From 2efde746810a0264ad2cf09dc9b003bfcd17a4d5 Mon Sep 17 00:00:00 2001 From: adamhrv Date: Sun, 6 Jan 2019 14:29:35 +0100 Subject: clean up 3d face plot --- .../notebooks/face_analysis/3d_face_plot.ipynb | 460 ++--- .../face_analysis/3d_face_plot_batch.ipynb | 291 --- .../face_analysis/3d_face_plot_batch_frames.ipynb | 2055 -------------------- 3 files changed, 232 insertions(+), 2574 deletions(-) mode change 100755 => 100644 megapixels/notebooks/face_analysis/3d_face_plot.ipynb delete mode 100644 megapixels/notebooks/face_analysis/3d_face_plot_batch.ipynb delete mode 100644 megapixels/notebooks/face_analysis/3d_face_plot_batch_frames.ipynb diff --git a/megapixels/notebooks/face_analysis/3d_face_plot.ipynb b/megapixels/notebooks/face_analysis/3d_face_plot.ipynb old mode 100755 new mode 100644 index 537e471b..f136015f --- a/megapixels/notebooks/face_analysis/3d_face_plot.ipynb +++ b/megapixels/notebooks/face_analysis/3d_face_plot.ipynb @@ -11,18 +11,15 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 88, "metadata": {}, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'face_alignment'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m-------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mrandom\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/work/megapixels_dev/3rdparty/face-alignment'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mface_alignment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmpl_toolkits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmplot3d\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAxes3D\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'face_alignment'" + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" ] } ], @@ -33,6 +30,9 @@ "from os.path import join\n", "import sys\n", "import time\n", + "from random import randint\n", + "import random\n", + "\n", "import cv2 as cv\n", "import numpy as np\n", "import imutils\n", @@ -41,22 +41,24 @@ "from glob import glob\n", "from matplotlib import cbook\n", "from matplotlib import cm\n", - "from matplotlib.colors import LightSource\n", - "from random import randint\n", - "sys.path.append('/work/megapixels_dev/3rdparty/face-alignment')\n", + "#from matplotlib.colors import LightSource\n", "import face_alignment\n", "import numpy as np\n", + "\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import matplotlib.pyplot as plt\n", "import mpl_toolkits.mplot3d.axes3d as p3\n", "from matplotlib import animation\n", - "import random\n", - "from skimage import io" + "\n", + "from skimage import io\n", + "from tqdm import tqdm_notebook as tqdm\n", + "from IPython.display import clear_output\n", + "from pathlib import Path" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -68,151 +70,102 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ "# init 3d face\n", "# Run the 3D face alignment on a test image, without CUDA.\n", - "fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, \n", - " enable_cuda=True, flip_input=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "80\n" - ] - } - ], - "source": [ - "data_bodega = '../data_bodega/'\n", - "fp = join(data_bodega,'images/msceleb/**/*.jpg')\n", - "face_files = glob(fp,recursive=True)\n", - "face_files = [f for f in face_files if os.path.basename(f) == '0-FaceId-0.jpg']\n", - "print(len(face_files))" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "../data_bodega/images/msceleb/m.0k014/0-FaceId-0.jpg\n" - ] - } - ], - "source": [ - "fp_face = face_files[randint(0,len(face_files)-1)]\n", - "im = io.imread(fp_face)" + "fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, device='cuda:0', flip_input=True)" ] }, { "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "preds = fa.get_landmarks(im)[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 41, + "execution_count": 276, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "(31.0, 256.0) (97.0, 293.0) (-101.42858, 71.526764)\n" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 276, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "xmm = (np.min(preds[:,0]),np.max(preds[:,0]))\n", - "ymm = (np.min(preds[:,1]),np.max(preds[:,1]))\n", - "zmm = (np.min(preds[:,2]),np.max(preds[:,2]))\n", - "print(xmm,ymm,zmm)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "# # draw person\n", - "# fig = plt.figure(figsize=plt.figaspect(.5))\n", - "# ax = fig.add_subplot(1, 2, 1)\n", - "# ax.imshow(im)\n", - "# ax.plot(preds[0:17,0],preds[0:17,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", - "# ax.plot(preds[17:22,0],preds[17:22,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", - "# ax.plot(preds[22:27,0],preds[22:27,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", - "# ax.plot(preds[27:31,0],preds[27:31,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", - "# ax.plot(preds[31:36,0],preds[31:36,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", - "# ax.plot(preds[36:42,0],preds[36:42,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", - "# ax.plot(preds[42:48,0],preds[42:48,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", - "# ax.plot(preds[48:60,0],preds[48:60,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", - "# ax.plot(preds[60:68,0],preds[60:68,1],marker='o',markersize=6,linestyle='-',color='w',lw=2) \n", - "# ax.axis('off')\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "preds_orig = preds.copy()" + "fp_im = '/data_store_hdd/datasets/people/vgg_face2/media/original/test/n000009/0012_01.jpg'\n", + "im = cv.imread(fp_im)\n", + "plt.imshow(im)" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 287, "metadata": {}, "outputs": [], - "source": [ - "xmm_sc = (1.2*np.min(preds[:,0]),1.2*np.max(preds_orig[:,0]))\n", - "xmm = (np.min(preds_orig[:,0]),np.max(preds_orig[:,0]))\n", - "ymm = (np.min(preds_orig[:,1]),np.max(preds_orig[:,1]))\n", - "zmm = (np.min(preds_orig[:,2]),np.max(preds_orig[:,2]))\n" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 288, "metadata": {}, "outputs": [], "source": [ - "#$preds = np.array( [p[0]-xmm[0], p[1]-ymm[1], p[2]-zmm[0]] for p in preds_orig)\n", - "preds = np.zeros_like(preds_orig).astype(np.uint8)\n", - "for i,p in enumerate(preds_orig):\n", - " x,y,z = p\n", - " preds[i] = np.array([x - xmm[0], y - ymm[0], z - zmm[0]])\n", - " preds[i] = np.array([x - xmm[0], z - zmm[0], y - ymm[0]])\n", - " #preds[i] = np.array([x - xmm[0], abs((z - zmm[0])+zmm[0]), y - ymm[0]])\n", + "def generate_3d_face_plain(im, lm):\n", + " preds = lm\n", + " fig = plt.figure(figsize=plt.figaspect(.5))\n", + " ax = fig.add_subplot(1, 2, 1)\n", + " ax.imshow(im)\n", + " ax.plot(preds[0:17,0],preds[0:17,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", + " ax.plot(preds[17:22,0],preds[17:22,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", + " ax.plot(preds[22:27,0],preds[22:27,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", + " ax.plot(preds[27:31,0],preds[27:31,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", + " ax.plot(preds[31:36,0],preds[31:36,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", + " ax.plot(preds[36:42,0],preds[36:42,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", + " ax.plot(preds[42:48,0],preds[42:48,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", + " ax.plot(preds[48:60,0],preds[48:60,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)\n", + " ax.plot(preds[60:68,0],preds[60:68,1],marker='o',markersize=6,linestyle='-',color='w',lw=2) \n", + " ax.axis('off')\n", + "\n", + " ax = fig.add_subplot(1, 2, 2, projection='3d')\n", + " surf = ax.scatter(preds[:,0]*1.2,preds[:,1],preds[:,2],c=\"cyan\", alpha=1.0, edgecolor='b')\n", + " ax.plot3D(preds[:17,0]*1.2,preds[:17,1], preds[:17,2], color='blue' )\n", + " ax.plot3D(preds[17:22,0]*1.2,preds[17:22,1],preds[17:22,2], color='blue')\n", + " ax.plot3D(preds[22:27,0]*1.2,preds[22:27,1],preds[22:27,2], color='blue')\n", + " ax.plot3D(preds[27:31,0]*1.2,preds[27:31,1],preds[27:31,2], color='blue')\n", + " ax.plot3D(preds[31:36,0]*1.2,preds[31:36,1],preds[31:36,2], color='blue')\n", + " ax.plot3D(preds[36:42,0]*1.2,preds[36:42,1],preds[36:42,2], color='blue')\n", + " ax.plot3D(preds[42:48,0]*1.2,preds[42:48,1],preds[42:48,2], color='blue')\n", + " ax.plot3D(preds[48:,0]*1.2,preds[48:,1],preds[48:,2], color='blue' )\n", + " \n", + " # pad\n", + " xmm = (np.min(lm[:,0]),np.max(lm[:,0]))\n", + " ymm = (np.min(lm[:,1]),np.max(lm[:,1]))\n", + " zmm = (np.min(lm[:,2]),np.max(lm[:,2]))\n", " \n", - "#preds = np.rot90(preds,2,axes=(0,1))" + " print(xmm, ymm, zmm)\n", + "# ax.set_xticks([])\n", + "# ax.set_yticks([])\n", + "# ax.set_zticks([])\n", + " plt.setp( ax.get_xticklabels(), visible=False)\n", + " plt.setp( ax.get_yticklabels(), visible=False)\n", + " #ax.set_xlim(xmm[0]-50, xmm[1]+50)\n", + " #ax.set_ylim(ymm[0]-50, ymm[1]+50)\n", + " #ax.set_ylim(zmm[0]- .1*zmm[0],zmm[1] + .1*zmm[1])\n", + " #ax.set_ylim(103, 275)\n", + " #ax.set_zlim((-100,100))\n", + " ax.view_init(elev=15., azim=135.)\n", + "\n", + " plt.show()" ] }, { "cell_type": "code", - "execution_count": 49, - "metadata": { - "scrolled": false - }, + "execution_count": 367, + "metadata": {}, "outputs": [ { "data": { @@ -997,7 +950,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -1010,123 +963,167 @@ "name": "stdout", "output_type": "stream", "text": [ - "(31.0, 256.0) (97.0, 293.0) (-101.42858, 71.526764)\n", - "143.0 195.0 -15.428581237792969\n" + "(68.0, 201.0) (110.0, 225.0) (-61.021374, 41.419292)\n" ] } ], "source": [ - "#TODO: Make this nice\n", - "fig = plt.figure(figsize=(16,16),dpi=72)\n", - "ax = fig.add_subplot(111, projection='3d')\n", - "\n", - "preds_scaled = np.array([1.2*x,y,z] for x,y,z in preds)\n", - "\n", - "preds_plot = np.zeros_like(preds)\n", - "for i,p in enumerate(preds):\n", - " x,y,z = p\n", - " preds_plot[i] = np.array([x,y,z])\n", - "\n", - "clr1 = '#00ff00'\n", - "clr2 = '#00ff00'\n", - "\n", - "clr1 = '#ff0000'\n", - "clr2 = '#ff0000'\n", - "\n", - "ax.set_facecolor('black')\n", - "#ax.set_facecolor((1, 0, 0))\n", - "\n", - "ls = LightSource(270, 45)\n", - "# To use a custom hillshading mode, override the built-in shading and pass\n", - "# in the rgb colors of the shaded surface calculated from \"shade\".