notebooks/demo_depth_map.ipynb

{
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   "cell_type": "code",
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     "start_time": "2022-03-17T01:10:18.597524Z"
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   "source": [
    "import os, sys\n",
    "sys.path.append('../')\n",
    "os.environ[\"PYOPENGL_PLATFORM\"] = \"egl\"\n",
    "\n",
    "import numpy as np\n",
    "import json\n",
    "import math\n",
    "\n",
    "import torch\n",
    "\n",
    "import open3d as o3d\n",
    "import json\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "%matplotlib nbagg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:20.874077Z",
     "start_time": "2022-03-17T01:10:20.866707Z"
    },
    "code_folding": [
     0
    ]
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   "source": [
    "def plot_3d_point_cloud(x,\n",
    "                        y,\n",
    "                        z,\n",
    "                        show=True,\n",
    "                        show_axis=True,\n",
    "                        in_u_sphere=False,\n",
    "                        marker='.',\n",
    "                        s=8,\n",
    "                        alpha=.8,\n",
    "                        figsize=(5, 5),\n",
    "                        elev=10,\n",
    "                        azim=240,\n",
    "                        axis=None,\n",
    "                        title=None,\n",
    "                        lim=None,\n",
    "                        *args,\n",
    "                        **kwargs):\n",
    "\n",
    "    if axis is None:\n",
    "        fig = plt.figure(figsize=figsize)\n",
    "        ax = fig.add_subplot(111, projection='3d')\n",
    "    else:\n",
    "        ax = axis\n",
    "        fig = axis\n",
    "\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "\n",
    "    sc = ax.scatter(x, y, z, marker=marker, s=s, alpha=alpha, *args, **kwargs)\n",
    "    ax.view_init(elev=elev, azim=azim)\n",
    "\n",
    "    if lim:\n",
    "        ax.set_xlim3d(*lim[0])\n",
    "        ax.set_ylim3d(*lim[1])\n",
    "        ax.set_zlim3d(*lim[2])\n",
    "    elif in_u_sphere:\n",
    "        ax.set_xlim3d(-0.5, 0.5)\n",
    "        ax.set_ylim3d(-0.5, 0.5)\n",
    "        ax.set_zlim3d(-0.5, 0.5)\n",
    "    else:\n",
    "        lim = (min(np.min(x), np.min(y),\n",
    "                   np.min(z)), max(np.max(x), np.max(y), np.max(z)))\n",
    "        ax.set_xlim(1.3 * lim[0], 1.3 * lim[1])\n",
    "        ax.set_ylim(1.3 * lim[0], 1.3 * lim[1])\n",
    "        ax.set_zlim(1.3 * lim[0], 1.3 * lim[1])\n",
    "        plt.tight_layout()\n",
    "\n",
    "    if not show_axis:\n",
    "        plt.axis('off')\n",
    "\n",
    "    if show:\n",
    "        plt.show()\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:20.884899Z",
     "start_time": "2022-03-17T01:10:20.875690Z"
    }
   },
   "outputs": [],
   "source": [
    "def read_depth(depth_path):\n",
    "    depth_img = np.array(Image.open(depth_path))\n",
    "    depth_img = depth_img.astype(np.float32) * 0.001\n",
    "    \n",
    "    \n",
    "    rgbd = o3d.geometry.RGBDImage.create_from_color_and_depth(\n",
    "        o3d.geometry.Image(np.empty_like(depth_img)),\n",
    "        o3d.geometry.Image(depth_img),\n",
    "        depth_scale=1.0,\n",
    "        depth_trunc=0.7,\n",
    "        convert_rgb_to_intensity=False,\n",
    "    )\n",
    "    return rgbd\n",
    "\n",
    "def sum_downsample_points(point_list, voxel_size=0.01, nb_neighbors=20, std_ratio=2.0):\n",
