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@peter0749
Last active May 15, 2019 15:46
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3DMM Fitting
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import cv2\n",
"import scipy.optimize as opt\n",
"import h5py\n",
"from tqdm import tqdm_notebook\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"with h5py.File('./shapemodel.h5', 'r') as hp:\n",
" mean_shape = np.array(hp['mean_shape']).astype(np.float32)[...,0] # (159645,)\n",
" pca_basis = np.array(hp['pca_basis']).astype(np.float32) # (159645, 50)\n",
" keypoints = np.array(hp['keypoints']).astype(np.int32)[0] # (68,)\n",
" sigma = np.array(hp['sigma']).astype(np.float32)[...,0] # (50,)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"with open('./testface_landmarks.txt', 'r') as fp:\n",
" target_pts = list(map(lambda x: list(map(float, x.split(' '))), fp.readlines()))\n",
" target_pts = np.asarray(target_pts, dtype=np.float32).T # (2, 68)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2, 68)\n",
"(159645,)\n",
"(159645, 50)\n",
"(68,)\n",
"(50,)\n"
]
}
],
"source": [
"print(target_pts.shape)\n",
"print(mean_shape.shape)\n",
"print(pca_basis.shape)\n",
"print(keypoints.shape)\n",
"print(sigma.shape)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[884336.25 555884.6 436801.44 313216.6 276257.2 209015.34\n",
" 192705.58 181120.11 177736.72 161613.2 152266.27 143272.22\n",
" 129612.55 114983.5 106353.03 101484.695 98814.484 95429.88\n",
" 89034.36 87535.04 82250.93 76056.766 75095.54 73497.375\n",
" 70085.1 69617.87 67851.516 65234.703 60350.746 57751.39\n",
" 56826.504 53567.543 51958. 50399.527 49517.945 47552.816\n",
" 46419.6 45736.055 43672.63 42845.676 40993.934 39643.086\n",
" 37674.47 36911.35 35686.535 35331.3 34741.04 34151.2\n",
" 33042.02 32360.275]\n"
]
}
],
"source": [
"print(sigma)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from torch.autograd import Variable\n",
"def dist(params, target_=None, mean_face_=None, face_comp_=None, keyp_idx_=None, sigma_=None, return_pred=False):\n",
" f = Variable(torch.from_numpy(params[0:1].astype(np.float32)), requires_grad=True)\n",
" R = Variable(torch.from_numpy(params[1:10].astype(np.float32).reshape(3,3)), requires_grad=True)\n",
" t = Variable(torch.from_numpy(params[10:12].astype(np.float32)), requires_grad=True)\n",
" comp_coef = Variable(torch.from_numpy(params[12:].astype(np.float32)), requires_grad=True)\n",
" target = Variable(torch.from_numpy(target_.astype(np.float32)), requires_grad=False)\n",
" mean_face = Variable(torch.from_numpy(mean_face_.astype(np.float32)), requires_grad=False)\n",
" face_comp = Variable(torch.from_numpy(face_comp_.T.astype(np.float32)), requires_grad=False)\n",
" keyp_idx = Variable(torch.from_numpy(keyp_idx_.astype(np.int32)).long(), requires_grad=False)\n",
" sigma = Variable(torch.from_numpy(sigma_.astype(np.float32)), requires_grad=False)\n",
" P = Variable(torch.FloatTensor([[1,0,0],[0,1,0]]), requires_grad=False)\n",
" \n",
" S_keyp_x = comp_coef.unsqueeze(0)@face_comp[:,keyp_idx*3] + mean_face[keyp_idx*3] # (1, 68)\n",
" S_keyp_y = comp_coef.unsqueeze(0)@face_comp[:,keyp_idx*3+1] + mean_face[keyp_idx*3+1] # (1, 68)\n",
" S_keyp_z = comp_coef.unsqueeze(0)@face_comp[:,keyp_idx*3+2] + mean_face[keyp_idx*3+2] # (1, 68)\n",
" S = torch.cat([S_keyp_x, S_keyp_y, S_keyp_z], 0) # (3, 68)\n",
" \n",
" U_proj = f * P@R@S + t.unsqueeze(1)\n",
" loss = ((U_proj-target)**2).sum()**0.5 + ((comp_coef/sigma)**2).sum() \n",
" loss.backward()\n",
" gradients = torch.cat((f.grad, R.grad.view(-1), t.grad, comp_coef.grad), 0).detach().numpy().astype(np.float64)\n",
" \n",
" if return_pred:\n",
" return loss.item(), gradients ,U_proj.detach().numpy()\n",
" return loss.item(), gradients\n",
"def gen_head(coefs, comp, mean):\n",
" xyz = np.squeeze(coefs[np.newaxis]@comp.T + mean)\n",
" xyz = np.vstack([ xyz[0::3], xyz[1::3], xyz[2::3] ])\n",
" print(xyz.shape)\n",
" return xyz\n",
"class M:\n",
" def __init__(self, target=None, mean_face=None, face_comp=None, keyp_idx=None, sigma=None):\n",