\n", - "#rgb = ls.shade(preds[:,1], cmap=cm.gist_earth, vert_exag=0.1, blend_mode='soft')\n", - "rh = rhex()\n", - "surf = ax.scatter(preds_plot[:,0]*1.2,preds_plot[:,1],preds_plot[:,2],c=rh, \n", - " alpha=1.0, s=65, edgecolor=rh)\n", - "#facecolors=rgb,linewidth=0, antialiased=False, shade=Fals\n", - "\n", - "\n", - "#surf = ax.plot_surface(x, y, z, rstride=1, cstride=1, facecolors=rgb,\n", - "# linewidth=0, antialiased=False, shade=False)\n", - "\n", - "\n", - "#surf.actor.property.specular = 0.1\n", - "#surf.actor.property.specular_power = 5\n", - "\n", - "lw = 3\n", - "ax.plot3D(preds_plot[:17,0]*1.2,preds_plot[:17,1], preds_plot[:17,2], color=rhex(),linewidth=lw)\n", - "ax.plot3D(preds_plot[17:22,0]*1.2,preds_plot[17:22,1],preds_plot[17:22,2], color=rhex(),linewidth=lw)\n", - "ax.plot3D(preds_plot[22:27,0]*1.2,preds_plot[22:27,1],preds_plot[22:27,2], color=rhex(),linewidth=lw)\n", - "ax.plot3D(preds_plot[27:31,0]*1.2,preds_plot[27:31,1],preds_plot[27:31,2], color=rhex(),linewidth=lw)\n", - "ax.plot3D(preds_plot[31:36,0]*1.2,preds_plot[31:36,1],preds_plot[31:36,2], color=rhex(),linewidth=lw)\n", - "ax.plot3D(preds_plot[36:42,0]*1.2,preds_plot[36:42,1],preds_plot[36:42,2], color=rhex(),linewidth=lw)\n", - "ax.plot3D(preds_plot[42:48,0]*1.2,preds_plot[42:48,1],preds_plot[42:48,2], color=rhex(),linewidth=lw)\n", - "ax.plot3D(preds_plot[48:,0]*1.2,preds_plot[48:,1],preds_plot[48:,2], color=rhex(), linewidth=lw)\n", - "\n", - "\n", - "print(xmm,ymm,zmm)\n", - "# ax.set_xlim3d(left=xmm[0],right=xmm[1])\n", - "# ax.set_ylim3d(bottom=ymm[0],top=ymm[1])\n", - "# ax.set_zlim3d(bottom=zmm[0],top=zmm[1])\n", - "\n", - "cx = ((xmm[0] - xmm[1]) // 2) + xmm[1]\n", - "cy = ((ymm[1] - ymm[0]) // 2) + ymm[0]\n", - "cz = ((zmm[1] - zmm[0]) // 2) + zmm[0]\n", - "print(cx,cy,cz)\n", - "\n", - "xpts = [cx,cx]\n", - "ypts = [cy,cy]\n", - "zpts = [zmm[0],zmm[1]]\n", - "\n", - "#ax.plot3D(xpts,ypts,zpts,color='red')\n", - "\n", - "# need to make this dynamic, sigh\n", - "ax.view_init(elev=120., azim=70.)\n", - "#ax.view_init(elev=90., azim=40.)\n", - "\n", - "margin = 20\n", + "im = cv.imread(fp_im)\n", + "im_rgb = cv.cvtColor(im, cv.COLOR_BGR2RGB)\n", + "lm = fa.get_landmarks(im_rgb)[-1]\n", + "generate_3d_face_plain(im_rgb, lm)" + ] + }, + { + "cell_type": "code", + "execution_count": 393, + "metadata": {}, + "outputs": [], + "source": [ + " # line weight\n", + "def generate_3d_face(lm, fp_out, num_frames=30, dpi=72, stroke_weight=2, size=(480,480),\n", + " mark_size=10, mark_type='.', mark_clr=(0,255,0), fps=10, transparent=False):\n", + " '''Generates 3D plot of face landmarks\n", + " '''\n", + " # convert opencv BGR numpy image to RGB\n", + " bg_color = '#%02x%02x%02x' % (0,0,0)\n", + " mark_clr = '#%02x%02x%02x' % (0,255,255)\n", + " \n", + " # scale to make larger\n", + " #lm = np.array([1.2*x,y,z] for x,y,z in list(lm))\n", + " \n", + " # center x,y,z\n", + " xmm = (np.min(lm[:,0]),np.max(lm[:,0]))\n", + " ymm = (np.min(lm[:,1]),np.max(lm[:,1]))\n", + " zmm = (np.min(lm[:,2]),np.max(lm[:,2]))\n", + " \n", + " # make copy of landmarks\n", + " lm_orig = lm.copy()\n", + " xmm = (np.min(lm_orig[:,0]),np.max(lm_orig[:,0]))\n", + " ymm = (np.min(lm_orig[:,1]),np.max(lm_orig[:,1]))\n", + " zmm = (np.min(lm_orig[:,2]),np.max(lm_orig[:,2]))\n", + " \n", + " # swap the y and z components to improve 3d rotation angles for matplotlib\n", + " lm = np.zeros_like(lm_orig).astype(np.uint8)\n", + " for i,p in enumerate(lm_orig):\n", + " x,y,z = p\n", + " lm[i] = np.array([x - xmm[0], z - zmm[0], y - ymm[0]])\n", + " \n", + " # Create plot\n", + " figsize = (size[0]/dpi, size[1]/dpi )\n", + " fig = plt.figure(figsize=figsize, dpi=dpi) # frameon=False\n", + " #fig.set_size_inches(100/100, 1, forward=False)\n", + " fig.tight_layout()\n", + " fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=None, hspace=None)\n", + " ax = fig.add_subplot(111, projection='3d')\n", + " ax.set_facecolor(bg_color) # background color\n", + " \n", + " xscale, yscale, zscale = (1.2, 1.0, 1.0)\n", + " \n", + " # scatter plot the dots\n", + " # jaw line\n", + " mark_clr = '#%02x%02x%02x' % (0,255,0) # green\n", + " ax.plot3D(lm[:17,0]*1.2,lm[:17,1], lm[:17,2],\n", + " marker=mark_type, markersize=mark_size, color=mark_clr,linewidth=stroke_weight)\n", + " # stage-right eyebrow\n", + " mark_clr = '#%02x%02x%02x' % (255,0,0) # green\n", + " ax.plot3D(lm[17:22,0]*1.2,lm[17:22,1],lm[17:22,2],\n", + " marker=mark_type, markersize=mark_size, color=mark_clr,linewidth=stroke_weight)\n", + " # stage-left eyebrow\n", + " mark_clr = '#%02x%02x%02x' % (255,255,0) # yellow\n", + " ax.plot3D(lm[22:27,0]*1.2,lm[22:27,1],lm[22:27,2], \n", + " marker=mark_type, markersize=mark_size, color=mark_clr,linewidth=stroke_weight)\n", + " # nose ridge\n", + " mark_clr = '#%02x%02x%02x' % (0,0,255) # blue\n", + " ax.plot3D(lm[27:31,0]*1.2,lm[27:31,1],lm[27:31,2],\n", + " marker=mark_type, markersize=mark_size, color=mark_clr,linewidth=stroke_weight)\n", + " # nose-bottom\n", + " mark_clr = '#%02x%02x%02x' % (255,0,255) # magenta\n", + " ax.plot3D(lm[31:36,0]*1.2,lm[31:36,1],lm[31:36,2],\n", + " marker=mark_type, markersize=mark_size, color=mark_clr,linewidth=stroke_weight)\n", + " # stage-left eye\n", + " mark_clr = '#%02x%02x%02x' % (0,255,255) # cyan\n", + " px, py, pz = lm[36:42,0]*1.2,lm[36:42,1],lm[36:42,2]\n", + " px = np.append(px, lm[36,0]*1.2)\n", + " py = np.append(py, lm[36,1])\n", + " pz = np.append(pz, lm[36,2])\n", + " ax.plot3D(px, py, pz, marker=mark_type, markersize=mark_size, color=mark_clr,linewidth=stroke_weight)\n", + " \n", + " # stage-right eye\n", + " mark_clr = '#%02x%02x%02x' % (255,255,255) # white\n", + " px, py, pz = lm[42:48,0]*1.2,lm[42:48,1],lm[42:48,2]\n", + " px = np.append(px, lm[42,0]*1.2)\n", + " py = np.append(py, lm[42,1])\n", + " pz = np.append(pz, lm[42,2])\n", + " ax.plot3D(px, py, pz, marker=mark_type, markersize=mark_size, color=mark_clr,linewidth=stroke_weight)\n", + " \n", + " # mouth\n", + " mark_clr = '#%02x%02x%02x' % (255,125,0) # orange?