_X, 0) - np.amin(new_X, 0))\n", |
| | | "print np.ptp(new_X, 0)\n", |
| | | "print new_X.shape" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 84, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [ |
| | | { |
| | | "name": "stdout", |
| | | "output_type": "stream", |
| | | "text": [ |
| | | "(10, 122415)\n", |
| | | "[0 0 0 0 0 0 0 0 0 0]\n", |
| | | "[59 59 59 59 59 59 59 59 59 59]\n" |
| | | ] |
| | | } |
| | | ], |
| | | "source": [ |
| | | "maxs = np.amax(new_X, 0)\n", |
| | | "mins = np.amin(new_X, 0)\n", |
| | | "dividers = 60\n", |
| | | "step_sizes = np.ptp(new_X, 0) / (dividers - 2)\n", |
| | | "\n", |
| | | "bins = []\n", |
| | | "for idx in xrange(new_X.shape[1]):\n", |
| | | " rng = range(int(mins[idx]), int(maxs[idx]) + 1, int(step_sizes[idx]))\n", |
| | | " bins.append(np.digitize(new_X[:,idx], rng))\n", |
| | | " \n", |
| | | "bins = np.array(bins)\n", |
| | | "print bins.shape\n", |
| | | "print np.amin(bins, 1)\n", |
| | | "print np.amax(bins, 1)" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": null, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [], |
| | | "source": [] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 88, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [], |
| | | "source": [ |
| | | "# Save the new PCA binned representation\n", |
| | | "idx = 0\n", |
| | | "for name in shape_params_dict:\n", |
| | | " pose_path = os.path.join(TRAIN_DATA_DIR, line + '_pose.npy')\n", |
| | | " np.save(pose_path, bins[:,idx])\n", |
| | | " idx += 1" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": null, |
| | | "metadata": { |
| | | "collapsed": true |
| | | }, |
| | | "outputs": [], |
| | | "source": [] |
| | | } |
| | | ], |
| | | "metadata": { |
| | | "anaconda-cloud": {}, |
| | | "kernelspec": { |
| | | "display_name": "Python [conda root]", |
| | | "language": "python", |
| | | "name": "conda-root-py" |
| | | }, |
| | | "language_info": { |
| | | "codemirror_mode": { |
| | | "name": "ipython", |
| | | "version": 2 |
| | | }, |
| | | "file_extension": ".py", |
| | | "mimetype": "text/x-python", |
| | | "name": "python", |
| | | "nbconvert_exporter": "python", |
| | | "pygments_lexer": "ipython2", |
| | | "version": "2.7.12" |
| | | } |
| | | }, |
| | | "nbformat": 4, |
| | | "nbformat_minor": 1 |
| | | } |
| | |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 128, |
| | | "execution_count": 23, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [ |
| | | { |
| | | "name": "stdout", |
| | | "output_type": "stream", |
| | | "text": [ |
| | | "[ 1.69706825e+06 -6.53330156e+04 -4.11682719e+05 4.34919781e+05\n", |
| | | " 2.50819281e+05 3.26359844e+04 1.24291895e+04 -2.52986391e+05\n", |
| | | " -6.15043477e+04 -1.59830828e+05 2.63572094e+05 1.71440203e+05\n", |
| | | " -4.88330117e+04 -1.97090562e+05 2.38590391e+04 4.10180391e+04\n", |
| | | " 1.01344531e+05 2.08940688e+05 1.22712875e+05 -2.61847754e+04\n", |
| | | " 5.90344609e+04 -1.15512295e+04 -4.96931484e+04 5.07356172e+04\n", |
| | | " 3.72444492e+04 -5.69681289e+04 6.74712188e+04 -4.20915156e+04\n", |
| | | " -3.63388320e+04 -2.46653613e+04 2.91140586e+04 -3.11200098e+04\n", |
| | | " 1.61020850e+04 -2.50033203e+02 -4.18961016e+04 -4.14161211e+04\n", |
| | | " 6.62841797e+01 1.20681201e+04 -3.56137744e+03 -2.65995938e+04\n", |
| | | " 2.30893848e+03 -1.03691650e+03 -1.16494004e+04 2.12929688e+04\n", |
| | | " 1.52232812e+04 3.64335430e+04 1.74000312e+04 1.89377402e+04\n", |
| | | " -1.34393281e+04 1.66427832e+04 -8.03712305e+03 -5.96380493e+02\n", |
| | | " 4.30957910e+03 2.24528638e+03 1.95483027e+04 -1.38550059e+04\n", |
| | | " -1.93898594e+04 -3.69277188e+04 7.44611182e+03 1.19638623e+04\n", |
