#include
#include
#include
#include "opencv2/opencv_modules.hpp"
#include
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/stitching/detail/autocalib.hpp"
#include "opencv2/stitching/detail/blenders.hpp"
#include "opencv2/stitching/detail/timelapsers.hpp"
#include "opencv2/stitching/detail/camera.hpp"
#include "opencv2/stitching/detail/exposure_compensate.hpp"
#include "opencv2/stitching/detail/matchers.hpp"
#include "opencv2/stitching/detail/motion_estimators.hpp"
#include "opencv2/stitching/detail/seam_finders.hpp"
#include "opencv2/stitching/detail/warpers.hpp"
#include "opencv2/stitching/warpers.hpp"
#ifdef HAVE_OPENCV_XFEATURES2D
#include "opencv2/xfeatures2d/nonfree.hpp"
#endif
#define ENABLE_LOG 1
#define LOG(msg) std::cout << msg
#define LOGLN(msg) std::cout << msg << std::endl
using namespace std;
using namespace cv;
using namespace cv::detail;
// Default command line args
#if 1
#define DLL_API __declspec(dllexport)
#else
#define DLL_API __declspec(dllimport)
#endif
extern "C" { //由于编译过程的原因，python一般只支持c的接口
typedef struct ImageBase {
int w; //图像的宽
int h; //图像的高
int c; //通道数
unsigned char *data; //我们要写python和c++交互的数据结构，0-255的单字符指针
}ImageMeta;
//typedef ImageBase ImageMeta;
DLL_API int Stitch(ImageMeta *im1, ImageMeta *im2);//函数导出，要改
};
//vector img_names;
int num_images;
bool preview = false;
bool try_cuda = false;
double work_megapix = 0.6;
double seam_megapix = 0.1;
double compose_megapix = -1;
float conf_thresh = 1.f;
#ifdef HAVE_OPENCV_XFEATURES2D
string features_type = "surf";
#else
string features_type = "orb";
#endif
string matcher_type = "homography";
string estimator_type = "homography";
string ba_cost_func = "ray";
string ba_refine_mask = "xxxxx";
bool do_wave_correct = true;
WaveCorrectKind wave_correct = detail::WAVE_CORRECT_HORIZ;
bool save_graph = false;
std::string save_graph_to;
string warp_type = "spherical";
int expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
int expos_comp_nr_feeds = 1;
int expos_comp_nr_filtering = 2;
int expos_comp_block_size = 32;
float match_conf = 0.3f;
string seam_find_type = "gc_color";
int blend_type = Blender::MULTI_BAND;
int timelapse_type = Timelapser::AS_IS;//延时摄影
float blend_strength = 5;
string result_name = "D:/result.jpg";//
bool timelapse = false;//首先定义timelapse的默认布尔类型为False
int range_width = -1;
DLL_API int Stitch(ImageMeta *im1, ImageMeta *im2)//入参两个数组指针，出参一个数组指针
//vector img_list
{//一个int数；一个图片类型的列表
//predict先判断长度 然后长度作为一个参数传给
//preview = true;
//try_cuda = true;
//preview = true;
//result = 'D:/result.jpg';
//work_megapix = -1;
//features_type = "orb";
Mat img1 = Mat::zeros(Size(im1->w, im1->h), CV_8UC3);
//先从输入的指针对象提取w,h，data；将python传来的参数转变成C处理的格式。用的是相同的结构：结构体。
img1.data = im1->data;
Mat img2 = Mat::zeros(Size(im2->w, im2->h), CV_8UC3);
//先从输入的指针对象提取w,h，data；将python传来的参数转变成C处理的格式。用的是相同的结构：结构体。
img2.data = im2->data;
//Mat img1, img2;
//img1 = imread("D:/1Hill.jpg");
//img2 = imread("D:/2Hill.jpg");
vector ALLimages(2);
ALLimages[0] = img1.clone();
ALLimages[1] = img2.clone();
//img_names.push_back("D:/1Hill.jpg");
//img_names.push_back("D:/2Hill.jpg");//??
