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MNN/test/speed/ImageProcessSpeedTest.cpp

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//
// ImageProcessSpeedTest.cpp
// MNNTests
//
// Created by MNN on 2019/01/10.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include <MNN/ImageProcess.hpp>
#include <cmath>
#include <memory>
#include "MNNTestSuite.h"
#define MNN_OPEN_TIME_TRACE
#include <MNN/AutoTime.hpp>
using namespace MNN;
using namespace MNN::CV;
class ImageProcessSpeedGrayToGrayTest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedGrayToGrayTest() = default;
virtual bool run(int precision) {
int w = 1080, h = 720, size = w * h;
std::vector<uint8_t> integers(size);
for (int i = 0; i < size; ++i) {
int magic = (i * 67 % 255);
integers[i] = magic;
}
std::vector<float> floats(size * 4);
std::shared_ptr<MNN::Tensor> tensor(
MNN::Tensor::create<float>(std::vector<int>{1, 1, h, w}, floats.data(), Tensor::CAFFE_C4));
ImageProcess::Config config;
config.sourceFormat = GRAY;
config.destFormat = GRAY;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
process->convert(integers.data(), w, h, 0, tensor.get());
for (int i = 0; i < floats.size() / 4; ++i) {
int s = floats[4 * i + 0];
if (s != integers[i]) {
MNN_ERROR("Error for turn gray to float:%d, %d -> %f\n", i, integers[i], floats[4 * i]);
return false;
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(integers.data(), w, h, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedGrayToGrayTest, "speed/cv/image_process/gray_to_gray");
class ImageProcessSpeedGrayToGrayBilinearTransformTest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedGrayToGrayBilinearTransformTest() = default;
virtual bool run(int precision) {
ImageProcess::Config config;
config.sourceFormat = GRAY;
config.destFormat = GRAY;
config.filterType = BILINEAR;
config.wrap = CLAMP_TO_EDGE;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
int sw = 1280;
int sh = 720;
int dw = 360;
int dh = 640;
Matrix tr;
tr.setScale(1.0 / sw, 1.0 / sh);
tr.postRotate(30, 0.5f, 0.5f);
tr.postScale(dw, dh);
tr.invert(&tr);
process->setMatrix(tr);
std::shared_ptr<unsigned char> integers(new unsigned char[sw * sh * 4]);
auto pixels = integers.get();
for (int y = 0; y < sh; ++y) {
auto pixelY = pixels + sw * y;
int magicY = ((sh - y) * (sh - y)) % 79;
for (int x = 0; x < sw; ++x) {
auto pixelX = pixelY + 4 * x;
int magicX = (x * x) % 113;
for (int p = 0; p < 1; ++p) {
int magic = (magicX + magicY + p * p * p) % 255;
pixelX[p] = magic;
}
}
}
std::shared_ptr<Tensor> tensor(
Tensor::create<float>(std::vector<int>{1, 1, dw, dh}, nullptr, Tensor::CAFFE_C4));
process->convert(integers.get(), sw, sh, 0, tensor.get());
auto floats = tensor->host<float>();
int expects[] = {18, 36, 14, 36, 18, 44, 30, 60, 50, 24};
for (int v = 0; v < 10; ++v) {
if (fabsf(floats[4 * v] - (float)expects[v]) >= 2) {
MNN_ERROR("Error for %d, %.f, correct=%d\n", v, floats[4 * v], expects[v]);
return false;
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(integers.get(), sw, sh, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedGrayToGrayBilinearTransformTest,
"speed/cv/image_process/gray_to_gray_bilinear_transorm");
class ImageProcessSpeedGrayToGrayNearestTransformTest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedGrayToGrayNearestTransformTest() = default;
virtual bool run(int precision) {
ImageProcess::Config config;
config.sourceFormat = GRAY;
config.destFormat = GRAY;
config.filterType = NEAREST;
config.wrap = ZERO;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
int sw = 1280;
int sh = 720;
int dw = 360;
int dh = 640;
Matrix tr;
tr.setScale(1.0 / sw, 1.0 / sh);
tr.postRotate(90, 0.5f, 0.5f);
tr.postScale(dw, dh);
tr.invert(&tr);
process->setMatrix(tr);
std::shared_ptr<unsigned char> integers(new unsigned char[sw * sh]);
auto pixels = integers.get();
