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267 lines
10 KiB
267 lines
10 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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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 paddle |
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import numpy as np |
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import math |
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import cv2 |
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from paddlers_slim.models.ppdet.core.workspace import register, create |
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from .meta_arch import BaseArch |
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from ..keypoint_utils import transform_preds |
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from .. import layers as L |
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__all__ = ['TopDownHRNet'] |
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@register |
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class TopDownHRNet(BaseArch): |
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__category__ = 'architecture' |
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__inject__ = ['loss'] |
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def __init__(self, |
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width, |
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num_joints, |
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backbone='HRNet', |
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loss='KeyPointMSELoss', |
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post_process='HRNetPostProcess', |
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flip_perm=None, |
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flip=True, |
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shift_heatmap=True, |
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use_dark=True): |
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""" |
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HRNet network, see https://arxiv.org/abs/1902.09212 |
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Args: |
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backbone (nn.Layer): backbone instance |
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post_process (object): `HRNetPostProcess` instance |
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flip_perm (list): The left-right joints exchange order list |
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use_dark(bool): Whether to use DARK in post processing |
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""" |
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super(TopDownHRNet, self).__init__() |
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self.backbone = backbone |
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self.post_process = HRNetPostProcess(use_dark) |
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self.loss = loss |
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self.flip_perm = flip_perm |
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self.flip = flip |
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self.final_conv = L.Conv2d(width, num_joints, 1, 1, 0, bias=True) |
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self.shift_heatmap = shift_heatmap |
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self.deploy = False |
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@classmethod |
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def from_config(cls, cfg, *args, **kwargs): |
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# backbone |
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backbone = create(cfg['backbone']) |
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return {'backbone': backbone, } |
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def _forward(self): |
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feats = self.backbone(self.inputs) |
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hrnet_outputs = self.final_conv(feats[0]) |
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if self.training: |
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return self.loss(hrnet_outputs, self.inputs) |
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elif self.deploy: |
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outshape = hrnet_outputs.shape |
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max_idx = paddle.argmax( |
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hrnet_outputs.reshape( |
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(outshape[0], outshape[1], outshape[2] * outshape[3])), |
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axis=-1) |
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return hrnet_outputs, max_idx |
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else: |
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if self.flip: |
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self.inputs['image'] = self.inputs['image'].flip([3]) |
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feats = self.backbone(self.inputs) |
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output_flipped = self.final_conv(feats[0]) |
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output_flipped = self.flip_back(output_flipped.numpy(), |
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self.flip_perm) |
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output_flipped = paddle.to_tensor(output_flipped.copy()) |
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if self.shift_heatmap: |
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output_flipped[:, :, :, 1:] = output_flipped.clone( |
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)[:, :, :, 0:-1] |
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hrnet_outputs = (hrnet_outputs + output_flipped) * 0.5 |
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imshape = (self.inputs['im_shape'].numpy() |
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)[:, ::-1] if 'im_shape' in self.inputs else None |
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center = self.inputs['center'].numpy( |
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) if 'center' in self.inputs else np.round(imshape / 2.) |
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scale = self.inputs['scale'].numpy( |
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) if 'scale' in self.inputs else imshape / 200. |
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outputs = self.post_process(hrnet_outputs, center, scale) |
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return outputs |
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def get_loss(self): |
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return self._forward() |
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def get_pred(self): |
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res_lst = self._forward() |
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outputs = {'keypoint': res_lst} |
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return outputs |
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def flip_back(self, output_flipped, matched_parts): |
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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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class HRNetPostProcess(object): |
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def __init__(self, use_dark=True): |
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self.use_dark = use_dark |
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def get_max_preds(self, heatmaps): |
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'''get predictions from score maps |
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Args: |
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heatmaps: numpy.ndarray([batch_size, num_joints, height, width]) |
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Returns: |
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preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords |
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maxvals: numpy.ndarray([batch_size, num_joints, 2]), the maximum confidence of the keypoints |
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''' |
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assert isinstance(heatmaps, |
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np.ndarray), 'heatmaps should be numpy.ndarray' |
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assert heatmaps.ndim == 4, 'batch_images should be 4-ndim' |
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batch_size = heatmaps.shape[0] |
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num_joints = heatmaps.shape[1] |
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width = heatmaps.shape[3] |
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heatmaps_reshaped = heatmaps.reshape((batch_size, num_joints, -1)) |