\n", + " px, py, pz = lm[48:,0]*1.2,lm[48:,1],lm[48:,2]\n", + " px = np.append(px, lm[48,0]*1.2)\n", + " py = np.append(py, lm[48,1])\n", + " pz = np.append(pz, lm[48,2])\n", + " ax.plot3D(px, py, pz, marker=mark_type, markersize=mark_size, color=mark_clr, linewidth=stroke_weight)\n", + " \n", + " rh = '#00ff00' # edge color\n", + " #ax.scatter(lm[:,0]*xscale,lm[:,1]*yscale,lm[:,2]*zscale, c=rh, alpha=1.0, s=35, edgecolor=rh)\n", + " #ax.scatter(lm[:,0]*xscale,lm[:,1]*yscale,lm[:,2]*zscale, c=rh, alpha=1.0, s=1)\n", + " \n", + " # center center x,y,z points\n", + " cx = ((xmm[0] - xmm[1]) // 2) + xmm[1]\n", + " cy = ((ymm[1] - ymm[0]) // 2) + ymm[0]\n", + " cz = ((zmm[1] - zmm[0]) // 2) + zmm[0]\n", + " \n", + " # set initial plot view\n", + " ax.view_init(elev=120., azim=70.)\n", + " \n", + " # remove ticks\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " ax.set_zticks([])\n", + " \n", + " # remove axis\n", + " ax.set_frame_on(False)\n", + " ax.set_axis_off()\n", "\n", - "#ax.set_xlim(xmm[0] - margin,xmm[1] + margin)\n", - "#ax.set_ylim(ymm[0] - margin,ymm[1] + margin)\n", - "#ax.set_zlim(zmm[0] - margin,zmm[1] + margin)\n", + " # rotation increments: from 0 to 360 in num_frames\n", + " phi = np.linspace(0, 2*np.pi, num_frames)\n", "\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])\n", - "ax.set_zticks([])\n", - "ax.set_axis_off()\n", - "ax.set_xlabel('x')\n", - "ax.set_ylabel('y')\n", - "ax.set_zlabel('z')\n", - "plt.savefig('test.png', dpi=80)\n", - "plt.show()\n" + " def update(phi):\n", + " ax.view_init(180,phi*180./np.pi)\n", + " \n", + " ani = matplotlib.animation.FuncAnimation(fig, update, frames=phi)\n", + " savefig_kwargs = {'pad_inches': 0, 'transparent': transparent}\n", + " ani.save(fp_out, writer='imagemagick', fps=fps, savefig_kwargs=savefig_kwargs)\n", + " clear_output()" ] }, { "cell_type": "code", - "execution_count": 50, - "metadata": {}, + "execution_count": 394, + "metadata": { + "scrolled": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "../data_bodega/output/m.0k014.gif\n" + "time: 0.0017\n", + "Saved file to /home/adam/Downloads/0012_01.gif\n" ] } ], "source": [ - "fname_out = join(data_bodega,'output', '{}.gif'.format(os.path.basename(os.path.dirname(fp_face))))\n", - "\n", - "phi = np.linspace(0, 2*np.pi,60)\n", - "\n", - "def update(phi):\n", - " ax.view_init(180,phi*180./np.pi)\n", - " #ax.view_init(90,phi*90./np.pi)\n", - " #ax.view_init(elev=90., azim=90.)\n", + "# filepaths\n", + "dir_out = '/home/adam/Downloads/'\n", + "fp_out = join(dir_out, '{}.gif'.format(Path(fp_im).stem))\n", "\n", - "ani = matplotlib.animation.FuncAnimation(fig, update, frames=phi)\n", - "ani.save(fname_out, writer='imagemagick', fps=10)\n", - "#plt.show()" + "# generate 3D face\n", + "st = time.time()\n", + "generate_3d_face(lm,fp_out, num_frames=20, fps=10)\n", + "print('time: {:.4f}'.format((time.time()-st)/1000))\n", + "print(f'Saved file to {fp_out}')" ] }, { @@ -1136,6 +1133,13 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -1146,9 +1150,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python [conda env:megapixels]", "language": "python", - "name": "python3" + "name": "conda-env-megapixels-py" }, "language_info": { "codemirror_mode": { diff --git a/megapixels/notebooks/face_analysis/3d_face_plot_batch.ipynb b/megapixels/notebooks/face_analysis/3d_face_plot_batch.ipynb deleted file mode 100644 index 45e309d3..00000000 --- a/megapixels/notebooks/face_analysis/3d_face_plot_batch.ipynb +++ /dev/null @@ -1,291 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3D Face Plot\n", - "\n", - "Attenzione visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "import os\n", - "from os.path import join\n", - "import sys\n", - "import time\n", - "import cv2 as cv\n", - "import numpy as np\n", - "import imutils\n", - "import matplotlib.animation\n", - "%matplotlib notebook\n", - "from glob import glob\n", - "from matplotlib import cbook\n", - "from matplotlib import cm\n", - "from matplotlib.colors import LightSource\n", - "from random import randint\n", - "sys.path.append('/megapixels/3rdparty/face-alignment')\n", - "import face_alignment\n", - "import numpy as np\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "import matplotlib.pyplot as plt\n", - "import mpl_toolkits.mplot3d.axes3d as p3\n", - "from matplotlib import animation\n", - "import random\n", - "from skimage import io\n", - "from tqdm import tqdm_notebook as tqdm\n", - "from IPython.display import clear_output\n", - "from pathlib import Path\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate random hex colors\n", - "def rhex():\n", - " r = lambda: random.randint(0,255)\n", - " return '#%02X%02X%02X' % (r(), r(), r())" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading the Face Alignment Network(FAN). Please wait...\n", - "Downloading the Face Alignment depth Network (FAN-D). Please wait...\n" - ] - } - ], - "source": [ - "# init 3d face\n", - "# Run the 3D face alignment on a test image, without CUDA.