//img_names.push_back("D:/3Hill.jpg");//??
num_images = 2;
#if ENABLE_LOG
int64 app_start_time = getTickCount();
#endif
#if 0
cv::setBreakOnError(true);
#endif
//int retval = parseCmdArgs(argc, argv);
//if (retval)
//return retval;
// Check if have enough images
//int num_images = static_cast(img_names.size());
if (num_images < 2)
{
LOGLN("Need more images");
return -1;
}
double work_scale = 1, seam_scale = 1, compose_scale = 1;
bool is_work_scale_set = false, is_seam_scale_set = false, is_compose_scale_set = false;
LOGLN("Finding features...");
#if ENABLE_LOG
int64 t = getTickCount();
#endif
Ptr finder;
if (features_type == "orb")
{
finder = ORB::create();
}
else if (features_type == "akaze")
{
finder = AKAZE::create();
}
#ifdef HAVE_OPENCV_XFEATURES2D
else if (features_type == "surf")
{
finder = xfeatures2d::SURF::create();
}
else if (features_type == "sift") {
finder = xfeatures2d::SIFT::create();
}
#endif
else
{
cout << "Unknown 2D features type: '" << features_type << "'.\n";
return -1;
}
Mat full_img, img;
vector features(num_images);
vector images(num_images);
vector full_img_sizes(num_images);
double seam_work_aspect = 1;
for (int i = 0; i < num_images; ++i)
{
full_img = ALLimages[i];
//获取一张图。imread_img -------->Mat
//先把一边调通了再去组合调试，分治
//full_img = img_list;
//python传进来n张图片的base64,可以转成读取后的图片。
//先在c中定义图像HWC结构数组数组转一次 Mat, dll返回Mat结果，Mat转一次结构体
//main输入 Mat1,Mat2
//dll返回数组，python转化成cv2image,然后输出image2base64
//full_image里面是读取的imread_img类型
//base64的size容易确定
//先在predict前提取到图片的整个Mat传给DLL
full_img_sizes[i] = full_img.size();//结果：full_img_sizes = [(500,300),(200,100)]
if (full_img.empty())
{
//LOGLN("Can't open image " << img_names[i]);//访问了空指针，和img_names有关
return -2;
}
if (work_megapix < 0)
{
img = full_img;
work_scale = 1;
is_work_scale_set = true;
}
else
{
if (!is_work_scale_set)
{
work_scale = min(1.0, sqrt(work_megapix * 1e6 / full_img.size().area()));
is_work_scale_set = true;
}
resize(full_img, img, Size(), work_scale, work_scale, INTER_LINEAR_EXACT);
}
if (!is_seam_scale_set)
{
seam_scale = min(1.0, sqrt(seam_megapix * 1e6 / full_img.size().area()));
seam_work_aspect = seam_scale / work_scale;
is_seam_scale_set = true;
}
computeImageFeatures(finder, img, features[i]);
features[i].img_idx = i;
LOGLN("Features in image #" << i + 1 << ": " << features[i].keypoints.size());
resize(full_img, img, Size(), seam_scale, seam_scale, INTER_LINEAR_EXACT);
images[i] = img.clone();
//循环是为了找到每张图的特征，然后把图片copy到images里
}
full_img.release();
img.release();
LOGLN("Finding features, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
LOG("Pairwise matching");
#if ENABLE_LOG
t = getTickCount();
#endif
vector pairwise_matches;
Ptr matcher;
if (matcher_type == "affine")
matcher = makePtr(false, try_cuda, match_conf);
else if (range_width == -1)
matcher = makePtr(try_cuda, match_conf);
else
matcher = makePtr(range_width, try_cuda, match_conf);
(*matcher)(features, pairwise_matches);
matcher->collectGarbage();
LOGLN("Pairwise matching, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
// Check if we should save matches graph
//if (save_graph)
//{
// LOGLN("Saving matches graph...");
// ofstream f(save_graph_to.c_str());
// f << matchesGraphAsString(img_names, pairwise_matches, conf_thresh);
//}
