From 9dd4ed18e27eadac3a28c9f6b2ae3e273b707aaf Mon Sep 17 00:00:00 2001 From: Klaus Greff Date: Wed, 23 Mar 2022 21:19:33 +0100 Subject: [PATCH] more movi documentation --- challenges/movi/README.md | 259 +++++++++++++++++++++++++++- challenges/movi/images/movi_d_1.gif | Bin 0 -> 671116 bytes challenges/movi/images/movi_d_2.gif | Bin 0 -> 761609 bytes challenges/movi/images/movi_d_3.gif | Bin 0 -> 779963 bytes challenges/movi/movi_a.py | 17 +- challenges/movi/movi_b.py | 64 +++++-- 6 files changed, 320 insertions(+), 20 deletions(-) create mode 100644 challenges/movi/images/movi_d_1.gif create mode 100644 challenges/movi/images/movi_d_2.gif create mode 100644 challenges/movi/images/movi_d_3.gif diff --git a/challenges/movi/README.md b/challenges/movi/README.md index 22c14421..2bfe21e8 100644 --- a/challenges/movi/README.md +++ b/challenges/movi/README.md @@ -1,10 +1,11 @@ # Multi-Object Video (MOVi) datasets -The MOVi dataset is really a series of six datasets (MOVi-A to MOVi-E) with increasing complexity. +The MOVi dataset is really a series of five datasets (MOVi-A to MOVi-E) with increasing complexity. Each dataset consists of random scenes, each being a 2 second rigid body simulation with a few objects falling. The variants differ in various dimensions including the number and type of objects, background, camera position/movement, and wether all objects are tossed or if some remain static. -## MOVi-A +## Variants +### MOVi-A ![](images/movi_a_1.gif) ![](images/movi_a_2.gif) ![](images/movi_a_3.gif) @@ -100,7 +101,7 @@ ds = tfds.load("movi_a", data_dir="gs://kubric-public/tfds") -## MOVi-B +### MOVi-B ![](images/movi_b_1.gif) ![](images/movi_b_2.gif) ![](images/movi_b_3.gif) @@ -230,6 +231,7 @@ ds = tfds.load("movi_c", data_dir="gs://kubric-public/tfds") "height": 256, "width": 256 }, + "background": str "camera": { "field_of_view": 0.85755605, "focal_length": 35.0, @@ -276,8 +278,253 @@ ds = tfds.load("movi_c", data_dir="gs://kubric-public/tfds") -## MOVi-D +### MOVi-D +![](images/movi_d_1.gif) +![](images/movi_d_2.gif) +![](images/movi_d_3.gif) + +MOVi-D slightly increases scene complexity by using more objects with most of them starting the scene lying still on the floor. +Concretely each scene consists of 10-20 static objects and only 1-3 dynamic objects that are tossed onto the scene like in the other datasets. +This variant is meant to test for larger number of objects, and the models ability to handle non-moving objects. + +Generate single scene with the [movi_de_worker.py](movi_de_worker.py) script: +```shell +docker run --rm --interactive \ + --user $(id -u):$(id -g) \ + --volume "$(pwd):/kubric" \ + kubricdockerhub/kubruntu \ + /usr/bin/python3 challenges/movi/movi_de_worker.py \ + --camera=fixed_random +``` +See [movi_d.py](movi_d.py) for the TFDS definition / conversion. + +``` python +ds = tfds.load("movi_d", data_dir="gs://kubric-public/tfds") +``` + +
+ Sample format and shapes + +``` python +{ + "metadata": { + "video_name": int, + "depth_range": (2,), + "forward_flow_range": (2,), + "backward_flow_range": (2,), + "num_frames": 24, + "num_instances": int, + "height": 256, + "width": 256 + }, + "background": str + "camera": { + "field_of_view": 0.85755605, + "focal_length": 35.0, + "positions": (24, 3), + "quaternions": (24, 4), + "sensor_width": 32.0 + }, + "instances": { + "angular_velocities": (nr_instances, 24, 3), + "bbox_frames": TensorShape([nr_instances, None]), + "bboxes": TensorShape([nr_instances, None, 4]), + "bboxes_3d": (nr_instances, 24, 8, 3), + "category": (nr_instances,), + "friction": (nr_instances,), + "image_positions": (nr_instances, 24, 2), + "is_dynamic': (nr_instances,)" + "mass": (nr_instances,), + "positions": (nr_instances, 24, 3), + "quaternions": (nr_instances, 24, 4), + "restitution": (nr_instances,), + "scale": (nr_instances,), + "velocities": (nr_instances, 24, 3), + "visibility": (nr_instances, 24) + }, + + "events": { + "collisions": { + "contact_normal": (2778, 3), + "force": (2778,), + "frame": (2778,), + "image_position": (2778, 2), + "instances": (2778, 2), + "position": (2778, 3) + } + }, + "depth": (24, 256, 256, 1), + "forward_flow": (24, 256, 256, 2), + "backward_flow": (24, 256, 256, 2), + "normal": (24, 256, 256, 3), + "object_coordinates": (24, 256, 256, 3), + "segmentations": (24, 256, 256, 1), + "video": (24, 256, 256, 3) +} + ``` +