for (int y = 0; y < sh; ++y) {
auto pixelY = pixels + sw * y;
int magicY = ((sh - y) * (sh - y)) % 79;
for (int x = 0; x < sw; ++x) {
auto pixelX = pixelY + x;
int magicX = (x * x) % 113;
int magic = (magicX + magicY) % 255;
pixelX[0] = magic;
}
}
std::shared_ptr<Tensor> tensor(
Tensor::create<float>(std::vector<int>{1, 1, dw, dh}, nullptr, Tensor::CAFFE_C4));
process->convert(integers.get(), sw, sh, 0, tensor.get());
auto floats = tensor->host<float>();
int expect[] = {0, 4, 16, 36, 64, 21, 65, 38, 19, 8};
for (int v = 0; v < 10; ++v) {
if ((int)(floats[4 * v]) != expect[v]) {
MNN_ERROR("Error for %d, %.f, correct=%d\n", v, floats[4 * v], expect[v]);
return false;
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(integers.get(), sw, sh, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedGrayToGrayNearestTransformTest,
"speed/cv/image_process/gray_to_gray_nearest_transorm");
class ImageProcessSpeedGrayToRGBATest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedGrayToRGBATest() = default;
virtual bool run(int precision) {
int w = 1080, h = 720, size = w * h;
std::vector<uint8_t> gray(size);
for (int i = 0; i < size; ++i) {
int magic = (i * 67 % 255);
gray[i + 0] = (3 * magic + 0) % 255;
}
std::vector<uint8_t> rgba(size * 4);
std::shared_ptr<MNN::Tensor> tensor(MNN::Tensor::create<uint8_t>(std::vector<int>{1, h, w, 4}, rgba.data()));
ImageProcess::Config config;
config.sourceFormat = GRAY;
config.destFormat = RGBA;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
process->convert(gray.data(), w, h, 0, tensor.get());
for (int i = 0; i < size; ++i) {
int s = gray[i];
int r = rgba[4 * i + 0];
int g = rgba[4 * i + 1];
int b = rgba[4 * i + 2];
int y = s;
int a = rgba[4 * i + 3];
if (y != r || y != g || y != b || a != 255) {
MNN_ERROR("Turn gray to RGBA:%d, %d -> %d,%d,%d,%d\n", i, s, r, g, b, a);
return false;
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(gray.data(), w, h, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedGrayToRGBATest, "speed/cv/image_process/gray_to_rgba");
class ImageProcessSpeedBGRToGrayTest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedBGRToGrayTest() = default;
virtual bool run(int precision) {
int w = 1080, h = 720, size = w * h;
std::vector<uint8_t> bgr(size * 3);
for (int i = 0; i < size; ++i) {
int magic = (i * 67 % 255);
bgr[3 * i + 0] = (3 * magic + 0) % 255;
bgr[3 * i + 1] = (3 * magic + 32) % 255;
bgr[3 * i + 2] = (3 * magic + 64) % 255;
}
std::vector<uint8_t> gray(size);
std::shared_ptr<MNN::Tensor> tensor(MNN::Tensor::create<uint8_t>(std::vector<int>{1, h, w, 1}, gray.data()));
ImageProcess::Config config;
config.sourceFormat = BGR;
config.destFormat = GRAY;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
process->convert(bgr.data(), w, h, 0, tensor.get());
for (int i = 0; i < size; ++i) {
int s = gray[i];
int r = bgr[3 * i + 2];
int g = bgr[3 * i + 1];
int b = bgr[3 * i + 0];
int y = (19 * r + 38 * g + 7 * b) >> 6;
if (abs(y - s) >= 2) {
MNN_ERROR("Turn BGR to gray:%d, %d,%d,%d -> %d\n", i, r, g, b, s);
return false;
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(bgr.data(), w, h, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedBGRToGrayTest, "speed/cv/image_process/bgr_to_gray");
class ImageProcessSpeedRGBToBGRTest : public MNNTestCase {
public:
virtual bool run(int precision) {
int w = 1081, h = 719, size = w * h;
std::vector<uint8_t> integers(size * 3);
for (int i = 0; i < size; ++i) {
int magic = (i * 67 % 255);
integers[3 * i + 0] = (3 * magic + 0) % 255;
integers[3 * i + 1] = (3 * magic + 1) % 255;
integers[3 * i + 2] = (3 * magic + 2) % 255;
}
std::vector<uint8_t> resultData(size * 3);
std::shared_ptr<MNN::Tensor> tensor(
MNN::Tensor::create<uint8_t>(std::vector<int>{1, h, w, 3}, resultData.data(), Tensor::TENSORFLOW));
ImageProcess::Config config;
config.sourceFormat = RGB;
