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MNN/test/cv/ImageProcessTest.cpp

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//
// ImageProcessTest.cpp
// MNNTests
//
// Created by MNN on 2019/01/10.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include <MNN/ImageProcess.hpp>
#include <cmath>
#include <memory>
#include <map>
#include "MNNTestSuite.h"
using namespace MNN;
using namespace MNN::CV;
static std::vector<uint8_t> genSourceData(int h, int w, int bpp) {
std::vector<uint8_t> source(h * w * bpp);
for (int y = 0; y < h; ++y) {
auto pixelY = source.data() + w * y * bpp;
int magicY = ((h - y) * (h - y)) % 79;
for (int x = 0; x < w; ++x) {
auto pixelX = pixelY + x * bpp;
int magicX = (x * x) % 113;
for (int p = 0; p < bpp; ++p) {
int magic = (magicX + magicY + p * p * p) % 255;
pixelX[p] = magic;
}
}
}
return source;
}
// format in {YUV_NV21, YUV_NV12, YUV_I420}
// dstFormat in {RGBA, BGRA, RGB, BGR, GRAY}
static int genYUVData(int h, int w, ImageFormat format, ImageFormat dstFormat,
std::vector<uint8_t>& source, std::vector<uint8_t>& dest, int extraOffset = 0) {
// https://www.jianshu.com/p/e67f79f10c65
if (format != YUV_NV21 && format != YUV_NV12 && /* YUV420sp(bi-planer): NV12, NV21 */
format != YUV_I420 /* YUV420p(planer): I420 or YV12 */) {
return -1;
}
bool yuv420p = (format != YUV_NV12 && format != YUV_NV21);
int bpp = 0;
if (dstFormat == RGBA || dstFormat == BGRA) {
bpp = 4;
} else if (dstFormat == RGB || dstFormat == BGR) {
bpp = 3;
} else if (dstFormat == GRAY) {
bpp = 1;
}
if (bpp == 0) {
return -2;
}
// YUV420, Y: h*w, UV: (h/2)*(w/2)*2
int ySize = h * w, uvSize = (h/2)*(w/2)*2;
source.resize(h * (w + extraOffset) + (h/2)*(w+extraOffset));
::memset(source.data(), 0, source.size());
dest.resize(h * w * bpp);
auto dstData = dest.data();
for (int y = 0; y < h; ++y) {
auto pixelY = source.data() + (w + extraOffset) * y;
auto pixelUV = source.data() + (w + extraOffset) * h + (y / 2) * (yuv420p ? w / 2 : (w + extraOffset));
int magicY = ((h - y) * (h - y)) % 79;
for (int x = 0; x < w; ++x) {
int magicX = ((x % 113) * (x % 113)) % 113, xx = x / 2;
int yVal = (magicX + magicY) % 255;
int uVal, vVal;
int uIndex = (yuv420p ? xx : 2 * xx);
int vIndex = (yuv420p ? xx + (h/2)*(w/2) : 2 * xx + 1);
if (format != YUV_NV12 && format != YUV_I420) {
std::swap(uIndex, vIndex);
}
if (y % 2 == 0 && x % 2 == 0) {
magicX = ((((xx % 283) * (xx % 283)) % 283) * (((xx % 283) * (xx % 283)) % 283)) % 283;
uVal = (magicX + magicY) % 255;
vVal = (magicX + magicY * 179) % 255;
pixelUV[uIndex] = uVal;
pixelUV[vIndex] = vVal;
} else {
uVal = pixelUV[uIndex];
vVal = pixelUV[vIndex];
}
pixelY[x] = yVal;
int Y = yVal, U = uVal - 128, V = vVal - 128;
auto dstData = dest.data() + (y * w + x) * bpp;
if (dstFormat == GRAY) {
dstData[0] = Y;
continue;
}
Y = Y << 6;
#define CLAMP(x, minVal, maxVal) std::min(std::max((x), (minVal)), (maxVal))
int r = CLAMP((Y + 73 * V) >> 6, 0, 255);
int g = CLAMP((Y - 25 * U - 37 * V) >> 6, 0, 255);
int b = CLAMP((Y + 130 * U) >> 6, 0, 255);
dstData[0] = r;
dstData[1] = g;
dstData[2] = b;
if (dstFormat == BGRA || dstFormat == BGR) {
std::swap(dstData[0], dstData[2]);
}
if (bpp == 4) {
dstData[3] = 255;
}
}
}
return 0;
}
class ImageProcessGrayToGrayTest : public MNNTestCase {
public:
virtual ~ImageProcessGrayToGrayTest() = default;
virtual bool run(int precision) {
int w = 27, h = 1, size = w * h;
auto integers = genSourceData(h, w, 1);
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;
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessGrayToGrayTest, "cv/image_process/gray_to_gray");
class ImageProcessGrayToGrayBilinearTransformTest : public MNNTestCase {
public:
virtual ~ImageProcessGrayToGrayBilinearTransformTest() = 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);
auto integers = genSourceData(sh, sw, 1);
std::shared_ptr<Tensor> tensor(
Tensor::create<float>(std::vector<int>{1, 1, dw, dh}, nullptr, Tensor::CAFFE_C4));
for (int i = 0; i < 10; ++i) {
process->convert(integers.data(), 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;
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessGrayToGrayBilinearTransformTest, "cv/image_process/gray_to_gray_bilinear_transorm");
class ImageProcessGrayToGrayNearestTransformTest : public MNNTestCase {
public:
virtual ~ImageProcessGrayToGrayNearestTransformTest() = 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);
auto integers = genSourceData(sh, sw, 1);
std::shared_ptr<Tensor> tensor(
Tensor::create<float>(std::vector<int>{1, 1, dw, dh}, nullptr, Tensor::CAFFE_C4));
for (int i = 0; i < 10; ++i) {
process->convert(integers.data(), 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;
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessGrayToGrayNearestTransformTest, "cv/image_process/gray_to_gray_nearest_transorm");
class ImageProcessGrayToRGBATest : public MNNTestCase {
public:
virtual ~ImageProcessGrayToRGBATest() = default;
virtual bool run(int precision) {
int w = 15, h = 1, size = w * h;
auto gray = genSourceData(h, w, 1);
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;
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessGrayToRGBATest, "cv/image_process/gray_to_rgba");
class ImageProcessBGRToGrayTest : public MNNTestCase {
public:
virtual ~ImageProcessBGRToGrayTest() = default;
virtual bool run(int precision) {
int w = 15, h = 1, size = w * h;
auto bgr = genSourceData(h, w, 3);
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;
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessBGRToGrayTest, "cv/image_process/bgr_to_gray");
class ImageProcessRGBToBGRTest : public MNNTestCase {
public:
virtual bool run(int precision) {
int w = 27, h = 1, size = w * h;
auto integers = genSourceData(h, w, 3);
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;
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessRGBToBGRTest, "cv/image_process/rgb_to_bgr");
class ImageProcessRGBAToBGRATest : public MNNTestCase {
public:
virtual ~ImageProcessRGBAToBGRATest() = default;
virtual bool run(int precision) {
int w = 27, h = 1, size = w * h;
auto integers = genSourceData(h, w, 4);
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;
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessRGBAToBGRATest, "cv/image_process/rgba_to_bgra");
class ImageProcessBGRToBGRTest : public MNNTestCase {
public:
virtual ~ImageProcessBGRToBGRTest() = default;
virtual bool run(int precision) {
int w = 27, h = 1, size = w * h;
auto integers = genSourceData(h, w, 3);
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 %d,%d,%d->%f, %f, %f, %f\n", 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;
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessBGRToBGRTest, "cv/image_process/bgr_to_bgr");
class ImageProcessRGBToGrayTest : public MNNTestCase {
public:
virtual ~ImageProcessRGBToGrayTest() = default;
virtual bool run(int precision) {
int w = 15, h = 1, size = w * h;
auto rgb = genSourceData(h, w, 3);
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;
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessRGBToGrayTest, "cv/image_process/rgb_to_gray");
class ImageProcessRGBAToGrayTest : public MNNTestCase {
public:
virtual ~ImageProcessRGBAToGrayTest() = default;
virtual bool run(int precision) {
int w = 15, h = 1, size = w * h;
auto rgba = genSourceData(h, w, 4);
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;
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessRGBAToGrayTest, "cv/image_process/rgba_to_gray");
class ImageProcessRGBAToGrayBilinearTransformTest : public MNNTestCase {
public:
virtual ~ImageProcessRGBAToGrayBilinearTransformTest() = 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);
auto integers = genSourceData(sh, sw, 4);
std::shared_ptr<Tensor> tensor(
Tensor::create<float>(std::vector<int>{1, 1, dw, dh}, nullptr, Tensor::CAFFE_C4));
process->convert(integers.data(), 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;
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessRGBAToGrayBilinearTransformTest, "cv/image_process/rgba_to_gray_bilinear_transorm");
class ImageProcessRGBAToGrayNearestTransformTest : public MNNTestCase {
public:
virtual ~ImageProcessRGBAToGrayNearestTransformTest() = 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);
auto integers = genSourceData(sh, sw, 4);
std::shared_ptr<Tensor> tensor(
Tensor::create<float>(std::vector<int>{1, 1, dw, dh}, nullptr, Tensor::CAFFE_C4));
for (int i = 0; i < 10; ++i) {
process->convert(integers.data(), 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;