+ +### MOVi-E (coming soon) -## MOVi-E -(coming soon) \ No newline at end of file + +## Annotations and Format +Each sample is a dictionary which contains the following video-format data +(`s: sequence length, h: height, w: width`): + +- **"video"**: `(s, h, w, 3) [uint8]` + The RGB frames. +- **"segmentations"**: `(s, h, w, 1) [uint8]` + Instance segmentation as per-pixel object-id with `background=0`. + *Note: because of this the instance IDs used here are one higher than their + corresponding index in `sample["instances"]`.* +- **"depth"**: `(s, h, w, 1) [uint16]` + Distance of each pixel from the center of the camera. + *(Note this is different from the z-value sometimes used, which measures the + distance to the camera **plane**.)* + The values are stored as `uint16` and span the range specified in + `sample["metadata"]["depth_range"]`. To convert them back to world-units + use: + ```python + minv, maxv = sample["metadata"]["depth_range"] + depth = sample["depth"] / 65535 * (maxv - minv) + minv + ``` +- **"forward_flow"**: `(s, h, w, 2) [uint16]` + Forward optical flow in the form `(delta_row, delta_column)`. + The values are stored as `uint16` and span the range specified in + `sample["metadata"]["forward_flow_range"]`. To convert them back to pixels use: + ```python + minv, maxv = sample["metadata"]["forward_flow_range"] + depth = sample["forward_flow"] / 65535 * (maxv - minv) + minv + ``` +- **"backward_flow"**: `(s, h, w, 2) [uint16]` + Backward optical flow in the form `(delta_row, delta_column)`. + The values are stored as `uint16` and span the range specified in + `sample["metadata"]["backward_flow_range"]`. To convert them back to pixels use: + ```python + minv, maxv = sample["metadata"]["backward_flow_range"] + depth = sample["backward_flow"] / 65535 * (maxv - minv) + minv + ``` +- **"normal"**: `(s, h, w, 3) [uint16]` + Surface normals for each pixel in world coordinates. +- **"object_coordinates"**: (s, h, w, 3) [uint16]` + Object coordinates encode the position of each point relative to the objects + bounding box (i.e. back-left-top (X=Y=Z=1) corner is white, + while front-right-bottom (X=Y=Z=0) corner is black.) + +Additionally there is rich instance-level information within `sample["instances"]`. +*Note that the instances are sorted by their total visibility (descending), +i.e. the total number pixels they occupy throughout the video.* +- **"mass"**: `[float32]` + Mass of the object used for simulation. +- **"friction"**: `[float32]` + Friction coefficient used for simulation. +- **"restitution"**: `[float32]` + Restitution coefficient (bounciness) used for simulation. +- **"positions"**: `(s, 3) [float32]` + Position of the object for each frame in world-coordinates. +- **"quaternions"**: `(s, 4) [float32]` + Rotation of the object for each frame as quaternions. +- **"velocities"**: `(s, 3) [float32]` + Velocity of the object for each frame. +- **"angular_velocities"**: `(s, 3) [float32]` + Angular velocity of the object for each frame. +- **"bboxes_3d"**: `(s, 8, 3) [float32]` + World-space corners of the 3D bounding box around the object. +- **"image_positions"**: `(s, 2) [float32]` + Normalized (0, 1) image-space (2D) coordinates of the center of mass of the + object for each frame. +- **"bboxes"**: `(None, 4) [float32]` + The normalized image-space (2D) coordinates of the bounding box + `[ymin, xmin, ymax, xmax]` for all the frames in which the object is visible + (as specified in bbox_frames). +- **"bbox_frames"**: `(None,) [int]` + A list of all the frames the object is visible. +- **"visibility"**: `(s,) [uint16]` + Visibility of the object in number of pixels for each frame (can be 0). + +Information about the camera in `sample["camera"]` +(given for each