pybind11用python调用C++代码
2024-05-11 05:44:02
C++代码
#include <pybind11/pybind11.h>
#include<opencv2/opencv.hpp>
#include<pybind11/numpy.h>using namespace std;
using namespace cv;
namespace py = pybind11;void BilinearInsert(Mat& src, Mat& dst, float ux, float uy, int i, int j)
{auto Abs = [&](float f) {return f > 0 ? f : -f;};int height = src.rows-1;int width = src.cols-1;int c = src.channels();if (c == 3){//存储图像得浮点坐标CvPoint2D32f uv;CvPoint3D32f f1;CvPoint3D32f f2;//取整数int iu = (int)ux;int iv = (int)uy;if (iu > width){iu = width;}if (iv > height){iv = height;}uv.x = iu + 1;uv.y = iv + 1;//step图象像素行的实际宽度 三个通道进行计算(0 , 1 2 三通道)/*cout << "src.data" << src.data << std::endl;//C++ unsigned char ＊是表示无符号字符指针的意思//unsigned若省略后一个关键字，大多数编译器都会认为是unsigned int。/*cout << "src.data.type" << typeid(src.data).name() << std::endl;*/f1.x = ((uchar*)(src.data + src.step * iv))[iu * 3] * (1 - Abs(uv.x - iu)) + \((uchar*)(src.data + src.step * iv))[(iu + 1) * 3] * (uv.x - iu);f1.y = ((uchar*)(src.data + src.step * iv))[iu * 3 + 1] * (1 - Abs(uv.x - iu)) + \((uchar*)(src.data + src.step * iv))[(iu + 1) * 3 + 1] * (uv.x - iu);f1.z = ((uchar*)(src.data + src.step * iv))[iu * 3 + 2] * (1 - Abs(uv.x - iu)) + \((uchar*)(src.data + src.step * iv))[(iu + 1) * 3 + 2] * (uv.x - iu);f2.x = ((uchar*)(src.data + src.step * (iv + 1)))[iu * 3] * (1 - Abs(uv.x - iu)) + \/*cout << "f1.x" << f1.x << std::endl;*/((uchar*)(src.data + src.step * (iv + 1)))[(iu + 1) * 3] * (uv.x - iu);f2.y = ((uchar*)(src.data + src.step * (iv + 1)))[iu * 3 + 1] * (1 - Abs(uv.x - iu)) + \((uchar*)(src.data + src.step * (iv + 1)))[(iu + 1) * 3 + 1] * (uv.x - iu);f2.z = ((uchar*)(src.data + src.step * (iv + 1)))[iu * 3 + 2] * (1 - Abs(uv.x - iu)) + \((uchar*)(src.data + src.step * (iv + 1)))[(iu + 1) * 3 + 2] * (uv.x - iu);//step指的字节数,一个图像是3通道，每个通道是16位，那么//其elemsize就是3* （16 / 8） = 6，即每个元素占6个字节((uchar*)(dst.data + dst.step * j))[i * 3] = f1.x * (1 - Abs(uv.y - iv)) + f2.x * (Abs(uv.y - iv)); //三个通道进行赋值((uchar*)(dst.data + dst.step * j))[i * 3 + 1] = f1.y * (1 - Abs(uv.y - iv)) + f2.y * (Abs(uv.y - iv));((uchar*)(dst.data + dst.step * j))[i * 3 + 2] = f1.z * (1 - Abs(uv.y - iv)) + f2.z * (Abs(uv.y - iv));}
}
Mat LocalTranslationWarp_Face(Mat& img, int warpX, int warpY, int endX, int endY, float radius)
{Mat dst = img.clone();//平移距离 float ddradius = radius * radius;//计算|m-c|^2size_t mc = (endX - warpX) * (endX - warpX) + (endY - warpY) * (endY - warpY);//计算 图像的高 宽 通道数量int height = img.rows;int width = img.cols;int chan = img.channels();auto Abs = [&](float f) {return f > 0 ? f : -f;};for (int i = 0; i < width; i++){for (int j = 0; j < height; j++){// # 计算该点是否在形变圆的范围之内//# 优化，第一步，直接判断是会在（startX, startY)的矩阵框中if ((Abs(i - warpX) > radius) && (Abs(j - warpY) > radius))continue;float distance = (i - warpX) * (i - warpX) + (j - warpY) * (j - warpY);if (distance < ddradius){//# 计算出（i, j）坐标的原坐标//# 计算公式中右边平方号里的部分float ratio = (ddradius - distance) / (ddradius - distance + mc);ratio *= ratio;//映射原位置float UX = i - ratio * (endX - warpX);float UY = j - ratio * (endY - warpY);//根据双线性插值得到UX UY的值BilinearInsert(img, dst, UX, UY, i, j);//改变当前的值}}}return dst;}
