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""" |
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@Author : Qingping Zheng |
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@Contact : [email protected] |
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@File : transforms.py |
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@Time : 10/01/21 00:00 PM |
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@Desc : |
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@License : Licensed under the Apache License, Version 2.0 (the "License"); |
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@Copyright : Copyright 2022 The Authors. All Rights Reserved. |
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""" |
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from __future__ import absolute_import |
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from __future__ import division |
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from __future__ import print_function |
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import numpy as np |
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import cv2 |
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def flip_back(output_flipped, matched_parts): |
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''' |
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ouput_flipped: numpy.ndarray(batch_size, num_joints, height, width) |
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''' |
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assert output_flipped.ndim == 4,\ |
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'output_flipped should be [batch_size, num_joints, height, width]' |
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output_flipped = output_flipped[:, :, :, ::-1] |
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for pair in matched_parts: |
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tmp = output_flipped[:, pair[0], :, :].copy() |
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output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :] |
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output_flipped[:, pair[1], :, :] = tmp |
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return output_flipped |
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def transform_parsing(pred, center, scale, width, height, input_size): |
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if center is not None: |
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trans = get_affine_transform(center, scale, 0, input_size, inv=1) |
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target_pred = cv2.warpAffine( |
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pred, |
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trans, |
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(int(width), int(height)), |
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flags=cv2.INTER_NEAREST, |
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borderMode=cv2.BORDER_CONSTANT, |
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borderValue=(0)) |
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else: |
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target_pred = cv2.resize(pred, (int(width), int(height)), interpolation=cv2.INTER_NEAREST) |
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return target_pred |
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def get_affine_transform(center, |
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scale, |
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rot, |
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output_size, |
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shift=np.array([0, 0], dtype=np.float32), |
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inv=0): |
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if not isinstance(scale, np.ndarray) and not isinstance(scale, list): |
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print(scale) |
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scale = np.array([scale, scale]) |
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scale_tmp = scale |
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src_w = scale_tmp[0] |
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dst_w = output_size[1] |
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dst_h = output_size[0] |
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rot_rad = np.pi * rot / 180 |
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src_dir = get_dir([0, src_w * -0.5], rot_rad) |
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dst_dir = np.array([0, dst_w * -0.5], np.float32) |
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src = np.zeros((3, 2), dtype=np.float32) |
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dst = np.zeros((3, 2), dtype=np.float32) |
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src[0, :] = center + scale_tmp * shift |
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src[1, :] = center + src_dir + scale_tmp * shift |
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dst[0, :] = [dst_w * 0.5, dst_h * 0.5] |
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dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir |
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src[2:, :] = get_3rd_point(src[0, :], src[1, :]) |
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dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :]) |
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if inv: |
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trans = cv2.getAffineTransform(np.float32(dst), np.float32(src)) |
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else: |
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trans = cv2.getAffineTransform(np.float32(src), np.float32(dst)) |
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return trans |
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def affine_transform(pt, t): |
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new_pt = np.array([pt[0], pt[1], 1.]).T |
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new_pt = np.dot(t, new_pt) |
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return new_pt[:2] |
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def get_3rd_point(a, b): |
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direct = a - b |
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return b + np.array([-direct[1], direct[0]], dtype=np.float32) |
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def get_dir(src_point, rot_rad): |
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sn, cs = np.sin(rot_rad), np.cos(rot_rad) |
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src_result = [0, 0] |
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src_result[0] = src_point[0] * cs - src_point[1] * sn |
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src_result[1] = src_point[0] * sn + src_point[1] * cs |
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return src_result |
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def crop(img, center, scale, output_size, rot=0): |
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trans = get_affine_transform(center, scale, rot, output_size) |
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dst_img = cv2.warpAffine(img, |
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trans, |
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(int(output_size[1]), int(output_size[0])), |
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flags=cv2.INTER_LINEAR) |
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return dst_img |
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