\n", - "fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, \n", - " enable_cuda=True, flip_input=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100\n" - ] - } - ], - "source": [ - "data_bodega = '/megapixels/data_bodega/'\n", - "fp = join(data_bodega,'images/senators/*.jpg')\n", - "face_files = glob(fp,recursive=True)\n", - "print(len(face_files))" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_3d_face(fpath,output_dir):\n", - " # load image\n", - " im = io.imread(fpath)\n", - " \n", - " # generate 3d predictions\n", - " preds = fa.get_landmarks(im)\n", - " if preds is None:\n", - " return\n", - " preds = preds[-1]\n", - " \n", - " # plot style\n", - " num_frames = 60\n", - " lw = 2 # line weight\n", - " bg_color = '#%02x%02x%02x' % (60,59,110)\n", - " mark_clr = '#%02x%02x%02x' % (255,255,255) \n", - " mark_type='$\\star$'\n", - " mark_size = 20\n", - " print(bg_color)\n", - " \n", - " dpi = 72\n", - " figsize = (16,16)\n", - " \n", - " # center x,y,z\n", - " xmm = (np.min(preds[:,0]),np.max(preds[:,0]))\n", - " ymm = (np.min(preds[:,1]),np.max(preds[:,1]))\n", - " zmm = (np.min(preds[:,2]),np.max(preds[:,2]))\n", - " \n", - " preds_orig = preds.copy()\n", - " xmm_sc = (1.2*np.min(preds[:,0]),1.2*np.max(preds_orig[:,0]))\n", - " xmm = (np.min(preds_orig[:,0]),np.max(preds_orig[:,0]))\n", - " ymm = (np.min(preds_orig[:,1]),np.max(preds_orig[:,1]))\n", - " zmm = (np.min(preds_orig[:,2]),np.max(preds_orig[:,2]))\n", - " \n", - " # swap the y and z components to improve 3d rotation angles\n", - " preds = np.zeros_like(preds_orig).astype(np.uint8)\n", - " for i,p in enumerate(preds_orig):\n", - " x,y,z = p\n", - " #preds[i] = np.array([x - xmm[0], y - ymm[0], z - zmm[0]]) # ?\n", - " preds[i] = np.array([x - xmm[0], z - zmm[0], y - ymm[0]])\n", - " \n", - " # Create plot\n", - " fig = plt.figure(figsize=figsize,dpi=dpi)\n", - " fig.tight_layout()\n", - " ax = fig.add_subplot(111, projection='3d')\n", - " ax.set_facecolor(bg_color) # background color\n", - " \n", - " preds_plot = np.zeros_like(preds)\n", - " for i,p in enumerate(preds):\n", - " x,y,z = p\n", - " preds_plot[i] = np.array([x,y,z])\n", - "\n", - " \n", - " # scatter plot the dots\n", - " ax.plot3D(preds_plot[:17,0]*1.2,preds_plot[:17,1], preds_plot[:17,2],\n", - " marker=mark_type,markersize=mark_size,color=mark_clr,linewidth=lw)\n", - " ax.plot3D(preds_plot[17:22,0]*1.2,preds_plot[17:22,1],preds_plot[17:22,2],\n", - " marker=mark_type,markersize=mark_size,color=mark_clr,linewidth=lw)\n", - " ax.plot3D(preds_plot[22:27,0]*1.2,preds_plot[22:27,1],preds_plot[22:27,2], \n", - " marker=mark_type,markersize=mark_size,color=mark_clr,linewidth=lw)\n", - " ax.plot3D(preds_plot[27:31,0]*1.2,preds_plot[27:31,1],preds_plot[27:31,2],\n", - " marker=mark_type,markersize=mark_size,color=mark_clr,linewidth=lw)\n", - " ax.plot3D(preds_plot[31:36,0]*1.2,preds_plot[31:36,1],preds_plot[31:36,2],\n", - " marker=mark_type,markersize=mark_size,color=mark_clr,linewidth=lw)\n", - " ax.plot3D(preds_plot[36:42,0]*1.2,preds_plot[36:42,1],preds_plot[36:42,2],\n", - " marker=mark_type,markersize=mark_size,color=mark_clr,linewidth=lw)\n", - " ax.plot3D(preds_plot[42:48,0]*1.2,preds_plot[42:48,1],preds_plot[42:48,2],\n", - " marker=mark_type,markersize=mark_size,color=mark_clr,linewidth=lw)\n", - " ax.plot3D(preds_plot[48:,0]*1.2,preds_plot[48:,1],preds_plot[48:,2],\n", - " marker=mark_type,markersize=mark_size,color=mark_clr, linewidth=lw)\n", - "\n", - " \n", - " rh = '#ffffff'\n", - " ax.scatter(preds_plot[:,0]*1.2,preds_plot[:,1],preds_plot[:,2],c=rh, alpha=1.0, s=35, edgecolor=rh)\n", - " \n", - " # center points\n", - " cx = ((xmm[0] - xmm[1]) // 2) + xmm[1]\n", - " cy = ((ymm[1] - ymm[0]) // 2) + ymm[0]\n", - " cz = ((zmm[1] - zmm[0]) // 2) + zmm[0]\n", - "\n", - " #xpts = [cx,cx]\n", - " #ypts = [cy,cy]\n", - " #zpts = [zmm[0],zmm[1]]\n", - "\n", - " ax.view_init(elev=120., azim=70.)\n", - " \n", - " ax.set_xticks([])\n", - " ax.set_yticks([])\n", - " ax.set_zticks([])\n", - " ax.set_axis_off()\n", - " ax.set_xlabel('x')\n", - " ax.set_ylabel('y')\n", - " ax.set_zlabel('z')\n", - "\n", - " # write animation to disk\n", - " fname_out = join(output_dir, '{}.gif'.format(Path(fpath).stem))\n", - " print(fname_out)\n", - " # rotation increments\n", - " phi = np.linspace(0, 2*np.pi, num_frames)\n", - "\n", - " def update(phi):\n", - " ax.view_init(180,phi*180./np.pi)\n", - " \n", - " ani = matplotlib.animation.FuncAnimation(fig, update, frames=phi)\n", - " ani.save(fname_out, writer='imagemagick', fps=10)\n", - " #del ani" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "output_dir = join(data_bodega,'output','senators_3d_points')" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "for f in tqdm(face_files):\n", - " generate_3d_face(f,output_dir)\n", - " clear_output()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cv.L" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:megapixels]", - "language": "python", - "name": "conda-env-megapixels-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/megapixels/notebooks/face_analysis/3d_face_plot_batch_frames.ipynb b/megapixels/notebooks/face_analysis/3d_face_plot_batch_frames.ipynb deleted file mode 100644 index 6ade79dc..00000000 --- a/megapixels/notebooks/face_analysis/3d_face_plot_batch_frames.ipynb +++ /dev/null @@ -1,2055 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3D Face Plot\n", - "\n", - "Process faces for Georgetown Color of Surveillance" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "import os\n", - "from os.path import join\n", - "import sys\n", - "import time\n", - "import cv2 as