// Leave only images we are sure are from the same panorama
vector indices = leaveBiggestComponent(features, pairwise_matches, conf_thresh);
if (indices.size() != 2)//判断两个图片的相关性
return -1;
if (num_images < 2)
{
LOGLN("Need more images");
return -1;
}
Ptr estimator;
if (estimator_type == "affine")
estimator = makePtr();
else
estimator = makePtr();
vector cameras;
if (!(*estimator)(features, pairwise_matches, cameras))
{
cout << "Homography estimation failed.\n";
return -1;
}
for (size_t i = 0; i < cameras.size(); ++i)
{
Mat R;
cameras[i].R.convertTo(R, CV_32F);
cameras[i].R = R;
//LOGLN("Initial camera intrinsics #" << indices[i] + 1 << ":\nK:\n" << cameras[i].K() << "\nR:\n" << cameras[i].R);
}
Ptr adjuster;
if (ba_cost_func == "reproj") adjuster = makePtr();
else if (ba_cost_func == "ray") adjuster = makePtr();
else if (ba_cost_func == "affine") adjuster = makePtr();
else if (ba_cost_func == "no") adjuster = makePtr();
else
{
cout << "Unknown bundle adjustment cost function: '" << ba_cost_func << "'.\n";
return -1;
}
adjuster->setConfThresh(conf_thresh);
Mat_ refine_mask = Mat::zeros(3, 3, CV_8U);
if (ba_refine_mask[0] == 'x') refine_mask(0, 0) = 1;
if (ba_refine_mask[1] == 'x') refine_mask(0, 1) = 1;
if (ba_refine_mask[2] == 'x') refine_mask(0, 2) = 1;
if (ba_refine_mask[3] == 'x') refine_mask(1, 1) = 1;
if (ba_refine_mask[4] == 'x') refine_mask(1, 2) = 1;
adjuster->setRefinementMask(refine_mask);
if (!(*adjuster)(features, pairwise_matches, cameras))
{
cout << "Camera parameters adjusting failed.\n";
return -1;
}
// Find median focal length
vector focals;
for (size_t i = 0; i < cameras.size(); ++i)
{
//LOGLN("Camera #" << indices[i] + 1 << ":\nK:\n" << cameras[i].K() << "\nR:\n" << cameras[i].R);
focals.push_back(cameras[i].focal);
}
sort(focals.begin(), focals.end());
float warped_image_scale;
if (focals.size() % 2 == 1)
warped_image_scale = static_cast(focals[focals.size() / 2]);
else
warped_image_scale = static_cast(focals[focals.size() / 2 - 1] + focals[focals.size() / 2]) * 0.5f;
if (do_wave_correct)
{
vector rmats;
for (size_t i = 0; i < cameras.size(); ++i)
rmats.push_back(cameras[i].R.clone());
waveCorrect(rmats, wave_correct);
for (size_t i = 0; i < cameras.size(); ++i)
cameras[i].R = rmats[i];
}
LOGLN("Warping images (auxiliary)... ");
#if ENABLE_LOG
t = getTickCount();
#endif
vector corners(num_images);
vector masks_warped(num_images);
vector images_warped(num_images);
vector sizes(num_images);
vector masks(num_images);
// Prepare images masks
for (int i = 0; i < num_images; ++i)
{
masks[i].create(images[i].size(), CV_8U);
masks[i].setTo(Scalar::all(255));
}
// Warp images and their masks
Ptr warper_creator;
#ifdef HAVE_OPENCV_CUDAWARPING
if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
{
if (warp_type == "plane")
warper_creator = makePtr();
else if (warp_type == "cylindrical")
warper_creator = makePtr();
else if (warp_type == "spherical")
warper_creator = makePtr();
}
else
#endif
{
if (warp_type == "plane")
warper_creator = makePtr();
else if (warp_type == "affine")
warper_creator = makePtr();
else if (warp_type == "cylindrical")
warper_creator = makePtr();
else if (warp_type == "spherical")
warper_creator = makePtr();
else if (warp_type == "fisheye")
warper_creator = makePtr();
