Change cropping method to imporve accuracy

@@ -126,12 +126,12 @@ def verify():
             break
 
     if verified_id != None:
-        sql = "SELECT DATE(attendance_time) FROM student_attendance WHERE (lecture_id=%s) AND (student_id=%s) AND (DATE(attendance_time) = CURDATE());"
-        cursor.execute(sql, ('0', verified_id))
         sql = "SELECT student_name FROM student WHERE student_id = %s"
         cursor.execute(sql, (verified_id))
         row_data = cursor.fetchone()
         verified_name = row_data['student_name']
+        sql = "SELECT DATE(attendance_time) FROM student_attendance WHERE (lecture_id=%s) AND (student_id=%s) AND (DATE(attendance_time) = CURDATE());"
+        cursor.execute(sql, ('0', verified_id))
         if cursor.rowcount == 0:
             sql = "INSERT INTO student_attendance(lecture_id, student_id, status) VALUES (%s, %s, %s)"
             # TODO: attend / late 처리

 [verification_server]
 model=models/20200816-080621
-threshold=0.75
+threshold=0.765
 image_size=160

@@ -6,8 +6,6 @@
 <style>
 #container {
 	margin: 0px auto;
-	width: 640px;
-	height: 480px;
 	border: 10px #333 solid;
 }
 #videoInput {
@@ -24,7 +22,9 @@
 
 function init()
 {
-    let video = document.getElementById("videoInput");
+    let video = document.getElementById('videoInput');
+    let container = document.getElementById('container');
+    let canvasOutput = document.getElementById("canvasOutput");
     if (navigator.mediaDevices.getUserMedia){
         navigator.mediaDevices.getUserMedia({ video: true })
 	    .then(function (stream) {
@@ -32,6 +32,10 @@ function init()
 	        video.addEventListener('canplay', () => {
                     video.width = video.videoWidth;
                     video.height = video.videoHeight;
+                    container.style.width = video.videoWidth + 'px';
+                    container.style.height = video.videoHeight + 'px';
+                    canvasOutput.width = video.videoWidth;
+                    canvasOutput.height = video.videoHeight;
                     load_cascade();
 	        });
 	    }).catch(function (err0r) {
@@ -59,7 +63,7 @@ let canvasOutput = document.getElementById("canvasOutput");
 let canvasContext = canvasOutput.getContext('2d');
 let src = new cv.Mat(video.height, video.width, cv.CV_8UC4);
 let dst = new cv.Mat(video.height, video.width, cv.CV_8UC4);
-let gray = new cv.Mat();
+// let gray = new cv.Mat();
 let cap = new cv.VideoCapture(video);
 let faces = new cv.RectVector();
 let classifier = new cv.CascadeClassifier();
@@ -109,22 +113,21 @@ function processVideo() {
         let begin = Date.now();
         // start processing.
         cap.read(src);
+        cv.flip(src, src, 1);
         src.copyTo(dst);
-        cv.cvtColor(dst, gray, cv.COLOR_RGBA2GRAY, 0);
+        // cv.cvtColor(dst, gray, cv.COLOR_RGBA2GRAY, 0);
         // detect faces.
-        let msize = new cv.Size(120, 120);
-        classifier.detectMultiScale(gray, faces, 1.1, 3, 0, msize);
+        let msize = new cv.Size(video.width / 4, video.height / 4);
+        classifier.detectMultiScale(dst, faces, 1.1, 3, 0, msize);
         // draw faces.
 	console.log('draw faces');
         for (let i = 0; i < faces.size(); ++i) {
             let face = faces.get(i);
             let point1 = new cv.Point(face.x, face.y);
             let point2 = new cv.Point(face.x + face.width, face.y + face.height);
-            cv.rectangle(dst, point1, point2, [255, 0, 0, 255]);
+            cv.rectangle(dst, point1, point2, [255, 0, 0, 255], 8);
             let cropped = new cv.Mat();
-            let margin_x = face.width / 5;
-            let margin_y = face.height / 5;
-            let rect = new cv.Rect(Math.max(face.x - margin_x, 0), Math.max(face.y - margin_y, 0), face.width + margin_x, face.height + margin_y);
+            let rect = new cv.Rect(face.x, face.y, face.width, face.height);
             cropped = src.roi(rect);
             let tempCanvas = document.createElement("canvas");
             cv.imshow(tempCanvas,cropped);

@@ -38,18 +38,18 @@ function detect_face()
 	ctx.drawImage(tempImage, 0, 0);
 	let src = cv.imread(canvas);
 	let dst = new cv.Mat(src.cols, src.rows, cv.CV_8UC4);
-	let gray = new cv.Mat();
+	// let gray = new cv.Mat();
 	let faces = new cv.RectVector();
 	let classifier = new cv.CascadeClassifier();
 	classifier.load('haarcascade_frontalface_default.xml');
 	src.copyTo(dst);
-	cv.cvtColor(dst, gray, cv.COLOR_RGBA2GRAY, 0);
-	let msize = new cv.Size(120, 120);
+	// cv.cvtColor(dst, gray, cv.COLOR_RGBA2GRAY, 0);
+	let msize = new cv.Size(tempImage.width / 4, tempImage.height / 4);
 	// detect faces.
-	classifier.detectMultiScale(gray, faces, 1.1, 3, 0, msize);
+	classifier.detectMultiScale(dst, faces, 1.1, 3, 0, msize);
 	if (faces.size() == 0)
 	{
-	    alert('얼굴이 인식되지 않았습니다.');
+	    alert('얼굴이 인식되지 않았습니다. 얼굴 이미지가 작지 않은지 확인해주세요.');
 	}
 	else if (faces.size() > 1)
 	{
@@ -60,10 +60,8 @@ function detect_face()
 		let face = faces.get(i);
 		let point1 = new cv.Point(face.x, face.y);
 		let point2 = new cv.Point(face.x + face.width, face.y + face.height);
-		cv.rectangle(dst, point1, point2, [255, 0, 0, 255]);
-		let margin_x = face.width / 5;
-                let margin_y = face.height / 5;
-		let rect = new cv.Rect(Math.max(face.x - margin_x, 0), Math.max(face.y - margin_y, 0), face.width + margin_x, face.height + margin_y);
+		cv.rectangle(dst, point1, point2, [255, 0, 0, 255], 8);
+		let rect = new cv.Rect(face.x, face.y, face.width, face.height);
                 let cropped = src.roi(rect);
                 cv.imshow(tempCanvas,cropped);
 	}