Mat LocalTranslationWarp_Eye(Mat& img, int warpX, int warpY, int endX, int endY, float radius)
{//平移距离 Mat dst = img.clone();float ddradius = radius * radius;//计算|m-c|^2size_t mc = (endX - warpX) * (endX - warpX) + (endY - warpY) * (endY - warpY);//计算 图像的高 宽 通道数量int height = img.rows;int width = img.cols;int chan = img.channels();auto Abs = [&](float f) {return f > 0 ? f : -f;};for (int i = 0; i < width; i++){for (int j = 0; j < height; j++){// # 计算该点是否在形变圆的范围之内//# 优化，第一步，直接判断是会在（startX, startY)的矩阵框中if ((Abs(i - warpX) > radius) && (Abs(j - warpY) > radius))continue;float distance = (i - warpX) * (i - warpX) + (j - warpY) * (j - warpY);if (distance < ddradius){float rnorm = sqrt(distance) / radius;float ratio = 1 - (rnorm - 1) * (rnorm - 1) * 0.5;//映射原位置float UX = warpX + ratio * (i - warpX);float UY = warpY + ratio * (j - warpY);//根据双线性插值得到UX UY的值BilinearInsert(img, dst, UX, UY, i, j);}}}return dst;}
Mat numpy_uint8_3c_to_cv_mat(py::array_t<unsigned char>& input) {if (input.ndim() != 3)throw std::runtime_error("1-channel image must be 2 dims ");py::buffer_info buf = input.request();Mat mat(buf.shape[0], buf.shape[1], CV_8UC3, (unsigned char*)buf.ptr);//imshow("显示读取图片", mat);return mat;
}
Mat numpy_uint8_1c_to_cv_mat(py::array_t<unsigned char>& input) {if (input.ndim() != 2)throw std::runtime_error("1-channel image must be 2 dims ");py::buffer_info buf = input.request();Mat mat(buf.shape[0], buf.shape[1], CV_8UC1, (unsigned char*)buf.ptr);return mat;
}
py::array_t<unsigned char> cv_mat_uint8_1c_to_numpy(cv::Mat& input) {py::array_t<unsigned char> dst = py::array_t<unsigned char>({ input.rows,input.cols }, input.data);return dst;
}py::array_t<unsigned char> cv_mat_uint8_3c_to_numpy(cv::Mat& input) {py::array_t<unsigned char> dst = py::array_t<unsigned char>({ input.rows,input.cols,3 }, input.data);return dst;
}py::array_t<unsigned char> eyebig(py::array_t<unsigned char>& input, int warpX, int warpY, int endX, int endY, float radius) {Mat img_rgb = numpy_uint8_3c_to_cv_mat(input);//cout << "input.rows" << img_rgb.rows << "input.cols" << img_rgb.cols << endl;Mat dst = LocalTranslationWarp_Eye(img_rgb, warpX, warpY, endX, endY, radius);//imshow("显示读取图片2", dst);return cv_mat_uint8_3c_to_numpy(dst);
}
