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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# elasticplay"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import json\n",
"%matplotlib inline\n",
"\n",
"\n",
"SILENT = 1\n",
"NONSILENT = 0\n",
"float_factor = 100*0.5 # multiply a float_fact to get a rough integer value for dp\n",
"\n",
"# define data structure for elasticplay\n",
"# the global timing is based 0.01 second\n",
"\n",
"class ep_shot(object):\n",
" def __init__(self, idx, start, end, accumulated_score):\n",
" self.idx = idx\n",
" self.start = start\n",
" self.end = end\n",
" self.accumulated_score = accumulated_score\n",
" self.duration = end - start\n",
" \n",
"class audio_ep_shot(ep_shot):\n",
" def setIfSilent(self, ifsilent):\n",
" self.ifsilent = ifsilent\n",
" \n",
" def __repr__(self):\n",
" return \"(start: \" + str(self.start) + \" end: \" + str(self.end) + \" score: \" + str(self.accumulated_score) + \" duration: \" + str(self.duration) + \")\"\n",
"\n",
" def __str__(self):\n",
" return \"(start: \" + str(self.start) + \" end: \" + str(self.end) + \" score: \" + str(self.accumulated_score) + \" duration: \" + str(self.duration) + \")\"\n",
"\n",
"#input should be an array of ep_shot\n",
"def vis_plot_shots_totalscore(shots):\n",
" data_x = []\n",
" data_y = []\n",
" for i, shot in enumerate(shots):\n",
" data_x.append(shot.start)\n",
" data_y.append(0)\n",
" data_x.append(shot.start)\n",
" data_y.append(shot.accumulated_score)\n",
" data_x.append(shot.start+shot.duration) #shot.start + shot.duration may not be equal to shot.end because of the speed up.\n",
" data_y.append(shot.accumulated_score)\n",
" data_x.append(shot.start+shot.duration)\n",
" data_y.append(0)\n",
" plt.ylabel('accumulated_score')\n",
" plt.xlabel('time(sec)')\n",
" plt.plot(data_x, data_y)\n",
"# plt.show()\n",
"\n",
"#input should be an array of ep_shot\n",
"def vis_plot_shots_avgscore(shots):\n",
" data_x = []\n",
" data_y = []\n",
" for i, shot in enumerate(shots):\n",
" data_x.append(shot.start)\n",
" data_y.append(0)\n",
" data_x.append(shot.start)\n",
" data_y.append(float(shot.accumulated_score)/float(shot.end-shot.start))\n",
" data_x.append(shot.start+shot.duration)\n",
" data_y.append(float(shot.accumulated_score)/float(shot.end-shot.start))\n",
" data_x.append(shot.start+shot.duration)\n",
" data_y.append(0)\n",
" plt.ylabel('accumulated_score')\n",
" plt.xlabel('time(sec)')\n",
" plt.plot(data_x, data_y)\n",
" \n",
"# for mm:ss format\n",
"def getSec(s):\n",
" l = s.split(':')\n",
" return int(l[0]) * 60 + int(l[1])\n"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bagged the following items\n",
" apple\n",
" banana\n",
" camera\n",
" cheese\n",
" compass\n",
" glucose\n",
" map\n",
" note-case\n",
" sandwich\n",
" socks\n",
" sunglasses\n",
" suntan cream\n",
" water\n",
" waterproof overclothes\n",
" waterproof trousers\n",
"for a total value of 1130 and a total weight of 490\n",
"Bagged the following items\n",
" apple\n",
" banana\n",
" camera\n",
" cheese\n",
" compass\n",
" glucose\n",
" map\n",
" note-case\n",
" sandwich\n",
" socks\n",
" sunglasses\n",
" suntan cream\n",
" water\n",
" waterproof overclothes\n",
" waterproof trousers\n",
"for a total value of 1130 and a total weight of 490\n"
]
}
],
"source": [
"bagsize = 500\n",
"\n",
"#knapsack algorithm\n",
"\n",
"try:\n",
" xrange\n",