cv\n", - "import numpy as np\n", - "import imutils\n", - "import matplotlib.animation\n", - "%matplotlib notebook\n", - "from glob import glob\n", - "from matplotlib import cbook\n", - "from matplotlib import cm\n", - "from matplotlib.colors import LightSource\n", - "from random import randint\n", - "sys.path.append('/megapixels/3rdparty/face-alignment')\n", - "import face_alignment\n", - "import numpy as np\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "import matplotlib.pyplot as plt\n", - "import mpl_toolkits.mplot3d.axes3d as p3\n", - "from matplotlib import animation\n", - "import random\n", - "from skimage import io\n", - "from tqdm import tqdm_notebook as tqdm\n", - "from IPython.display import clear_output\n", - "from pathlib import Path\n" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [], - "source": [ - "# init 3d face\n", - "# Run the 3D face alignment on a test image, without CUDA.\n", - "fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, \n", - " enable_cuda=True, flip_input=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100\n" - ] - } - ], - "source": [ - "data_bodega = '/megapixels/data_bodega/'\n", - "fp = join(data_bodega,'images/senators/*.jpg')\n", - "face_files = glob(fp,recursive=True)\n", - "print(len(face_files))" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "def ensure_dir(d):\n", - " \"\"\"Create directory if not exist\n", - " :param d: path to directory\n", - " \"\"\"\n", - " if not os.path.exists(d):\n", - " os.makedirs(d, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_3d_face(fpath,output_dir,anim=False,ext='png'):\n", - " # load image\n", - " im = io.imread(fpath)\n", - " \n", - " # generate 3d predictions\n", - " preds = fa.get_landmarks(im)\n", - " if preds is None:\n", - " return\n", - " preds = preds[-1]\n", - " \n", - " # plot style\n", - " num_frames = 60\n", - " lw = 2 # line weight\n", - " bg_color = '#%02x%02x%02x' % (60,59,110)\n", - " mark_clr = '#%02x%02x%02x' % (255,255,255)\n", - " mark_clr = '#%02x%02x%02x' % (0,255,0)\n", - " #mark_type='$\\star$'\n", - " #mark_size = 20\n", - " mark_type='o'\n", - " mark_size = 10\n", - " rh = '#ffffff'\n", - " dpi = 72\n", - " figsize = (16,16)\n", - " \n", - " # center x,y,z\n", - " xmm = (np.min(preds[:,0]),np.max(preds[:,0]))\n", - " ymm = (np.min(preds[:,1]),np.max(preds[:,1]))\n", - " zmm = (np.min(preds[:,2]),np.max(preds[:,2]))\n", - " \n", - " preds_orig = preds.copy()\n", - " xmm_sc = (1.2*np.min(preds[:,0]),1.2*np.max(preds_orig[:,0]))\n", - " xmm = (np.min(preds_orig[:,0]),np.max(preds_orig[:,0]))\n", - " ymm = (np.min(preds_orig[:,1]),np.max(preds_orig[:,1]))\n", - " zmm = (np.min(preds_orig[:,2]),np.max(preds_orig[:,2]))\n", - " \n", - " # swap the y and z components to improve 3d rotation angles\n", - " preds = np.zeros_like(preds_orig).astype(np.uint8)\n", - " for i,p in enumerate(preds_orig):\n", - " x,y,z = p\n", - " #preds[i] = np.array([x - xmm[0], y - ymm[0], z - zmm[0]]) # ?\n", - " preds[i] = np.array([x - xmm[0], z - zmm[0], y - ymm[0]])\n", - " \n", - " # Create plot\n", - " fig = plt.figure(figsize=figsize,dpi=dpi)\n", - " fig.tight_layout()\n", - " ax = fig.add_subplot(111, projection='3d')\n", - " ax.set_facecolor(bg_color) # background color\n", - " \n", - " preds_plot = np.zeros_like(preds)\n", - " for i,p in enumerate(preds):\n", - " x,y,z = p\n", - " preds_plot[i] = np.array([x,y,z])\n", - "\n", - " \n", - " # scatter plot the dots\n", - " ax.plot3D(preds_plot[:17,0]*1.2,preds_plot[:17,1], preds_plot[:17,2],\n", - " marker=mark_type,markersize=mark_size,color=mark_clr,linewidth=lw)\n", - " ax.plot3D(preds_plot[17:22,0]*1.2,preds_plot[17:22,1],preds_plot[17:22,2],\n", - " marker=mark_type,markersize=mark_size,color=mark_clr,linewidth=lw)\n", - " ax.plot3D(preds_plot[22:27,0]*1.2,preds_plot[22:27,1],preds_plot[22:27,2], \n", - " marker=mark_type,markersize=mark_size,color=mark_clr,linewidth=lw)\n", - " ax.plot3D(preds_plot[27:31,0]*1.2,preds_plot[27:31,1],preds_plot[27:31,2],\n", - " marker=mark_type,markersize=mark_size,color=mark_clr,linewidth=lw)\n", - " ax.plot3D(preds_plot[31:36,0]*1.2,preds_plot[31:36,1],preds_plot[31:36,2],\n", - " marker=mark_type,markersize=mark_size,color=mark_clr,linewidth=lw)\n", - " ax.plot3D(preds_plot[36:42,0]*1.2,preds_plot[36:42,1],preds_plot[36:42,2],\n", - " marker=mark_type,markersize=mark_size,color=mark_clr,linewidth=lw)\n", - " ax.plot3D(preds_plot[42:48,0]*1.2,preds_plot[42:48,1],preds_plot[42:48,2],\n", - " marker=mark_type,markersize=mark_size,color=mark_clr,linewidth=lw)\n", - " ax.plot3D(preds_plot[48:,0]*1.2,preds_plot[48:,1],preds_plot[48:,2],\n", - " marker=mark_type,markersize=mark_size,color=mark_clr, linewidth=lw)\n", - "\n", - " \n", - " ax.scatter(preds_plot[:,0]*1.2,preds_plot[:,1],preds_plot[:,2],c=rh, alpha=1.0, s=35, edgecolor=rh)\n", - " \n", - " # center points\n", - " cx = ((xmm[0] - xmm[1]) // 2) + xmm[1]\n", - " cy = ((ymm[1] - ymm[0]) // 2) + ymm[0]\n", - " cz = ((zmm[1] - zmm[0]) // 2) + zmm[0]\n", - " \n", - " # ?\n", - " ax.view_init(elev=120., azim=70.)\n", - " \n", - " ax.set_xticks([])\n", - " ax.set_yticks([])\n", - " ax.set_zticks([])\n", - " ax.set_axis_off()\n", - " ax.set_xlabel('x')\n", - " ax.set_ylabel('y')\n", - " ax.set_zlabel('z')\n", - "\n", - " \n", - " phis = np.linspace(0, 2*np.pi, num_frames)\n", - " \n", - " if anim:\n", - " def update(phi):\n", - " ax.view_init(180,phi*180./np.pi)\n", - " fname_out = join(output_dir,'{}.gif'.format(Path(fpath).stem))\n", - " ani = matplotlib.animation.FuncAnimation(fig, update, frames=phis)\n", - " ani.save(fname_out, writer='imagemagick', fps=10)\n", - " del ani\n", - " else:\n", - " for i,phi in enumerate(phis):\n", - " # ext (jpg,png,pdf)\n", - " person_name = Path(fpath).stem\n", - " fname_out = join(output_dir,person_name,'{}.{}'.format(str(i).zfill(4), ext))\n", - " ensure_dir(Path(fname_out).parent)\n", - " ax.view_init(180,phi*180./np.pi)\n", - " fig.savefig(fname_out, transparent=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [], - "source": [ - "output_dir = join(data_bodega,'output','senators_3d_points_frames')" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
