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Running
on
Zero
Running
on
Zero
File size: 2,561 Bytes
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import copy
import numpy as np
import torch
import torch.nn.functional as F
class encoder_default:
def __init__(self, image_height, image_width, scale=0.25, sigma=1.5):
self.image_height = image_height
self.image_width = image_width
self.scale = scale
self.sigma = sigma
def generate_heatmap(self, points):
# points = (num_pts, 2)
h, w = self.image_height, self.image_width
pointmaps = []
for i in range(len(points)):
pointmap = np.zeros([h, w], dtype=np.float32)
# align_corners: False.
point = copy.deepcopy(points[i])
point[0] = max(0, min(w - 1, point[0]))
point[1] = max(0, min(h - 1, point[1]))
pointmap = self._circle(pointmap, point, sigma=self.sigma)
pointmaps.append(pointmap)
pointmaps = np.stack(pointmaps, axis=0) / 255.0
pointmaps = torch.from_numpy(pointmaps).float().unsqueeze(0)
pointmaps = F.interpolate(pointmaps, size=(int(w * self.scale), int(h * self.scale)), mode='bilinear',
align_corners=False).squeeze()
return pointmaps
def _circle(self, img, pt, sigma=1.0, label_type='Gaussian'):
# Check that any part of the gaussian is in-bounds
tmp_size = sigma * 3
ul = [int(pt[0] - tmp_size), int(pt[1] - tmp_size)]
br = [int(pt[0] + tmp_size + 1), int(pt[1] + tmp_size + 1)]
if (ul[0] > img.shape[1] - 1 or ul[1] > img.shape[0] - 1 or
br[0] - 1 < 0 or br[1] - 1 < 0):
# If not, just return the image as is
return img
# Generate gaussian
size = 2 * tmp_size + 1
x = np.arange(0, size, 1, np.float32)
y = x[:, np.newaxis]
x0 = y0 = size // 2
# The gaussian is not normalized, we want the center value to equal 1
if label_type == 'Gaussian':
g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
else:
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]] = 255 * g[g_y[0]:g_y[1], g_x[0]:g_x[1]]
return img
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