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Running
on
Zero
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import numpy as np | |
| from typing import Optional, Tuple | |
| import cv2 | |
| from densepose.structures import DensePoseDataRelative | |
| from ..structures import DensePoseChartPredictorOutput | |
| from .base import Boxes, Image, MatrixVisualizer | |
| class DensePoseOutputsVisualizer: | |
| def __init__( | |
| self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, to_visualize=None, **kwargs | |
| ): | |
| assert to_visualize in "IUV", "can only visualize IUV" | |
| self.to_visualize = to_visualize | |
| if self.to_visualize == "I": | |
| val_scale = 255.0 / DensePoseDataRelative.N_PART_LABELS | |
| else: | |
| val_scale = 1.0 | |
| self.mask_visualizer = MatrixVisualizer( | |
| inplace=inplace, cmap=cmap, val_scale=val_scale, alpha=alpha | |
| ) | |
| def visualize( | |
| self, | |
| image_bgr: Image, | |
| dp_output_with_bboxes: Tuple[Optional[DensePoseChartPredictorOutput], Optional[Boxes]], | |
| ) -> Image: | |
| densepose_output, bboxes_xywh = dp_output_with_bboxes | |
| if densepose_output is None or bboxes_xywh is None: | |
| return image_bgr | |
| assert isinstance( | |
| densepose_output, DensePoseChartPredictorOutput | |
| ), "DensePoseChartPredictorOutput expected, {} encountered".format(type(densepose_output)) | |
| S = densepose_output.coarse_segm | |
| I = densepose_output.fine_segm # noqa | |
| U = densepose_output.u | |
| V = densepose_output.v | |
| N = S.size(0) | |
| assert N == I.size( | |
| 0 | |
| ), "densepose outputs S {} and I {}" " should have equal first dim size".format( | |
| S.size(), I.size() | |
| ) | |
| assert N == U.size( | |
| 0 | |
| ), "densepose outputs S {} and U {}" " should have equal first dim size".format( | |
| S.size(), U.size() | |
| ) | |
| assert N == V.size( | |
| 0 | |
| ), "densepose outputs S {} and V {}" " should have equal first dim size".format( | |
| S.size(), V.size() | |
| ) | |
| assert N == len( | |
| bboxes_xywh | |
| ), "number of bounding boxes {}" " should be equal to first dim size of outputs {}".format( | |
| len(bboxes_xywh), N | |
| ) | |
| for n in range(N): | |
| Sn = S[n].argmax(dim=0) | |
| In = I[n].argmax(dim=0) * (Sn > 0).long() | |
| segmentation = In.cpu().numpy().astype(np.uint8) | |
| mask = np.zeros(segmentation.shape, dtype=np.uint8) | |
| mask[segmentation > 0] = 1 | |
| bbox_xywh = bboxes_xywh[n] | |
| if self.to_visualize == "I": | |
| vis = segmentation | |
| elif self.to_visualize in "UV": | |
| U_or_Vn = {"U": U, "V": V}[self.to_visualize][n].cpu().numpy().astype(np.float32) | |
| vis = np.zeros(segmentation.shape, dtype=np.float32) | |
| for partId in range(U_or_Vn.shape[0]): | |
| vis[segmentation == partId] = ( | |
| U_or_Vn[partId][segmentation == partId].clip(0, 1) * 255 | |
| ) | |
| # pyre-fixme[61]: `vis` may not be initialized here. | |
| image_bgr = self.mask_visualizer.visualize(image_bgr, mask, vis, bbox_xywh) | |
| return image_bgr | |
| class DensePoseOutputsUVisualizer(DensePoseOutputsVisualizer): | |
| def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs): | |
| super().__init__(inplace=inplace, cmap=cmap, alpha=alpha, to_visualize="U", **kwargs) | |
| class DensePoseOutputsVVisualizer(DensePoseOutputsVisualizer): | |
| def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs): | |
| super().__init__(inplace=inplace, cmap=cmap, alpha=alpha, to_visualize="V", **kwargs) | |
| class DensePoseOutputsFineSegmentationVisualizer(DensePoseOutputsVisualizer): | |
| def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs): | |
| super().__init__(inplace=inplace, cmap=cmap, alpha=alpha, to_visualize="I", **kwargs) | |