py::array_t<unsigned char> facethin(py::array_t<unsigned char>& input, int warpX, int warpY, int endX, int endY, float radius) {Mat img_rgb = numpy_uint8_3c_to_cv_mat(input);//cout << "input.rows" << img_rgb.rows << "input.cols" << img_rgb.cols << endl;Mat dst = LocalTranslationWarp_Face(img_rgb, warpX, warpY, endX, endY, radius);//imshow("显示读取图片2", dst);return cv_mat_uint8_3c_to_numpy(dst);
}
py::array_t<unsigned char> test_rgb_to_gray(py::array_t<unsigned char>& input) {Mat img_rgb = numpy_uint8_3c_to_cv_mat(input);Mat dst;cv::cvtColor(img_rgb, dst, cv::COLOR_RGB2GRAY);return cv_mat_uint8_1c_to_numpy(dst);
}PYBIND11_MODULE(py2cpp, m) {m.doc() = "pybind11 example";m.def("LocalTranslationWarp_Eye", &LocalTranslationWarp_Eye, "LocalTranslationWarp_Eye");m.def("test_rgb_to_gray", &test_rgb_to_gray,"test_rgb_to_grayt");m.def("eyebig", &eyebig, "eyebig");m.def("facethin", &facethin, "facethin");}
python代码
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import py2cpp as obj
import dlib
import cv2
import numpy as np
import math
import time
predictor_path = 'shape_predictor_68_face_landmarks.dat'# 使用dlib自带的frontal_face_detector作为我们的特征提取器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)def landmark_dec_dlib_fun(img_src):#转灰度图img_gray = cv2.cvtColor(img_src, cv2.COLOR_BGR2GRAY)#cv2.imshow('grayimg',img_gray)land_marks = []rects = detector(img_gray, 0)print(rects)for i in range(len(rects)):land_marks_node = np.matrix([[p.x, p.y] for p in predictor(img_gray, rects[i]).parts()])# for idx,point in enumerate(land_marks_node):# # 68点坐标# pos = (point[0,0],point[0,1])# print(idx,pos)# # 利用cv2.circle给每个特征点画一个圈，共68个# cv2.circle(img_src, pos, 5, color=(0, 255, 0))# # 利用cv2.putText输出1-68# font = cv2.FONT_HERSHEY_SIMPLEX# cv2.putText(img_src, str(idx + 1), pos, font, 0.5, (0, 0, 0), 1, cv2.LINE_AA)land_marks.append(land_marks_node)return land_marks'''
方法1： Interactive Image Warping 局部放大算法
'''
def localTranslationWarp1(srcImg, startX, startY, endX, endY, radius):ddradius = float(radius * radius)copyImg = np.zeros(srcImg.shape, np.uint8)copyImg = srcImg.copy()t1 = time.time()# 计算公式中的|m-c|^2ddmc = (endX - startX) * (endX - startX) + (endY - startY) * (endY - startY)H, W, C = srcImg.shapefor i in range(W):for j in range(H):# 计算该点是否在形变圆的范围之内# 优化，第一步，直接判断是会在（startX,startY)的矩阵框中if math.fabs(i - startX) > radius and math.fabs(j - startY) > radius:continuedistance = (i - startX) * (i - startX) + (j - startY) * (j - startY)if (distance < ddradius):# 计算出（i,j）坐标的原坐标# 计算公式中右边平方号里的部分# ratio = (ddradius - distance) / (ddradius - distance + ddmc)# ratio = ratio * ratiornorm = math.sqrt(distance) / radiusratio = 1 - (rnorm - 1)*(rnorm - 1)*0.5# 映射原位置UX = startX + ratio * (i - startX)UY = startY + ratio * (j - startY)# 根据双线性插值法得到UX，UY的值value = BilinearInsert(srcImg, UX, UY)# 改变当前 i ，j的值copyImg[j, i] = valuereturn copyImg
'''
方法2： Interactive Image Warping 局部平移算法
'''
