zamba.models.densepose.densepose_manager

DensePoseManager

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

class DensePoseManager:
    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. See: https://zamba.drivendata.org/docs/stable/models/densepose/#installation"
            )

        # 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

    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)

    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

    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

    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

    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

    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

    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

    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

    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

    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

__init__(model=MODELS['chimps'], model_cache_dir=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.

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. See: https://zamba.drivendata.org/docs/stable/models/densepose/#installation"
        )

    # 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(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(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

predict(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(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(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(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

visualize_image(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(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