"except:\n",
" xrange = range\n",
" \n",
"def totalvalue(comb):\n",
" ' Totalise a particular combination of items'\n",
" totwt = totval = 0\n",
" for item, wt, val in comb:\n",
" totwt += wt\n",
" totval += val\n",
" return (totval, -totwt)\n",
" \n",
"items = (\n",
" (\"map\", 9, 150), (\"compass\", 13, 35), (\"water\", 153, 200), (\"sandwich\", 50, 160),\n",
" (\"glucose\", 15, 60), (\"tin\", 68, 45), (\"banana\", 27, 60), (\"apple\", 39, 40),\n",
" (\"cheese\", 23, 30), (\"beer\", 52, 10), (\"suntan cream\", 11, 70), (\"camera\", 32, 30),\n",
" (\"t-shirt\", 24, 15), (\"trousers\", 48, 10), (\"umbrella\", 73, 40),\n",
" (\"waterproof trousers\", 42, 70), (\"waterproof overclothes\", 43, 75),\n",
" (\"note-case\", 22, 80), (\"sunglasses\", 7, 20), (\"towel\", 18, 12),\n",
" (\"socks\", 4, 50), (\"book\", 30, 10),\n",
" )\n",
" \n",
"def knapsack01_dp_init(items, max_limit):\n",
" table = [[0 for w in range(max_limit + 1)] for j in xrange(len(items) + 1)]\n",
" \n",
" for j in xrange(1, len(items) + 1):\n",
" item, wt, val = items[j-1]\n",
" for w in xrange(1, max_limit + 1):\n",
" if wt > w:\n",
" table[j][w] = table[j-1][w]\n",
" else:\n",
" table[j][w] = max(table[j-1][w],\n",
" table[j-1][w-wt] + val)\n",
" return table\n",
"\n",
"def knapsack01_dp_getsolution(items, limit, table):\n",
" result = []\n",
" w = limit\n",
" for j in range(len(items), 0, -1):\n",
" was_added = table[j][w] != table[j-1][w]\n",
" \n",
" if was_added:\n",
" item, wt, val = items[j-1]\n",
" result.append(items[j-1])\n",
" w -= wt\n",
" \n",
" return result\n",
" \n",
"def knapsack01_dp(items, limit):\n",
" table = [[0 for w in range(limit + 1)] for j in xrange(len(items) + 1)]\n",
" \n",
" for j in xrange(1, len(items) + 1):\n",
" item, wt, val = items[j-1]\n",
" for w in xrange(1, limit + 1):\n",
" if wt > w:\n",
" table[j][w] = table[j-1][w]\n",
" else:\n",
" table[j][w] = max(table[j-1][w],\n",
" table[j-1][w-wt] + val)\n",
" \n",
" result = []\n",
" w = limit\n",
" for j in range(len(items), 0, -1):\n",
" was_added = table[j][w] != table[j-1][w]\n",
" \n",
" if was_added:\n",
" item, wt, val = items[j-1]\n",
" result.append(items[j-1])\n",
" w -= wt\n",
" \n",
" return result\n",
"\n",
"\n",
"\n",
"bagged = knapsack01_dp(items, bagsize)\n",
"print(\"Bagged the following items\\n \" +\n",
" '\\n '.join(sorted(item for item,_,_ in bagged)))\n",
"val, wt = totalvalue(bagged)\n",
"print(\"for a total value of %i and a total weight of %i\" % (val, -wt))\n",
"\n",
"\n",
"table = knapsack01_dp_init(items, bagsize)\n",
"bagged = knapsack01_dp_getsolution(items, bagsize, table)\n",
"print(\"Bagged the following items\\n \" +\n",
" '\\n '.join(sorted(item for item,_,_ in bagged)))\n",
"val, wt = totalvalue(bagged)\n",
"print(\"for a total value of %i and a total weight of %i\" % (val, -wt))"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# the annotation is per frame.\n",
"dict_anno = {}\n",
"dict_info = {}\n",
"dict_framerate = {}\n",
"\n",
"# load video info\n",
"with open(\"../tvsum/data/ydata-tvsum50-info.tsv\") as finfo:\n",
" for line in finfo:\n",
" if line.startswith('cate'):\n",
" continue\n",
" fields = line.split('\\t')\n",
" # print fields[1], fields[4], getSec(fields[4])\n",
" dict_info[fields[1]] = getSec(fields[4])\n",
" \n",
"# load shot scores\n",
"with open(\"../tvsum/data/ydata-tvsum50-anno.tsv\") as fanno:\n",
" for line in fanno:\n",
