{
 "metadata": {
  "name": "",
  "signature": "sha256:a585965d204fc21b799bc19b67e762f91ce3ec9ee91edc6478699f1c287d4345"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import cv2\n",
      "import matplotlib.pyplot as plt\n",
      "import os\n",
      "import cv\n",
      "import sys\n",
      "try:\n",
      "    import cv\n",
      "    CAP_FRAME_COUNT = cv.CV_CAP_PROP_FRAME_COUNT\n",
      "    CAP_FRAME_WIDTH  = cv.CV_CAP_PROP_FRAME_WIDTH\n",
      "    CAP_FRAME_HEIGHT = cv.CV_CAP_PROP_FRAME_HEIGHT\n",
      "    CAP_FPS = cv.CV_CAP_PROP_FPS\n",
      "    CAP_POS_FRAMES = cv.CV_CAP_PROP_POS_FRAMES\n",
      "except:\n",
      "    CAP_FRAME_COUNT = cv2.CAP_PROP_FRAME_COUNT\n",
      "    CAP_FRAME_WIDTH  = cv2.CAP_PROP_FRAME_WIDTH\n",
      "    CAP_FRAME_HEIGHT = cv2.CAP_PROP_FRAME_HEIGHT\n",
      "    CAP_FPS = cv2.CAP_PROP_FPS\n",
      "    CAP_POS_FRAMES = cv2.CAP_PROP_POS_FRAMES"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html.widgets import interact, interactive, fixed\n",
      "from IPython.display import clear_output, display, HTML\n",
      "from IPython.html import widgets\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from io import BytesIO\n",
      "import PIL\n",
      "from IPython.display import display, Image\n",
      "def img_to_png(ima, cvt=None):\n",
      "    if cvt:\n",
      "        ima = cv2.cvtColor(ima, cvt)\n",
      "    im = PIL.Image.fromarray(ima)\n",
      "    bio = BytesIO()\n",
      "    im.save(bio, format='png')\n",
      "    return bio.getvalue()\n",
      "def display_img_array(ima, cvt=None, **kwargs):\n",
      "    display(Image(img_to_png(ima, cvt=cvt) , format='png', **kwargs))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def normalize(im):    \n",
      "    im=cv2.cvtColor(im, cv2.CV_32F)\n",
      "    im=cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\n",
      "    #im=cv2.equalizeHist(im)\n",
      "    return im"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def gray2vector(img):\n",
      "    v = img.reshape(-1).astype(float) \n",
      "    v = v - np.average(v) \n",
      "    return v/np.linalg.norm(v)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def diff_i(gray, i):\n",
      "    v1 = gray2vector(gray)\n",
      "    return np.dot(v1, slides_v[i])\n",
      "\n",
      "def compare_slides(gray, slides_v):\n",
      "    v1 = gray2vector(gray)\n",
      "    r = np.dot(slides_v, v1)\n",
      "    i = np.argmax(r)\n",
      "    return r[i], i\n",
      "\n",
      "def compare_absdiff(gray):\n",
      "    return sorted( (cv2.absdiff(slides[i], gray).sum() , i)  for i in range(len(slides)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def frame_to_time(f, r):\n",
      "    s = f/r\n",
      "    m = int(s/60)\n",
      "    s = s -60*m\n",
      "    return \"%d:%04.1f\"%(m,s)\n",
      "def sync_video( fn, p, slides, slides_v, threshold=0.8, step=10, dark=1000, STOP=-1, debug=False):\n",
      "    cap = cv2.VideoCapture(fn)\n",
      "    frame_rate = cap.get(cv.CV_CAP_PROP_FPS)\n",
      "    print \"frame_rate\", frame_rate\n",
      "    num_of_frames = cap.get(cv.CV_CAP_PROP_FRAME_COUNT)\n",
      "    frame_index =-1\n",
      "    last_slide = -1\n",
      "    last_start = -1\n",
      "    frame_list = []\n",
      "    progress = widgets.IntProgressWidget(min=0, max = num_of_frames - 1, value=0)\n",
      "    progress_text = widgets.TextWidget()\n",
      "    progress.set_css('background', 'black')\n",
      "    display(progress)\n",
      "    display(progress_text)   \n",
      "    img_widget = widgets.ImageWidget()\n",
      "    display(img_widget)\n",
      "    slide_h, slide_w = original_slides[0].shape[:2]\n",
