mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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60b430f7be
* [Android] Add VisSegmentation NEON support * [ARM] change vqaddq_u8 -> vaddq_u8 * [ARM] change vqaddq_u8 -> vaddq_u8 * [Bug Fix] add FDASSERT * update assert info * add QuantizeBlendingWeight8 * Update QuantizeBlendingWeight8 * Update VisSegmentation * [Visualize] add DefaultVisualizeType and EnableFastVisuzlie * fix typos * fix typo * Update VisSegmentation * [Android] Add omp parallel support for Android * Add omp schedule(static)
205 lines
8.0 KiB
C++
205 lines
8.0 KiB
C++
// 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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#ifdef ENABLE_VISION_VISUALIZE
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#include "fastdeploy/vision/visualize/visualize.h"
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc/imgproc.hpp"
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#ifdef __ARM_NEON
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#include <arm_neon.h>
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#endif
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namespace fastdeploy {
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namespace vision {
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#ifdef __ARM_NEON
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static inline void QuantizeBlendingWeight8(
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float weight, uint8_t* old_multi_factor, uint8_t* new_multi_factor) {
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// Quantize the weight to boost blending performance.
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// if 0.0 < w <= 1/8, w ~ 1/8=1/(2^3) shift right 3 mul 1, 7
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// if 1/8 < w <= 2/8, w ~ 2/8=1/(2^3) shift right 3 mul 2, 6
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// if 2/8 < w <= 3/8, w ~ 3/8=1/(2^3) shift right 3 mul 3, 5
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// if 3/8 < w <= 4/8, w ~ 4/8=1/(2^3) shift right 3 mul 4, 4
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// Shift factor is always 3, but the mul factor is different.
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// Moving 7 bits to the right tends to result in a zero value,
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// So, We choose to shift 3 bits to get an approximation.
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uint8_t weight_quantize = static_cast<uint8_t>(weight * 8.0f);
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*new_multi_factor = weight_quantize;
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*old_multi_factor = (8 - weight_quantize);
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}
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static cv::Mat FastVisSegmentationNEON(
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const cv::Mat& im, const SegmentationResult& result,
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float weight, bool quantize_weight = true) {
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int64_t height = result.shape[0];
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int64_t width = result.shape[1];
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auto vis_img = cv::Mat(height, width, CV_8UC3);
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int32_t size = static_cast<int32_t>(height * width);
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uint8_t *vis_ptr = static_cast<uint8_t*>(vis_img.data);
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const uint8_t *label_ptr = static_cast<const uint8_t*>(result.label_map.data());
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const uint8_t *im_ptr = static_cast<const uint8_t*>(im.data);
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if (!quantize_weight) {
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#pragma omp parallel for num_threads(2) schedule(static)
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for (int i = 0; i < size - 15; i += 16) {
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uint8x16_t labelx16 = vld1q_u8(label_ptr + i); // 16 bytes
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// e.g 0b00000001 << 7 -> 0b10000000 128;
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uint8x16x3_t vbgrx16x3;
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vbgrx16x3.val[0] = vshlq_n_u8(labelx16, 7);
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vbgrx16x3.val[1] = vshlq_n_u8(labelx16, 4);
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vbgrx16x3.val[2] = vshlq_n_u8(labelx16, 3);
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vst3q_u8(vis_ptr + i * 3, vbgrx16x3);
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}
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for (int i = size - 15; i < size; i++) {
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uint8_t label = label_ptr[i];
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vis_ptr[i * 3 + 0] = (label << 7);
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vis_ptr[i * 3 + 1] = (label << 4);
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vis_ptr[i * 3 + 2] = (label << 3);
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}
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// Blend colors use opencv
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cv::addWeighted(im, 1.0 - weight, vis_img, weight, 0, vis_img);
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return vis_img;
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}
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// Quantize the weight to boost blending performance.
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// After that, we can directly use shift instructions
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// to blend the colors from input im and mask. Please
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// check QuantizeBlendingWeight8 for more details.
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uint8_t old_multi_factor, new_multi_factor;
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QuantizeBlendingWeight8(weight, &old_multi_factor,
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&new_multi_factor);
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if (new_multi_factor == 0) {
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return im; // Only keep origin image.
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}
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if (new_multi_factor == 8) {
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// Only keep mask, no need to blending with origin image.
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#pragma omp parallel for num_threads(2) schedule(static)
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for (int i = 0; i < size - 15; i += 16) {
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uint8x16_t labelx16 = vld1q_u8(label_ptr + i); // 16 bytes
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// e.g 0b00000001 << 7 -> 0b10000000 128;
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uint8x16_t mbx16 = vshlq_n_u8(labelx16, 7);
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uint8x16_t mgx16 = vshlq_n_u8(labelx16, 4);
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uint8x16_t mrx16 = vshlq_n_u8(labelx16, 3);
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uint8x16x3_t vbgr16x3;
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vbgr16x3.val[0] = mbx16;
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vbgr16x3.val[1] = mgx16;
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vbgr16x3.val[2] = mrx16;
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vst3q_u8(vis_ptr + i * 3, vbgr16x3);
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}
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for (int i = size - 15; i < size; i++) {
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uint8_t label = label_ptr[i];
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vis_ptr[i * 3 + 0] = (label << 7);
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vis_ptr[i * 3 + 1] = (label << 4);
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vis_ptr[i * 3 + 2] = (label << 3);
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}
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return vis_img;
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}
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uint8x16_t old_mulx16 = vdupq_n_u8(old_multi_factor);
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uint8x16_t new_mulx16 = vdupq_n_u8(new_multi_factor);
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// Blend the two colors together with quantize 'weight'.
