동영상 프레임 단위로 분할 후 디텍션 api에 request

 const express = require('express');
 const express = require('express');
 const fs = require('fs');
 const fs = require('fs');
-const {
-    cv,
-    getDataFilePath,
-    drawBlueRect,
-    drawGreenRect
-} = require('./utils');
-
-const openCV = require('opencv4nodejs');
+const ffmpeg = require('ffmpeg');
+const request = require('request')
 
 
 const router = express.Router();
 const router = express.Router();
-
+const path = require('path');
 //================================================================
 //================================================================
 
 
 router.post('/videoResult', function (req, res) {
 router.post('/videoResult', function (req, res) {
 
 
     try {
     try {
-    let preview = req.body[0].preview;
-
-    str = preview.replace(/^data:(.*?);base64,/, "");
-    str = str.replace(/ /g, '+');
-
+        let preview = req.body[0].preview;
+
+        str = preview.replace(/^data:(.*?);base64,/, "");
+        str = str.replace(/ /g, '+');
+
+
+        fs.writeFileSync(`./data/temp.mp4`, str, 'base64', function (err) {
+            if (err) throw err;
+            console.log("video saved");
+            return;
+        })
+
+        let process = new ffmpeg(`./data/temp.mp4`);
+
+
+        detectedImgFile = "test.jpg"; // null로 바꿀 것
+        process.then(function (video) {
+            video.fnExtractFrameToJPG(__dirname + "/data",
+                {
+                    every_n_seconds: 1,
+                    file_name: 'frame_%s'
+                }, function (error, files) {
+                    if (!error)
+                        console.log('###1 Frames =>' + files);
+                    console.log("###2 갯수 => " + files.length)
+
+
+                    console.log("###3 첫번째 파일 => " + files[0]); // 마지막 파일은 영상임
+                    let base64str = base64_encode(files[0]);
+                    console.log("###4 base64str => " + base64str);
+                    console.log("##### for")
+                    for(var i=0;i<files.length-1;i++){
+                        request.post({
+                            url: 'http://101.101.210.73/process',
+                            form: {
+                                'data': base64_encode(files[0])
+                            },
+                            json: true
+                        }, (err, response, body) => {
+                            console.log(body)
+                        })
+                    }
+                    /**
+                     * TODO
+                     * 반복문돌면서 각 프레임 파이썬 api 요청하고 응답받아서
+                     * 하나라도 true나오면 프론트에 감지된 이미지파일주소 응답
+                     */
+                    detectedImgFile=null;
+                }
+            )
+        })
 
 
-    fs.writeFile(`./data/temp.mp4`, str, 'base64', function (err) {
-        if (err) throw err;
-        console.log("saved");
-        const vCap = new openCV.VideoCapture('./data/temp.mp4')
-        const delay = 1000;
-        let done = false;
-        let cnt = 0;
-        while (!done) {
-            let frame = vCap.read();
-            cv.imwrite('./data/' + cnt + '.jpg');
-            cnt++;
-            if (frame.empty) {
-                vCap.reset();
-                frame = vCap.read();
-            }
-        }
-    });
     } catch (err) {
     } catch (err) {
-        console.log("err : " + err);
+        console.error(err);
     }
     }
 
 
-    return res.json({ data: 'myData' });
+    return res.json({ data: detectedImgFile });
 });
 });
 
 
-//================================================================
-
-// router.post('/faceRecognition', function (req, res) {
-
-//     try {
-//         let preview = req.body[0].preview;
-
-//         str = preview.replace(/^data:(.*?);base64,/, "");
-//         str = str.replace(/ /g, '+');
-
-//         // 임시파일 저장
-//         fs.writeFile(`./data/temp.jpg`, str, 'base64', function (err) {
-//             if (err) throw err;
-//             console.log("saved");
-//             detectFaceAndEyes('./data/temp.jpg');
-//         });
-
-
-
-//     } catch (err) {
-//         console.log('err: ' + err);
-//     }
-
-//     return res.json({ data: 'myData' });
-// });
-
-//================================================================
-
-function base64encode(plaintext) {
-    return Buffer.from(plaintext, "utf8").toString('base64');
-}
-
-function base64decode(base64text) {
-    console.log(base64text.length);
-    return Buffer.from(base64text, 'base64').toString('utf8');
+function base64_encode(file) {
+    // read binary data
+    var bitmap = fs.readFileSync(file);
+    // convert binary data to base64 encoded string
+    return new Buffer(bitmap).toString('base64');
 }
 }
 