      "    while cap.isOpened():\n",
      "        frame_index +=1\n",
      "        ret, frame = cap.read()\n",
      "        if not ret:\n",
      "            break        \n",
      "        if frame_index%step ==0:\n",
      "            if STOP!=-1 and frame_index > STOP:\n",
      "                break                        \n",
      "            gray = cv2.resize(normalize(frame)[p[0]:p[2],p[1]:p[3]], (256,256))        \n",
      "            darklevel = np.linalg.norm(gray.reshape(-1).astype(float)) \n",
      "            if darklevel < dark:\n",
      "                # too dark                \n",
      "                this_slide, v, i = -1, -1, 0\n",
      "            else:            \n",
      "                v, i = compare_slides(gray, slides_v)\n",
      "                this_slide = i if v > threshold else -1 \n",
      "            if debug:\n",
      "                if i>=0:\n",
      "                    frame2 = frame.copy()            \n",
      "                    frame2[p[0]:p[2], p[1]:p[3]] = cv2.resize(original_slides[i][q[0]:q[2], q[1]:q[3]], (p[3]-p[1], p[2]-p[0]))\n",
      "                    outp = np.concatenate( (frame2, cv2.addWeighted(frame,0.5,frame2, 0.5,0), frame), axis = 1)\n",
      "                    display_img_array(outp, width=1200, cvt=cv2.COLOR_BGR2RGB)\n",
      "                else:\n",
      "                    display_img_array(frame, width=400, cvt=cv2.COLOR_BGR2RGB)\n",
      "                print v,i\n",
      "            if frame_index%100 ==0:                \n",
      "                frame2 = original_slides[this_slide] if this_slide >=0 else np.zeros_like(original_slides[0])\n",
      "                frame2 = cv2.resize(frame2, (slide_w*frame.shape[0]/slide_h, frame.shape[0]))                                \n",
      "                outp = np.concatenate( (frame, frame2), axis = 1)\n",
      "                img_widget.value = img_to_png(outp, cvt=cv2.COLOR_BGR2RGB)\n",
      "                img_widget.height=600\n",
      "                progress.value = frame_index            \n",
      "                progress_text.value = \"%d/%d (%.1f)\"%(frame_index, num_of_frames, 100.0*frame_index/num_of_frames)\n",
      "                if i >=0:\n",
      "                    progress_text.value += \" match: %d, %s\"%(i, v)\n",
      "                    \n",
      "            if this_slide != last_slide:\n",
      "                # update\n",
      "                frame_list.append( (last_start,  frame_index-1, last_slide))\n",
      "                \n",
      "                # display information                \n",
      "                if last_slide >=0:                \n",
      "                    fl = frame_list[-1]\n",
      "                    t1, t2 = frame_to_time(fl[0], frame_rate), frame_to_time(fl[1], frame_rate)\n",
      "                    print fl, \"=(%s, %s)\"%(t1,t2), \"v=%f\"%v, \"dark=%d\"%darklevel\n",
      "                last_start = frame_index                            \n",
      "        last_slide = this_slide\n",
      "    # last update\n",
      "    frame_list.append( (last_start,  frame_index-1, last_slide))\n",
      "    cap.release()\n",
      "    return frame_list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def write_file(fn, p,q, outfn, original_slides, sync_result, M=20, fourcc=\"XVID\", SKIP=None, WH=None):    \n",
      "    # W,H = 1920, 1080\n",
      "    cap = cv2.VideoCapture(fn)\n",
      "    SW, SH = int(cap.get(cv.CV_CAP_PROP_FRAME_WIDTH)), int(cap.get(cv.CV_CAP_PROP_FRAME_HEIGHT))\n",
      "    if WH==None:\n",
      "        W, H = SW, SH\n",
