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# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import numpy as np
from typing import List, Optional, Tuple
import cv2
import torch
from densepose.structures import DensePoseDataRelative
from ..structures import DensePoseChartResult
from .base import Boxes, Image, MatrixVisualizer
class DensePoseResultsVisualizer:
def visualize(
self,
image_bgr: Image,
results_and_boxes_xywh: Tuple[Optional[List[DensePoseChartResult]], Optional[Boxes]],
) -> Image:
densepose_result, boxes_xywh = results_and_boxes_xywh
if densepose_result is None or boxes_xywh is None:
return image_bgr
boxes_xywh = boxes_xywh.cpu().numpy()
context = self.create_visualization_context(image_bgr)
for i, result in enumerate(densepose_result):
iuv_array = torch.cat(
(result.labels[None].type(torch.float32), result.uv * 255.0)
).type(torch.uint8)
self.visualize_iuv_arr(context, iuv_array.cpu().numpy(), boxes_xywh[i])
image_bgr = self.context_to_image_bgr(context)
return image_bgr
def create_visualization_context(self, image_bgr: Image):
return image_bgr
def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh) -> None:
pass
def context_to_image_bgr(self, context):
return context
def get_image_bgr_from_context(self, context):
return context
class DensePoseMaskedColormapResultsVisualizer(DensePoseResultsVisualizer):
def __init__(
self,
data_extractor,
segm_extractor,
inplace=True,
cmap=cv2.COLORMAP_PARULA,
alpha=0.7,
val_scale=1.0,
**kwargs,
):
self.mask_visualizer = MatrixVisualizer(
inplace=inplace, cmap=cmap, val_scale=val_scale, alpha=alpha
)
self.data_extractor = data_extractor
self.segm_extractor = segm_extractor
def context_to_image_bgr(self, context):
return context
def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh) -> None:
image_bgr = self.get_image_bgr_from_context(context)
matrix = self.data_extractor(iuv_arr)
segm = self.segm_extractor(iuv_arr)
mask = np.zeros(matrix.shape, dtype=np.uint8)
mask[segm > 0] = 1
image_bgr = self.mask_visualizer.visualize(image_bgr, mask, matrix, bbox_xywh)
def _extract_i_from_iuvarr(iuv_arr):
return iuv_arr[0, :, :]
def _extract_u_from_iuvarr(iuv_arr):
return iuv_arr[1, :, :]
def _extract_v_from_iuvarr(iuv_arr):
return iuv_arr[2, :, :]
class DensePoseResultsMplContourVisualizer(DensePoseResultsVisualizer):
def __init__(self, levels=10, **kwargs):
self.levels = levels
self.plot_args = kwargs
def create_visualization_context(self, image_bgr: Image):
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
context = {}
context["image_bgr"] = image_bgr
dpi = 100
height_inches = float(image_bgr.shape[0]) / dpi
width_inches = float(image_bgr.shape[1]) / dpi
fig = plt.figure(figsize=(width_inches, height_inches), dpi=dpi)
plt.axes([0, 0, 1, 1])
plt.axis("off")
context["fig"] = fig
canvas = FigureCanvas(fig)
context["canvas"] = canvas
extent = (0, image_bgr.shape[1], image_bgr.shape[0], 0)
plt.imshow(image_bgr[:, :, ::-1], extent=extent)
return context
def context_to_image_bgr(self, context):
fig = context["fig"]
w, h = map(int, fig.get_size_inches() * fig.get_dpi())
canvas = context["canvas"]
canvas.draw()
image_1d = np.fromstring(canvas.tostring_rgb(), dtype="uint8")