else if (warp_type == "stereographic")
warper_creator = makePtr();
else if (warp_type == "compressedPlaneA2B1")
warper_creator = makePtr(2.0f, 1.0f);
else if (warp_type == "compressedPlaneA1.5B1")
warper_creator = makePtr(1.5f, 1.0f);
else if (warp_type == "compressedPlanePortraitA2B1")
warper_creator = makePtr(2.0f, 1.0f);
else if (warp_type == "compressedPlanePortraitA1.5B1")
warper_creator = makePtr(1.5f, 1.0f);
else if (warp_type == "paniniA2B1")
warper_creator = makePtr(2.0f, 1.0f);
else if (warp_type == "paniniA1.5B1")
warper_creator = makePtr(1.5f, 1.0f);
else if (warp_type == "paniniPortraitA2B1")
warper_creator = makePtr(2.0f, 1.0f);
else if (warp_type == "paniniPortraitA1.5B1")
warper_creator = makePtr(1.5f, 1.0f);
else if (warp_type == "mercator")
warper_creator = makePtr();
else if (warp_type == "transverseMercator")
warper_creator = makePtr();
}
if (!warper_creator)
{
cout << "Can't create the following warper '" << warp_type << "'\n";
return 1;
}
Ptr warper = warper_creator->create(static_cast(warped_image_scale * seam_work_aspect));
for (int i = 0; i < num_images; ++i)
{
Mat_ K;
cameras[i].K().convertTo(K, CV_32F);
float swa = (float)seam_work_aspect;
K(0, 0) *= swa; K(0, 2) *= swa;
K(1, 1) *= swa; K(1, 2) *= swa;
corners[i] = warper->warp(images[i], K, cameras[i].R, INTER_LINEAR, BORDER_REFLECT, images_warped[i]);
sizes[i] = images_warped[i].size();
warper->warp(masks[i], K, cameras[i].R, INTER_NEAREST, BORDER_CONSTANT, masks_warped[i]);
}
vector images_warped_f(num_images);
for (int i = 0; i < num_images; ++i)
images_warped[i].convertTo(images_warped_f[i], CV_32F);
LOGLN("Warping images, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
LOGLN("Compensating exposure...");
#if ENABLE_LOG
t = getTickCount();
#endif
Ptr compensator = ExposureCompensator::createDefault(expos_comp_type);
if (dynamic_cast(compensator.get()))
{
GainCompensator* gcompensator = dynamic_cast(compensator.get());
gcompensator->setNrFeeds(expos_comp_nr_feeds);
}
if (dynamic_cast(compensator.get()))
{
ChannelsCompensator* ccompensator = dynamic_cast(compensator.get());
ccompensator->setNrFeeds(expos_comp_nr_feeds);
}
if (dynamic_cast(compensator.get()))
{
BlocksCompensator* bcompensator = dynamic_cast(compensator.get());
bcompensator->setNrFeeds(expos_comp_nr_feeds);
bcompensator->setNrGainsFilteringIterations(expos_comp_nr_filtering);
bcompensator->setBlockSize(expos_comp_block_size, expos_comp_block_size);
}
compensator->feed(corners, images_warped, masks_warped);
LOGLN("Compensating exposure, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
LOGLN("Finding seams...");
#if ENABLE_LOG
t = getTickCount();
#endif
Ptr seam_finder;
if (seam_find_type == "no")
seam_finder = makePtr();
else if (seam_find_type == "voronoi")
seam_finder = makePtr();
else if (seam_find_type == "gc_color")
{
#ifdef HAVE_OPENCV_CUDALEGACY
if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
seam_finder = makePtr(GraphCutSeamFinderBase::COST_COLOR);
else
#endif
seam_finder = makePtr(GraphCutSeamFinderBase::COST_COLOR);
}
else if (seam_find_type == "gc_colorgrad")
{
#ifdef HAVE_OPENCV_CUDALEGACY
if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
seam_finder = makePtr(GraphCutSeamFinderBase::COST_COLOR_GRAD);
else
#endif