" fields = line.split('\\t')\n",
" if fields[0] in dict_anno:\n",
" # print fields[2]\n",
" # dict_anno[fields[0]] += 1\n",
" arr = np.fromstring(fields[2], dtype=int, sep=\",\")\n",
" # print arr[:10]\n",
" dict_anno[fields[0]] = np.vstack((arr, dict_anno[fields[0]]))\n",
" else:\n",
"# print fields[0], len(fields[2].split(',')), dict_info[fields[0]], \"framerate\", float(len(fields[2].split(',')))/dict_info[fields[0]], \"round\", round(float(len(fields[2].split(',')))/dict_info[fields[0]])\n",
" dict_framerate[fields[0]] = round(float(len(fields[2].split(',')))/dict_info[fields[0]])\n",
" arr = np.fromstring(fields[2], dtype=int, sep=\",\")\n",
" dict_anno[fields[0]] = arr"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# select the example video id.\n",
"vid = '-esJrBWj2d8'"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"# audio segmentation method 1: sphix \n",
"# based on the audio segment to calculate the score for each segment.\n",
"# recommended method for audio segmentation: cmu sphinx -> sphinx_cont_seg\n",
"# library at: http://sourceforge.net/projects/cmusphinx/files/sphinxbase/5prealpha/\n",
"# interface description at: http://www.speech.cs.cmu.edu/sphinx/doc/doxygen/sphinxbase/cont__fileseg_8c-source.html\n",
"# ffmpeg to extract the wmv file: \"ffmpeg -i 3eYKfiOEJNs.mp4 -vn -ar 44100 -ac 2 -ab 192k -f wav test.wav\"\n",
"# segment with sphix: \"sphinx_cont_seg -infile test.wav\" .details at: http://cmusphinx.sourceforge.net/wiki/tutorial\n",
"\n",
"\n",
"# after several experimentations, i think sphix is not really a good choice. it cannot seperate music very well.\n"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1148b7e90>]"
]
},
"execution_count": 201,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1148b7350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# audio segmentation method 2: python version\n",
"# load audio scores\n",
"# pyaudioanalysis scikit-learn==0.16.1\n",
"# for the speedup, we can use fragmented segments. so we should prefer the high configure score threshold.\n",
"# although many long sentences would be splitted into multiple segments, but we can capture the silent segments.\n",
"# set the smooth window seconds = 1 second , svm probability = 0.4\n",
"plot_data_x = []\n",
"plot_data_y = []\n",
"with open(\"../audio_info.json\") as json_file:\n",
" json_data = json.load(json_file)\n",
" vid_data = {float(k):v for k,v in json_data[vid].items()}\n",
"# print vid_data\n",
"\n",
"\n",
"\n",
"audioshots = []\n",
"total_si = 0\n",
"total_nonsi = 0\n",
"# data visualization\n",
"i = 0\n",
"for key, value in sorted( vid_data.iteritems()):\n",
"# print key, value['len'], value, type(value)\n",
" cur_shot = audio_ep_shot(i, float(key), float(value['end']), 0)\n",
" i = i+1\n",
" plot_data_x.append(key)\n",
" plot_data_x.append(value['end'])\n",
" if value['ifsilent'] == 'silent':\n",
" plot_data_y.append(0)\n",
" plot_data_y.append(0)\n",
" total_si += float(value['len'])\n",
" cur_shot.setIfSilent(SILENT)\n",
" else :\n",
" plot_data_y.append(1)\n",
" plot_data_y.append(1)\n",
" total_nonsi += float(value['len'])\n",
" cur_shot.setIfSilent(NONSILENT)\n",
" audioshots.append(cur_shot)\n",
" \n",
"plt.ylim((0,1.5))\n",
"plt.ylabel('IfSilent')\n",
"plt.xlabel('time (sec)')\n",
"plt.plot(plot_data_x, plot_data_y)\n"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# for i, item in enumerate(audioshots):\n",
