treyhuffine

import gql from 'graphql-tag';

export const QUERY_LAUNCH_PROFILE = gql`
  query LaunchProfile($id: String!) {
    launch(id: $id) {
      flight_number
      mission_name
      launch_year
      launch_success
      details

treyhuffine

import gql from 'graphql-tag';

export const QUERY_LAUNCH_LIST = gql`
  query LaunchList {
    launches {
      flight_number
      mission_name
      launch_year
    }
  }

treyhuffine

overwrite: true
schema: 'https://spacexdata.herokuapp.com/graphql'
documents: './src/components/**/*.ts'
generates:
  src/generated/graphql.tsx:
    plugins:
      - 'typescript'
      - 'typescript-operations'
      - 'typescript-react-apollo'
    config:

treyhuffine

interface FunctionComponent<P = {}> {
  (props: P & { children?: ReactNode }, context?: any): ReactElement | null;
  propTypes?: WeakValidationMap<P>;
  contextTypes?: ValidationMap<any>;
  defaultProps?: Partial<P>;
  displayName?: string;
}

treyhuffine

vector<Point3f> objectPointsForReprojection {
  objectPoints[2],                   // tip of nose
  objectPoints[2] + Point3f(0,0,15), // nose and Z-axis
  objectPoints[2] + Point3f(0,15,0), // nose and Y-axis
  objectPoints[2] + Point3f(15,0,0)  // nose and X-axis
};
//...
vector<Point2f> projectionOutput(objectPointsForReprojection.size());
projectPoints(objectPointsForReprojection, rvec, tvec, K, Mat(), projectionOutput);
arrowedLine(out, projectionOutput[0], projectionOutput[1], Scalar(255,255,0));

treyhuffine

vector<Point3f> objectPoints {
  {8.27412, 1.33849, 10.63490},    //left eye corner
  {-8.27412, 1.33849, 10.63490},   //right eye corner
  {0, -4.47894, 17.73010},         //nose tip
  {-4.61960, -10.14360, 12.27940}, //right mouth corner
  {4.61960, -10.14360, 12.27940},  //left mouth corner
};
vector<int> landmarksIDsFor3DPoints {45, 36, 30, 48, 54}; // 0-index
// ...
vector<Point2f> points2d;

treyhuffine

vector<Rect> faces;
faceDetector(img, faces, face_cascade);

// Check if faces detected or not
if (faces.size() != 0) {
// We assume a single face so we look at the first only
  cv::rectangle(img, faces[0], Scalar(255, 0, 0), 2);
  vector<vector<Point2f> > shapes;
  
  if (facemark->fit(img, faces, shapes)) {

treyhuffine

#include <opencv2/face.hpp>
using namespace cv::face;
// ...
const string facemark_filename = "data/lbfmodel.yaml";
Ptr<Facemark> facemark = createFacemarkLBF();
facemark->loadModel(facemark_filename);
cout << "Loaded facemark LBF model" << endl;

treyhuffine

const string cascade_name = "$OPENCV_ROOT/data/haarcascades/haarcascade_frontalface_default.xml";
CascadeClassifier face_cascade;
if (not face_cascade.load(cascade_name)) {
     cerr << "Cannot load cascade classifier from file: " <<    cascade_name << endl;
return -1;
}
// ... obtain an image in img
vector<Rect> faces;
faceDetector(img, faces, face_cascade);
// Check if any faces were detected or not

treyhuffine

void faceDetector(const Mat& image,
  std::vector<Rect> &faces,
  CascadeClassifier &face_cascade
) {
  Mat gray;
  // The cascade classifier works best on grayscale images
  if (image.channels() > 1) {
    cvtColor(image, gray, COLOR_BGR2GRAY);
  } else {
    gray = image.clone();