      "    else:\n",
      "        W,H = WH\n",
      "    print \"(W,H)\", W,H\n",
      "    sys.stdout.flush()\n",
      "    p2 = ( p[0]*H/SH, p[1]*W/SW, p[2]*H/SH, p[3]*W/SW)\n",
      "    pw, ph = p2[3]-p2[1], p2[2]-p2[0]\n",
      "    print p2, q\n",
      "    fourcc = cv.FOURCC(*fourcc)\n",
      "    num_of_frames = cap.get(cv.CV_CAP_PROP_FRAME_COUNT)\n",
      "    frame_rate = cap.get(cv.CV_CAP_PROP_FPS)\n",
      "    print \"frame_rate\", frame_rate\n",
      "    sys.stdout.flush()\n",
      "    out = cv2.VideoWriter(outfn, fourcc, frame_rate, (W, H))\n",
      "    frame_index =-1\n",
      "    last_slide = -1\n",
      "    last_start = -1\n",
      "    frame_list = []\n",
      "    result_index = 0\n",
      "    progress = widgets.IntProgressWidget(min=0, max = num_of_frames - 1, value=0)\n",
      "    progress_text = widgets.TextWidget()\n",
      "    progress.set_css('background', 'black')\n",
      "    display(progress)\n",
      "    display(progress_text)\n",
      "    img_widget = widgets.ImageWidget()\n",
      "    display(img_widget)\n",
      "    while cap.isOpened():\n",
      "        frame_index +=1\n",
      "        ret, frame = cap.read()\n",
      "        if not ret:\n",
      "            break\n",
      "        while result_index < len(sync_result) and sync_result[result_index][1] < frame_index:\n",
      "            result_index += 1            \n",
      "        the_slide = (-1,-1,-1) if result_index >= len(sync_result) else sync_result[result_index]\n",
      "        if SKIP and the_slide[2] in SKIP:\n",
      "            the_slide = (-1,-1,-1)\n",
      "        original_frame = cv2.resize(frame, (W, H), interpolation = cv2.INTER_CUBIC)\n",
      "        if the_slide[2] >=0 and the_slide[1]-the_slide[0]>3*M:\n",
      "            slide = original_slides[the_slide[2]]\n",
      "            inner_frame = cv2.resize(slide[q[0]:q[2], q[1]:q[3]],  (pw, ph), interpolation = cv2.INTER_CUBIC )\n",
      "            d = min(frame_index-the_slide[0], the_slide[1]-frame_index)\n",
      "            out_frame = original_frame.copy()\n",
      "            out_frame[p2[0]:p2[2], p2[1]:p2[3]] = inner_frame\n",
      "            if d < M:\n",
      "                out_frame = cv2.addWeighted(out_frame, d*1.0/M , original_frame, 1- d*1.0/M, 0)\n",
      "        else:\n",
      "            out_frame = original_frame\n",
      "        out.write(out_frame)\n",
      "        if frame_index%100 ==0:\n",
      "            progress.value = frame_index            \n",
      "            progress_text.value = \"%d/%d (%.1f)\"%(frame_index, num_of_frames, 100.0*frame_index/num_of_frames)\n",
      "            disp_frame = np.concatenate((out_frame[:, :W/2], original_frame[:,W/2:]), axis=1)            \n",
      "            img_widget.value = img_to_png(disp_frame, cvt=cv2.COLOR_BGR2RGB)  \n",
      "            img_widget.width = \"800\"\n",
      "    cap.release()\n",
      "    out.release()\n",
      "            \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os.path\n",
      "def load_original_slides(name):\n",
      "    original_slides = []\n",
      "    i = 0\n",
      "    #progress = widgets.IntProgressWidget(min=0, max = num_of_frames - 1, value=0)\n",
      "    progress_text = widgets.TextWidget()\n",
      "    #progress.set_css('background', 'black')\n",
      "    #display(progress)\n",
      "    display(progress_text)\n",
      "    while True:         \n",
      "        progress_text.value = \"loading %d\"%i\n",
      "        img = cv2.imread(\"%s/%s-%d.png\"%(name, name, i))\n",
      "        if img is None:\n",
      "            break        \n",