    "    points = np.concatenate([np.asarray(x.points) for x in point_list], axis=0)\n",
    "    pcd = o3d.geometry.PointCloud()\n",
    "    pcd.points = o3d.utility.Vector3dVector(points)\n",
    "    pcd = pcd.voxel_down_sample(voxel_size=voxel_size)\n",
    "    pcd, _ = pcd.remove_statistical_outlier(nb_neighbors=nb_neighbors, std_ratio=std_ratio)\n",
    "    return pcd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:20.902631Z",
     "start_time": "2022-03-17T01:10:20.886038Z"
    }
   },
   "outputs": [],
   "source": [
    "def normalize(tensor: torch.Tensor, dim: int) -> torch.Tensor:\n",
    "    return tensor / ((tensor ** 2).sum(dim, keepdim=True).sqrt() + 1.0e-5)\n",
    "\n",
    "def vector_to_rotation(vector):\n",
    "    z = np.array(vector)\n",
    "    z = z / np.linalg.norm(z)\n",
    "    x = np.array([1, 0, 0])\n",
    "    x = x - z*(x.dot(z)/z.dot(z))\n",
    "    x = x / np.linalg.norm(x)\n",
    "    y = np.cross(z, x)\n",
    "    return np.c_[x, y, z]\n",
    "\n",
    "def add_r_joint_to_scene(scene,\n",
    "                             axis,\n",
    "                             pivot_point,\n",
    "                             length,\n",
    "                             radius=0.01,\n",
    "                             joint_color=[200, 0, 0, 180],\n",
    "                             recenter=False):\n",
    "    if recenter:\n",
    "        pivot_point = np.cross(axis, np.cross(pivot_point, axis))\n",
    "    rotation_mat = vector_to_rotation(axis)\n",
    "    screw_tran = np.eye(4)\n",
    "    screw_tran[:3, :3] = rotation_mat\n",
    "    screw_tran[:3, 3] = pivot_point\n",
    "    \n",
    "    axis_cylinder = trimesh.creation.cylinder(radius, height=length)\n",
    "    axis_arrow = trimesh.creation.cone(radius * 2, radius * 4)\n",
    "    arrow_trans = np.eye(4)\n",
    "    arrow_trans[2, 3] = length / 2\n",
    "    axis_arrow.apply_transform(arrow_trans)\n",
    "    axis_obj = trimesh.Scene((axis_cylinder, axis_arrow))\n",
    "    screw = as_mesh(axis_obj)\n",
    "    \n",
    "    # screw.apply_translation([0, 0, 0.1])\n",
    "    screw.apply_transform(screw_tran)\n",
    "    screw.visual.face_colors = np.array(joint_color, dtype=np.uint8)\n",
    "    scene.add_geometry(screw)\n",
    "    return screw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:21.159181Z",
     "start_time": "2022-03-17T01:10:20.904046Z"
    }
   },
   "outputs": [],
   "source": [
    "# read data\n",
    "root = '../data/real_laptop'\n",
    "num_point_per_depth = 2048\n",
    "\n",
    "with open(os.path.join(root, 'camera2base.json')) as f:\n",
    "    transform_dict = json.load(f)\n",
    "\n",
    "src_img_dir = os.path.join(root, 'imgs_1')\n",
    "dst_img_dir = os.path.join(root, 'imgs_2')\n",
    "\n",
    "transforms = transform_dict['camera2base']\n",
    "intrinsics = o3d.camera.PinholeCameraIntrinsic(\n",
    "    width=640,\n",
    "    height=480,\n",
    "    fx=635.647,\n",
    "    fy=635.647,\n",
    "    cx=321.300,\n",
    "    cy=241.436,\n",
    ")\n",
    "# read data\n",
    "src_data = []\n",
    "for idx in range(len(transforms)):\n",
    "    rgbd = read_depth(os.path.join(src_img_dir, f'{idx}_depth.png'))\n",
    "    src_data.append((rgbd, np.array(transforms[idx])))\n",
    "\n",
    "dst_data = []\n",
    "for idx in range(len(transforms)):\n",
    "    rgbd = read_depth(os.path.join(dst_img_dir, f'{idx}_depth.png'))\n",
    "    dst_data.append((rgbd, np.array(transforms[idx])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:21.361851Z",
     "start_time": "2022-03-17T01:10:21.160570Z"
    }
   },
   "outputs": [],