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessRGBAToGrayNearestTransformTest, "cv/image_process/rgba_to_gray_nearest_transorm");
class ImageProcessRGBAToBGRTest : public MNNTestCase {
public:
virtual ~ImageProcessRGBAToBGRTest() = default;
virtual bool run(int precision) {
int w = 15, h = 1, size = w * h;
auto rgba = genSourceData(h, w, 4);
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;
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessRGBAToBGRTest, "cv/image_process/rgba_to_bgr");
// Test for _blitC3ToFloatC3
class ImageProcessBGRToBGRFloatBlitterTest : public MNNTestCase {
public:
virtual ~ImageProcessBGRToBGRFloatBlitterTest() = default;
virtual bool run(int precision) {
int w = 27, h = 27, size = w * h;
auto integers = genSourceData(h, w, 3);
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 -> %f, right: %f\n", integers[3 * i + j], result,
right);
return false;
}
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessBGRToBGRFloatBlitterTest, "cv/image_process/bgr_to_bgr_blitter");
// Test for _blitC1ToFloatC1
class ImageProcessGrayToGrayFloatBlitterTest : public MNNTestCase {
public:
virtual ~ImageProcessGrayToGrayFloatBlitterTest() = default;
virtual bool run(int precision) {
int w = 27, h = 27, size = w * h;
auto integers = genSourceData(h, w, 1);
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("raw: %d, result: %f, right: %f\n", integers[i], result, right);
MNN_ERROR("Error for blitter gray to gray\n");
return false;
}
}
return true;
}
};
MNNTestSuiteRegister(ImageProcessGrayToGrayFloatBlitterTest, "cv/image_process/gray_to_gray_blitter");
class ImageProcessYUVTestCommmon : public MNNTestCase {
protected:
virtual ~ImageProcessYUVTestCommmon() = default;
bool test(ImageFormat sourceFormat, ImageFormat destFormat, int bpp, int sw, int sh) {
std::map<ImageFormat, std::string> formatMap = {
{RGBA, "RGBA"}, {RGB, "RGB"}, {BGRA, "BGRA"}, {BGR, "BGR"}, {GRAY, "GRAY"},
{YUV_NV21, "NV21"}, {YUV_NV12, "NV12"}, {YUV_I420, "I420"}
};
auto sourceStr = formatMap[sourceFormat].c_str(), destStr = formatMap[destFormat].c_str();
//MNN_PRINT("%s_to_%s\n", sourceStr, destStr);
ImageProcess::Config config;
config.sourceFormat = sourceFormat;
config.destFormat = destFormat;
//config.filterType = NEAREST;
//config.wrap = CLAMP_TO_EDGE;
std::shared_ptr<ImageProcess> process(ImageProcess::create(config));
//Matrix tr;
//process->setMatrix(tr);
std::vector<uint8_t> src, dst;
int extraOffset = 0;
if (sourceFormat != YUV_I420) {
extraOffset = 16;
}
int stride = sw + extraOffset;
genYUVData(sh, sw, sourceFormat, destFormat, src, dst, extraOffset);
std::shared_ptr<Tensor> tensor(
Tensor::create<uint8_t>(std::vector<int>{1, sh, sw, bpp}, nullptr, Tensor::TENSORFLOW));
process->convert(src.data(), sw, sh, stride, tensor.get());
for (int y = 0; y < sh; ++y) {
auto srcY_Y = src.data() + y * sw;
auto srcY_UV = src.data() + (y / 2) * (sw / 2) * 2 + sw * sh;
for (int x = 0; x < sw; ++x) {
auto rightData = dst.data() + (y * sw + x) * bpp;
auto testData = tensor->host<uint8_t>() + (y * sw + x) * bpp;
bool wrong = false;
for (int i = 0; i < bpp && !wrong; ++i) {
if (abs(rightData[i] - testData[i]) > 5) {
wrong = true;
}
}
if (wrong) {
int Y = srcY_Y[x], U = srcY_UV[(x / 2) * 2], V = srcY_UV[(x / 2) * 2 + 1];
MNN_ERROR("Error for %s to %s (%d, %d): %d, %d, %d -> ", sourceStr, destStr, y, x, Y, U, V);
for (int i = 0; i < bpp; ++i) {
MNN_ERROR("%d, ", rightData[i]);
}
MNN_ERROR("wrong:");
for (int i = 0; i < bpp; ++i) {
MNN_ERROR(" %d%s", testData[i], (i < bpp ? ",": ""));
}
MNN_ERROR("\n");
return false;
}
}
}
return true;
}
};
class ImageProcessYUVBlitterTest : public ImageProcessYUVTestCommmon {
public:
virtual ~ImageProcessYUVBlitterTest() = default;
virtual bool run(int precision) {
std::vector<ImageFormat> srcFromats = {YUV_NV21, YUV_NV12, YUV_I420};
std::vector<ImageFormat> dstFormats = {RGBA, RGB, BGRA, BGR, GRAY};
std::vector<int> bpps = {4, 3, 4, 3, 1};
bool succ = true;
for (auto srcFormat : srcFromats) {
for (int i = 0; i < dstFormats.size(); ++i) {
succ = succ && test(srcFormat, dstFormats[i], bpps[i], 1920, 1080);
}
}
return succ;
}
};
// {YUV_NV21, YUV_NV12, YUV_I420} -> {RGBA, RGB, BGRA, BGR, GRAY} unit test
MNNTestSuiteRegister(ImageProcessYUVBlitterTest, "cv/image_process/yuv_blitter");
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