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| import math | |
| import cv2 | |
| import munkres | |
| import numpy as np | |
| import torch | |
| # solution proposed in https://github.com/pytorch/pytorch/issues/229#issuecomment-299424875 | |
| def flip_tensor(tensor, dim=0): | |
| """ | |
| flip the tensor on the dimension dim | |
| """ | |
| inv_idx = torch.arange(tensor.shape[dim] - 1, -1, -1).to(tensor.device) | |
| return tensor.index_select(dim, inv_idx) | |
| # | |
| # derived from https://github.com/leoxiaobin/deep-high-resolution-net.pytorch | |
| def flip_back(output_flipped, matched_parts): | |
| assert len(output_flipped.shape) == 4, 'output_flipped has to be [batch_size, num_joints, height, width]' | |
| output_flipped = flip_tensor(output_flipped, dim=-1) | |
| for pair in matched_parts: | |
| tmp = output_flipped[:, pair[0]].clone() | |
| output_flipped[:, pair[0]] = output_flipped[:, pair[1]] | |
| output_flipped[:, pair[1]] = tmp | |
| return output_flipped | |
| def fliplr_joints(joints, joints_vis, width, matched_parts): | |
| # Flip horizontal | |
| joints[:, 0] = width - joints[:, 0] - 1 | |
| # Change left-right parts | |
| for pair in matched_parts: | |
| joints[pair[0], :], joints[pair[1], :] = \ | |
| joints[pair[1], :], joints[pair[0], :].copy() | |
| joints_vis[pair[0], :], joints_vis[pair[1], :] = \ | |
| joints_vis[pair[1], :], joints_vis[pair[0], :].copy() | |
| return joints * joints_vis, joints_vis | |
| def get_affine_transform(center, scale, pixel_std, rot, output_size, shift=np.array([0, 0], dtype=np.float32), inv=0): | |
| if not isinstance(scale, np.ndarray) and not isinstance(scale, list): | |
| print(scale) | |
| scale = np.array([scale, scale]) | |
| scale_tmp = scale * 1.0 * pixel_std # It was scale_tmp = scale * 200.0 | |
| src_w = scale_tmp[0] | |
| dst_w = output_size[0] | |
| dst_h = output_size[1] | |
| rot_rad = np.pi * rot / 180 | |
| src_dir = get_dir([0, src_w * -0.5], rot_rad) | |
| dst_dir = np.array([0, dst_w * -0.5], np.float32) | |
| src = np.zeros((3, 2), dtype=np.float32) | |
| dst = np.zeros((3, 2), dtype=np.float32) | |
| src[0, :] = center + scale_tmp * shift | |
| src[1, :] = center + src_dir + scale_tmp * shift | |
| dst[0, :] = [dst_w * 0.5, dst_h * 0.5] | |
| dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir | |
| src[2:, :] = get_3rd_point(src[0, :], src[1, :]) | |
| dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :]) | |
| if inv: | |
| trans = cv2.getAffineTransform(np.float32(dst), np.float32(src)) | |
| else: | |
| trans = cv2.getAffineTransform(np.float32(src), np.float32(dst)) | |
| return trans | |
| def affine_transform(pt, t): | |
| new_pt = np.array([pt[0], pt[1], 1.]).T | |
| new_pt = np.dot(t, new_pt) | |
| return new_pt[:2] | |
| def get_3rd_point(a, b): | |
| direct = a - b | |
| return b + np.array([-direct[1], direct[0]], dtype=np.float32) | |
| def get_dir(src_point, rot_rad): | |
| sn, cs = np.sin(rot_rad), np.cos(rot_rad) | |
| src_result = [0, 0] | |
| src_result[0] = src_point[0] * cs - src_point[1] * sn | |
| src_result[1] = src_point[0] * sn + src_point[1] * cs | |
| return src_result | |