   "source": [
    "# crop out the object\n",
    "bbox = o3d.geometry.AxisAlignedBoundingBox(min_bound=(0.3, -0.5, 0.03), max_bound=(0.7, 0.16, 1))\n",
    "\n",
    "# find bounding box of data\n",
    "src_pc_list = []\n",
    "for rgbd, trans in src_data:\n",
    "    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd, intrinsics)\n",
    "    # pcd, tmp = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=0.01)\n",
    "    pcd.transform(trans)\n",
    "    pcd = pcd.crop(bbox)\n",
    "    pc = np.asarray(pcd.points)\n",
    "    pc_idx = np.random.randint(0, pc.shape[0], size=(2048, ))\n",
    "    pc = pc[pc_idx]\n",
    "    src_pc_list.append(pc)\n",
    "src_fused_pc = np.concatenate(src_pc_list, axis=0)\n",
    "center = (np.min(src_fused_pc, 0) + np.max(src_fused_pc, 0)) / 2\n",
    "scale = (np.max(src_fused_pc, 0) - np.min(src_fused_pc, 0)).max()\n",
    "scale *= 1.1\n",
    "\n",
    "# back project and normalize point cloud\n",
    "\n",
    "src_pcd_list = []\n",
    "for rgbd, trans in src_data:\n",
    "    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd, intrinsics)\n",
    "    pcd.transform(trans)\n",
    "    pcd = pcd.crop(bbox)\n",
    "    center_transform = np.eye(4)\n",
    "    center_transform[:3, 3] = -center\n",
    "    pcd.transform(center_transform)\n",
    "    pcd.scale(1 / scale, np.zeros((3, 1)))\n",
    "    src_pcd_list.append(pcd)\n",
    "    \n",
    "dst_pcd_list = []\n",
    "for rgbd, trans in dst_data:\n",
    "    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd, intrinsics)\n",
    "    # pcd, tmp = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=0.01)\n",
    "    pcd.transform(trans)\n",
    "    pcd = pcd.crop(bbox)\n",
    "    center_transform = np.eye(4)\n",
    "    center_transform[:3, 3] = -center\n",
    "    pcd.transform(center_transform)\n",
    "    pcd.scale(1 / scale, np.zeros((3, 1)))\n",
    "    dst_pcd_list.append(pcd)\n",
    "    \n",
    "src_pcd = sum_downsample_points(src_pcd_list, 0.02, 50, 0.1)\n",
    "dst_pcd = sum_downsample_points(dst_pcd_list, 0.02, 50, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:21.434993Z",
     "start_time": "2022-03-17T01:10:21.363155Z"
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   },
   "outputs": [
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       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\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 < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
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     "output_type": "display_data"
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize crop results\n",
    "# tune crop box to get better isolated objects\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 2, 1, projection='3d')\n",
    "plot_3d_point_cloud(*np.asarray(src_pcd.points).T,\n",
    "                    axis=ax,\n",
    "                    azim=30,\n",
    "                    elev=30,\n",
    "                    lim=[(-0.5, 0.5)] * 3)\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2, projection='3d')\n",
    "plot_3d_point_cloud(*np.asarray(dst_pcd.points).T,\n",
    "                    axis=ax,\n",
    "                    azim=30,\n",
    "                    elev=30,\n",
    "                    lim=[(-0.5, 0.5)] * 3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:21.834409Z",
     "start_time": "2022-03-17T01:10:21.436638Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from hydra.experimental import initialize, initialize_config_module, initialize_config_dir, compose\n",
    "from omegaconf import OmegaConf\n",
