zamba.models.densepose.densepose_manager¶

`DENSEPOSE_AVAILABLE` ¶

`DensePoseOutputsTextureVisualizer` ¶

`DensePoseOutputsVertexVisualizer` ¶

`MODELS` ¶

Classes¶

`DensePoseManager` ¶

Methods¶

init(self, model = {'config': '/home/runner/work/zamba/zamba/zamba/models/densepose/assets/densepose_rcnn_R_50_FPN_soft_chimps_finetune_4k.yaml', 'densepose_weights_url': 'https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_chimps_finetune_4k/253146869/model_final_52f649.pkl', 'weights': 'zamba_densepose_model_final_52f649.pkl', 'viz_class': None, 'viz_class_kwargs': {'texture_atlases_dict': {'chimp_5029': None}}, 'anatomy_color_mapping': '/home/runner/work/zamba/zamba/zamba/models/densepose/assets/chimp_5029_parts.csv'}, model_cache_dir: Path = PosixPath('.zamba_cache'), download_region = <RegionEnum.us: 'us'>) `special` ¶

Create a DensePoseManager object.

Parameters¶

model : dict, optional (default MODELS['chimps']) A dictionary with the densepose model defintion like those defined in MODELS.

Source code in zamba/models/densepose/densepose_manager.py

def __init__(
    self,
    model=MODELS["chimps"],
    model_cache_dir: Path = Path(".zamba_cache"),
    download_region=RegionEnum("us"),
):
    """Create a DensePoseManager object.

    Parameters
    ----------
    model : dict, optional (default MODELS['chimps'])
        A dictionary with the densepose model defintion like those defined in MODELS.
    """
    if not DENSEPOSE_AVAILABLE:
        raise ImportError(
            "Densepose not installed; install it as an extra with `pip install zamba[densepose]`."
        )

    # setup configuration for densepose
    self.cfg = get_cfg()
    add_densepose_config(self.cfg)

    self.cfg.merge_from_file(model["config"])

    if not (model_cache_dir / model["weights"]).exists():
        model_cache_dir.mkdir(parents=True, exist_ok=True)
        self.cfg.MODEL.WEIGHTS = download_weights(
            model["weights"], model_cache_dir, download_region
        )

    # automatically use CPU if no cuda available
    if not torch.cuda.is_available():
        self.cfg.MODEL.DEVICE = "cpu"

    self.cfg.freeze()

    logging.getLogger("fvcore").setLevel("CRITICAL")  # silence noisy detectron2 logging
    # set up predictor with the configuration
    self.predictor = DefaultPredictor(self.cfg)

    # we have a specific texture atlas for chimps with relevant regions
    # labeled that we can use instead of the default segmentation
    self.visualizer = model["viz_class"](
        self.cfg,
        device=self.cfg.MODEL.DEVICE,
        **model.get("viz_class_kwargs", {}),
    )

    # set up utilities for use with visualizer
    self.vis_extractor = create_extractor(self.visualizer)
    self.vis_embedder = build_densepose_embedder(self.cfg)
    self.vis_class_to_mesh_name = get_class_to_mesh_name_mapping(self.cfg)
    self.vis_mesh_vertex_embeddings = {
        mesh_name: self.vis_embedder(mesh_name).to(self.cfg.MODEL.DEVICE)
        for mesh_name in self.vis_class_to_mesh_name.values()
        if self.vis_embedder.has_embeddings(mesh_name)
    }

    if "anatomy_color_mapping" in model:
        self.anatomy_color_mapping = pd.read_csv(model["anatomy_color_mapping"], index_col=0)
    else:
        self.anatomy_color_mapping = None

`anatomize_image(self, visualized_img_arr, outputs, output_path = None)` ¶

Convert the pose information into the percent of pixels in the detection bounding box that correspond to each part of the anatomy in an image.

Parameters¶

visualized_img_arr : numpy array (unit8) BGR The numpy array the image after the texture has been visualized (by calling DensePoseManager.visualize_image). outputs : The outputs from running DensePoseManager.predict*

Returns¶

pandas.DataFrame DataFrame with percent of pixels of the bounding box that correspond to each anatomical part

Source code in zamba/models/densepose/densepose_manager.py

def anatomize_image(self, visualized_img_arr, outputs, output_path=None):
    """Convert the pose information into the percent of pixels in the detection
        bounding box that correspond to each part of the anatomy in an image.

