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# Modified from:
# https://github.com/anibali/pytorch-stacked-hourglass
# https://github.com/bearpaw/pytorch-pose
import numpy as np
from .misc import to_numpy, to_torch
from .pilutil import imread, imresize
from kornia.geometry.subpix import dsnt
import torch
def im_to_numpy(img):
img = to_numpy(img)
img = np.transpose(img, (1, 2, 0)) # H*W*C
return img
def im_to_torch(img):
img = np.transpose(img, (2, 0, 1)) # C*H*W
img = to_torch(img).float()
if img.max() > 1:
img /= 255
return img
def load_image(img_path):
# H x W x C => C x H x W
return im_to_torch(imread(img_path, mode='RGB'))
# =============================================================================
# Helpful functions generating groundtruth labelmap
# =============================================================================
def gaussian(shape=(7,7),sigma=1):
"""
2D gaussian mask - should give the same result as MATLAB's
fspecial('gaussian',[shape],[sigma])
"""
m,n = [(ss-1.)/2. for ss in shape]
y,x = np.ogrid[-m:m+1,-n:n+1]
h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )
h[ h < np.finfo(h.dtype).eps*h.max() ] = 0
return to_torch(h).float()
def draw_labelmap_orig(img, pt, sigma, type='Gaussian'):
# Draw a 2D gaussian
# Adopted from https://github.com/anewell/pose-hg-train/blob/master/src/pypose/draw.py
# maximum value of the gaussian is 1
img = to_numpy(img)
# Check that any part of the gaussian is in-bounds
ul = [int(pt[0] - 3 * sigma), int(pt[1] - 3 * sigma)]
br = [int(pt[0] + 3 * sigma + 1), int(pt[1] + 3 * sigma + 1)]
if (ul[0] >= img.shape[1] or ul[1] >= img.shape[0] or
br[0] < 0 or br[1] < 0):
# If not, just return the image as is
return to_torch(img), 0
# Generate gaussian
size = 6 * sigma + 1
x = np.arange(0, size, 1, float)
y = x[:, np.newaxis]
x0 = y0 = size // 2
# The gaussian is not normalized, we want the center value to equal 1
if type == 'Gaussian':
g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
elif type == 'Cauchy':
g = sigma / (((x - x0) ** 2 + (y - y0) ** 2 + sigma ** 2) ** 1.5)
# Usable gaussian range
g_x = max(0, -ul[0]), min(br[0], img.shape[1]) - ul[0]
g_y = max(0, -ul[1]), min(br[1], img.shape[0]) - ul[1]
# Image range
img_x = max(0, ul[0]), min(br[0], img.shape[1])
img_y = max(0, ul[1]), min(br[1], img.shape[0])
img[img_y[0]:img_y[1], img_x[0]:img_x[1]] = g[g_y[0]:g_y[1], g_x[0]:g_x[1]]
return to_torch(img), 1
def draw_labelmap(img, pt, sigma, type='Gaussian'):
# Draw a 2D gaussian
# real probability distribution: the sum of all values is 1
img = to_numpy(img)
if not type == 'Gaussian':
raise NotImplementedError
# Check that any part of the gaussian is in-bounds
ul = [int(pt[0] - 3 * sigma), int(pt[1] - 3 * sigma)]
br = [int(pt[0] + 3 * sigma + 1), int(pt[1] + 3 * sigma + 1)]
if (ul[0] >= img.shape[1] or ul[1] >= img.shape[0] or
br[0] < 0 or br[1] < 0):
# If not, just return the image as is
return to_torch(img), 0
# Generate gaussian
# img_new = dsnt.render_gaussian2d(mean=torch.tensor([[-1, 0]]).float(), std=torch.tensor([[sigma, sigma]]).float(), size=(img.shape[0], img.shape[1]), normalized_coordinates=False)
img_new = dsnt.render_gaussian2d(mean=torch.tensor([[pt[0], pt[1]]]).float(), \
std=torch.tensor([[sigma, sigma]]).float(), \
size=(img.shape[0], img.shape[1]), \
normalized_coordinates=False)
img_new = img_new[0, :, :] # this is a torch image
return img_new, 1
def draw_multiple_labelmaps(out_res, pts, sigma, type='Gaussian'):
# Draw a 2D gaussian
# real probability distribution: the sum of all values is 1
if not type == 'Gaussian':
raise NotImplementedError
# Generate gaussians
n_pts = pts.shape[0]
imgs_new = dsnt.render_gaussian2d(mean=pts[:, :2], \
std=torch.tensor([[sigma, sigma]]).float().repeat((n_pts, 1)), \
size=(out_res[0], out_res[1]), \
normalized_coordinates=False) # shape: (n_pts, out_res[0], out_res[1])
visibility_orig = imgs_new.sum(axis=2).sum(axis=1) # shape: (n_pts)
visibility = torch.zeros((n_pts, 1), dtype=torch.float32)
visibility[visibility_orig>=0.99999] = 1.0
# import pdb; pdb.set_trace()
return imgs_new, visibility.int() |