config.destFormat = BGR;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
process->convert(integers.data(), w, h, 0, tensor.get());
for (int i = 0; i < size; ++i) {
int r = resultData[3 * i + 2];
int g = resultData[3 * i + 1];
int b = resultData[3 * i + 0];
if (r != integers[3 * i + 0] || g != integers[3 * i + 1] || b != integers[3 * i + 2]) {
MNN_ERROR("Error for turn rgb to bgr:\n %d,%d,%d->%d, %d, %d\n", integers[3 * i + 0],
integers[3 * i + 1], integers[3 * i + 2], r, g, b);
return false;
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(integers.data(), w, h, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedRGBToBGRTest, "speed/cv/image_process/rgb_to_bgr");
class ImageProcessSpeedRGBAToBGRATest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedRGBAToBGRATest() = default;
virtual bool run(int precision) {
int w = 1081, h = 720, size = w * h;
std::vector<uint8_t> integers(size * 4);
for (int i = 0; i < size; ++i) {
int magic = (i * 67 % 255);
integers[4 * i + 0] = (4 * magic + 0) % 255;
integers[4 * i + 1] = (4 * magic + 1) % 255;
integers[4 * i + 2] = (4 * magic + 2) % 255;
integers[4 * i + 3] = (4 * magic + 3) % 255;
}
std::vector<uint8_t> floats(size * 4);
std::shared_ptr<MNN::Tensor> tensor(
MNN::Tensor::create<uint8_t>(std::vector<int>{1, h, w, 4}, floats.data(), Tensor::TENSORFLOW));
ImageProcess::Config config;
config.sourceFormat = RGBA;
config.destFormat = BGRA;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
process->convert(integers.data(), w, h, 0, tensor.get());
for (int i = 0; i < floats.size() / 4; ++i) {
int r = floats[4 * i + 2];
int g = floats[4 * i + 1];
int b = floats[4 * i + 0];
if (r != integers[4 * i + 0] || g != integers[4 * i + 1] || b != integers[4 * i + 2]) {
MNN_ERROR("Error for turn rgba to bgra:\n %d,%d,%d->%d, %d, %d, %d\n", integers[4 * i + 0],
integers[4 * i + 1], integers[4 * i + 2], floats[4 * i + 0], floats[4 * i + 1],
floats[4 * i + 2], floats[4 * i + 3]);
return false;
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(integers.data(), w, h, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedRGBAToBGRATest, "speed/cv/image_process/rgba_to_bgra");
class ImageProcessSpeedBGRToBGRTest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedBGRToBGRTest() = default;
virtual bool run(int precision) {
int w = 1020, h = 960, size = w * h;
std::vector<uint8_t> integers(size * 3);
for (int i = 0; i < size; ++i) {
int magic = (i * 67 % 255);
integers[3 * i + 0] = (3 * magic + 0) % 255;
integers[3 * i + 1] = (3 * magic + 1) % 255;
integers[3 * i + 2] = (3 * magic + 2) % 255;
}
std::vector<float> floats(size * 4);
std::shared_ptr<MNN::Tensor> tensor(
MNN::Tensor::create<float>(std::vector<int>{1, 1, h, w}, floats.data(), Tensor::CAFFE_C4));
ImageProcess::Config config;
config.sourceFormat = BGR;
config.destFormat = BGR;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
process->convert(integers.data(), w, h, 0, tensor.get());
for (int i = 0; i < floats.size() / 4; ++i) {
int r = floats[4 * i + 0];
int g = floats[4 * i + 1];
int b = floats[4 * i + 2];
if (r != integers[3 * i + 0] || g != integers[3 * i + 1] || b != integers[3 * i + 2]) {
MNN_ERROR("Error for turn rgb to float:\n, i:%d, %d,%d,%d->%f, %f, %f, %f\n", i, integers[3 * i + 0],
integers[3 * i + 1], integers[3 * i + 2], floats[4 * i + 0], floats[4 * i + 1],
floats[4 * i + 2], floats[4 * i + 3]);
return false;
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(integers.data(), w, h, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedBGRToBGRTest, "speed/cv/image_process/bgr_to_bgr");
class ImageProcessSpeedRGBToGrayTest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedRGBToGrayTest() = default;
virtual bool run(int precision) {
int w = 1080, h = 720, size = w * h;
std::vector<uint8_t> rgb(size * 3);
for (int i = 0; i < size; ++i) {
int magic = (i * 67 % 255);
rgb[3 * i + 0] = (3 * magic + 0) % 255;