    Parameters
    ----------
    visualized_img_arr : numpy array (unit8) BGR
        The numpy array the image after the texture has been visualized (by calling DensePoseManager.visualize_image).
    outputs :
        The outputs from running DensePoseManager.predict*

    Returns
    -------
    pandas.DataFrame
        DataFrame with percent of pixels of the bounding box that correspond to each anatomical part
    """
    if self.anatomy_color_mapping is None:
        raise ValueError(
            "No anatomy_color_mapping provided to track anatomy; did you mean to use a different MODEL?"
        )

    # no detections, return empty df for joining later (e.g., in anatomize_video)
    if not outputs:
        return pd.DataFrame([])

    _, _, N, bboxes_xywh, pred_classes = self.visualizer.extract_and_check_outputs_and_boxes(
        self.vis_extractor(outputs)
    )

    all_detections = []
    for n in range(N):
        x, y, w, h = bboxes_xywh[n].int().cpu().numpy()
        detection_area = visualized_img_arr[y : y + h, x : x + w]

        detection_stats = {
            name: (detection_area == np.array([[[color.B, color.G, color.R]]]))
            .all(axis=-1)
            .sum()
            / (h * w)  # calc percent of bounding box with this color
            for name, color in self.anatomy_color_mapping.iterrows()
        }

        detection_stats["x"] = x
        detection_stats["y"] = y
        detection_stats["h"] = h
        detection_stats["w"] = w

        all_detections.append(detection_stats)

    results = pd.DataFrame(all_detections)

    if output_path is not None:
        results.to_csv(output_path, index=False)

    return results

`anatomize_video(self, visualized_video_arr, outputs, output_path = None, fps = 30)` ¶

Convert the pose information into the percent of pixels in the detection bounding box that correspond to each part of the anatomy in a video.

Parameters¶

visualized_video_arr : numpy array (unit8) BGR The numpy array the video after the texture has been visualized (by calling DensePoseManager.visualize_video). outputs : The outputs from running DensePoseManager.predict*

Returns¶

numpy array (unit8) BGR DensePose outputs visualized on top of the image.

Source code in zamba/models/densepose/densepose_manager.py

def anatomize_video(self, visualized_video_arr, outputs, output_path=None, fps=30):
    """Convert the pose information into the percent of pixels in the detection
        bounding box that correspond to each part of the anatomy in a video.

    Parameters
    ----------
    visualized_video_arr : numpy array (unit8) BGR
        The numpy array the video after the texture has been visualized (by calling DensePoseManager.visualize_video).
    outputs :
        The outputs from running DensePoseManager.predict*

    Returns
    -------
    numpy array (unit8) BGR
        DensePose outputs visualized on top of the image.
    """
    all_detections = []

    for ix in range(visualized_video_arr.shape[0]):
        detection_df = self.anatomize_image(visualized_video_arr[ix, ...], outputs[ix])
        detection_df["frame"] = ix
        detection_df["seconds"] = ix / fps
        all_detections.append(detection_df)

    results = pd.concat(all_detections)

    if output_path is not None:
        results.to_csv(output_path, index=False)

    return results

`deserialize_output(self, instances_dict = None, filename = None)` ¶

Source code in zamba/models/densepose/densepose_manager.py

def deserialize_output(self, instances_dict=None, filename=None):
    if filename is not None:
        with Path(filename).open("r") as f:
            instances_dict = json.load(f)

    # handle image case
    is_image = False
    if "frames" not in instances_dict:
        instances_dict = {"frames": [instances_dict]}
        is_image = True

    frames = []
    for frame in instances_dict["frames"]:
        heights, widths, boxes, scores, labels, embeddings, segmentations = zip(
            *[
                (
                    i["img_height"],
                    i["img_width"],
                    i["box"],
                    i["score"],
                    i["label"]["value"],
                    i["embedding"] if i["embedding"] is not None else [np.nan],
                    i["segmentation"] if i["segmentation"] is not None else [np.nan],
                )
                for i in frame["instances"]
            ]
        )

        frames.append(
            Instances(
                (heights[0], widths[0]),
                pred_boxes=boxes,
                scores=scores,
                pred_classes=labels,
                pred_densepose=DensePoseEmbeddingPredictorOutput(
                    embedding=torch.tensor(embeddings),
                    coarse_segm=torch.tensor(segmentations),
                ),
            )
        )

    # if image or single frame, just return the instance
    if is_image:
        return frames[0]
    else:
        return frames

`predict(self, image_arr)` ¶

Main call to DensePose for inference. Runs inference on an image array.