image_rgb = image_1d.reshape(h, w, 3)
image_bgr = image_rgb[:, :, ::-1].copy()
return image_bgr
def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh: Boxes) -> None:
import matplotlib.pyplot as plt
u = _extract_u_from_iuvarr(iuv_arr).astype(float) / 255.0
v = _extract_v_from_iuvarr(iuv_arr).astype(float) / 255.0
extent = (
bbox_xywh[0],
bbox_xywh[0] + bbox_xywh[2],
bbox_xywh[1],
bbox_xywh[1] + bbox_xywh[3],
)
plt.contour(u, self.levels, extent=extent, **self.plot_args)
plt.contour(v, self.levels, extent=extent, **self.plot_args)
class DensePoseResultsCustomContourVisualizer(DensePoseResultsVisualizer):
"""
Contour visualization using marching squares
"""
def __init__(self, levels=10, **kwargs):
# TODO: colormap is hardcoded
cmap = cv2.COLORMAP_PARULA
if isinstance(levels, int):
self.levels = np.linspace(0, 1, levels)
else:
self.levels = levels
if "linewidths" in kwargs:
self.linewidths = kwargs["linewidths"]
else:
self.linewidths = [1] * len(self.levels)
self.plot_args = kwargs
img_colors_bgr = cv2.applyColorMap((self.levels * 255).astype(np.uint8), cmap)
self.level_colors_bgr = [
[int(v) for v in img_color_bgr.ravel()] for img_color_bgr in img_colors_bgr
]
def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh: Boxes) -> None:
image_bgr = self.get_image_bgr_from_context(context)
segm = _extract_i_from_iuvarr(iuv_arr)
u = _extract_u_from_iuvarr(iuv_arr).astype(float) / 255.0
v = _extract_v_from_iuvarr(iuv_arr).astype(float) / 255.0
self._contours(image_bgr, u, segm, bbox_xywh)
self._contours(image_bgr, v, segm, bbox_xywh)
def _contours(self, image_bgr, arr, segm, bbox_xywh):
for part_idx in range(1, DensePoseDataRelative.N_PART_LABELS + 1):
mask = segm == part_idx
if not np.any(mask):
continue
arr_min = np.amin(arr[mask])
arr_max = np.amax(arr[mask])
I, J = np.nonzero(mask)
i0 = np.amin(I)
i1 = np.amax(I) + 1
j0 = np.amin(J)
j1 = np.amax(J) + 1
if (j1 == j0 + 1) or (i1 == i0 + 1):
continue
Nw = arr.shape[1] - 1
Nh = arr.shape[0] - 1
for level_idx, level in enumerate(self.levels):
if (level < arr_min) or (level > arr_max):
continue
vp = arr[i0:i1, j0:j1] >= level
bin_codes = vp[:-1, :-1] + vp[1:, :-1] * 2 + vp[1:, 1:] * 4 + vp[:-1, 1:] * 8
mp = mask[i0:i1, j0:j1]
bin_mask_codes = mp[:-1, :-1] + mp[1:, :-1] * 2 + mp[1:, 1:] * 4 + mp[:-1, 1:] * 8
it = np.nditer(bin_codes, flags=["multi_index"])
color_bgr = self.level_colors_bgr[level_idx]
linewidth = self.linewidths[level_idx]
while not it.finished:
if (it[0] != 0) and (it[0] != 15):
i, j = it.multi_index
if bin_mask_codes[i, j] != 0:
self._draw_line(
image_bgr,
arr,
mask,
level,
color_bgr,
linewidth,
it[0],
it.multi_index,
bbox_xywh,
Nw,
Nh,
(i0, j0),
)
it.iternext()
def _draw_line(
self,
image_bgr,
arr,
mask,
v,
color_bgr,
linewidth,
bin_code,
multi_idx,
bbox_xywh,
Nw,
Nh,
offset,
):
lines = self._bin_code_2_lines(arr, v, bin_code, multi_idx, Nw, Nh, offset)
x0, y0, w, h = bbox_xywh
x1 = x0 + w
y1 = y0 + h
for line in lines:
x0r, y0r = line[0]
x1r, y1r = line[1]
pt0 = (int(x0 + x0r * (x1 - x0)), int(y0 + y0r * (y1 - y0)))
pt1 = (int(x0 + x1r * (x1 - x0)), int(y0 + y1r * (y1 - y0)))
cv2.line(image_bgr, pt0, pt1, color_bgr, linewidth)