"# print i, item.idx, item.start, item.end, item.accumulated_score, item.ifsilent, \"avg: \", item.accumulated_score/item.duration"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(6912,) 6900.0\n"
]
}
],
"source": [
"annotations_multiple = dict_anno[vid]\n",
"video_len = dict_info[vid]\n",
"frame_rate = dict_framerate[vid]\n",
"total_frame = dict_info[vid] * dict_framerate[vid]\n",
"avg_annotations = annotations_multiple.mean(axis=0)\n",
"print avg_annotations.shape, total_frame\n",
"\n",
"cur_shot = 0\n",
"#calculate the score for each shot\n",
"for i, val in enumerate(avg_annotations):\n",
" cur_time = float(i)/frame_rate\n",
"# print cur_time\n",
" if cur_time >= audioshots[cur_shot].start and cur_time <= audioshots[cur_shot].end:\n",
" audioshots[cur_shot].accumulated_score += float(val)\n",
" elif cur_time > audioshots[cur_shot].end:\n",
" cur_shot += 1\n",
" if cur_time >= audioshots[cur_shot].start and cur_time <= audioshots[cur_shot].end:\n",
" audioshots[cur_shot].accumulated_score += float(val)\n",
" else:\n",
" print \"error\"\n",
" break\n",
"\n",
"\n",
" \n",
"# for key, value in dict_anno[].iteritems():\n",
"# print key, dict_info[key], dict_framerate[key], dict_info[key] * dict_framerate[key], value.shape,\n",
"# value = value.mean(axis=0) #average the score from multiple label resources (20 people label each video)\n",
"# shot_id\n",
"# for i, val in enumerate(value):\n",
" \n",
" \n",
"\n",
"# # print \"==========start to generate========\"\n",
"# dict_final = {} \n",
"# # dict_final; key is video id, value is the array of scores for each second. the size of the array should be equal to the video duration \n",
"# for key, value in dict_anno.iteritems():\n",
"# # if key != \"AwmHb44_ouw\":\n",
"# # continue\n",
"# # print key, dict_info[key], dict_framerate[key], dict_info[key] * dict_framerate[key], value.shape,\n",
"\n",
"# value = value.mean(axis=0)\n",
"# idx = 0\n",
"# scores = []\n",
"# for i in range(0, value.size, int(dict_framerate[key])):\n",
"# # print np.average(value[i:i+dict_framerate[key]]), value[i]\n",
"# # scores.append(np.average(value[i:i+dict_framerate[key]]))\n",
"# scores.append(np.average(value[i]))\n",
"# dict_final[key] = scores\n",
"# # print len(dict_final[key])\n",
"\n",
"\n",
"# #score visualization\n",
"# plt.plot(dict_final[vid])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x110380190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dp_items = [] #item, weight, value, \n",
"for i, value in enumerate(audioshots):\n",
" dp_items.append( (value, int(value.duration*float_factor), value.accumulated_score) ) #multiply by float_factor to elimiate float\n",
"# print value.accumulated_score\n",
"\n",
"cur_ep_shots = [x[0] for x in dp_items]\n",
"vis_plot_shots_totalscore(cur_ep_shots) \n",
"vis_plot_shots_avgscore(cur_ep_shots) \n",
"# print cur_ep_shots"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pre sum: 13638.45 weight, 229.44\n",
"videolen: 230 budget: 207.0 totalvalue: \n",
"for a total value of 13638 and a total weight budget of 229\n",