| | | " 2.36314219e+04 -3.99684766e+03 3.02203174e+03 1.81975000e+04\n", |
| | | " 3.92823359e+04 1.03367803e+04 -7.48786230e+03 -7.82242627e+03\n", |
| | | " -2.20080430e+04 -1.80566052e+03 -4.29961572e+03 6.22520508e+03\n", |
| | | " 1.32062817e+03 -1.19227490e+04 -1.03479248e+04 2.38821729e+03\n", |
| | | " 1.38015742e+04 -3.97805591e+03 -1.45742676e+03 2.98669434e+03\n", |
| | | " -1.31951680e+04 7.29575732e+03 1.37428066e+04 -1.81346204e+03\n", |
| | | " -5.99187988e+03 -6.37990234e+03 1.08937734e+04 3.90912183e+03\n", |
| | | " -1.82016113e+03 -6.95491650e+03 1.01266547e+03 5.55246487e+03\n", |
| | | " -4.09280261e+03 -7.49072026e+03 -3.33481183e+03 3.81758888e+03\n", |
| | | " -1.42652571e+04 3.25190696e+03 -8.06668851e+03 -8.09302285e+01\n", |
| | | " 6.90776884e+02 6.18540532e+03 -4.45203709e+02 -1.19316644e+02\n", |
| | | " 3.22066688e+03 -3.87281505e+03 2.97684886e+03 3.94476455e+02\n", |
| | | " -1.11272038e+03 -1.27330615e+01 4.55350247e+03 -2.89555926e+03\n", |
| | | " 6.14798443e+03 5.82692202e+03 -1.44661964e+03 3.56454615e+02\n", |
| | | " 6.34426954e+03 2.79352582e+03 4.14583497e+03 1.43854894e+03\n", |
| | | " -5.16225113e+03 -4.57522410e+03 3.23370728e+03 -9.63488065e+02\n", |
| | | " -2.74134713e+03 3.08936095e+03 3.61066414e+03 -4.00078795e+03\n", |
| | | " -9.83102206e+02 -2.74329951e+03 -1.55174369e+03 -6.84434853e+02\n", |
| | | " -2.33597127e+03 3.73775421e+03 1.02427317e+02 2.92204155e+03\n", |
| | | " 1.44912830e+03 -1.83596196e+03 -1.16512661e+03 -3.51094402e+03\n", |
| | | " 9.48400384e+02 -1.08330687e+03 2.43350889e+02 2.19268855e+03\n", |
| | | " -9.90789830e+02 1.16296530e+03 1.56232074e+03 -2.00964088e+03\n", |
| | | " 6.12623762e+02 1.47080665e+03 -2.00006729e+03 1.00312895e+02\n", |
| | | " -1.91954703e+03 -1.46687661e+03 4.03627700e+02 1.64068147e+02\n", |
| | | " -1.99043744e+02 -6.39154809e+02 6.22728972e+02 5.76263786e+02\n", |
| | | " 1.16506023e+03 -7.05723234e+02 -1.77256142e+03 -3.22557899e+03\n", |
| | | " 1.35091605e+03 -1.53005243e+03 9.81808337e+02 -1.36305791e+03\n", |
| | | " 3.81452081e+00 1.61325594e+02 -4.98502076e+02 -2.12832902e+03\n", |
| | | " 7.85335724e+01 8.88980592e+02 2.47593342e+02 1.35603187e+02\n", |
| | | " -3.51698007e+02 8.47661598e+02 -3.56308426e+01 8.76648817e+02\n", |
| | | " 1.07030326e+03 3.70701145e+02 -1.79440077e+01 -2.66410893e+01\n", |
| | | " 1.14185129e+03 -1.03180266e+03 1.73052681e+02 1.09740767e+02\n", |
| | | " 5.53453894e+02 -9.60119278e+02 5.19062592e+01 -9.51568374e+02\n", |
| | | " 4.98827716e+02 -5.43007005e+01 -5.09670817e+02 4.49387407e+02\n", |
| | | " -6.79402151e+02 -2.53117505e+02 4.95405405e+02]\n" |
| | | ] |
| | | }, |
| | | { |
| | | "data": { |
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FkGXZztSTOLkzMMs4tQQuutjE5+8CGsbSOUhjSW9utr5BSJ73TNs0PrjYcGls44Vx3eeO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\nl85zaZf2Ke3/BzEvuGvrMr5NAAAAAElFTkSuQmCC\n", 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GJMgYGESoCofWDldUlGV9TJsGuq6jDwmjHfViyelqSRLwYaSwYFygKApSGBiCZ24LjHEY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\nYfv/AaCvv6eVms+OAAAAAElFTkSuQmCC\n", |
| | | "text/plain": [ |
| | | "<matplotlib.figure.Figure at 0x7fac60267550>" |
| | | "<matplotlib.figure.Figure at 0x7fb3cd210e50>" |
| | | ] |
| | | }, |
| | | "metadata": {}, |
| | |
| | | "name": "stdout", |
| | | "output_type": "stream", |
| | | "text": [ |
| | | "[ 56 -85 619 478]\n", |
| | | "[270 316 613 659]\n", |
| | | "['roi', 'Shape_Para', 'Pose_Para', 'Exp_Para', '__header__', '__globals__', 'Color_Para', 'Illum_Para', 'pt2d', '__version__', 'Tex_Para']\n", |
| | | "pitch, yaw, roll: -0.978765 18.7533 9.07337\n" |
