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on
Zero
Running
on
Zero
| 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 | |