Parameters¶

image_arr : numpy array BGR image array

Returns¶

Instances Detection instances with boxes, scores, and densepose estimates.

Source code in zamba/models/densepose/densepose_manager.py

def predict(self, image_arr):
    """Main call to DensePose for inference. Runs inference on an image array.

    Parameters
    ----------
    image_arr : numpy array
        BGR image array

    Returns
    -------
    Instances
        Detection instances with boxes, scores, and densepose estimates.
    """
    with torch.no_grad():
        instances = self.predictor(image_arr)["instances"]

    return instances

`predict_image(self, image)` ¶

Run inference to get the densepose results for an image.

Parameters¶

image : numpy array (unit8) of an image in BGR format or path to an image

Returns¶

tuple Returns the image array as passed or loaded and the the densepose Instances as results.

Source code in zamba/models/densepose/densepose_manager.py

def predict_image(self, image):
    """Run inference to get the densepose results for an image.

    Parameters
    ----------
    image :
        numpy array (unit8) of an image in BGR format or path to an image

    Returns
    -------
    tuple
        Returns the image array as passed or loaded and the the densepose Instances as results.
    """
    if isinstance(image, (str, Path)):
        image = read_image(image, format="BGR")

    return image, self.predict(image)

`predict_video(self, video, video_loader_config = None, pbar = True)` ¶

Run inference to get the densepose results for a video.

Parameters¶

video : numpy array (uint8) of a a video in BGR layout with time dimension first or path to a video video_loader_config : VideoLoaderConfig, optional A video loader config for loading videos (uses all defaults except pix_fmt="bgr24") pbar : bool, optional Whether to display a progress bar, by default True

Returns¶

tuple Tuple of (video_array, list of densepose results per frame)

Source code in zamba/models/densepose/densepose_manager.py

def predict_video(self, video, video_loader_config=None, pbar=True):
    """Run inference to get the densepose results for a video.

    Parameters
    ----------
    video :
        numpy array (uint8) of a a video in BGR layout with time dimension first or path to a video
    video_loader_config : VideoLoaderConfig, optional
        A video loader config for loading videos (uses all defaults except pix_fmt="bgr24")
    pbar : bool, optional
        Whether to display a progress bar, by default True

    Returns
    -------
    tuple
        Tuple of (video_array, list of densepose results per frame)
    """
    if isinstance(video, (str, Path)):
        video = load_video_frames(video, config=video_loader_config)

    pbar = tqdm if pbar else lambda x, **kwargs: x

    return video, [
        self.predict_image(img)[1] for img in pbar(video, desc="Frames")
    ]  # just the predictions

`serialize_image_output(self, instances, filename = None, write_embeddings = False)` ¶

Convert the densepose output into Python-native objects that can be written and read with json.

Parameters¶

instances : Instance The output from the densepose model filename : (str, Path), optional If not None, the filename to write the output to, by default None

Source code in zamba/models/densepose/densepose_manager.py

def serialize_image_output(self, instances, filename=None, write_embeddings=False):
    """Convert the densepose output into Python-native objects that can
        be written and read with json.