| | | "pitch, yaw, roll: 3.42978 -26.1518 11.9776\n" |
| | | ] |
| | | } |
| | | ], |
| | | "source": [ |
| | | "img_name = 'AFW_5144909700_3_0'\n", |
| | | "img_name = 'AFW_2417212918_1_2'\n", |
| | | "img_path = os.path.join(AFW, img_name + '.jpg')\n", |
| | | "mat_path = os.path.join(AFW, img_name + '.mat')\n", |
| | | "\n", |
| | |
| | | "\n", |
| | | "cropped_img = img[int(y_min):int(y_max), int(x_min):int(x_max), :]\n", |
| | | "\n", |
| | | "print mat['Shape_Para'][:,0]\n", |
| | | "\n", |
| | | "plt.imshow(img)\n", |
| | | "plt.imshow(cropped_img)\n", |
| | | "plt.show()\n", |
New file |
| | |
| | | { |
| | | "cells": [ |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 1, |
| | | "metadata": { |
| | | "collapsed": true |
| | | }, |
| | | "outputs": [], |
| | | "source": [ |
| | | "%matplotlib inline\n", |
| | | "import numpy as np\n", |
| | | "import torch\n", |
| | | "from torch.utils.serialization import load_lua\n", |
| | | "import os\n", |
| | | "import scipy.io as sio\n", |
| | | "import cv2\n", |
| | | "import math\n", |
| | | "from matplotlib import pyplot as plt\n", |
| | | "from torch.utils.data.dataset import Dataset\n", |
| | | "\n", |
| | | "from sklearn.decomposition import PCA" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 2, |
| | | "metadata": { |
| | | "collapsed": true |
| | | }, |
| | | "outputs": [], |
| | | "source": [ |
| | | "TRAIN_DATA_DIR = '/Data/nruiz9/data/facial_landmarks/300W_LP/'" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 4, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [], |
| | | "source": [ |
| | | "shape_params_list = []\n", |
| | | "names = []\n", |
| | | "\n", |
| | | "with open(os.path.join(TRAIN_DATA_DIR, 'filename_list_filtered.txt')) as f:\n", |
| | | " for idx, line in enumerate(f):\n", |
| | | " original_line = line\n", |
| | | " line = line.strip('\\n')\n", |
| | | " mat_path = os.path.join(TRAIN_DATA_DIR, line + '.mat')\n", |
| | | " mat = sio.loadmat(mat_path)\n", |
| | | "\n", |
| | | " shape_params_list.append(np.array(mat['Shape_Para'][:,0]))\n", |
| | | " names.append(line)" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 5, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [ |
| | | { |
| | | "name": "stdout", |
| | | "output_type": "stream", |
| | | "text": [ |
| | | "122415\n" |
| | | ] |
| | | } |
| | | ], |
| | | "source": [ |
| | | "X = [sp for sp in shape_params_list]\n", |
| | | "print len(X)" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 6, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [ |
| | | { |
| | | "name": "stdout", |
| | | "output_type": "stream", |
| | | "text": [ |
| | | "[ 0.4954199 0.13912562 0.11082269 0.07658631 0.04858431 0.02813001\n", |
| | | " 0.01758898 0.01631346 0.01002331 0.00814171]\n", |
| | | "0.950736293253\n" |
| | | ] |
| | | } |
| | | ], |
| | | "source": [ |
| | | "pca = PCA(n_components=10)\n", |
| | | "pca.fit(X)\n", |
| | | "print(pca.explained_variance_ratio_)\n", |
| | | "print sum(pca.explained_variance_ratio_)" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 7, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [ |
| | | { |
| | | "name": "stdout", |
| | | "output_type": "stream", |
| | | "text": [ |
| | | "122415\n" |
| | | ] |
| | | } |
| | | ], |
| | | "source": [ |
| | | "new_X = pca.transform(X)\n", |
| | | "print len(new_X)" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 8, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [ |
| | | { |
| | | "name": "stdout", |
| | | "output_type": "stream", |
| | | "text": [ |
| | | "[-2556323.53165033 -1140679.77655202 -1371614.65446089 -1119583.33472875\n", |
| | | " -754535.15912456 -821857.44375021 -534676.82835068 -499987.22801558\n", |