    "import hydra\n",
    "\n",
    "from src.third_party.ConvONets.conv_onet.generation_two_stage import Generator3D\n",
    "from src.utils.misc import sample_point_cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:22.145791Z",
     "start_time": "2022-03-17T01:10:21.838704Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/data2/zhenyu/anaconda3/envs/Ditto/lib/python3.8/site-packages/hydra/experimental/initialize.py:35: UserWarning: hydra.experimental.initialize() is no longer experimental. Use hydra.initialize()\n",
      "  warnings.warn(\n",
      "/mnt/data2/zhenyu/anaconda3/envs/Ditto/lib/python3.8/site-packages/hydra/experimental/compose.py:16: UserWarning: hydra.experimental.compose() is no longer experimental. Use hydra.compose()\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "with initialize(config_path='../configs/'):\n",
    "    config = compose(\n",
    "        config_name='config',\n",
    "        overrides=[\n",
    "            'experiment=Ditto_s2m.yaml',\n",
    "        ], return_hydra_config=True)\n",
    "config.datamodule.opt.train.data_dir = '../data/'\n",
    "config.datamodule.opt.val.data_dir = '../data/'\n",
    "config.datamodule.opt.test.data_dir = '../data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:25.530752Z",
     "start_time": "2022-03-17T01:10:22.146932Z"
    }
   },
   "outputs": [],
   "source": [
    "model = hydra.utils.instantiate(config.model)\n",
    "ckpt = torch.load('../data/Ditto_s2m.ckpt')\n",
    "device = torch.device(0)\n",
    "model.load_state_dict(ckpt['state_dict'], strict=True)\n",
    "model = model.eval().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:25.551329Z",
     "start_time": "2022-03-17T01:10:25.534806Z"
    }
   },
   "outputs": [],
   "source": [
    "generator = Generator3D(\n",
    "    model.model,\n",
    "    device=device,\n",
    "    threshold=0.4,\n",
    "    seg_threshold=0.5,\n",
    "    input_type='pointcloud',\n",
    "    refinement_step=0,\n",
    "    padding=0.1,\n",
    "    resolution0=32\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:25.567740Z",
     "start_time": "2022-03-17T01:10:25.553360Z"
    }
   },
   "outputs": [],
   "source": [
    "pc_start = np.asarray(src_pcd.points)\n",
    "pc_end = np.asarray(dst_pcd.points)\n",
    "\n",
    "bound_max = np.maximum(pc_start.max(0), pc_end.max(0))\n",
    "bound_min = np.minimum(pc_start.min(0), pc_end.min(0))\n",
    "norm_center = (bound_max + bound_min) / 2\n",
    "norm_scale = (bound_max - bound_min).max() * 1.1\n",
    "pc_start = (pc_start - norm_center) / norm_scale\n",
    "pc_end = (pc_end - norm_center) / norm_scale\n",
    "\n",
    "pc_start, _ = sample_point_cloud(pc_start, 8192)\n",
    "pc_end, _ = sample_point_cloud(pc_end, 8192)\n",
    "sample = {\n",
    "    'pc_start': torch.from_numpy(pc_start).unsqueeze(0).to(device).float(),\n",
    "    'pc_end': torch.from_numpy(pc_end).unsqueeze(0).to(device).float()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:30.315208Z",
     "start_time": "2022-03-17T01:10:25.569368Z"
    }
   },
   "outputs": [],
   "source": [
    "mesh_dict, mobile_points_all, c, stats_dict = generator.generate_mesh(sample)\n",
    "with torch.no_grad():\n",
    "    joint_type_logits, joint_param_revolute, joint_param_prismatic = model.model.decode_joints(mobile_points_all, c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:31.797089Z",
     "start_time": "2022-03-17T01:10:30.318226Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - 2022-03-16 20:10:30,497 - acceleratesupport - No OpenGL_accelerate module loaded: No module named 'OpenGL_accelerate'\n"