      "        original_slides.append(img)\n",
      "        i+=1\n",
      "    print \"load original slides\", len(original_slides)\n",
      "    return original_slides\n",
      "def prepare_slides(original_slides, q, blur_factor):\n",
      "    normalized_slides = (cv2.blur(normalize(s), (blur_factor, blur_factor))  for s in original_slides)\n",
      "    slides = [cv2.resize(s[q[0]:q[2], q[1]:q[3]], (256,256), interpolation = cv2.INTER_CUBIC)  for s in normalized_slides]\n",
      "    slides_v = np.array([gray2vector(s) for s in slides])\n",
      "    print \"slides prepared\"\n",
      "    return slides, slides_v\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "original_slides, original_slides_name, result = None, None, None"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def auto_sync(NAME, p1, q1, blur_factor, p2=None, q2=None,  threshold=0.8, step=10, dark=1500, STOP=-1, debug=False, \n",
      "              SKIP=None, M=20, PASS=[3],  fourcc=\"XVID\",  EXT=\"avi\", WH=None):\n",
      "    global original_slides, result, original_slides_name\n",
      "    print \"NAME=\", NAME       \n",
      "    if 0 in PASS or not os.path.isfile(\"%s/%s-0.png\"%(NAME,NAME)) : # 0 Extract PDF\n",
      "        print \"extract slides\"\n",
      "        sys.stdout.flush()\n",
      "        print os.system(\"convert -density 200 %s/%s.pdf  %s/%s.png\"%(NAME,NAME,NAME,NAME))\n",
      "    result_slides_name = original_slides_name\n",
      "    if 1 in PASS or original_slides_name != NAME:\n",
      "        print \"load original png\"\n",
      "        original_slides = load_original_slides(NAME)\n",
      "        original_slides_name = NAME    \n",
      "    fn_base = \"%s/%s\"%(NAME,NAME)    \n",
      "    if  os.path.isfile(fn_base+\".mp4\"):\n",
      "        fn = fn_base+\".mp4\"\n",
      "    elif os.path.isfile(fn_base+\".avi\"):\n",
      "        fn = fn_base+\".avi\"\n",
      "    else:\n",
      "        print \"original video file does not exist\"\n",
      "        return\n",
      "    outfn = \"%s/better_%s.%s\"%(NAME, NAME, EXT)\n",
      "        \n",
      "\n",
      "    if 2 in PASS or result_slides_name != NAME: # Sync Video and Slides\n",
      "        print \"prepare slides\"\n",
      "        slides, slides_v = prepare_slides(original_slides, q1, blur_factor)        \n",
      "        print \"syncing video\"\n",
      "        result = sync_video(fn, p1, slides, slides_v, threshold=threshold, step=step, dark=dark, STOP=STOP, debug=debug)\n",
      "        print \"sync_video done\"   \n",
      "        \n",
      "    if p2 is None:   # full screen\n",
      "        p2 = p1\n",
      "    if q2 is None:  # full screen\n",
      "        q2 = q1\n",
      "        \n",
      "    if 3 in PASS or original_slides_name != NAME:\n",
      "        print \"start writing and converting\"\n",
      "        TEMP_OUT = \"temp_out.\"+EXT\n",
      "        write_file(fn, p1, q1, TEMP_OUT, original_slides, result, M=M, fourcc=fourcc, SKIP=SKIP, WH=WH)\n",
      "        print \"write done\"\n",
      "        sys.stdout.flush()\n",
      "        retcode = os.system(\"avconv -y -i %s -i %s -map 0:v -map 1:a -c:v copy -c:a copy %s\"%(TEMP_OUT, fn, outfn))\n",
      "        print \"covert done\", retcode"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1,q1, blur_factor = (10, 160, 1080, 1754) , (0, 40, 2112, 2844) , 10 \n",
      "p2, q2 = p1, q1\n",
      "auto_sync(\"tulip\",  p1,q1, blur_factor, p2,q2, threshold=0.9, M=5, fourcc=\"x264\", EXT=\"mp4\", SKIP=[28], PASS=[2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1,q1,blur_factor = (10, 159, 1080, 1754) , (0, 39, 2112, 2844) , 16\n",