');\n", - " this._root_extra_style(this.root)\n", - " this.root.attr('style', 'display: inline-block');\n", - "\n", - " $(parent_element).append(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", - " fig.send_message(\"send_image_mode\", {});\n", - " if (mpl.ratio != 1) {\n", - " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", - " }\n", - " fig.send_message(\"refresh\", {});\n", - " }\n", - "\n", - " this.imageObj.onload = function() {\n", - " if (fig.image_mode == 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function() {\n", - " fig.ws.close();\n", - " }\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "}\n", - "\n", - "mpl.figure.prototype._init_header = function() {\n", - " var titlebar = $(\n", - " '
');\n", - " var titletext = $(\n", - " '
');\n", - " titlebar.append(titletext)\n", - " this.root.append(titlebar);\n", - " this.header = titletext[0];\n", - "}\n", - "\n", - "\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._init_canvas = function() {\n", - " var fig = this;\n", - "\n", - " var canvas_div = $('
');\n", - "\n", - " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", - "\n", - " function canvas_keyboard_event(event) {\n", - " return fig.key_event(event, event['data']);\n", - " }\n", - "\n", - " canvas_div.keydown('key_press', canvas_keyboard_event);\n", - " canvas_div.keyup('key_release', canvas_keyboard_event);\n", - " this.canvas_div = canvas_div\n", - " this._canvas_extra_style(canvas_div)\n", - " this.root.append(canvas_div);\n", - "\n", - " var canvas = $('');\n", - " canvas.addClass('mpl-canvas');\n", - " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", - "\n", - " this.canvas = canvas[0];\n", - " this.context = canvas[0].getContext(\"2d\");\n", - "\n", - " var backingStore = this.context.backingStorePixelRatio ||\n", - "\tthis.context.webkitBackingStorePixelRatio ||\n", - "\tthis.context.mozBackingStorePixelRatio ||\n", - "\tthis.context.msBackingStorePixelRatio ||\n", - "\tthis.context.oBackingStorePixelRatio ||\n", - "\tthis.context.backingStorePixelRatio || 1;\n", - "\n", - " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband = $('');\n", - " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", - "\n", - " var pass_mouse_events = true;\n", - "\n", - " canvas_div.resizable({\n", - " start: function(event, ui) {\n", - " pass_mouse_events = false;\n", - " },\n", - " resize: function(event, ui) {\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " stop: function(event, ui) {\n", - " pass_mouse_events = true;\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " });\n", - "\n", - " function mouse_event_fn(event) {\n", - " if (pass_mouse_events)\n", - " return fig.mouse_event(event, event['data']);\n", - " }\n", - "\n", - " rubberband.mousedown('button_press', mouse_event_fn);\n", - " rubberband.mouseup('button_release', mouse_event_fn);\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband.mousemove('motion_notify', mouse_event_fn);\n", - "\n", - " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", - " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", - "\n", - " canvas_div.on(\"wheel\", function (event) {\n", - " event = event.originalEvent;\n", - " event['data'] = 'scroll'\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " mouse_event_fn(event);\n", - " });\n", - "\n", - " canvas_div.append(canvas);\n", - " canvas_div.append(rubberband);\n", - "\n", - " this.rubberband = rubberband;\n", - " this.rubberband_canvas = rubberband[0];\n", - " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", - " this.rubberband_context.strokeStyle = \"#000000\";\n", - "\n", - " this._resize_canvas = function(width, height) {\n", - " // Keep the size of the canvas, canvas container, and rubber band\n", - " // canvas in synch.\n", - " canvas_div.css('width', width)\n", - " canvas_div.css('height', height)\n", - "\n", - " canvas.attr('width', width * mpl.ratio);\n", - " canvas.attr('height', height * mpl.ratio);\n", - " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", - "\n", - " rubberband.attr('width', width);\n", - " rubberband.attr('height', height);\n", - " }\n", - "\n", - " // Set the figure to an initial 600x600px, this will subsequently be updated\n", - " // upon first draw.\n", - " this._resize_canvas(600, 600);\n", - "\n", - " // Disable right mouse context menu.\n", - " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", - " return false;\n", - " });\n", - "\n", - " function set_focus () {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "}\n", - "\n", - "mpl.figure.prototype._init_toolbar = function() {\n", - " var fig = this;\n", - "\n", - " var nav_element = $('