rgb[3 * i + 1] = (3 * magic + 32) % 255;
rgb[3 * i + 2] = (3 * magic + 64) % 255;
}
std::vector<uint8_t> gray(size);
std::shared_ptr<MNN::Tensor> tensor(MNN::Tensor::create<uint8_t>(std::vector<int>{1, h, w, 1}, gray.data()));
ImageProcess::Config config;
config.sourceFormat = RGB;
config.destFormat = GRAY;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
process->convert(rgb.data(), w, h, 0, tensor.get());
for (int i = 0; i < size; ++i) {
int s = gray[i];
int r = rgb[3 * i + 0];
int g = rgb[3 * i + 1];
int b = rgb[3 * i + 2];
int y = (19 * r + 38 * g + 7 * b) >> 6;
if (abs(y - s) >= 2) {
MNN_ERROR("Error: Turn RGB to gray:%d, %d,%d,%d -> %d\n", i, r, g, b, s);
return false;
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(rgb.data(), w, h, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedRGBToGrayTest, "speed/cv/image_process/rgb_to_gray");
class ImageProcessSpeedRGBAToGrayTest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedRGBAToGrayTest() = default;
virtual bool run(int precision) {
int w = 1080, h = 720, size = w * h;
std::vector<uint8_t> rgba(size * 4);
for (int i = 0; i < size; ++i) {
int magic = (i * 67 % 255);
rgba[4 * i + 0] = (4 * magic + 0) % 255;
rgba[4 * i + 1] = (4 * magic + 32) % 255;
rgba[4 * i + 2] = (4 * magic + 64) % 255;
rgba[4 * i + 3] = 0;
}
std::vector<uint8_t> gray(size);
std::shared_ptr<MNN::Tensor> tensor(MNN::Tensor::create<uint8_t>(std::vector<int>{1, h, w, 1}, gray.data()));
ImageProcess::Config config;
config.sourceFormat = RGBA;
config.destFormat = GRAY;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
process->convert(rgba.data(), w, h, 0, tensor.get());
for (int i = 0; i < size; ++i) {
int s = gray[i];
int r = rgba[4 * i + 0];
int g = rgba[4 * i + 1];
int b = rgba[4 * i + 2];
int y = (19 * r + 38 * g + 7 * b) >> 6;
if (abs(y - s) >= 2) {
MNN_ERROR("Turn RGBA to gray:%d, %d,%d,%d -> %d\n", i, r, g, b, s);
return false;
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(rgba.data(), w, h, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedRGBAToGrayTest, "speed/cv/image_process/rgba_to_gray");
class ImageProcessSpeedRGBAToGrayBilinearTransformTest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedRGBAToGrayBilinearTransformTest() = default;
virtual bool run(int precision) {
ImageProcess::Config config;
config.sourceFormat = RGBA;
config.destFormat = GRAY;
config.filterType = BILINEAR;
config.wrap = CLAMP_TO_EDGE;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
int sw = 1280;
int sh = 720;
int dw = 360;
int dh = 640;
Matrix tr;
tr.setScale(1.0 / sw, 1.0 / sh);
tr.postRotate(30, 0.5f, 0.5f);
tr.postScale(dw, dh);
tr.invert(&tr);
process->setMatrix(tr);
std::shared_ptr<unsigned char> integers(new unsigned char[sw * sh * 4]);
auto pixels = integers.get();
for (int y = 0; y < sh; ++y) {
auto pixelY = pixels + 4 * sw * y;
int magicY = ((sh - y) * (sh - y)) % 79;
for (int x = 0; x < sw; ++x) {
auto pixelX = pixelY + 4 * x;
int magicX = (x * x) % 113;
for (int p = 0; p < 4; ++p) {
int magic = (magicX + magicY + p * p * p) % 255;
pixelX[p] = magic;
}
}
}
std::shared_ptr<Tensor> tensor(
Tensor::create<float>(std::vector<int>{1, 1, dw, dh}, nullptr, Tensor::CAFFE_C4));
process->convert(integers.get(), sw, sh, 0, tensor.get());
auto floats = tensor->host<float>();
int expect[] = {19, 37, 15, 37, 19, 45, 31, 61, 51, 25};
for (int v = 0; v < 10; ++v) {
if (fabsf(floats[4 * v] - (float)expect[v]) >= 2) {
MNN_ERROR("Error for %d, %.f, correct=%d\n", v, floats[4 * v], expect[v]);
return false;
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(integers.get(), sw, sh, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedRGBAToGrayBilinearTransformTest,
"speed/cv/image_process/rgba_to_gray_bilinear_transorm");
class ImageProcessSpeedRGBAToGrayNearestTransformTest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedRGBAToGrayNearestTransformTest() = default;