"[(start: 0.0 end: 2.85 score: 168.95 duration: 2.85), (start: 2.85 end: 3.8 score: 58.0 duration: 0.95), (start: 3.8 end: 6.05 score: 157.85 duration: 2.25), (start: 6.05 end: 7.2 score: 64.75 duration: 1.15), (start: 7.2 end: 14.0 score: 323.4 duration: 6.8), (start: 14.0 end: 15.4 score: 88.2 duration: 1.4), (start: 15.4 end: 15.6 score: 12.6 duration: 0.2), (start: 15.6 end: 23.55 score: 495.0 duration: 7.95), (start: 23.55 end: 23.9 score: 19.8 duration: 0.35), (start: 23.9 end: 29.45 score: 302.6 duration: 5.55), (start: 29.45 end: 33.2 score: 191.2 duration: 3.75), (start: 33.2 end: 33.85 score: 36.1 duration: 0.65), (start: 33.85 end: 35.5 score: 99.5 duration: 1.65), (start: 35.5 end: 36.6 score: 70.5 duration: 1.1), (start: 36.6 end: 40.55 score: 256.3 duration: 3.95), (start: 40.55 end: 41.9 score: 84.05 duration: 1.35), (start: 41.9 end: 46.9 score: 418.65 duration: 5.0), (start: 46.9 end: 48.1 score: 91.5 duration: 1.2), (start: 48.1 end: 48.55 score: 39.0 duration: 0.45), (start: 48.55 end: 50.0 score: 132.0 duration: 1.45), (start: 50.0 end: 50.95 score: 75.6 duration: 0.95), (start: 50.95 end: 52.55 score: 127.2 duration: 1.6), (start: 52.55 end: 55.6 score: 227.4 duration: 3.05), (start: 55.6 end: 56.55 score: 71.2 duration: 0.95), (start: 56.55 end: 59.75 score: 236.2 duration: 3.2), (start: 59.75 end: 62.95 score: 208.8 duration: 3.2), (start: 62.95 end: 67.0 score: 306.1 duration: 4.05), (start: 67.0 end: 72.8 score: 546.9 duration: 5.8), (start: 72.8 end: 73.45 score: 58.9 duration: 0.65), (start: 73.45 end: 74.35 score: 83.7 duration: 0.9), (start: 74.35 end: 75.15 score: 74.4 duration: 0.8), (start: 75.15 end: 76.4 score: 117.2 duration: 1.25), (start: 76.4 end: 76.65 score: 21.35 duration: 0.25), (start: 76.65 end: 78.0 score: 125.05 duration: 1.35), (start: 78.0 end: 80.75 score: 189.9 duration: 2.75), (start: 80.75 end: 84.4 score: 202.5 duration: 3.65), (start: 84.4 end: 86.9 score: 138.15 duration: 2.5), (start: 86.9 end: 92.2 score: 303.15 duration: 5.3), (start: 92.2 end: 93.0 score: 46.8 duration: 0.8), (start: 93.0 end: 96.65 score: 201.65 duration: 3.65), (start: 96.65 end: 97.6 score: 53.65 duration: 0.95), (start: 97.6 end: 97.95 score: 18.5 duration: 0.35), (start: 97.95 end: 98.8 score: 46.9 duration: 0.85), (start: 98.8 end: 99.65 score: 45.0 duration: 0.85), (start: 99.65 end: 100.7 score: 60.75 duration: 1.05), (start: 100.7 end: 104.0 score: 202.05 duration: 3.3), (start: 104.0 end: 105.0 score: 54.0 duration: 1.0), (start: 105.0 end: 111.5 score: 419.25 duration: 6.5), (start: 111.5 end: 112.5 score: 82.5 duration: 1.0), (start: 112.5 end: 114.95 score: 204.45 duration: 2.45), (start: 114.95 end: 115.3 score: 26.4 duration: 0.35), (start: 115.3 end: 120.7 score: 404.7 duration: 5.4), (start: 120.7 end: 121.4 score: 48.3 duration: 0.7), (start: 121.4 end: 122.05 score: 43.55 duration: 0.65), (start: 122.05 end: 124.5 score: 158.35 duration: 2.45), (start: 124.5 end: 125.0 score: 31.5 duration: 0.5), (start: 125.0 end: 125.25 score: 14.7 duration: 0.25), (start: 125.25 end: 128.05 score: 205.95 duration: 2.8), (start: 128.05 end: 129.8 score: 87.45 duration: 1.75), (start: 129.8 end: 133.75 score: 174.5 duration: 3.95), (start: 133.75 end: 136.1 score: 109.9 duration: 2.35), (start: 136.1 end: 136.3 score: 9.0 duration: 0.2), (start: 136.3 end: 138.9 score: 121.05 duration: 2.6), (start: 138.9 end: 139.6 score: 34.65 duration: 0.7), (start: 139.6 end: 141.35 score: 87.8 duration: 1.75), (start: 141.35 end: 143.2 score: 89.8 duration: 1.85), (start: 143.2 end: 146.55 score: 162.2 duration: 3.35), (start: 146.55 end: 147.55 score: 60.0 duration: 1.0), (start: 147.55 