def localTranslationWarp2(srcImg, startX, startY, endX, endY, radius):ddradius = float(radius * radius)copyImg = np.zeros(srcImg.shape, np.uint8)copyImg = srcImg.copy()# 计算公式中的|m-c|^2ddmc = (endX - startX) * (endX - startX) + (endY - startY) * (endY - startY)H, W, C = srcImg.shapefor i in range(W):for j in range(H):# 计算该点是否在形变圆的范围之内# 优化，第一步，直接判断是会在（startX,startY)的矩阵框中if math.fabs(i - startX) > radius and math.fabs(j - startY) > radius:continuedistance = (i - startX) * (i - startX) + (j - startY) * (j - startY)if (distance < ddradius):# 计算出（i,j）坐标的原坐标# 计算公式中右边平方号里的部分ratio = (ddradius - distance) / (ddradius - distance + ddmc)ratio = ratio * ratio# 映射原位置UX = i - ratio * (endX - startX)UY = j - ratio * (endY - startY)# 根据双线性插值法得到UX，UY的值value = BilinearInsert(srcImg, UX, UY)# 改变当前 i ，j的值copyImg[j, i] = valuereturn copyImgpredictor_path = 'shape_predictor_68_face_landmarks.dat'# 使用dlib自带的frontal_face_detector作为我们的特征提取器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)def landmark_dec_dlib_fun(img_src):#转灰度图img_gray = cv2.cvtColor(img_src, cv2.COLOR_BGR2GRAY)#cv2.imshow('grayimg',img_gray)land_marks = []rects = detector(img_gray, 0)#print(rects)for i in range(len(rects)):land_marks_node = np.matrix([[p.x, p.y] for p in predictor(img_gray, rects[i]).parts()])# for idx,point in enumerate(land_marks_node):# # 68点坐标# pos = (point[0,0],point[0,1])# print(idx,pos)# # 利用cv2.circle给每个特征点画一个圈，共68个# cv2.circle(img_src, pos, 5, color=(0, 255, 0))# # 利用cv2.putText输出1-68# font = cv2.FONT_HERSHEY_SIMPLEX# cv2.putText(img_src, str(idx + 1), pos, font, 0.5, (0, 0, 0), 1, cv2.LINE_AA)land_marks.append(land_marks_node)return land_marks# 双线性插值法
def BilinearInsert(src, ux, uy):w, h, c = src.shapeif c == 3:x1 = int(ux)x2 = x1 + 1y1 = int(uy)y2 = y1 + 1part1 = src[y1, x1].astype(np.float64) * (float(x2) - ux) * (float(y2) - uy)part2 = src[y1, x2].astype(np.float64) * (ux - float(x1)) * (float(y2) - uy)part3 = src[y2, x1].astype(np.float64) * (float(x2) - ux) * (uy - float(y1))part4 = src[y2, x2].astype(np.float64) * (ux - float(x1)) * (uy - float(y1))insertValue = part1 + part2 + part3 + part4return insertValue.astype(np.int8)src = cv2.imread('1.jpg')landmarks = landmark_dec_dlib_fun(src)
# if len(landmarks) == 0:
# returnfor landmarks_node in landmarks:# 第4个点左left_landmark = landmarks_node[37]# 第6个点左left_landmark_down = landmarks_node[27]# 第14个点右right_landmark = landmarks_node[44]# 第16个点右right_landmark_down = landmarks_node[27]# 第31个点鼻尖endPt = landmarks_node[30]
def face_big_auto(x):#68个关键点二维数组#landmarks = landmark_dec_dlib_fun(src)if len(landmarks) == 0:returnw = cv2.getTrackbarPos('Bigeye', 'How big the eyes do you want')# 计算第4个点到第6个点的距离作为瘦脸距离# r_left = math.sqrt(# (left_landmark[0, 0] - left_landmark_down[0, 0]) * (left_landmark[0, 0] - left_landmark_down[0, 0]) +# (left_landmark[0, 1] - left_landmark_down[0, 1]) * (left_landmark[0, 