virtual bool run(int precision) {
ImageProcess::Config config;
config.sourceFormat = RGBA;
config.destFormat = GRAY;
config.filterType = NEAREST;
config.wrap = CLAMP_TO_EDGE;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
int sw = 1280;
int sh = 720;
int dw = 360;
int dh = 640;
Matrix tr;
tr.setScale(1.0 / sw, 1.0 / sh);
tr.postRotate(60, 0.5f, 0.5f);
tr.postScale(dw, dh);
tr.invert(&tr);
process->setMatrix(tr);
std::shared_ptr<unsigned char> integers(new unsigned char[sw * sh * 4]);
auto pixels = integers.get();
for (int y = 0; y < sh; ++y) {
auto pixelY = pixels + 4 * sw * y;
int magicY = ((sh - y) * (sh - y)) % 79;
for (int x = 0; x < sw; ++x) {
auto pixelX = pixelY + 4 * x;
int magicX = (x * x) % 113;
for (int p = 0; p < 4; ++p) {
int magic = (magicX + magicY + p * p * p) % 255;
pixelX[p] = magic;
}
}
}
std::shared_ptr<Tensor> tensor(
Tensor::create<float>(std::vector<int>{1, 1, dw, dh}, nullptr, Tensor::CAFFE_C4));
process->convert(integers.get(), sw, sh, 0, tensor.get());
auto floats = tensor->host<float>();
int expect[] = {3, 50, 26, 17, 5, 1, 5, 10, 26, 50};
for (int v = 0; v < 10; ++v) {
if ((int)(floats[4 * v]) != expect[v]) {
MNN_ERROR("Error for %d, %.f, correct=%d\n", v, floats[4 * v], expect[v]);
return false;
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(integers.get(), sw, sh, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedRGBAToGrayNearestTransformTest,
"speed/cv/image_process/rgba_to_gray_nearest_transorm");
class ImageProcessSpeedRGBAToBGRTest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedRGBAToBGRTest() = default;
virtual bool run(int precision) {
int w = 1500, h = 1080, size = w * h;
std::vector<uint8_t> rgba(size * 4);
for (int i = 0; i < size; ++i) {
int magic = (i * 67 % 255);
rgba[4 * i + 0] = (3 * magic + 32 * 0) % 255;
rgba[4 * i + 1] = (3 * magic + 32 * 1) % 255;
rgba[4 * i + 2] = (3 * magic + 32 * 2) % 255;
rgba[4 * i + 3] = (3 * magic + 32 * 3) % 255;
}
std::vector<uint8_t> bgr(size * 3);
std::shared_ptr<MNN::Tensor> tensor(MNN::Tensor::create<uint8_t>(std::vector<int>{1, h, w, 3}, bgr.data()));
ImageProcess::Config config;
config.sourceFormat = RGBA;
config.destFormat = BGR;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
process->convert(rgba.data(), w, h, 0, tensor.get());
for (int i = 0; i < size; ++i) {
if (rgba[4 * i + 0] != bgr[3 * i + 2] || rgba[4 * i + 1] != bgr[3 * i + 1] ||
rgba[4 * i + 2] != bgr[3 * i + 0]) {
MNN_ERROR("Error: Turn RGBA to BGR:%d, %d,%d,%d,%d -> %d,%d,%d\n", i, rgba[4 * i + 0], rgba[4 * i + 1],
rgba[4 * i + 2], rgba[4 * i + 3], bgr[3 * i + 0], bgr[3 * i + 1], bgr[3 * i + 2]);
return false;
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(rgba.data(), w, h, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedRGBAToBGRTest, "speed/cv/image_process/rgba_to_bgr");
class ImageProcessSpeedNV21ToRGBTest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedNV21ToRGBTest() = default;
virtual bool run(int precision) {
ImageProcess::Config config;
config.sourceFormat = YUV_NV21;
config.destFormat = RGB;
config.filterType = NEAREST;
config.wrap = CLAMP_TO_EDGE;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
int sw = 1920;
int sh = 1080;
Matrix tr;
process->setMatrix(tr);
std::shared_ptr<unsigned char> nv12(new unsigned char[sw * sh + (sw / 2) * (sh / 2) * 2]);
auto pixels = nv12.get();
for (int y = 0; y < sh; ++y) {
auto pixelY = pixels + sw * y;
auto pixelUV = pixels + sw * sh + (y / 2) * sw;
int magicY = ((sh - y) * (sh - y)) % 79;
for (int x = 0; x < sw; ++x) {
auto pixelX = pixelY + x;
int magicX = (x * x) % 113;
int magic = (magicX + magicY) % 255;
pixelX[0] = magic;
}
for (int x = 0; x < sw / 2; ++x) {
auto pixelX = pixelUV + 2 * x;
int magicX = ((((x % 283) * (x % 283)) % 283) * (((x % 283) * (x % 283)) % 283)) % 283;
int magic0 = (magicX + magicY) % 255;
