|
|
|
|
|
|
|
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 |
|
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 |
|
) |
|
|
|
|
|
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) |
|
|