      "p2, q2 =  (10, 131, 1080, 1750) , (0, 0, 2115, 2844),\n",
      "auto_sync(\"graphtool\", p1,q1, blur_factor, p2, q2, threshold=0.7, SKIP=[7, 90, 91,92,93,94,95,96,97], PASS=[2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1,q1, blur_factor = (10, 159, 1080, 1750) , (0, 35, 2115, 2844), 16\n",
      "p2,q2 = (10, 138, 1080, 1750) , (0, 0, 2115, 2844) # test abcdefg\n",
      "auto_sync(\"ls\",  p1,q1, blur_factor, p2,q2, threshold=0.8, SKIP=[4,35,36] , PASS=[2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1,q1, blur_factor = (211, 281, 958, 1723) , (0, 68, 2133, 2838) , 18\n",
      "p2,q2 = None, None \n",
      "auto_sync(\"fabric\",  p1,q1, blur_factor, p2,q2, threshold=0.8, SKIP=[19], M=40)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1,q1, blur_factor = (145, 160, 954, 1751) , (0, 27, 1125, 2000) , 12\n",
      "p2, q2 = None, None\n",
      "auto_sync(\"vote\",  p1,q1, blur_factor, p2,q2, threshold=0.75, M=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1,q1, blur_factor = (11, 144, 1080, 1752) , (0, 0, 1485, 2000) , 16\n",
      "p2, q2 = p1, q1\n",
      "auto_sync(\"mezz\",  p1,q1, blur_factor, p2,q2, threshold=0.9, M=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1,q1, blur_factor = (11, 135, 1080, 1753) , (0, 0, 2113, 2844) , 16\n",
      "p2, q2 = p1, q1\n",
      "auto_sync(\"summly\",  p1,q1, blur_factor, p2,q2, threshold=0.935, M=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1,q1, blur_factor = (19, 160, 1067, 1672) , (0, 80, 2070, 2844), 36\n",
      "p2, q2 = p1, q1\n",
      "auto_sync(\"StreetVoice\",  p1,q1, blur_factor, p2,q2, threshold=0.5, M=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1,q1, blur_factor = (19, 108, 1080, 1688) , (0, 0, 2084, 2844), 26\n",
      "p2, q2 = p1, q1\n",
      "auto_sync(\"grs\",  p1,q1, blur_factor, p2,q2, threshold=0.7, M=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1,q1, blur_factor=(73, 7, 1028, 1429), (8, 32, 2133, 2842), 13\n",
      "p2,q2=(69, 0, 1028, 1430), [0, 17, 2133, 2844]\n",
      "auto_sync(\"dmhs\",  p1,q1, blur_factor, p2,q2, threshold=0.6, M=20, SKIP=[0, 6, 17], PASS=[3])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1,q1, blur_factor=(87, 5, 404, 633), (9, 28, 1125, 2000), 14\n",
      "p2,q2=(84, 0, 404, 633), [0, 9, 1125, 2000]\n",
      "auto_sync(\"present\",  p1,q1, blur_factor, p2,q2, threshold=0.7, M=20, WH=(1440,1080), PASS=[2,3])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1,q1, blur_factor=(33, 4, 453, 635), (8, 30, 2119, 2844), 18\n",
      "p2,q2=(31, 0, 456, 635), [0, 8, 2133, 2844]\n",
      "auto_sync(\"hai\",  p1,q1, blur_factor, p2,q2, threshold=0.7, M=20, WH=(1440,1080))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1,q1, blur_factor=(34, 2, 451, 629), (4, 8, 1500, 2000), 14\n",
      "p2,q2=(32, 0, 451, 629), [0, 0, 1500, 2000]\n",
      "auto_sync(\"vim\",  p1,q1, blur_factor, p2,q2, threshold=0.8, M=20, WH=(1440,1080))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "NAME= vim\n",
        "load original png\n",
        "load original slides"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17\n",
        "prepare slides\n",
        "slides prepared"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "syncing video\n",
        "frame_rate 29.97002997\n",
        "(510, 1489, 0)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(0:17.0, 0:49.7) v=0.880750 dark=10112\n",
        "(1490, 2859, 1)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(0:49.7, 1:35.4) v=0.874783 dark=7511\n",
        "(2860, 3809, 2)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(1:35.4, 2:07.1) v=0.992393 dark=40593\n",