     ]
    }
   ],
   "source": [
    "import trimesh\n",
    "from src.utils.joint_estimation import aggregate_dense_prediction_r\n",
    "\n",
    "from utils3d.mesh.utils import as_mesh\n",
    "from utils3d.render.pyrender import get_pose, PyRenderer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:32.251424Z",
     "start_time": "2022-03-17T01:10:31.798720Z"
    }
   },
   "outputs": [],
   "source": [
    "renderer = PyRenderer(light_kwargs={'color': np.array([1., 1., 1.]), 'intensity': 9})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:32.268455Z",
     "start_time": "2022-03-17T01:10:32.253181Z"
    }
   },
   "outputs": [],
   "source": [
    "# compute articulation model\n",
    "mesh_dict[1].visual.face_colors = np.array([84, 220, 83, 255], dtype=np.uint8)\n",
    "joint_type_prob = joint_type_logits.sigmoid().mean()\n",
    "\n",
    "if joint_type_prob.item()< 0.5:\n",
    "    # axis voting\n",
    "    joint_r_axis = (\n",
    "        normalize(joint_param_revolute[:, :, :3], -1)[0].cpu().numpy()\n",
    "    )\n",
    "    joint_r_t = joint_param_revolute[:, :, 3][0].cpu().numpy()\n",
    "    joint_r_p2l_vec = (\n",
    "        normalize(joint_param_revolute[:, :, 4:7], -1)[0].cpu().numpy()\n",
    "    )\n",
    "    joint_r_p2l_dist = joint_param_revolute[:, :, 7][0].cpu().numpy()\n",
    "    p_seg = mobile_points_all[0].cpu().numpy()\n",
    "\n",
    "    pivot_point = p_seg + joint_r_p2l_vec * joint_r_p2l_dist[:, np.newaxis]\n",
    "    (\n",
    "        joint_axis_pred,\n",
    "        pivot_point_pred,\n",
    "        config_pred,\n",
    "    ) = aggregate_dense_prediction_r(\n",
    "        joint_r_axis, pivot_point, joint_r_t, method=\"mean\"\n",
    "    )\n",
    "# prismatic\n",
    "else:\n",
    "    # axis voting\n",
    "    joint_p_axis = (\n",
    "        normalize(joint_param_prismatic[:, :, :3], -1)[0].cpu().numpy()\n",
    "    )\n",
    "    joint_axis_pred = joint_p_axis.mean(0)\n",
    "    joint_p_t = joint_param_prismatic[:, :, 3][0].cpu().numpy()\n",
    "    config_pred = joint_p_t.mean()\n",
    "    \n",
    "    pivot_point_pred = mesh_dict[1].bounds.mean(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:32.299348Z",
     "start_time": "2022-03-17T01:10:32.269856Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<trimesh.Trimesh(vertices.shape=(100, 3), faces.shape=(192, 3))>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scene = trimesh.Scene()\n",
    "static_part = mesh_dict[0].copy()\n",
    "mobile_part = mesh_dict[1].copy()\n",
    "scene.add_geometry(static_part)\n",
    "scene.add_geometry(mobile_part)\n",
    "add_r_joint_to_scene(scene, joint_axis_pred, pivot_point_pred, 1.0, recenter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:32.562232Z",
     "start_time": "2022-03-17T01:10:32.300555Z"
    }
   },
   "outputs": [],
   "source": [
    "# render result mesh\n",
    "camera_pose = get_pose(1.5, ax=np.pi / 3, ay=0, az=np.pi/2)\n",
    "light_pose = get_pose(1.5, ax=np.pi / 3, ay=0, az=np.pi/2)\n",
    "rgb, depth = renderer.render_mesh(scene, camera_pose, light_pose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-17T01:10:32.607604Z",
     "start_time": "2022-03-17T01:10:32.563420Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=640x480 at 0x7F030C7F4A60>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image.fromarray(rgb)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:Ditto]",
   "language": "python",
   "name": "conda-env-Ditto-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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}