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import copy
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import numpy as np
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import torch
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import torch.nn.functional as F
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class encoder_default:
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def __init__(self, image_height, image_width, scale=0.25, sigma=1.5):
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self.image_height = image_height
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self.image_width = image_width
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self.scale = scale
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self.sigma = sigma
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def generate_heatmap(self, points):
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h, w = self.image_height, self.image_width
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pointmaps = []
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for i in range(len(points)):
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pointmap = np.zeros([h, w], dtype=np.float32)
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point = copy.deepcopy(points[i])
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point[0] = max(0, min(w - 1, point[0]))
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point[1] = max(0, min(h - 1, point[1]))
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pointmap = self._circle(pointmap, point, sigma=self.sigma)
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pointmaps.append(pointmap)
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pointmaps = np.stack(pointmaps, axis=0) / 255.0
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pointmaps = torch.from_numpy(pointmaps).float().unsqueeze(0)
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pointmaps = F.interpolate(pointmaps, size=(int(w * self.scale), int(h * self.scale)), mode='bilinear',
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align_corners=False).squeeze()
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return pointmaps
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def _circle(self, img, pt, sigma=1.0, label_type='Gaussian'):
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tmp_size = sigma * 3
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ul = [int(pt[0] - tmp_size), int(pt[1] - tmp_size)]
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br = [int(pt[0] + tmp_size + 1), int(pt[1] + tmp_size + 1)]
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if (ul[0] > img.shape[1] - 1 or ul[1] > img.shape[0] - 1 or
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br[0] - 1 < 0 or br[1] - 1 < 0):
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return img
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size = 2 * tmp_size + 1
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x = np.arange(0, size, 1, np.float32)
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y = x[:, np.newaxis]
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x0 = y0 = size // 2
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if label_type == 'Gaussian':
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g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
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else:
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g = sigma / (((x - x0) ** 2 + (y - y0) ** 2 + sigma ** 2) ** 1.5)
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g_x = max(0, -ul[0]), min(br[0], img.shape[1]) - ul[0]
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g_y = max(0, -ul[1]), min(br[1], img.shape[0]) - ul[1]
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img_x = max(0, ul[0]), min(br[0], img.shape[1])
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img_y = max(0, ul[1]), min(br[1], img.shape[0])
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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]]
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return img
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