        "(3810, 4639, 3)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(2:07.1, 2:34.8) v=0.880929 dark=9820\n",
        "(4640, 5879, 4)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(2:34.8, 3:16.2) v=0.874664 dark=9693\n",
        "(5880, 6299, 5)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(3:16.2, 3:30.2) v=0.314825 dark=27658\n",
        "(9020, 9049, 5)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(5:01.0, 5:01.9) v=0.894895 dark=8972\n",
        "(9050, 9919, 6)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(5:02.0, 5:31.0) v=0.314081 dark=27138\n",
        "(10510, 10569, 6)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(5:50.7, 5:52.7) v=0.891409 dark=9972\n",
        "(10570, 11779, 7)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(5:52.7, 6:33.0) v=0.312722 dark=26447\n",
        "(15110, 15209, 7)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(8:24.2, 8:27.5) v=0.314112 dark=26469\n",
        "(17290, 17909, 7)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(9:36.9, 9:57.6) v=0.318960 dark=24984\n",
        "(19270, 19319, 7)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(10:43.0, 10:44.6) v=0.886876 dark=8421\n",
        "(19320, 19349, 8)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(10:44.6, 10:45.6) v=0.890548 dark=9772\n",
        "(19350, 20189, 7)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(10:45.6, 11:13.6) v=0.886495 dark=8399\n",
        "(20190, 20369, 8)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(11:13.7, 11:19.6) v=0.674841 dark=7892\n",
        "(20380, 20689, 8)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(11:20.0, 11:30.3) v=0.300560 dark=26469\n",
        "(21740, 22079, 8)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(12:05.4, 12:16.7) v=0.882859 dark=8892\n",
        "(22080, 22389, 9)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(12:16.7, 12:27.0) v=0.314180 dark=26330\n",
        "(23570, 23749, 9)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(13:06.5, 13:12.4) v=0.282210 dark=22812\n",
        "(24200, 24289, 9)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(13:27.5, 13:30.4) v=0.332195 dark=24593\n",
        "(24850, 24909, 9)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(13:49.2, 13:51.1) v=0.884287 dark=8687\n",
        "(24910, 25269, 10)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(13:51.2, 14:03.1) v=0.312524 dark=26418\n",
        "(26450, 26489, 10)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(14:42.5, 14:43.8) v=0.236490 dark=11876\n",
        "(26630, 26639, 9)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(14:48.6, 14:48.9) v=0.720991 dark=7883\n",
        "(26650, 27539, 10)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(14:49.2, 15:18.9) v=0.670329 dark=7777\n",
        "(27550, 27689, 10)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(15:19.3, 15:23.9) v=0.884074 dark=7866\n",
        "(27690, 27899, 11)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(15:23.9, 15:30.9) v=0.885193 dark=8865\n",
        "(27900, 27949, 10)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(15:30.9, 15:32.6) v=0.883748 dark=7886\n",
        "(27950, 28209, 11)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(15:32.6, 15:41.2) v=0.352887 dark=13769\n",
        "(29280, 29289, 11)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(16:17.0, 16:17.3) v=0.323675 dark=12192\n",
        "(29440, 31019, 11)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(16:22.3, 17:15.0) v=0.883191 dark=8352\n",
        "(31020, 31909, 12)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(17:15.0, 17:44.7) v=0.271830 dark=26401\n",
