densepose/vis/densepose_data_points.py

# Copyright (c) Facebook, Inc. and its affiliates.

# pyre-unsafe
import numpy as np
from typing import Iterable, Optional, Tuple
import cv2

from densepose.structures import DensePoseDataRelative

from .base import Boxes, Image, MatrixVisualizer, PointsVisualizer


class DensePoseDataCoarseSegmentationVisualizer:
    """
    Visualizer for ground truth segmentation
    """

    def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs):
        self.mask_visualizer = MatrixVisualizer(
            inplace=inplace,
            cmap=cmap,
            val_scale=255.0 / DensePoseDataRelative.N_BODY_PARTS,
            alpha=alpha,
        )

    def visualize(
        self,
        image_bgr: Image,
        bbox_densepose_datas: Optional[Tuple[Iterable[Boxes], Iterable[DensePoseDataRelative]]],
    ) -> Image:
        if bbox_densepose_datas is None:
            return image_bgr
        for bbox_xywh, densepose_data in zip(*bbox_densepose_datas):
            matrix = densepose_data.segm.numpy()
            mask = np.zeros(matrix.shape, dtype=np.uint8)
            mask[matrix > 0] = 1
            image_bgr = self.mask_visualizer.visualize(image_bgr, mask, matrix, bbox_xywh.numpy())
        return image_bgr


class DensePoseDataPointsVisualizer:
    def __init__(self, densepose_data_to_value_fn=None, cmap=cv2.COLORMAP_PARULA, **kwargs):
        self.points_visualizer = PointsVisualizer()
        self.densepose_data_to_value_fn = densepose_data_to_value_fn
        self.cmap = cmap

    def visualize(
        self,
        image_bgr: Image,
        bbox_densepose_datas: Optional[Tuple[Iterable[Boxes], Iterable[DensePoseDataRelative]]],
    ) -> Image:
        if bbox_densepose_datas is None:
            return image_bgr
        for bbox_xywh, densepose_data in zip(*bbox_densepose_datas):
            x0, y0, w, h = bbox_xywh.numpy()
            x = densepose_data.x.numpy() * w / 255.0 + x0
            y = densepose_data.y.numpy() * h / 255.0 + y0
            pts_xy = zip(x, y)
            if self.densepose_data_to_value_fn is None:
                image_bgr = self.points_visualizer.visualize(image_bgr, pts_xy)
            else:
                v = self.densepose_data_to_value_fn(densepose_data)
                img_colors_bgr = cv2.applyColorMap(v, self.cmap)
                colors_bgr = [
                    [int(v) for v in img_color_bgr.ravel()] for img_color_bgr in img_colors_bgr
                ]
                image_bgr = self.points_visualizer.visualize(image_bgr, pts_xy, colors_bgr)
        return image_bgr


def _densepose_data_u_for_cmap(densepose_data):
    u = np.clip(densepose_data.u.numpy(), 0, 1) * 255.0
    return u.astype(np.uint8)


def _densepose_data_v_for_cmap(densepose_data):
    v = np.clip(densepose_data.v.numpy(), 0, 1) * 255.0
    return v.astype(np.uint8)


def _densepose_data_i_for_cmap(densepose_data):
    i = (
        np.clip(densepose_data.i.numpy(), 0.0, DensePoseDataRelative.N_PART_LABELS)
        * 255.0
        / DensePoseDataRelative.N_PART_LABELS
    )
    return i.astype(np.uint8)


class DensePoseDataPointsUVisualizer(DensePoseDataPointsVisualizer):
    def __init__(self, **kwargs):
        super(DensePoseDataPointsUVisualizer, self).__init__(
            densepose_data_to_value_fn=_densepose_data_u_for_cmap, **kwargs
        )


class DensePoseDataPointsVVisualizer(DensePoseDataPointsVisualizer):
    def __init__(self, **kwargs):
        super(DensePoseDataPointsVVisualizer, self).__init__(
            densepose_data_to_value_fn=_densepose_data_v_for_cmap, **kwargs
        )


class DensePoseDataPointsIVisualizer(DensePoseDataPointsVisualizer):
    def __init__(self, **kwargs):
        super(DensePoseDataPointsIVisualizer, self).__init__(
            densepose_data_to_value_fn=_densepose_data_i_for_cmap, **kwargs
        )