end: 149.9 score: 110.65 duration: 2.35), (start: 149.9 end: 150.2 score: 13.65 duration: 0.3), (start: 150.2 end: 150.7 score: 23.25 duration: 0.5), (start: 150.7 end: 152.4 score: 79.65 duration: 1.7), (start: 152.4 end: 153.75 score: 64.0 duration: 1.35), (start: 153.75 end: 154.35 score: 28.8 duration: 0.6), (start: 154.35 end: 154.95 score: 28.8 duration: 0.6), (start: 154.95 end: 156.15 score: 59.4 duration: 1.2), (start: 156.15 end: 157.65 score: 92.25 duration: 1.5), (start: 157.65 end: 158.6 score: 59.45 duration: 0.95), (start: 158.6 end: 160.3 score: 97.8 duration: 1.7), (start: 160.3 end: 161.3 score: 39.0 duration: 1.0), (start: 161.3 end: 164.1 score: 112.5 duration: 2.8), (start: 164.1 end: 167.45 score: 144.3 duration: 3.35), (start: 167.45 end: 167.85 score: 18.0 duration: 0.4), (start: 167.85 end: 168.8 score: 42.3 duration: 0.95), (start: 168.8 end: 169.1 score: 13.05 duration: 0.3), (start: 169.1 end: 171.2 score: 91.35 duration: 2.1), (start: 171.2 end: 171.35 score: 5.8 duration: 0.15), (start: 171.35 end: 171.9 score: 24.65 duration: 0.55), (start: 171.9 end: 181.25 score: 397.0 duration: 9.35), (start: 181.25 end: 187.15 score: 243.75 duration: 5.9), (start: 187.15 end: 187.9 score: 36.8 duration: 0.75), (start: 187.9 end: 191.7 score: 173.1 duration: 3.8), (start: 191.7 end: 193.45 score: 71.9 duration: 1.75), (start: 193.45 end: 195.15 score: 81.6 duration: 1.7), (start: 195.15 end: 196.15 score: 50.6 duration: 1.0), (start: 196.15 end: 197.25 score: 52.8 duration: 1.1), (start: 197.25 end: 198.9 score: 82.7 duration: 1.65), (start: 198.9 end: 200.25 score: 71.15 duration: 1.35), (start: 200.25 end: 201.3 score: 68.8 duration: 1.05), (start: 201.3 end: 203.05 score: 107.15 duration: 1.75), (start: 203.05 end: 203.95 score: 54.0 duration: 0.9), (start: 203.95 end: 206.1 score: 136.75 duration: 2.15), (start: 206.1 end: 206.95 score: 56.25 duration: 0.85), (start: 206.95 end: 210.25 score: 210.05 duration: 3.3), (start: 210.25 end: 211.0 score: 49.45 duration: 0.75), (start: 211.0 end: 212.25 score: 79.2 duration: 1.25), (start: 212.25 end: 212.45 score: 12.6 duration: 0.2), (start: 212.45 end: 213.7 score: 79.8 duration: 1.25), (start: 213.7 end: 221.95 score: 475.0 duration: 8.25), (start: 221.95 end: 226.3 score: 189.95 duration: 4.35), (start: 226.3 end: 230.458 score: 180.45 duration: 4.158)]\n"
]
},
{
"data": {
"image/png": 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C\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110b4fad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val, wt = totalvalue(dp_items)\n",
"print \"pre sum: \", val, \" weight, \", -wt/float_factor\n",
"#start to run dp\n",
"print \"videolen: \", video_len, \" budget: \", video_len*0.9, \"totalvalue: \"\n",
"\n",
"bagged = knapsack01_dp(dp_items, int(video_len*float_factor))\n",
"# print(\"Bagged the following items\\n \" +\n",
"# '\\n '.join(sorted(item for item,_,_ in bagged)))\n",
"val, wt = totalvalue(bagged)\n",
"print(\"for a total value of %i and a total weight budget of %i\" % (val, -wt/float_factor))\n",
"bagged_shots = [x[0] for x in bagged[::-1]]\n",
"print bagged_shots\n",
"vis_plot_shots_totalscore(bagged_shots) \n"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x115404890>]"
]
},