 
-// function detectFaceAndEyes(filePath) {
-//     const image = cv.imread(filePath);
-//     const faceClassifier = new cv.CascadeClassifier(cv.HAAR_FRONTALFACE_DEFAULT);
-//     const eyeClassifier = new cv.CascadeClassifier(cv.HAAR_EYE);
-
-//     // detect faces
-//     const faceResult = faceClassifier.detectMultiScale(image.bgrToGray());
-
-//     if (!faceResult.objects.length) {
-//         throw new Error('No faces detected!');
-//     }
-
-//     const sortByNumDetections = result => result.numDetections
-//         .map((num, idx) => ({ num, idx }))
-//         .sort(((n0, n1) => n1.num - n0.num))
-//         .map(({ idx }) => idx);
-
-//     // get best result
-//     const faceRect = faceResult.objects[sortByNumDetections(faceResult)[0]];
-//     console.log('faceRects:', faceResult.objects);
-//     console.log('confidences:', faceResult.numDetections);
-
-//     // detect eyes
-//     const faceRegion = image.getRegion(faceRect);
-//     const eyeResult = eyeClassifier.detectMultiScale(faceRegion);
-//     console.log('eyeRects:', eyeResult.objects);
-//     console.log('confidences:', eyeResult.numDetections);
-
-//     // get best result
-//     const eyeRects = sortByNumDetections(eyeResult)
-//         .slice(0, 2)
-//         .map(idx => eyeResult.objects[idx]);
-
-
-//     // draw face detection
-//     drawBlueRect(image, faceRect);
-
-//     // draw eyes detection in face region
-//     eyeRects.forEach(eyeRect => drawGreenRect(faceRegion, eyeRect));
-
-//     cv.imwrite(`./data/temp2.jpg`, image);
-// }
-
 module.exports = router;
 module.exports = router;
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+{
+  "name": "back-end",
+  "version": "1.0.0",
+  "description": "",
+  "main": "apiRouter.js",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1",
+    "start": "node server.js"
+  },
+  "author": "",
+  "license": "ISC",
+  "dependencies": {
+    "body-parser": "^1.19.0",
+    "cors": "^2.8.5",
+    "express": "^4.17.1",
+    "ffmpeg": "0.0.4",
+    "opencv4nodejs": "^5.6.0",
+    "path": "^0.12.7",
+    "request": "^2.88.2"
+  }
+}

@@ -12,5 +12,5 @@ app.use(bodyParser.urlencoded({limit: '100mb', extended: true}));
 app.use(bodyParser());
 app.use(bodyParser());
 app.use('/api', api);
 app.use('/api', api);
 
 
-const port = 3002;
+const port = 3003;
 app.listen(port, () => console.log(`노드서버 시작 : ${port}`));
 app.listen(port, () => console.log(`노드서버 시작 : ${port}`));

-const path = require('path');
-const cv = require('opencv4nodejs');
-
-exports.cv = cv;
-
-const dataPath = path.resolve(__dirname, './data');
-exports.dataPath = dataPath;
-exports.getDataFilePath = fileName => {
-    let targetPath = path.resolve(dataPath, fileName);
-    return targetPath;
-}
-
-const grabFrames = (videoFile, delay, onFrame) => {
-  const cap = new cv.VideoCapture(videoFile);
-  let done = false;
-  const intvl = setInterval(() => {
-    let frame = cap.read();
-    // loop back to start on end of stream reached
-    if (frame.empty) {
-      cap.reset();
-      frame = cap.read();
-    }
-    onFrame(frame);
-
-    const key = cv.waitKey(delay);
-    done = key !== -1 && key !== 255;
-    if (done) {
-      clearInterval(intvl);
-      console.log('Key pressed, exiting.');
-    }
-  }, 0);
-};
-exports.grabFrames = grabFrames;
-
-exports.runVideoDetection = (src, detect) => {
-  grabFrames(src, 1, frame => {
-    detect(frame);
-  });
-};
-
-exports.drawRectAroundBlobs = (binaryImg, dstImg, minPxSize, fixedRectWidth) => {
-  const {
-    centroids,
-    stats
-  } = binaryImg.connectedComponentsWithStats();
-
-  // pretend label 0 is background
-  for (let label = 1; label < centroids.rows; label += 1) {
-    const [x1, y1] = [stats.at(label, cv.CC_STAT_LEFT), stats.at(label, cv.CC_STAT_TOP)];
-    const [x2, y2] = [
-      x1 + (fixedRectWidth || stats.at(label, cv.CC_STAT_WIDTH)),
-      y1 + (fixedRectWidth || stats.at(label, cv.CC_STAT_HEIGHT))
-    ];
-    const size = stats.at(label, cv.CC_STAT_AREA);
-    const blue = new cv.Vec(255, 0, 0);
-    if (minPxSize < size) {
-      dstImg.drawRectangle(
-        new cv.Point(x1, y1),
-        new cv.Point(x2, y2),
-        { color: blue, thickness: 2 }
-      );
-    }
-  }
-};
-
-const drawRect = (image, rect, color, opts = { thickness: 2 }) =>
-  image.drawRectangle(
-    rect,
-    color,
-    opts.thickness,
-    cv.LINE_8
-  );
-
-exports.drawRect = drawRect;
-exports.drawBlueRect = (image, rect, opts = { thickness: 2 }) =>
-  drawRect(image, rect, new cv.Vec(255, 0, 0), opts);
-exports.drawGreenRect = (image, rect, opts = { thickness: 2 }) =>
-  drawRect(image, rect, new cv.Vec(0, 255, 0), opts);
-exports.drawRedRect = (image, rect, opts = { thickness: 2 }) =>
-  drawRect(image, rect, new cv.Vec(0, 0, 255), opts);
\ No newline at end of file