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idx = np.argmax(heatmaps_reshaped, 2) |
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maxvals = np.amax(heatmaps_reshaped, 2) |
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maxvals = maxvals.reshape((batch_size, num_joints, 1)) |
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idx = idx.reshape((batch_size, num_joints, 1)) |
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preds = np.tile(idx, (1, 1, 2)).astype(np.float32) |
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preds[:, :, 0] = (preds[:, :, 0]) % width |
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preds[:, :, 1] = np.floor((preds[:, :, 1]) / width) |
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pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2)) |
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pred_mask = pred_mask.astype(np.float32) |
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preds *= pred_mask |
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return preds, maxvals |
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def gaussian_blur(self, heatmap, kernel): |
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border = (kernel - 1) // 2 |
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batch_size = heatmap.shape[0] |
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num_joints = heatmap.shape[1] |
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height = heatmap.shape[2] |
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width = heatmap.shape[3] |
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for i in range(batch_size): |
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for j in range(num_joints): |
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origin_max = np.max(heatmap[i, j]) |
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dr = np.zeros((height + 2 * border, width + 2 * border)) |
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dr[border:-border, border:-border] = heatmap[i, j].copy() |
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dr = cv2.GaussianBlur(dr, (kernel, kernel), 0) |
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heatmap[i, j] = dr[border:-border, border:-border].copy() |
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heatmap[i, j] *= origin_max / np.max(heatmap[i, j]) |
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return heatmap |
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def dark_parse(self, hm, coord): |
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heatmap_height = hm.shape[0] |
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heatmap_width = hm.shape[1] |
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px = int(coord[0]) |
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py = int(coord[1]) |
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if 1 < px < heatmap_width - 2 and 1 < py < heatmap_height - 2: |
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dx = 0.5 * (hm[py][px + 1] - hm[py][px - 1]) |
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dy = 0.5 * (hm[py + 1][px] - hm[py - 1][px]) |
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dxx = 0.25 * (hm[py][px + 2] - 2 * hm[py][px] + hm[py][px - 2]) |
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dxy = 0.25 * (hm[py+1][px+1] - hm[py-1][px+1] - hm[py+1][px-1] \ |
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+ hm[py-1][px-1]) |
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dyy = 0.25 * ( |
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hm[py + 2 * 1][px] - 2 * hm[py][px] + hm[py - 2 * 1][px]) |
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derivative = np.matrix([[dx], [dy]]) |
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hessian = np.matrix([[dxx, dxy], [dxy, dyy]]) |
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if dxx * dyy - dxy**2 != 0: |
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hessianinv = hessian.I |
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offset = -hessianinv * derivative |
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offset = np.squeeze(np.array(offset.T), axis=0) |
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coord += offset |
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return coord |
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def dark_postprocess(self, hm, coords, kernelsize): |
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'''DARK postpocessing, Zhang et al. Distribution-Aware Coordinate |
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Representation for Human Pose Estimation (CVPR 2020). |
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''' |
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hm = self.gaussian_blur(hm, kernelsize) |
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hm = np.maximum(hm, 1e-10) |
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hm = np.log(hm) |
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for n in range(coords.shape[0]): |
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for p in range(coords.shape[1]): |
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coords[n, p] = self.dark_parse(hm[n][p], coords[n][p]) |
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return coords |
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def get_final_preds(self, heatmaps, center, scale, kernelsize=3): |
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"""the highest heatvalue location with a quarter offset in the |
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direction from the highest response to the second highest response. |
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Args: |
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heatmaps (numpy.ndarray): The predicted heatmaps |
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center (numpy.ndarray): The boxes center |
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scale (numpy.ndarray): The scale factor |
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Returns: |
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preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords |
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maxvals: numpy.ndarray([batch_size, num_joints, 1]), the maximum confidence of the keypoints |
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""" |
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coords, maxvals = self.get_max_preds(heatmaps) |
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heatmap_height = heatmaps.shape[2] |
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heatmap_width = heatmaps.shape[3] |
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if self.use_dark: |
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coords = self.dark_postprocess(heatmaps, coords, kernelsize) |
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else: |
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for n in range(coords.shape[0]): |
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for p in range(coords.shape[1]): |
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hm = heatmaps[n][p] |
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px = int(math.floor(coords[n][p][0] + 0.5)) |
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py = int(math.floor(coords[n][p][1] + 0.5)) |
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if 1 < px < heatmap_width - 1 and 1 < py < heatmap_height - 1: |
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diff = np.array([ |
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hm[py][px + 1] - hm[py][px - 1], |
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hm[py + 1][px] - hm[py - 1][px] |
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]) |
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coords[n][p] += np.sign(diff) * .25 |
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preds = coords.copy() |
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# Transform back |
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for i in range(coords.shape[0]): |
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preds[i] = transform_preds(coords[i], center[i], scale[i], |
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[heatmap_width, heatmap_height]) |
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return preds, maxvals |
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def __call__(self, output, center, scale): |
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preds, maxvals = self.get_final_preds(output.numpy(), center, scale) |
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outputs = [[ |
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np.concatenate( |
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(preds, maxvals), axis=-1), np.mean( |
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maxvals, axis=1) |
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]] |
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return outputs
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