"execution_count": 206,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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/Iu4qZ+BmVt1694a//AU22yw1aT34YN4RVRcvZWJmBtx2G3zzm3DaafDtb7tJ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112a5eed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_dp_data_x = []\n",
"plot_dp_data_y = []\n",
"\n",
"table = knapsack01_dp_init(dp_items, int(video_len*float_factor))\n",
"# plot importance score performance\n",
"for i in range(int(video_len)):\n",
" bagged = knapsack01_dp_getsolution(dp_items, int(i*float_factor), table) \n",
" val, wt = totalvalue(bagged)\n",
"# print(\"for a total value of %i and a total weight budget of %i\" % (val, -wt/float_factor))\n",
" plot_dp_data_x.append(i)\n",
" plot_dp_data_y.append(val)\n",
"# print i, val\n",
" \n",
"# plt.ylim((plot_dp_data_y.min(), plot_dp_data_y.max())\n",
"plt.ylabel('total score')\n",
"plt.xlabel('time (sec)')\n",
"plt.xlim(max(plot_dp_data_x), min(plot_dp_data_x))\n",
"plt.plot(plot_dp_data_x, plot_dp_data_y)"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"99.1596666667\n"
]
}
],
"source": [
"# print \"silent:\", total_si, \"nonsilent:\", total_nonsi, \"total:\", dict_info[vid]\n",
"SILENT_RATIO = 6\n",
"NONSILENT_RATIO = 1.5\n",
"\n",
"tb_noskiptime = total_si/SILENT_RATIO + total_nonsi/NONSILENT_RATIO\n",
"\n",
"print tb_noskiptime\n"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import math\n",
"\n",
"budget = video_len *0.5\n",
"\n",
"# calculate the speedup rate:\n",
"\n",
"def calc_speedup(si, nonsi, budget):\n",
" T1 = total_si\n",
" T2 = total_nonsi\n",
"\n",
" B = budget\n",
" c1 = (math.sqrt( math.pow((9*B - T1 - 10 * T2), 2) + 36 * B * T1 ) - 9*B + T1 + 10*T2)/(2*B)\n",
" c2 = (c1+9)/10\n",
" return c1, c2"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import copy\n",
"#input should be an array of ep_shot\n",
"def get_vis_shot_data(shots):\n",
" data_x = []\n",
" data_y = []\n",
" for i, shot in enumerate(shots):\n",
" data_x.append(shot.start)\n",
" data_y.append(0)\n",
" data_x.append(shot.start)\n",
" data_y.append(shot.accumulated_score)\n",
" data_x.append(shot.start+shot.duration) #shot.start + shot.duration may not be equal to shot.end because of the speed up.\n",
" data_y.append(shot.accumulated_score)\n",
" data_x.append(shot.start+shot.duration)\n",
" data_y.append(0)\n",
" return data_x, data_y\n",
"# plt.ylabel('accumulated_score')\n",
"# plt.xlabel('time(sec)')\n",
"# plt.plot(data_x, data_y)\n",
"\n",
"def vis_strategy_with_speedup(budget):\n",
" tb_noskiptime = total_si/SILENT_RATIO + total_nonsi/NONSILENT_RATIO\n",
" if budget > tb_noskiptime:\n",
" c1, c2 = calc_speedup(total_si, total_nonsi, budget)\n",
" # calculate the speedup rate:\n",
" print c1, c2, budget\n",
" nospeedup_ep_shots = [x[0] for x in dp_items]\n",
" speedup_ep_shots = [] #copy datastructure to hold the content\n",
"# nospeedupx, nospeedupy = get_vis_shot_data(nospeedup_ep_shots) \n",
" print nospeedup_ep_shots[0]\n",
" for i, value in enumerate(nospeedup_ep_shots):\n",
" speedup_ep_shots.append(copy.copy(value))\n",
" if speedup_ep_shots[i].ifsilent == SILENT:\n",
" speedup_ep_shots[i].duration = (speedup_ep_shots[i].end-speedup_ep_shots[i].start)/c1\n",
" else:\n",
" speedup_ep_shots[i].duration = (speedup_ep_shots[i].end-speedup_ep_shots[i].start)/c2\n",
" speedupx, speedupy = get_vis_shot_data(speedup_ep_shots) \n",
"# print speedup_ep_shots[0]\n",
" vis_plot_shots_totalscore(speedup_ep_shots)\n",