int magic1 = (magicX + magicY * 179) % 255;
pixelX[0] = magic0;
pixelX[1] = magic1;
}
}
std::shared_ptr<Tensor> tensor(
Tensor::create<uint8_t>(std::vector<int>{1, sh, sw, 3}, nullptr, Tensor::TENSORFLOW));
process->convert(nv12.get(), sw, sh, 0, tensor.get());
for (int y = 0; y < sh; ++y) {
auto dstY = tensor->host<uint8_t>() + 3 * y * sw;
auto srcY_Y = nv12.get() + y * sw;
auto srcY_UV = nv12.get() + (y / 2) * (sw / 2) * 2 + sw * sh;
for (int x = 0; x < sw; ++x) {
auto dstX = dstY + 3 * x;
auto srcX_Y = srcY_Y + x;
auto srcX_UV = srcY_UV + (x / 2) * 2;
int Y = srcX_Y[0];
int U = (int)srcX_UV[1] - 128;
int V = (int)srcX_UV[0] - 128;
Y = Y << 6;
int r = (Y + 73 * V) >> 6;
int g = (Y - 25 * U - 37 * V) >> 6;
int b = (Y + 130 * U) >> 6;
r = r < 0 ? 0 : r;
r = r > 255 ? 255 : r;
g = g < 0 ? 0 : g;
g = g > 255 ? 255 : g;
b = b < 0 ? 0 : b;
b = b > 255 ? 255 : b;
auto diff = [](int a, int b) { return abs(a - b) > 5; };
if (diff(dstX[0], r) || diff(dstX[1], g) || diff(dstX[2], b)) {
MNN_ERROR("%d, Error for NV12 to RGB: %d: %d, %d, %d -> %d, %d, %d, wrong: %d, %d, %d\n", y, x, Y,
U, V, r, g, b, dstX[0], dstX[1], dstX[2]);
return false;
}
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(nv12.get(), sw, sh, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedNV21ToRGBTest, "speed/cv/image_process/nv21_to_rgb");
class ImageProcessSpeedNV12ToRGBTest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedNV12ToRGBTest() = default;
virtual bool run(int precision) {
ImageProcess::Config config;
config.sourceFormat = YUV_NV12;
config.destFormat = RGB;
config.filterType = NEAREST;
config.wrap = CLAMP_TO_EDGE;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
int sw = 1920;
int sh = 1080;
Matrix tr;
process->setMatrix(tr);
std::shared_ptr<unsigned char> nv12(new unsigned char[sw * sh + (sw / 2) * (sh / 2) * 2]);
auto pixels = nv12.get();
for (int y = 0; y < sh; ++y) {
auto pixelY = pixels + sw * y;
auto pixelUV = pixels + sw * sh + (y / 2) * sw;
int magicY = ((sh - y) * (sh - y)) % 79;
for (int x = 0; x < sw; ++x) {
auto pixelX = pixelY + x;
int magicX = (x * x) % 113;
int magic = (magicX + magicY) % 255;
pixelX[0] = magic;
}
for (int x = 0; x < sw / 2; ++x) {
auto pixelX = pixelUV + 2 * x;
int magicX = ((((x % 283) * (x % 283)) % 283) * (((x % 283) * (x % 283)) % 283)) % 283;
int magic0 = (magicX + magicY) % 255;
int magic1 = (magicX + magicY * 179) % 255;
pixelX[0] = magic0;
pixelX[1] = magic1;
}
}
std::shared_ptr<Tensor> tensor(
Tensor::create<uint8_t>(std::vector<int>{1, sh, sw, 3}, nullptr, Tensor::TENSORFLOW));
process->convert(nv12.get(), sw, sh, 0, tensor.get());
for (int y = 0; y < sh; ++y) {
auto dstY = tensor->host<uint8_t>() + 3 * y * sw;
auto srcY_Y = nv12.get() + y * sw;
auto srcY_UV = nv12.get() + (y / 2) * (sw / 2) * 2 + sw * sh;
for (int x = 0; x < sw; ++x) {
auto dstX = dstY + 3 * x;
auto srcX_Y = srcY_Y + x;
auto srcX_UV = srcY_UV + (x / 2) * 2;
int Y = srcX_Y[0];
int U = (int)srcX_UV[0] - 128;
int V = (int)srcX_UV[1] - 128;
Y = Y << 6;
int r = (Y + 73 * V) >> 6;
int g = (Y - 25 * U - 37 * V) >> 6;
int b = (Y + 130 * U) >> 6;
r = r < 0 ? 0 : r;
r = r > 255 ? 255 : r;
g = g < 0 ? 0 : g;
g = g > 255 ? 255 : g;
b = b < 0 ? 0 : b;
b = b > 255 ? 255 : b;
auto diff = [](int a, int b) { return abs(a - b) > 5; };
if (diff(dstX[0], r) || diff(dstX[1], g) || diff(dstX[2], b)) {
MNN_ERROR("%d, Error for NV12 to RGB: %d: %d, %d, %d -> %d, %d, %d, wrong: %d, %d, %d\n", y, x,
(int)srcX_Y[0], U, V, r, g, b, dstX[0], dstX[1], dstX[2]);
return false;
}
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(nv12.get(), sw, sh, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedNV12ToRGBTest, "speed/cv/image_process/nv12_to_rgb");
class ImageProcessSpeedNV12ToRGBATest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedNV12ToRGBATest() {
}
virtual bool run(int precision) {