    Parameters
    ----------
    instances : Instance
        The output from the densepose model
    filename : (str, Path), optional
        If not None, the filename to write the output to, by default None
    """
    if isinstance(instances, list):
        img_height, img_width = instances[0].image_size
    else:
        img_height, img_width = instances.image_size

    boxes = instances.get("pred_boxes").tensor
    scores = instances.get("scores").tolist()
    labels = instances.get("pred_classes").tolist()

    try:
        pose_result = instances.get("pred_densepose")
    except KeyError:
        pose_result = None

    # include embeddings + segmentation if they exist and they are requested
    write_embeddings = write_embeddings and (pose_result is not None)

    serialized = {
        "instances": [
            {
                "img_height": img_height,
                "img_width": img_width,
                "box": boxes[i].cpu().tolist(),
                "score": scores[i],
                "label": {
                    "value": labels[i],
                    "mesh_name": self.vis_class_to_mesh_name[labels[i]],
                },
                "embedding": pose_result.embedding[[i], ...].cpu().tolist()
                if write_embeddings
                else None,
                "segmentation": pose_result.coarse_segm[[i], ...].cpu().tolist()
                if write_embeddings
                else None,
            }
            for i in range(len(instances))
        ]
    }

    if filename is not None:
        with Path(filename).open("w") as f:
            json.dump(serialized, f, indent=2)

    return serialized

`serialize_video_output(self, instances, filename = None, write_embeddings = False)` ¶

Source code in zamba/models/densepose/densepose_manager.py

def serialize_video_output(self, instances, filename=None, write_embeddings=False):
    serialized = {
        "frames": [
            self.serialize_image_output(
                frame_instances, filename=None, write_embeddings=write_embeddings
            )
            for frame_instances in instances
        ]
    }

    if filename is not None:
        with Path(filename).open("w") as f:
            json.dump(serialized, f, indent=2)

    return serialized

`visualize_image(self, image_arr, outputs, output_path = None)` ¶

Visualize the pose information.

Parameters¶

image_arr : numpy array (unit8) BGR The numpy array representing the image. outputs : The outputs from running DensePoseManager.predict* output_path : str or Path, optional If not None, write visualization to this path; by default None

Returns¶

numpy array (unit8) BGR DensePose outputs visualized on top of the image.

Source code in zamba/models/densepose/densepose_manager.py

def visualize_image(self, image_arr, outputs, output_path=None):
    """Visualize the pose information.

    Parameters
    ----------
    image_arr : numpy array (unit8) BGR
        The numpy array representing the image.
    outputs :
        The outputs from running DensePoseManager.predict*
    output_path : str or Path, optional
        If not None, write visualization to this path; by default None

    Returns
    -------
    numpy array (unit8) BGR
        DensePose outputs visualized on top of the image.
    """
    bw_image = cv2.cvtColor(image_arr, cv2.COLOR_BGR2GRAY)
    bw_image = np.tile(bw_image[:, :, np.newaxis], [1, 1, 3])
    data = self.vis_extractor(outputs)
    image_vis = self.visualizer.visualize(bw_image, data)

    if output_path is not None:
        cv2.imwrite(str(output_path), image_vis)

    return image_vis

`visualize_video(self, video_arr, outputs, output_path = None, frame_size = None, fps = 30, pbar = True)` ¶

Visualize the pose information on a video

Parameters¶

video_arr : numpy array (unit8) BGR, time first The numpy array representing the video. outputs : The outputs from running DensePoseManager.predict* output_path : str or Path, optional If not None, write visualization to this path (should be .mp4); by default None frame_size : (innt, float), optional If frame_size is float, scale up or down by that float value; if frame_size is an integer, set width to that size and scale height appropriately. fps : int frames per second for output video if writing; defaults to 30 pbar : bool display a progress bar

Returns¶

numpy array (unit8) BGR DensePose outputs visualized on top of the image.

Source code in zamba/models/densepose/densepose_manager.py

def visualize_video(
    self, video_arr, outputs, output_path=None, frame_size=None, fps=30, pbar=True
):
    """Visualize the pose information on a video

    Parameters
    ----------
    video_arr : numpy array (unit8) BGR, time first
        The numpy array representing the video.
    outputs :
        The outputs from running DensePoseManager.predict*
    output_path : str or Path, optional
        If not None, write visualization to this path (should be .mp4); by default None
    frame_size : (innt, float), optional
        If frame_size is float, scale up or down by that float value; if frame_size is an integer,
        set width to that size and scale height appropriately.
    fps : int
        frames per second for output video if writing; defaults to 30
    pbar : bool
        display a progress bar