def _bin_code_2_lines(self, arr, v, bin_code, multi_idx, Nw, Nh, offset):
i0, j0 = offset
i, j = multi_idx
i += i0
j += j0
v0, v1, v2, v3 = arr[i, j], arr[i + 1, j], arr[i + 1, j + 1], arr[i, j + 1]
x0i = float(j) / Nw
y0j = float(i) / Nh
He = 1.0 / Nh
We = 1.0 / Nw
if (bin_code == 1) or (bin_code == 14):
a = (v - v0) / (v1 - v0)
b = (v - v0) / (v3 - v0)
pt1 = (x0i, y0j + a * He)
pt2 = (x0i + b * We, y0j)
return [(pt1, pt2)]
elif (bin_code == 2) or (bin_code == 13):
a = (v - v0) / (v1 - v0)
b = (v - v1) / (v2 - v1)
pt1 = (x0i, y0j + a * He)
pt2 = (x0i + b * We, y0j + He)
return [(pt1, pt2)]
elif (bin_code == 3) or (bin_code == 12):
a = (v - v0) / (v3 - v0)
b = (v - v1) / (v2 - v1)
pt1 = (x0i + a * We, y0j)
pt2 = (x0i + b * We, y0j + He)
return [(pt1, pt2)]
elif (bin_code == 4) or (bin_code == 11):
a = (v - v1) / (v2 - v1)
b = (v - v3) / (v2 - v3)
pt1 = (x0i + a * We, y0j + He)
pt2 = (x0i + We, y0j + b * He)
return [(pt1, pt2)]
elif (bin_code == 6) or (bin_code == 9):
a = (v - v0) / (v1 - v0)
b = (v - v3) / (v2 - v3)
pt1 = (x0i, y0j + a * He)
pt2 = (x0i + We, y0j + b * He)
return [(pt1, pt2)]
elif (bin_code == 7) or (bin_code == 8):
a = (v - v0) / (v3 - v0)
b = (v - v3) / (v2 - v3)
pt1 = (x0i + a * We, y0j)
pt2 = (x0i + We, y0j + b * He)
return [(pt1, pt2)]
elif bin_code == 5:
a1 = (v - v0) / (v1 - v0)
b1 = (v - v1) / (v2 - v1)
pt11 = (x0i, y0j + a1 * He)
pt12 = (x0i + b1 * We, y0j + He)
a2 = (v - v0) / (v3 - v0)
b2 = (v - v3) / (v2 - v3)
pt21 = (x0i + a2 * We, y0j)
pt22 = (x0i + We, y0j + b2 * He)
return [(pt11, pt12), (pt21, pt22)]
elif bin_code == 10:
a1 = (v - v0) / (v3 - v0)
b1 = (v - v0) / (v1 - v0)
pt11 = (x0i + a1 * We, y0j)
pt12 = (x0i, y0j + b1 * He)
a2 = (v - v1) / (v2 - v1)
b2 = (v - v3) / (v2 - v3)
pt21 = (x0i + a2 * We, y0j + He)
pt22 = (x0i + We, y0j + b2 * He)
return [(pt11, pt12), (pt21, pt22)]
return []
try:
import matplotlib
matplotlib.use("Agg")
DensePoseResultsContourVisualizer = DensePoseResultsMplContourVisualizer
except ModuleNotFoundError:
logger = logging.getLogger(__name__)
logger.warning("Could not import matplotlib, using custom contour visualizer")
DensePoseResultsContourVisualizer = DensePoseResultsCustomContourVisualizer
class DensePoseResultsFineSegmentationVisualizer(DensePoseMaskedColormapResultsVisualizer):
def __init__(self, inplace=False, cmap=cv2.COLORMAP_PARULA, alpha=1, **kwargs):
super(DensePoseResultsFineSegmentationVisualizer, self).__init__(
_extract_i_from_iuvarr,
_extract_i_from_iuvarr,
inplace,
cmap,
alpha,
val_scale=255.0 / DensePoseDataRelative.N_PART_LABELS,
**kwargs,
)
class DensePoseResultsUVisualizer(DensePoseMaskedColormapResultsVisualizer):
def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs):
super(DensePoseResultsUVisualizer, self).__init__(
_extract_u_from_iuvarr,
_extract_i_from_iuvarr,
inplace,
cmap,
alpha,
val_scale=1.0,
**kwargs,
)
class DensePoseResultsVVisualizer(DensePoseMaskedColormapResultsVisualizer):
def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs):
super(DensePoseResultsVVisualizer, self).__init__(
_extract_v_from_iuvarr,
_extract_i_from_iuvarr,
inplace,
cmap,
alpha,
val_scale=1.0,
**kwargs,
)
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