| | | " -426309.71017172 -446477.70288007]\n", |
| | | "[ 5002830.39467843 1820495.74291976 1441834.85901925 1429397.04589404\n", |
| | | " 1223356.93869817 924078.41303862 760271.63357968 805551.96259901\n", |
| | | " 466004.54029864 545186.01838144]\n", |
| | | "[-176340.23999369 440.18163591 -2284.73154146 -6407.94592961\n", |
| | | " -11806.29047248 -2078.74081526 -3059.95275478 -5356.3932487\n", |
| | | " -3081.66281823 2027.99147229]\n", |
| | | "[ 7559153.92632876 2961175.51947178 2813449.51348013 2548980.38062279\n", |
| | | " 1977892.09782273 1745935.85678882 1294948.46193037 1305539.19061459\n", |
| | | " 892314.25047037 991663.72126151]\n", |
| | | "[ 7559153.92632876 2961175.51947178 2813449.51348013 2548980.38062279\n", |
| | | " 1977892.09782273 1745935.85678882 1294948.46193037 1305539.19061459\n", |
| | | " 892314.25047037 991663.72126151]\n", |
| | | "(122415, 10)\n" |
| | | ] |
| | | } |
| | | ], |
| | | "source": [ |
| | | "print np.amin(new_X, 0)\n", |
| | | "print np.amax(new_X, 0)\n", |
| | | "print np.median(new_X, 0)\n", |
| | | "\n", |
| | | "print np.abs(np.amax(new_X, 0) - np.amin(new_X, 0))\n", |
| | | "print np.ptp(new_X, 0)\n", |
| | | "print new_X.shape" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 9, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [ |
| | | { |
| | | "name": "stdout", |
| | | "output_type": "stream", |
| | | "text": [ |
| | | "(10, 122415)\n", |
| | | "[0 0 0 0 0 0 0 0 0 0]\n", |
| | | "[59 59 59 59 59 59 59 59 59 59]\n" |
| | | ] |
| | | } |
| | | ], |
| | | "source": [ |
| | | "maxs = np.amax(new_X, 0)\n", |
| | | "mins = np.amin(new_X, 0)\n", |
| | | "dividers = 60\n", |
| | | "step_sizes = np.ptp(new_X, 0) / (dividers - 2)\n", |
| | | "\n", |
| | | "bins = []\n", |
| | | "for idx in xrange(new_X.shape[1]):\n", |
| | | " rng = range(int(mins[idx]), int(maxs[idx]) + 1, int(step_sizes[idx]))\n", |
| | | " bins.append(np.digitize(new_X[:,idx], rng))\n", |
| | | " \n", |
| | | "bins = np.array(bins)\n", |
| | | "print bins.shape\n", |
| | | "print np.amin(bins, 1)\n", |
| | | "print np.amax(bins, 1)" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 10, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [ |
| | | { |
| | | "name": "stdout", |
| | | "output_type": "stream", |
| | | "text": [ |
| | | "AFW_Flip/AFW_5083671561_5_5 AFW_Flip/AFW_1648807314_2_0\n" |
| | | ] |
| | | } |
| | | ], |
| | | "source": [ |
| | | "print names[0], names[1]" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": 12, |
| | | "metadata": { |
| | | "collapsed": false |
| | | }, |
| | | "outputs": [], |
| | | "source": [ |
| | | "# Save the new PCA binned representation\n", |
| | | "idx = 0\n", |
| | | "for name in names:\n", |
| | | " pose_path = os.path.join(TRAIN_DATA_DIR, name + '_shape.npy')\n", |
| | | " np.save(pose_path, bins[:,idx])\n", |
| | | " idx += 1" |
| | | ] |
| | | }, |
| | | { |
| | | "cell_type": "code", |
| | | "execution_count": null, |
| | | "metadata": { |
| | | "collapsed": true |
| | | }, |
| | | "outputs": [], |
| | | "source": [] |
| | | } |
| | | ], |
| | | "metadata": { |
| | | "anaconda-cloud": {}, |
| | | "kernelspec": { |
| | | "display_name": "Python [conda root]", |
| | | "language": "python", |
| | | "name": "conda-root-py" |
| | | }, |
| | | "language_info": { |
| | | "codemirror_mode": { |
| | | "name": "ipython", |
| | | "version": 2 |
| | | }, |
| | | "file_extension": ".py", |
| | | "mimetype": "text/x-python", |
| | | "name": "python", |
| | | "nbconvert_exporter": "python", |
| | | "pygments_lexer": "ipython2", |
| | | "version": "2.7.12" |
| | | } |
| | | }, |
| | | "nbformat": 4, |
| | | "nbformat_minor": 1 |
| | | } |