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- # coding: utf8
- # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- import numpy as np
- def human_seg_tracking(pre_gray, cur_gray, prev_cfd, dl_weights, disflow):
- """计算光流跟踪匹配点和光流图
- 输入参数:
- pre_gray: 上一帧灰度图
- cur_gray: 当前帧灰度图
- prev_cfd: 上一帧光流图
- dl_weights: 融合权重图
- disflow: 光流数据结构
- 返回值:
- is_track: 光流点跟踪二值图，即是否具有光流点匹配
- track_cfd: 光流跟踪图
- """
- check_thres = 8
- h, w = pre_gray.shape[:2]
- track_cfd = np.zeros_like(prev_cfd)
- is_track = np.zeros_like(pre_gray)
- flow_fw = disflow.calc(pre_gray, cur_gray, None)
- flow_bw = disflow.calc(cur_gray, pre_gray, None)
- flow_fw = np.round(flow_fw).astype(np.int)
- flow_bw = np.round(flow_bw).astype(np.int)
- y_list = np.array(range(h))
- x_list = np.array(range(w))
- yv, xv = np.meshgrid(y_list, x_list)
- yv, xv = yv.T, xv.T
- cur_x = xv + flow_fw[:, :, 0]
- cur_y = yv + flow_fw[:, :, 1]
- # 超出边界不跟踪
- not_track = (cur_x < 0) + (cur_x >= w) + (cur_y < 0) + (cur_y >= h)
- flow_bw[~not_track] = flow_bw[cur_y[~not_track], cur_x[~not_track]]
- not_track += (np.square(flow_fw[:, :, 0] + flow_bw[:, :, 0]) +
- np.square(flow_fw[:, :, 1] + flow_bw[:, :, 1])
- ) >= check_thres
- track_cfd[cur_y[~not_track], cur_x[~not_track]] = prev_cfd[~not_track]
- is_track[cur_y[~not_track], cur_x[~not_track]] = 1
- not_flow = np.all(np.abs(flow_fw) == 0,
- axis=-1) * np.all(np.abs(flow_bw) == 0, axis=-1)
- dl_weights[cur_y[not_flow], cur_x[not_flow]] = 0.05
- return track_cfd, is_track, dl_weights
- def human_seg_track_fuse(track_cfd, dl_cfd, dl_weights, is_track):
- """光流追踪图和人像分割结构融合
- 输入参数:
- track_cfd: 光流追踪图
- dl_cfd: 当前帧分割结果
- dl_weights: 融合权重图
- is_track: 光流点匹配二值图
- 返回
- cur_cfd: 光流跟踪图和人像分割结果融合图
- """
- fusion_cfd = dl_cfd.copy()
- is_track = is_track.astype(np.bool)
- fusion_cfd[is_track] = dl_weights[is_track] * dl_cfd[is_track] + (
- 1 - dl_weights[is_track]) * track_cfd[is_track]
- # 确定区域
- index_certain = ((dl_cfd > 0.9) + (dl_cfd < 0.1)) * is_track
- index_less01 = (dl_weights < 0.1) * index_certain
- fusion_cfd[index_less01] = 0.3 * dl_cfd[index_less01] + 0.7 * track_cfd[
- index_less01]
- index_larger09 = (dl_weights >= 0.1) * index_certain
- fusion_cfd[index_larger09] = 0.4 * dl_cfd[
- index_larger09] + 0.6 * track_cfd[index_larger09]
- return fusion_cfd
- def threshold_mask(img, thresh_bg, thresh_fg):
- dst = (img / 255.0 - thresh_bg) / (thresh_fg - thresh_bg)
- dst[np.where(dst > 1)] = 1
- dst[np.where(dst < 0)] = 0
- return dst.astype(np.float32)
- def postprocess(cur_gray, scoremap, prev_gray, pre_cfd, disflow, is_init):
- """光流优化
- Args:
- cur_gray : 当前帧灰度图
- pre_gray : 前一帧灰度图
- pre_cfd ：前一帧融合结果
- scoremap : 当前帧分割结果
- difflow : 光流
- is_init : 是否第一帧
- Returns:
- fusion_cfd : 光流追踪图和预测结果融合图
- """
- h, w = scoremap.shape
- cur_cfd = scoremap.copy()
- if is_init:
- if h <= 64 or w <= 64:
- disflow.setFinestScale(1)
- elif h <= 160 or w <= 160:
- disflow.setFinestScale(2)
- else:
- disflow.setFinestScale(3)
- fusion_cfd = cur_cfd
- else:
- weights = np.ones((h, w), np.float32) * 0.3
- track_cfd, is_track, weights = human_seg_tracking(
- prev_gray, cur_gray, pre_cfd, weights, disflow)
- fusion_cfd = human_seg_track_fuse(track_cfd, cur_cfd, weights,
- is_track)
- return fusion_cfd
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