        "(32770, 33209, 12)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(18:13.4, 18:28.1) v=0.885606 dark=8130\n",
        "(33210, 33759, 13)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(18:28.1, 18:46.4) v=0.313255 dark=27302\n",
        "(35280, 35349, 13)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(19:37.2, 19:39.5) v=0.880311 dark=9045\n",
        "(35350, 35369, 14) =(19:39.5, 19:40.1) v=0.883706 dark=7905\n",
        "(35370, 35509, 13)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(19:40.2, 19:44.8) v=0.296882 dark=21428\n",
        "(35850, 35899, 13)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(19:56.2, 19:57.8) v=0.879523 dark=9066\n",
        "(35900, 35949, 14)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(19:57.9, 19:59.5) v=0.884199 dark=7829\n",
        "(35950, 35959, 13) =(19:59.5, 19:59.8) v=0.879959 dark=8882\n",
        "(35960, 36919, 14)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(19:59.9, 20:31.9) v=0.306256 dark=24196\n",
        "(37580, 37589, 14)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(20:53.9, 20:54.2) v=0.288880 dark=10194\n",
        "(39230, 39269, 14)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(21:49.0, 21:50.3) v=0.340925 dark=11169\n",
        "(42970, 42999, 14)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(23:53.8, 23:54.7) v=0.477629 dark=8324\n",
        "(43100, 43119, 14)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(23:58.1, 23:58.7) v=0.223015 dark=2467\n",
        "(43130, 43249, 15)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(23:59.1, 24:03.1) v=0.299696 dark=11126\n",
        "(46260, 46309, 15)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(25:43.5, 25:45.2) v=0.303056 dark=12248\n",
        "(47900, 47939, 15)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(26:38.3, 26:39.6) v=0.397188 dark=23937\n",
        "(49930, 49949, 15)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(27:46.0, 27:46.6) v=0.319250 dark=6208\n",
        "(49960, 50129, 16)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " =(27:47.0, 27:52.6) v=0.312347 dark=13905\n",
        "sync_video done"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "start writing and converting\n",
        "(W,H)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1440 1080\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(76, 4, 1014, 1415) (4, 8, 1500, 2000)\n",
        "frame_rate 29.97002997\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "write done\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "covert done 0\n"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1,q1, blur_factor=(126, 1, 443, 631), (6, 12, 1125, 2000), 12\n",
      "p2,q2=(124, 0, 443, 631), [0, 6, 1125, 2000]\n",
      "auto_sync(\"openstack\",  p1,q1, blur_factor, p2,q2, threshold=0.7, M=20, WH=(1440,1080), SKIP=[3,14,35,36])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "NAME= openstack\n",
        "start writing and converting\n",
        "(W,H) 1440 1080\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(283, 2, 996, 1419) (6, 12, 1125, 2000)\n",
        "frame_rate 29.97002997\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "write done\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "covert done 0\n"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "[(-1, 1819, -1),\n",
        " (1820, 3119, 2),\n",
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        " (44160, 44859, 33),\n",
        " (44860, 44869, 32),\n",
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        " (44900, 45709, 34),\n",
        " (45710, 45949, 35),\n",
        " (45950, 48038, -1)]"
       ]
      }
     ],
     "prompt_number": 79
    }
   ],
   "metadata": {}
  }
 ]
}