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|
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
|
|
| import numpy as np |
| import cv2 |
|
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|
|
| def flip_back(output_flipped, matched_parts): |
| ''' |
| ouput_flipped: numpy.ndarray(batch_size, num_joints, height, width) |
| ''' |
| assert output_flipped.ndim == 4,\ |
| 'output_flipped should be [batch_size, num_joints, height, width]' |
|
|
| output_flipped = output_flipped[:, :, :, ::-1] |
|
|
| for pair in matched_parts: |
| tmp = output_flipped[:, pair[0], :, :].copy() |
| output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :] |
| output_flipped[:, pair[1], :, :] = tmp |
|
|
| return output_flipped |
|
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|
|
| def fliplr_joints(joints, joints_vis, width, matched_parts): |
| """ |
| flip coords |
| """ |
| |
| joints[:, 0] = width - joints[:, 0] - 1 |
|
|
| |
| 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 transform_preds(coords, center, scale, output_size): |
| target_coords = np.zeros(coords.shape) |
| trans = get_affine_transform(center, scale, 0, output_size, inv=1) |
| for p in range(coords.shape[0]): |
| target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans) |
| return target_coords |
|
|
|
|
| def get_affine_transform( |
| center, scale, rot, output_size, shift=np.array([0, 0], dtype=np.float32), inv=0 |
| ): |
| if not isinstance(scale, np.ndarray) and not isinstance(scale, list): |
| |
| scale = np.array([scale, scale]) |
|
|
| 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 |
|
|
|
|
| def crop(img, center, scale, output_size, rot=0): |
| trans = get_affine_transform(center, scale, rot, output_size) |
|
|
| dst_img = cv2.warpAffine( |
| img, trans, (int(output_size[0]), int(output_size[1])), flags=cv2.INTER_LINEAR |
| ) |
|
|
| return dst_img |
|
|