seam_finder = makePtr(GraphCutSeamFinderBase::COST_COLOR_GRAD);
}
else if (seam_find_type == "dp_color")
seam_finder = makePtr(DpSeamFinder::COLOR);
else if (seam_find_type == "dp_colorgrad")
seam_finder = makePtr(DpSeamFinder::COLOR_GRAD);
if (!seam_finder)
{
cout << "Can't create the following seam finder '" << seam_find_type << "'\n";
return 1;
}
seam_finder->find(images_warped_f, corners, masks_warped);
LOGLN("Finding seams, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
// Release unused memory
images.clear();
images_warped.clear();
images_warped_f.clear();
masks.clear();
LOGLN("Compositing...");
#if ENABLE_LOG
t = getTickCount();
#endif
Mat img_warped, img_warped_s;
Mat dilated_mask, seam_mask, mask, mask_warped;
Ptr blender;
Ptr timelapser;
//double compose_seam_aspect = 1;
double compose_work_aspect = 1;
for (int img_idx = 0; img_idx < num_images; ++img_idx)
{
//LOGLN("Compositing image #" << indices[img_idx] + 1);
// Read image and resize it if necessary
full_img = ALLimages[img_idx];
if (!is_compose_scale_set)
{
if (compose_megapix > 0)
compose_scale = min(1.0, sqrt(compose_megapix * 1e6 / full_img.size().area()));
is_compose_scale_set = true;
// Compute relative scales
//compose_seam_aspect = compose_scale / seam_scale;
compose_work_aspect = compose_scale / work_scale;
// Update warped image scale
warped_image_scale *= static_cast(compose_work_aspect);
warper = warper_creator->create(warped_image_scale);
// Update corners and sizes
for (int i = 0; i < num_images; ++i)
{
// Update intrinsics
cameras[i].focal *= compose_work_aspect;
cameras[i].ppx *= compose_work_aspect;
cameras[i].ppy *= compose_work_aspect;
// Update corner and size
Size sz = full_img_sizes[i];
Mat K;
cameras[i].K().convertTo(K, CV_32F);
Rect roi = warper->warpRoi(sz, K, cameras[i].R);
corners[i] = roi.tl();
sizes[i] = roi.size();
}
}
if (abs(compose_scale - 1) > 1e-1)//没用
resize(full_img, img, Size(), compose_scale, compose_scale, INTER_LINEAR_EXACT);
else
img = full_img;
full_img.release();
Size img_size = img.size();
Mat K;
cameras[img_idx].K().convertTo(K, CV_32F);
// Warp the current image
warper->warp(img, K, cameras[img_idx].R, INTER_LINEAR, BORDER_REFLECT, img_warped);
// Warp the current image mask
mask.create(img_size, CV_8U);
mask.setTo(Scalar::all(255));
warper->warp(mask, K, cameras[img_idx].R, INTER_NEAREST, BORDER_CONSTANT, mask_warped);
// Compensate exposure
compensator->apply(img_idx, corners[img_idx], img_warped, mask_warped);
img_warped.convertTo(img_warped_s, CV_16S);
img_warped.release();
img.release();
mask.release();
dilate(masks_warped[img_idx], dilated_mask, Mat());
resize(dilated_mask, seam_mask, mask_warped.size(), 0, 0, INTER_LINEAR_EXACT);
mask_warped = seam_mask & mask_warped;
if (!blender && !timelapse)//blender是False,timelapse也是False,这里运行了！
{//做multiband
blender = Blender::createDefault(blend_type, try_cuda);
Size dst_sz = resultRoi(corners, sizes).size();
float blend_width = sqrt(static_cast(dst_sz.area())) * blend_strength / 100.f;
if (blend_width < 1.f)
blender = Blender::createDefault(Blender::NO, try_cuda);
else if (blend_type == Blender::MULTI_BAND)
{
MultiBandBlender* mb = dynamic_cast(blender.get());
mb->setNumBands(static_cast(ceil(log(blend_width) / log(2.)) - 1.));