')\n", - " nav_element.attr('style', 'width: 100%');\n", - " this.root.append(nav_element);\n", - "\n", - " // Define a callback function for later on.\n", - " function toolbar_event(event) {\n", - " return fig.toolbar_button_onclick(event['data']);\n", - " }\n", - " function toolbar_mouse_event(event) {\n", - " return fig.toolbar_button_onmouseover(event['data']);\n", - " }\n", - "\n", - " for(var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " // put a spacer in here.\n", - " continue;\n", - " }\n", - " var button = $('');\n", - " button.click(method_name, toolbar_event);\n", - " button.mouseover(tooltip, toolbar_mouse_event);\n", - " nav_element.append(button);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = $('');\n", - " nav_element.append(status_bar);\n", - " this.message = status_bar[0];\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = $('
');\n", - " var button = $('');\n", - " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", - " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", - " buttongrp.append(button);\n", - " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", - " titlebar.prepend(buttongrp);\n", - "}\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(el){\n", - " var fig = this\n", - " el.on(\"remove\", function(){\n", - "\tfig.close_ws(fig, {});\n", - " });\n", - "}\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(el){\n", - " // this is important to make the div 'focusable\n", - " el.attr('tabindex', 0)\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " }\n", - " else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._key_event_extra = function(event, name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager)\n", - " manager = IPython.keyboard_manager;\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which == 13) {\n", - " this.canvas_div.blur();\n", - " event.shiftKey = false;\n", - " // Send a \"J\" for go to next cell\n", - " event.which = 74;\n", - " event.keyCode = 74;\n", - " manager.command_mode();\n", - " manager.handle_keydown(event);\n", - " }\n", - "}\n", - "\n", - "mpl.figure.prototype.handle_save = function(fig, msg) {\n", - " fig.ondownload(fig, null);\n", - "}\n", - "\n", - "\n", - "mpl.find_output_cell = function(html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "im[100][10] = (255,0,0)\n", - "plt.imshow(im)" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "metadata": {}, - "outputs": [], - "source": [ - "import pickle\n", - "from slugify import slugify" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function slugify in module slugify:\n", - "\n", - "slugify(string)\n", - " Slugify a unicode string.\n", - " \n", - " Example:\n", - " \n", - " >>> slugify(u\"Héllø Wörld\")\n", - " u\"hello-world\"\n", - "\n" - ] - } - ], - "source": [ - "help(slugify)" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": {}, - "outputs": [], - "source": [ - "f = '/data_store/datasets/gov/output/senators_obj_04/boozman_john_mesh.pkl'\n", - "with open(f, 'rb') as fp:\n", - " data = pickle.load(fp)" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "colors\n", - "width\n", - "keypoints\n", - "triangles\n", - "texture\n", - "vertices\n", - "uv_coords\n", - "save_vertices\n", - "position\n", - "pose\n", - "camera_matrix\n", - "height\n" - ] - } - ], - "source": [ - "for k,v in data.items():\n", - " print(k)" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[[0.37254902 0.30588235 0.24313725]\n", - " [0.44313725 0.53333333 0.59607843]\n", - " [0.0627451 0.05882353 0.04313725]\n", - " ...\n", - " [0.65490196 0.51764706 0.44313725]\n", - " [0.60392157 0.4627451 0.36078431]\n", - " [0.33333333 0.25882353 0.23529412]]\n", - "\n", - " [[0.38039216 0.28627451 0.19215686]\n", - " [0.43137255 0.53333333 0.59215686]\n", - " [0.09803922 0.09411765 0.0745098 ]\n", - " ...\n", - " [0.38039216 0.31372549 0.25098039]\n", - " [0.23921569 0.20784314 0.15686275]\n", - " [0.28235294 0.23137255 0.19607843]]\n", - "\n", - " [[0.32156863 0.25098039 0.16470588]\n", - " [0.10196078 0.10588235 0.08235294]\n", - " [0.11372549 0.11764706 0.09803922]\n", - " ...\n", - " [0.23529412 0.19607843 0.16078431]\n", - " [0.12156863 0.10980392 0.08235294]\n", - " [0.61960784 0.49803922 0.38823529]]\n", - "\n", - " ...\n", - "\n", - " [[0.38039216 0.45882353 0.50196078]\n", - " [0.35294118 0.43921569 0.49411765]\n", - " [0.23529412 0.2627451 0.30196078]\n", - " ...\n", - " [0.72156863 0.49803922 0.39215686]\n", - " [0.10196078 0.10196078 0.13333333]\n", - " [0.0627451 0.06666667 0.08235294]]\n", - "\n", - " [[0. 0. 0. ]\n", - " [0.0745098 0.07843137 0.09803922]\n", - " [0.07843137 0.0745098 0.09803922]\n", - " ...\n", - " [0.10588235 0.12156863 0.1254902 ]\n", - " [0.10196078 0.10196078 0.13333333]\n", - " [0.09803922 0.09411765 0.11372549]]\n", - "\n", - " [[0. 0. 0. ]\n", - " [0.07058824 0.0745098 0.09019608]\n", - " [0.06666667 0.07058824 0.08627451]\n", - " ...\n", - " [0.10588235 0.10196078 0.13333333]\n", - " [0.07058824 0.08235294 0.10980392]\n", - " [0.07843137 0.09411765 0.12941176]]]\n" - ] - } - ], - "source": [ - "print(data['texture'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:megapixels]", - "language": "python", - "name": "conda-env-megapixels-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} -- cgit v1.2.3-70-g09d2