ImageProcess::Config config;
config.sourceFormat = YUV_NV21;
config.destFormat = RGBA;
config.filterType = NEAREST;
config.wrap = CLAMP_TO_EDGE;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
int sw = 1920;
int sh = 1080;
Matrix tr;
process->setMatrix(tr);
std::shared_ptr<unsigned char> nv12(new unsigned char[sw * sh + (sw / 2) * (sh / 2) * 2]);
auto pixels = nv12.get();
for (int y = 0; y < sh; ++y) {
auto pixelY = pixels + sw * y;
auto pixelUV = pixels + sw * sh + (y / 2) * sw;
int magicY = ((sh - y) * (sh - y)) % 79;
for (int x = 0; x < sw; ++x) {
auto pixelX = pixelY + x;
int magicX = (x * x) % 113;
int magic = (magicX + magicY) % 255;
pixelX[0] = magic;
}
for (int x = 0; x < sw / 2; ++x) {
auto pixelX = pixelUV + 2 * x;
int magicX = ((((x % 283) * (x % 283)) % 283) * (((x % 283) * (x % 283)) % 283)) % 283;
int magic0 = (magicX + magicY) % 255;
int magic1 = (magicX + magicY * 179) % 255;
pixelX[0] = magic0;
pixelX[1] = magic1;
}
}
std::shared_ptr<Tensor> tensor(
Tensor::create<uint8_t>(std::vector<int>{1, sh, sw, 4}, nullptr, Tensor::TENSORFLOW));
process->convert(nv12.get(), sw, sh, 0, tensor.get());
for (int y = 0; y < sh; ++y) {
auto dstY = tensor->host<uint8_t>() + 4 * y * sw;
auto srcY_Y = nv12.get() + y * sw;
auto srcY_UV = nv12.get() + (y / 2) * (sw / 2) * 2 + sw * sh;
for (int x = 0; x < sw; ++x) {
auto dstX = dstY + 4 * x;
auto srcX_Y = srcY_Y + x;
auto srcX_UV = srcY_UV + (x / 2) * 2;
int Y = srcX_Y[0];
int U = (int)srcX_UV[1] - 128;
int V = (int)srcX_UV[0] - 128;
Y = Y << 6;
int r = (Y + 73 * V) >> 6;
int g = (Y - 25 * U - 37 * V) >> 6;
int b = (Y + 130 * U) >> 6;
r = r < 0 ? 0 : r;
r = r > 255 ? 255 : r;
g = g < 0 ? 0 : g;
g = g > 255 ? 255 : g;
b = b < 0 ? 0 : b;
b = b > 255 ? 255 : b;
auto diff = [](int a, int b) { return abs(a - b) > 5; };
if (diff(dstX[0], r) || diff(dstX[1], g) || diff(dstX[2], b)) {
MNN_ERROR("%d, Error for NV12 to RGBA: %d: %d, %d, %d -> %d, %d, %d, wrong: %d, %d, %d\n", y, x, Y,
U, V, r, g, b, dstX[0], dstX[1], dstX[2]);
return false;
}
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(nv12.get(), sw, sh, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedNV12ToRGBATest, "speed/cv/image_process/nv21_to_rgba");
// Test for _blitC3ToFloatC3
class ImageProcessSpeedBGRToBGRFloatBlitterTest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedBGRToBGRFloatBlitterTest() = default;
virtual bool run(int precision) {
int w = 1020, h = 756, size = w * h;
std::vector<uint8_t> integers(size * 3);
for (int i = 0; i < size; ++i) {
int magic = (i * 67 % 255);
integers[3 * i + 0] = (3 * magic + 0) % 255;
integers[3 * i + 1] = (3 * magic + 1) % 255;
integers[3 * i + 2] = (3 * magic + 2) % 255;
}
std::vector<float> floats(size * 3);
std::shared_ptr<MNN::Tensor> tensor(
MNN::Tensor::create<float>(std::vector<int>{1, h, w, 3}, floats.data(), Tensor::TENSORFLOW));
ImageProcess::Config config;
config.sourceFormat = BGR;
config.destFormat = BGR;
const float means[3] = {127.5f, 127.5f, 127.5f};
const float normals[3] = {2.0f / 255.0f, 2.0f / 255.0f, 2.0f / 255.0f};
memcpy(config.mean, means, sizeof(means));
memcpy(config.normal, normals, sizeof(normals));
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
process->convert(integers.data(), w, h, 0, tensor.get());
for (int i = 0; i < size; ++i) {
for (int j = 0; j < 3; ++j) {
float result = floats[3 * i + j];
float right = (integers[3 * i + j] - means[j]) * normals[j];
if (fabs(result - right) > 1e-6f) {
MNN_ERROR("Error for blitter bgr to bgr\n%d ->i:%d, %f, right: %f\n", i, integers[3 * i + j],
result, right);
return false;
}
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(integers.data(), w, h, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedBGRToBGRFloatBlitterTest, "speed/cv/image_process/bgr_to_bgr_blitter");
// Test for _blitC1ToFloatC1