frame even if the camera is static): + +- **"focal_length"**: `[float32]` + Focal length of the camera in millimeters. +- **"sensor_width"**: `[float32]` + Width of the sensor of the camera in millimeters. +- **"field_of_view"**: `[float32]` + (horizontal) field of view angle of the camera in degrees. +- **"positions"**: (s, 3) `[float32]` + Position of the camera for each frame in world-coordinates. +- **"quaternions"**: (s, 4) `[float32]` + Rotation of the camera for each frame as quaternions. + + +And finally information about collision events in `sample["events"]["collisions"]`: + +- **"instances"**: `(2,) [uint16]` + Indices of the two instance between which the collision happened. + Note that collisions with the floor/background objects are marked with 65535 +- **"frame"**: `[int32]` + Frame in which the collision happenend. +- **"force"**: `[float32]` + The force (strength) of the collision. +- **"position"**: `(3,) [float32]` + Position of the collision event in 3D world coordinates. +- **"image_position"**: `(2,) [float32]` + Position of the collision event projected onto normalized 2D image coordinates. +- **"contact_normal"**: `(3,) [float32]` + The normal-vector of the contact (direction of the force). + + +### Variant Specific Additional Information +#### MOVi-A +For each instance MOVi-A contains the following additional information: +- **"shape_label"**: + Choice of `["cube", "cylinder", "sphere"]` +- **"size_label"**: + Choice of `["small", "large"]` +- **"color"**: `(3,) [float32]` + Color of the object in RGB. +- **"color_label"**: + One of `["blue", "brown", "cyan", "gray", "green", "purple", "red", "yellow"]` +- **"material_label"**: + One of `["metal", "rubber"]` + +#### MOVi-B +MOVi-B has an additional (top-level) entry: +- **"background_color"**: `(3,) [float32]` + Color of the background in RGB. + +And for each instance MOVi-B contains the following additional information: +- **"shape_label"**: + One of `["cube", "cylinder", "sphere", "cone", "torus", "gear", "torus_knot", "sponge", "spot", "teapot", "suzanne"]` +- **"scale"**: `[float32]` + Scale of the object (between 0.7 and 1.4). +- **"color"**: `(3,) [float32]` + Color of the object in RGB. +- **"material_label"**: + One of `["metal", "rubber"]` + +#### MOVi-C +MOVi-C has an additional (top-level) entry: +- **"background"**: `str` + Name of the background HDRI. + +And for each instance MOVi-C contains the following additional information: +- **"asset_id"**: `str` + Asset id from Google Scanned Objects dataset. +- **"scale"**: `[float32]` + Scale of the object (between 0.75 and 3.0). +- **"category"**: + One of `["Action Figures", "Bag", "Board Games", + "Bottles and Cans and Cups", "Camera", + "Car Seat", "Consumer Goods", "Hat", + "Headphones", "Keyboard", "Legos", + "Media Cases", "Mouse", "None", "Shoe", + "Stuffed Toys", "Toys"]` + + +#### MOVi-D 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np.ndarray: img = img[:, :, None] return img +def rgb_from_hexstr(hexstr: str): + """Create a Color instance from a hex string like #ffaa22 or #11aa88ff. + + Supports both long and short form (i.e. #ffffff is the same as #fff), and also an optional + alpha value (e.g. #112233ff or #123f). + """ + if hexstr[0] == "#": # get rid of leading # + hexstr = hexstr[1:] + if len(hexstr) == 3: + r = int(hexstr[0], 16) / 15. + g = int(hexstr[1], 16) / 15. + b = int(hexstr[2], 16) / 15. + return r, g, b + elif len(hexstr) == 4: + r = int(hexstr[0], 16) / 15. + g = int(hexstr[1], 16) / 15. + b = int(hexstr[2], 16) / 15. + a = int(hexstr[3], 16) / 15. + return r, g, b + elif len(hexstr) == 6: + r = int(hexstr[0:2], 16) / 255.0 + g = int(hexstr[2:4], 16) / 255.0 + b = int(hexstr[4:6], 16) / 255.0 + return r, g, b + elif len(hexstr) == 8: + r = int(hexstr[0:2], 16) / 255.0 + g = int(hexstr[2:4], 16) / 255.0 + b = int(hexstr[4:6], 16) / 255.0 + a = int(hexstr[6:8], 16) / 255.0 + return r, g, b + else: + raise ValueError("invalid color hex string") \ No newline at end of file

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