    Returns
    -------
    numpy array (unit8) BGR
        DensePose outputs visualized on top of the image.
    """
    pbar = tqdm if pbar else lambda x, **kwargs: x

    out_frames = np.array(
        [
            self.visualize_image(
                image_arr,
                output,
            )
            for image_arr, output in pbar(
                zip(video_arr, outputs), total=video_arr.shape[0], desc="Visualize frames"
            )
        ]
    )

    if output_path is not None:
        # get new size for output video if scaling
        if frame_size is None:
            frame_size = video_arr.shape[2]  # default to same size

        # if float, scale as a multiple
        if isinstance(frame_size, float):
            frame_width = round(video_arr.shape[2] * frame_size)
            frame_height = round(video_arr.shape[1] * frame_size)

        # if int, use as width of the video and scale height proportionally
        elif isinstance(frame_size, int):
            frame_width = frame_size
            scale = frame_width / video_arr.shape[2]
            frame_height = round(video_arr.shape[1] * scale)

        # setup output for writing
        output_path = output_path.with_suffix(".mp4")
        out = cv2.VideoWriter(
            str(output_path),
            cv2.VideoWriter_fourcc(*"mp4v"),
            max(1, int(fps)),
            (frame_width, frame_height),
        )

        for f in pbar(out_frames, desc="Write frames"):
            if (f.shape[0] != frame_height) or (f.shape[1] != frame_width):
                f = cv2.resize(
                    f,
                    (frame_width, frame_height),
                    # https://stackoverflow.com/a/51042104/1692709
                    interpolation=(
                        cv2.INTER_LINEAR if f.shape[1] < frame_width else cv2.INTER_AREA
                    ),
                )
            out.write(f)

        out.release()

    return out_frames

`get_texture_atlas(x)` ¶

Source code in zamba/models/densepose/densepose_manager.py

get_texture_atlas = lambda x: None  # noqa: E731

zamba.models.densepose.densepose_manager¶

DENSEPOSE_AVAILABLE ¶

DensePoseOutputsTextureVisualizer ¶

DensePoseOutputsVertexVisualizer ¶

MODELS ¶

Classes¶

DensePoseManager ¶

Methods¶

Parameters¶

anatomize_image(self, visualized_img_arr, outputs, output_path = None) ¶

Parameters¶

Returns¶

anatomize_video(self, visualized_video_arr, outputs, output_path = None, fps = 30) ¶

Parameters¶

Returns¶

deserialize_output(self, instances_dict = None, filename = None) ¶

predict(self, image_arr) ¶

Parameters¶

Returns¶

predict_image(self, image) ¶

Parameters¶

Returns¶

predict_video(self, video, video_loader_config = None, pbar = True) ¶

Parameters¶

Returns¶

serialize_image_output(self, instances, filename = None, write_embeddings = False) ¶

Parameters¶

serialize_video_output(self, instances, filename = None, write_embeddings = False) ¶

visualize_image(self, image_arr, outputs, output_path = None) ¶

Parameters¶

Returns¶

visualize_video(self, video_arr, outputs, output_path = None, frame_size = None, fps = 30, pbar = True) ¶

Parameters¶

Returns¶

get_texture_atlas(x) ¶

`DENSEPOSE_AVAILABLE` ¶

`DensePoseOutputsTextureVisualizer` ¶

`DensePoseOutputsVertexVisualizer` ¶

`MODELS` ¶

`DensePoseManager` ¶

`anatomize_image(self, visualized_img_arr, outputs, output_path = None)` ¶

`anatomize_video(self, visualized_video_arr, outputs, output_path = None, fps = 30)` ¶

`deserialize_output(self, instances_dict = None, filename = None)` ¶

`predict(self, image_arr)` ¶

`predict_image(self, image)` ¶

`predict_video(self, video, video_loader_config = None, pbar = True)` ¶

`serialize_image_output(self, instances, filename = None, write_embeddings = False)` ¶

`serialize_video_output(self, instances, filename = None, write_embeddings = False)` ¶

`visualize_image(self, image_arr, outputs, output_path = None)` ¶

`visualize_video(self, video_arr, outputs, output_path = None, frame_size = None, fps = 30, pbar = True)` ¶

`get_texture_atlas(x)` ¶