" self.loss, self.grads = None, None\n",
" self.target=target\n",
" self.mean_face=mean_face\n",
" self.face_comp=face_comp\n",
" self.keyp_idx=keyp_idx\n",
" self.sigma=sigma\n",
" def get_loss(self,params):\n",
" self.loss, self.grads = dist(params,self.target,self.mean_face,self.face_comp,self.keyp_idx,self.sigma)\n",
" return self.loss\n",
" def get_grads(self,params):\n",
" grads = np.copy(self.grads)\n",
" self.loss, self.grads = None, None\n",
" return grads"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"best score: 439.86\n",
"best score: 439.86\n",
"best score: 431.78\n",
"best score: 258.65\n",
"best score: 258.65\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n",
"best score: 9.91\n"
]
}
],
"source": [
"metrics = M(target_pts, mean_shape, pca_basis, keypoints, sigma)\n",
"bounds = [(0, np.inf)] + [(-np.inf, np.inf)]*61\n",
"best_params = np.random.randn(62)\n",
"best_score = np.inf\n",
"tries = 100\n",
"max_iter = 30\n",
"for _ in range(tries):\n",
" params = np.random.randn(62)\n",
" params[0] = max(params[0],0)\n",
" for i in range(max_iter):\n",
" params, min_val, info = opt.fmin_l_bfgs_b(metrics.get_loss, params.flatten(),\n",
" fprime=metrics.get_grads,\n",
" bounds=bounds,\n",
" approx_grad=False,\n",
" m=1000,\n",
" factr=5,\n",
" maxfun=100,\n",
" maxiter=100,\n",
" maxls=30\n",
" )\n",
" if min_val<best_score:\n",
" best_score=min_val\n",
" best_params = np.copy(params)\n",
" print('best score: %.2f'%best_score)\n",
"params = np.copy(best_params)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"with open('result.txt', 'w') as fp:\n",
" fp.write('%.6f\\n'%params[0]) # 0\n",
" for i in range(9): # 1~9\n",
" fp.write('%.6f'%params[i+1] + (' ' if i<8 else '\\n'))\n",
" fp.write('%.6f %.6f\\n'%tuple(params[10:12])) # 10~11\n",
" for i in range(50): # 12~61\n",
" fp.write('%.6f'%params[i+12] + (' ' if i<49 else '\\n'))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"l, g, p = dist(params, target_pts, mean_shape, pca_basis, keypoints, sigma, return_pred=True)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 82.990524, 85.336945, 88.75433 , 92.03804 , 96.349915,\n",
" 103.06148 , 110.559296, 119.94623 , 133.5299 , 146.69676 ,\n",
" 155.38235 , 162.0281 , 167.18839 , 169.52237 , 170.82043 ,\n",
" 171.91164 , 171.8765 , 91.18661 , 95.79016 , 102.05474 ,\n",
" 108.25635 , 113.972336, 136.83688 , 142.29932 , 148.47462 ,\n",
" 155.17868 , 160.65918 , 126.68986 , 127.388336, 128.12009 ,\n",
" 128.71606 , 121.961685, 125.090996, 129.35777 , 133.44759 ,\n",
" 136.45967 , 100.50909 , 104.121735, 109.62882 , 115.028885,\n",
" 110.51174 , 104.78989 , 138.14378 , 143.04099 , 148.70699 ,\n",
" 152.97867 , 148.87558 , 143.0135 , 115.22777 , 120.31032 ,\n",
" 126.596405, 130.14531 , 133.55318 , 140.32329 , 145.86708 ,\n",
" 140.60522 , 136.07785 , 131.1408 , 126.18757 , 121.41486 ,\n",
" 116.73291 , 125.99402 , 130.41338 , 134.90953 , 144.82568 ,\n",
" 134.93849 , 130.57811 , 126.239624],\n",
" [ 91.74619 , 104.00964 , 115.04391 , 125.01329 , 135.72894 ,\n",
" 144.10666 , 148.92636 , 152.65721 , 153.9064 , 149.81635 ,\n",
" 144.4551 , 138.34586 , 128.89632 , 117.56758 , 107.05383 ,\n",
" 95.49657 , 82.87299 , 78.08236 , 73.76426 , 71.92559 ,\n",
" 71.94454 , 73.013016, 70.57167 , 68.34601 , 67.04851 ,\n",
" 67.603806, 71.017044, 83.76204 , 91.413 , 99.03494 ,\n",
" 105.48949 , 111.50735 , 111.914764, 112.367065, 111.06352 ,\n",
" 110.01369 , 86.29913 , 83.628235, 83.00177 , 85.1336 ,\n",
" 86.94494 , 87.785 , 82.809616, 79.57761 , 79.16373 ,\n",
" 81.11061 , 83.27724 , 83.69253 , 127.33172 , 123.230576,\n",
" 120.22967 , 120.588745, 119.533356, 121.2269 , 124.28018 ,\n",
" 127.55858 , 129.89758 , 130.7235 , 130.86948 , 129.50134 ,\n",
" 126.86642 , 124.449524, 123.91311 , 123.59231 , 124.09633 ,\n",
" 125.07207 , 125.74835 , 125.89669 ]], dtype=float32)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 83.03612 , 85.03618 , 88.40926 , 91.74295 , 95.92448 ,\n",
" 102.45331 , 109.84935 , 119.63641 , 133.63774 , 147.1751 ,\n",