LOGLN("Multi-band blender, number of bands: " << mb->numBands());
}
else if (blend_type == Blender::FEATHER)//未运行
{
FeatherBlender* fb = dynamic_cast(blender.get());
fb->setSharpness(1.f / blend_width);
LOGLN("Feather blender, sharpness: " << fb->sharpness());
}
blender->prepare(corners, sizes);
}
else if (!timelapser && timelapse)//timelapse是假，timelapser是什么？？没运行
{
timelapser = Timelapser::createDefault(timelapse_type);
timelapser->initialize(corners, sizes);
cout << "----------------------------运行---------------------------------" << endl;
}
// Blend the current image
if (timelapse)//默认是假
{
cout << "----------------------------运行2---------------------------------" << endl;
}
else
{//这里运行了两次，因为在循环体中，图片有两张
blender->feed(img_warped_s, mask_warped, corners[img_idx]);
cout << "----------------------------运行3---------------------------------" << endl;
}
}
if (!timelapse)//运行了
{
Mat result, result_mask;
blender->blend(result, result_mask);
LOGLN("Compositing, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
imwrite(result_name, result);
//(result.cols)*(result.rows)
memcpy(im2->data, result.clone().data, (result.cols)*(result.rows));//从哪里拷贝多少个字节。。
im2->w = result.cols;
im2->h = result.rows;
im2->c = 3;
}
LOGLN("Finished, total time: " << ((getTickCount() - app_start_time) / getTickFrequency()) << " sec");
return 0;
}
from ctypes import *
from io import BytesIO
import numpy as np
import cv2
# 写法是yolo的darknet.py里看到的，学以致用
def c_array(ctype, values): # 把图像的数据转化为内存连续的 列表 ， 使c++能使用这块内存
arr = (ctype * len(values))()
arr[:] = values
return arr
def array_to_image(arr):
c = arr.shape[2]
h = arr.shape[0]
w = arr.shape[1]
arr = arr.flatten()#转化成图片后成了一维的
data = c_array(c_uint8, arr)
im = IMAGE(w, h, c, data)#将读进来数组转化成c接受的形式，调用class IMAGE
return im
class IMAGE(Structure): # 这里和ImgSegmentation.hpp里面的结构体保持一致。
_fields_ = [("w", c_int),
("h", c_int),
("c", c_int),
("data", POINTER(c_uint8))]
img1 = cv2.imread('D:/1Hill.jpg')
img2 = cv2.imread('D:/2Hill.jpg')
#h, w, c = img.shape[0], img.shape[1], img.shape[2]
#h, w, c = img.shape[0], img.shape[1], img.shape[2]
#gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) * 0
#gray = np.reshape(gray, (h, w, 1)) # 一定要使用(h, w, 1)，最后的1别忘。
im1 = array_to_image(img1)#这里是将读进来的cv_imread格式图片转化成结构体，一维的
im2 = array_to_image(img2)
#gray_img = array_to_image(gray)
lib = cdll.LoadLibrary('./image_stiching.dll') # 读取动态库文件
lib.Stitch.argtypes = [POINTER(IMAGE), POINTER(IMAGE)] # 设置函数入参格式,声明采用指针传递。指定入参为2个数组指针，python里定义类型指针和C相反，类型在后。
#lib.Stitch.restype = c_int64
lib.Stitch(im1, im2) # 执行函数，这里直接修改gray_img的内存数据。入参是非指针，python提取地址作为输入。因为函数原型是传递的指针，这里相当于POINTER会自动取输入im1,im2的指针作为入参。
## 因此输入的内存数据会直接被改变。
y = im2.data # 获取data，被改变传递改变的对象名
array_length = im2.h * im2.w
#转化为numpy的ndarray
buffer_from_memory = pythonapi.PyMemoryView_FromMemory # 这个是python 3的使用方法，提取运算缓存
buffer_from_memory.restype = py_object #提取缓存返回的数据格式，以上两步是下一步从缓存中提取某个变量的结果必须的。
buffer = buffer_from_memory(y, array_length) #提取底层的缓存指针，指定提取缓存大小
img = np.frombuffer(buffer, dtype=np.uint8) #提取到缓存中的数组
print("----------------------")
print(img.shape)
img = np.reshape(img, (im2.h, im2.w,1)) #改变缓存数组的格式，用于显示
print("-------2---------")
print(img.shape)
print(img)
cv2.imshow('test', img)
cv2.imwrite("D:/RESULT_PY.JPG",img)
cv2.waitKey(0)