1] - left_landmark_down[0, 1]))## # 计算第14个点到第16个点的距离作为瘦脸距离# r_right = math.sqrt(# (right_landmark[0, 0] - right_landmark_down[0, 0]) * (right_landmark[0, 0] - right_landmark_down[0, 0]) +# (right_landmark[0, 1] - right_landmark_down[0, 1]) * (right_landmark[0, 1] - right_landmark_down[0, 1]))r_left = wr_right = w# 大左边眼# big_image = localTranslationWarp1(src, left_landmark[0, 0], left_landmark[0, 1], endPt[0, 0], endPt[0, 1],# r_left)big_image = obj.eyebig(src, left_landmark[0, 0], left_landmark[0, 1], endPt[0, 0], endPt[0, 1],r_left)# 大右边眼# big_image = localTranslationWarp1(big_image, right_landmark[0, 0], right_landmark[0, 1], endPt[0, 0],# endPt[0, 1], r_right)big_image = obj.eyebig(big_image, right_landmark[0, 0], right_landmark[0, 1], endPt[0, 0],endPt[0, 1], r_right)# 显示cv2.imshow('How big the eyes do you want', big_image)cv2.waitKey(1)def face_thin_auto(x):s = cv2.getTrackbarPos('Thinface', 'How big the eyes do you want')# 如果未检测到人脸关键点，就不进行瘦脸if len(landmarks) == 0:returnfor landmarks_node in landmarks:#第4个点左left_landmark = landmarks_node[3]#第6个点左left_landmark_down = landmarks_node[5]#第14个点右right_landmark = landmarks_node[13]#第16个点右right_landmark_down = landmarks_node[15]#第31个点鼻尖endPt = landmarks_node[30]# 计算第4个点到第6个点的距离作为瘦脸距离# r_left = math.sqrt(# (left_landmark[0, 0] - left_landmark_down[0, 0]) * (left_landmark[0, 0] - left_landmark_down[0, 0]) +# (left_landmark[0, 1] - left_landmark_down[0, 1]) * (left_landmark[0, 1] - left_landmark_down[0, 1]))## # 计算第14个点到第16个点的距离作为瘦脸距离# r_right = math.sqrt(# (right_landmark[0, 0] - right_landmark_down[0, 0]) * (right_landmark[0, 0] - right_landmark_down[0, 0]) +# (right_landmark[0, 1] - right_landmark_down[0, 1]) * (right_landmark[0, 1] - right_landmark_down[0, 1]))r_left = sr_right = s# 瘦左边脸# thin_image = localTranslationWarp2(src, left_landmark[0, 0], left_landmark[0, 1], endPt[0, 0], endPt[0, 1],# r_left)thin_image = obj.facethin(src, left_landmark[0, 0], left_landmark[0, 1], endPt[0, 0], endPt[0, 1],r_left)# 瘦右边脸# thin_image = localTranslationWarp2(thin_image, right_landmark[0, 0], right_landmark[0, 1], endPt[0, 0],# endPt[0, 1], r_right)thin_image = obj.facethin(thin_image, right_landmark[0, 0], right_landmark[0, 1], endPt[0, 0],endPt[0, 1], r_right)# 显示cv2.imshow('How big the eyes do you want', thin_image)cv2.waitKey(1)cv2.namedWindow('How big the eyes do you want')
cv2.createTrackbar('Bigeye', 'How big the eyes do you want', 0, 70, face_big_auto)
cv2.createTrackbar('Thinface', 'How big the eyes do you want', 0, 60, face_thin_auto)
cv2.imshow('How big the eyes do you want',src)
cv2.waitKey(0)
# while True:
# #cv2.imshow('How big the eyes do you want', window)
# ch = cv2.waitKey(1) & 0xFF
# if ch == 27:
# breakcv2.destroyAllWindows()
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