class ImageProcessSpeedGrayToGrayFloatBlitterTest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedGrayToGrayFloatBlitterTest() = default;
virtual bool run(int precision) {
int w = 1024, h = 1080, size = w * h;
std::vector<uint8_t> integers(size);
for (int i = 0; i < size; ++i) {
int magic = (i * 67 % 255);
integers[i] = magic;
}
std::vector<float> floats(size);
std::shared_ptr<MNN::Tensor> tensor(
MNN::Tensor::create<float>(std::vector<int>{1, h, w, 1}, floats.data(), Tensor::TENSORFLOW));
ImageProcess::Config config;
config.sourceFormat = GRAY;
config.destFormat = GRAY;
const float means[1] = {127.5f};
const float normals[1] = {2.0f / 255.0f};
memcpy(config.mean, means, sizeof(means));
memcpy(config.normal, normals, sizeof(normals));
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
process->convert(integers.data(), w, h, 0, tensor.get());
for (int i = 0; i < size; ++i) {
float result = floats[i];
float right = (integers[i] - means[0]) * normals[0];
if (fabs(result - right) > 1e-6f) {
MNN_PRINT("i:%d, raw: %d, result: %f, right: %f\n", i, integers[i], result, right);
MNN_ERROR("Error for blitter gray to gray\n");
return false;
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(integers.data(), w, h, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedGrayToGrayFloatBlitterTest, "speed/cv/image_process/gray_to_gray_blitter");
class ImageProcessSpeedI420ToRGBTest : public MNNTestCase {
public:
virtual ~ImageProcessSpeedI420ToRGBTest() = default;
virtual bool run(int precision) {
ImageProcess::Config config;
config.sourceFormat = YUV_I420;
config.destFormat = RGB;
config.filterType = NEAREST;
config.wrap = CLAMP_TO_EDGE;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
int sw = 1920;
int sh = 1080;
Matrix tr;
process->setMatrix(tr);
std::shared_ptr<unsigned char> nv12(new unsigned char[sw * sh + (sw / 2) * (sh / 2) * 2]);
auto pixels = nv12.get();
for (int y = 0; y < sh; ++y) {
auto pixelY = pixels + sw * y;
auto pixelUV = pixels + sw * sh + (y / 2) * sw;
int magicY = ((sh - y) * (sh - y)) % 79;
for (int x = 0; x < sw; ++x) {
auto pixelX = pixelY + x;
int magicX = (x * x) % 113;
int magic = (magicX + magicY) % 255;
pixelX[0] = magic;
}
for (int x = 0; x < sw / 2; ++x) {
auto pixelX = pixelUV + 2 * x;
int magicX = ((((x % 283) * (x % 283)) % 283) * (((x % 283) * (x % 283)) % 283)) % 283;
int magic0 = (magicX + magicY) % 255;
int magic1 = (magicX + magicY * 179) % 255;
pixelX[0] = magic0;
pixelX[1] = magic1;
}
}
std::shared_ptr<Tensor> tensor(
Tensor::create<uint8_t>(std::vector<int>{1, sh, sw, 3}, nullptr, Tensor::TENSORFLOW));
process->convert(nv12.get(), sw, sh, 0, tensor.get());
for (int y = 0; y < sh; ++y) {
auto dstY = tensor->host<uint8_t>() + 3 * y * sw;
auto srcY_Y = nv12.get() + y * sw;
auto srcY_U = nv12.get() + (y / 2) * (sw / 2) + sw * sh;
auto srcY_V = nv12.get() + (y / 2) * (sw / 2) + sw * sh + (sw / 2) * (sh / 2);
for (int x = 0; x < sw; ++x) {
auto dstX = dstY + 3 * x;
auto srcX_Y = srcY_Y + x;
auto srcX_U = srcY_U + (x / 2);
auto srcX_V = srcY_V + (x / 2);
int Y = srcX_Y[0];
int U = (int)srcX_U[0] - 128;
int V = (int)srcX_V[0] - 128;
Y = Y << 6;
int r = (Y + 73 * V) >> 6;
int g = (Y - 25 * U - 37 * V) >> 6;
int b = (Y + 130 * U) >> 6;
r = r < 0 ? 0 : r;
r = r > 255 ? 255 : r;
g = g < 0 ? 0 : g;
g = g > 255 ? 255 : g;
b = b < 0 ? 0 : b;
b = b > 255 ? 255 : b;
auto diff = [](int a, int b) { return abs(a - b) > 5; };
if (diff(dstX[0], r) || diff(dstX[1], g) || diff(dstX[2], b)) {
MNN_ERROR("%d, Error for I420 to RGB: %d: %d, %d, %d -> %d, %d, %d, wrong: %d, %d, %d\n", y, x, Y,
U, V, r, g, b, dstX[0], dstX[1], dstX[2]);
return false;
}
}
}
{
AUTOTIME;
for (int i = 0; i < 10; ++i) {
process->convert(nv12.get(), sw, sh, 0, tensor.get());
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessSpeedI420ToRGBTest, "speed/cv/image_process/I420_to_rgb");
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