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#pragma omp parallel for num_threads(2) schedule(static)
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for (int i = 0; i < size - 15; i += 16) {
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uint8x16x3_t bgrx16x3 = vld3q_u8(im_ptr + i * 3); // 48 bytes
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uint8x16_t labelx16 = vld1q_u8(label_ptr + i); // 16 bytes
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uint8x16_t ibx16 = bgrx16x3.val[0];
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uint8x16_t igx16 = bgrx16x3.val[1];
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uint8x16_t irx16 = bgrx16x3.val[2];
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// e.g 0b00000001 << 7 -> 0b10000000 128;
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uint8x16_t mbx16 = vshlq_n_u8(labelx16, 7);
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uint8x16_t mgx16 = vshlq_n_u8(labelx16, 4);
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uint8x16_t mrx16 = vshlq_n_u8(labelx16, 3);
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// TODO: keep the pixels of input im if mask = 0
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uint8x16_t ibx16_mshr, igx16_mshr, irx16_mshr;
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uint8x16_t mbx16_mshr, mgx16_mshr, mrx16_mshr;
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// Moving 7 bits to the right tends to result in zero,
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// So, We choose to shift 3 bits to get an approximation
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ibx16_mshr = vmulq_u8(vshrq_n_u8(ibx16, 3), old_mulx16);
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igx16_mshr = vmulq_u8(vshrq_n_u8(igx16, 3), old_mulx16);
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irx16_mshr = vmulq_u8(vshrq_n_u8(irx16, 3), old_mulx16);
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mbx16_mshr = vmulq_u8(vshrq_n_u8(mbx16, 3), new_mulx16);
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mgx16_mshr = vmulq_u8(vshrq_n_u8(mgx16, 3), new_mulx16);
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mrx16_mshr = vmulq_u8(vshrq_n_u8(mrx16, 3), new_mulx16);
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uint8x16x3_t vbgr16x3;
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vbgr16x3.val[0] = vaddq_u8(ibx16_mshr, mbx16_mshr);
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vbgr16x3.val[1] = vaddq_u8(igx16_mshr, mgx16_mshr);
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vbgr16x3.val[2] = vaddq_u8(irx16_mshr, mrx16_mshr);
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// Store the blended pixels to vis img
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vst3q_u8(vis_ptr + i * 3, vbgr16x3);
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}
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for (int i = size - 15; i < size; i++) {
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uint8_t label = label_ptr[i];
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vis_ptr[i * 3 + 0] = (im_ptr[i * 3 + 0] >> 3) * old_multi_factor
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+ ((label << 7) >> 3) * new_multi_factor;
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vis_ptr[i * 3 + 1] = (im_ptr[i * 3 + 1] >> 3) * old_multi_factor
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+ ((label << 4) >> 3) * new_multi_factor;
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vis_ptr[i * 3 + 2] = (im_ptr[i * 3 + 2] >> 3) * old_multi_factor
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+ ((label << 3) >> 3) * new_multi_factor;
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}
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return vis_img;
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}
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#endif
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static cv::Mat VisSegmentationCommonCpu(
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const cv::Mat& im, const SegmentationResult& result,
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float weight) {
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// Use the native c++ version without any optimization.
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auto color_map = GenerateColorMap(1000);
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int64_t height = result.shape[0];
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int64_t width = result.shape[1];
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auto vis_img = cv::Mat(height, width, CV_8UC3);
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int64_t index = 0;
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for (int i = 0; i < height; i++) {
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for (int j = 0; j < width; j++) {
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int category_id = result.label_map[index++];
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vis_img.at<cv::Vec3b>(i, j)[0] = color_map[3 * category_id + 0];
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vis_img.at<cv::Vec3b>(i, j)[1] = color_map[3 * category_id + 1];
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vis_img.at<cv::Vec3b>(i, j)[2] = color_map[3 * category_id + 2];
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}
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}
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cv::addWeighted(im, 1.0 - weight, vis_img, weight, 0, vis_img);
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return vis_img;
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}
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cv::Mat VisSegmentation(const cv::Mat& im, const SegmentationResult& result,
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float weight) {
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// TODO: Support SSE/AVX on x86_64 platforms
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#ifdef __ARM_NEON
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return FastVisSegmentationNEON(im, result, weight, true);
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#else
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return VisSegmentationCommonCpu(im, result, weight);
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#endif
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}
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cv::Mat Visualize::VisSegmentation(const cv::Mat& im,
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const SegmentationResult& result) {
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FDWARNING << "DEPRECATED: fastdeploy::vision::Visualize::VisSegmentation is "
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"deprecated, please use fastdeploy::vision:VisSegmentation "
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"function instead."
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<< std::endl;
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#ifdef __ARM_NEON
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return FastVisSegmentationNEON(im, result, 0.5f, true);
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#else
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return VisSegmentationCommonCpu(im, result, 0.5f);
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#endif
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}
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} // namespace vision
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} // namespace fastdeploy
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#endif
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