" 156.1813 , 162.64166 , 167.4982 , 169.5993 , 170.96332 ,\n",
" 172.19131 , 172.0603 , 92.0248 , 96.45194 , 102.56723 ,\n",
" 108.58327 , 114.105 , 136.28181 , 141.63455 , 147.71327 ,\n",
" 154.33627 , 160.02249 , 126.81945 , 127.493324, 128.17491 ,\n",
" 128.7587 , 122.20415 , 125.13395 , 129.33698 , 133.36876 ,\n",
" 136.32187 , 101.36092 , 104.66492 , 110.017105, 115.06465 ,\n",
" 110.81028 , 105.49862 , 138.31955 , 142.95775 , 148.41628 ,\n",
" 152.232 , 148.2602 , 142.87096 , 114.20859 , 119.798676,\n",
" 126.42747 , 130.1071 , 133.63448 , 140.94124 , 146.98422 ,\n",
" 140.90707 , 136.13348 , 131.07039 , 125.9701 , 121.18256 ,\n",
" 115.73125 , 125.48503 , 130.33305 , 135.2304 , 145.8141 ,\n",
" 135.06471 , 130.49536 , 126.00246 ],\n",
" [ 92.37785 , 104.43965 , 115.20203 , 124.88259 , 135.37062 ,\n",
" 143.4386 , 148.18266 , 152.06732 , 153.6329 , 149.21423 ,\n",
" 143.62576 , 137.72112 , 128.78337 , 117.80987 , 107.52499 ,\n",
" 96.01673 , 83.600204, 78.084755, 71.73718 , 69.746185,\n",
" 69.80023 , 71.16684 , 68.37154 , 65.81643 , 64.36582 ,\n",
" 65.2733 , 71.12403 , 84.87143 , 93.00882 , 100.69411 ,\n",
" 107.00471 , 112.76296 , 113.40149 , 113.928345, 112.52478 ,\n",
" 111.245895, 87.344986, 84.6894 , 84.347855, 85.881584,\n",
" 87.86682 , 88.72556 , 83.38808 , 80.725266, 80.22859 ,\n",
" 82.225464, 84.22447 , 84.5508 , 127.15328 , 123.54748 ,\n",
" 120.87188 , 121.16333 , 120.22595 , 121.41937 , 123.71601 ,\n",
" 126.75421 , 128.92944 , 129.76042 , 129.91055 , 128.67986 ,\n",
" 126.58298 , 124.32115 , 123.85308 , 123.4052 , 123.53551 ,\n",
" 124.720825, 125.44679 , 125.54184 ]], dtype=float32)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target_pts"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"img = cv2.imread('./testface_256.jpg')[...,::-1]\n",
"plt.imshow(img)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(img)\n",
"plt.scatter(target_pts[0], target_pts[1], marker='o')\n",
"plt.scatter(p[0], p[1], marker='x')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import plotly\n",
"import plotly.graph_objs as go\n",
"import plotly.offline as py\n",
"import plotly.plotly as py\n",
"plotly.tools.set_credentials_file(username='jengku', api_key='不用擔心我換了')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3, 53215)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/peter/anaconda3/envs/dnn/lib/python3.6/site-packages/plotly/plotly/plotly.py:248: UserWarning:\n",
"\n",
"Woah there! Look at all those points! Due to browser limitations, the Plotly SVG drawing functions have a hard time graphing more than 500k data points for line charts, or 40k points for other types of charts. Here are some suggestions:\n",
"(1) Use the `plotly.graph_objs.Scattergl` trace object to generate a WebGl graph.\n",
"(2) Trying using the image API to return an image instead of a graph URL\n",
"(3) Use matplotlib\n",
"(4) See if you can create your visualization with fewer data points\n",
"\n",
"If the visualization you're using aggregates points (e.g., box plot, histogram, etc.) you can disregard this warning.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~jengku/0 or inside your plot.ly account where it is named 'simple-3d-scatter'\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/peter/anaconda3/envs/dnn/lib/python3.6/site-packages/IPython/core/display.py:689: UserWarning:\n",
"\n",
"Consider using IPython.display.IFrame instead\n",
"\n"
]
},
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~jengku/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pts = gen_head(params[12:12+50], pca_basis, mean_shape)\n",
"sc = go.Mesh3d(\n",
" x=pts[0],\n",
" y=pts[1],\n",
" z=pts[2],\n",
" alphahull=-1,\n",
" color='#FFB6C1',\n",
" opacity=0.7\n",
")\n",
"fig = go.Figure(data=[sc])\n",
"py.iplot(fig, filename='simple-3d-scatter')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [3DMM plot](https://plot.ly/~jengku/0)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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