" vis_plot_shots_totalscore(nospeedup_ep_shots)\n",
" fig = plt.figure()\n",
" vis_plot_shots_totalscore(speedup_ep_shots)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.43525967627 1.34352596763 115.0\n",
"(start: 0.0 end: 2.85 score: 168.95 duration: 2.85)\n"
]
},
{
"data": {
"image/png": 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J6X2SNkl6RtJGSbMb9lkl6VlJ2ySN7aprZmZtULRk8WHgvoh4Ka3PBgYiYn3OrvuAfxcR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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111eedad0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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j+sx64pqFmZnlcs3CzMxyOVmYmVkuJwszM8vlZGFmZrmcLMzMLJeThZmZ5fr/\nXn2jPrKtrAMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1112c44d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"vis_strategy_with_speedup(budget)"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11185f850>]"
]
},
"execution_count": 195,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11185fb50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_dp_speedup_data_x = []\n",
"plot_dp_speedup_data_y = []\n",
"\n",
"table = knapsack01_dp_init(dp_items, int(video_len*float_factor))\n",
"# plot importance score performance\n",
"for i in range(1,int(video_len)):\n",
" if(i > tb_noskiptime):\n",
" plot_dp_speedup_data_x.append(i)\n",
" plot_dp_speedup_data_y.append(max(plot_dp_data_y))\n",
" else:\n",
"# print i\n",
" c1, c2 = calc_speedup(total_si, total_nonsi, i)\n",
" \n",
" dp_speedup_items = [] #item, weight, value, \n",
" for j, value in enumerate(audioshots):\n",
" shot_cpy = copy.copy(value)\n",
" if shot_cpy.ifsilent == SILENT:\n",
" shot_cpy.duration = (shot_cpy.end-shot_cpy.start)/c1\n",
" else:\n",
" shot_cpy.duration = (shot_cpy.end-shot_cpy.start)/c2 \n",
" dp_speedup_items.append( (shot_cpy, int(shot_cpy.duration*float_factor), shot_cpy.accumulated_score)) #multiply by 100 to elimiate float\n",
" table = knapsack01_dp_init(dp_items, int(i*float_factor)) \n",
" bagged = knapsack01_dp_getsolution(dp_items, int(i*float_factor), table) \n",
" val, wt = totalvalue(bagged)\n",
"# print(\"for a total value of %i and a total weight budget of %i\" % (val, -wt/float_factor))\n",
" plot_dp_speedup_data_x.append(i)\n",
" plot_dp_speedup_data_y.append(val)\n",
"plt.ylabel('total score')\n",
"plt.xlabel('time (sec)')\n",
"plt.xlim(max(plot_dp_data_x), min(plot_dp_data_x))\n",
"plt.plot(plot_dp_speedup_data_x, plot_dp_speedup_data_y)"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13616.4\n"
]
}
],
"source": [
"print max(plot_dp_data_y)"
]
},
{
"cell_type": "code",
"execution_count": 214,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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RFSlhYW7oRZ+xS4wpNJYETN7FxMDYsfD99+5xFE2awLhxdvlaQESC414BUzxY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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115b2d110>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.xlim(max(plot_dp_data_x), min(plot_dp_data_x))\n",
"plt.title('Total weight of different time budget')\n",
"plt.plot(plot_dp_data_x, plot_dp_data_y, 'r', label=\"nospeedup\")\n",
"plt.plot(plot_dp_speedup_data_x, plot_dp_speedup_data_y, 'b', label=\"speedup\")\n",
"legend = plt.legend(loc='upper right', shadow=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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