$fontDiff: (14px/16px);

$container-width: 100% - 20px;

darken(hsl(25, 100%, 80%), 30%) => hsl(25, 100%, 50%)
darken(#800, 20%) => #200

lighten(hsl(0, 0%, 0%), 30%) => hsl(0, 0, 30)
lighten(#800, 20%) => #e00

mix(#f00, #00f) => #7f007f
mix(#f00, #00f, 25%) => #3f00bf
mix(rgba(255, 0, 0, 0.5), #00f) => rgba(63, 0, 191, 0.75)

a.myAnchor {
    color: blue;

    &:hover {
        text-decoration: underline;
    }

    &:visited {
        color: purple;
    }
}

 >npm install ng2-file-upload --save

 >ng g c UploadFile/ng2-file-upload-test

import { FileUploadModule } from "ng2-file-upload";

@NgModule({
  imports: [
    FileUploadModule
  ]

import { FileUploader, FileUploaderOptions } from "ng2-file-upload";
import { Ticket } from "./../ticket";

export class Ng2FileUploadTestComponent implements OnInit {
  fileUploader: FileUploader;
  model = new Ticket();

    this.fileUploader = new FileUploader(
      <FileUploaderOptions>{
        url: "api/SimpleUpload/SaveTicket",
        headers: [
          { name: "X-XSRF-TOKEN", value: this.getCookie("XSRF-TOKEN") },
          { name: "Accept", value: "application/json" }
        ],
        isHTML5: true,
        // allowedMimeType: ["image/jpeg", "image/png", "application/pdf", "application/msword", "application/zip"]
        allowedFileType: [
          "application",
          "image",
          "video",
          "audio",
          "pdf",
          "compress",
          "doc",
          "xls",
          "ppt"
        ],
        removeAfterUpload: true,
        autoUpload: false,
        maxFileSize: 10 * 1024 * 1024
      }
    );

  getCookie(name: string): string {
    const value = "; " + document.cookie;
    const parts = value.split("; " + name + "=");
    if (parts.length === 2) {
      return decodeURIComponent(parts.pop().split(";").shift());
    }
  }

    this.fileUploader.onBuildItemForm = (fileItem, form) => {
      for (const key in this.model) {
        if (this.model.hasOwnProperty(key)) {
          form.append(key, this.model[key]);
        }
      }
    };

    this.fileUploader.onCompleteAll = () => {
      // clear the form
      // this.model = new Ticket();
    };

    this.fileUploader.onWhenAddingFileFailed = (item, filter, options) => {
      // msg: `You can't select ${item.name} file because of the ${filter.name} filter.`
    };

    this.fileUploader.onErrorItem = (fileItem, response, status, headers) => {
       //
    };

    this.fileUploader.onSuccessItem = (item, response, status, headers) => {
      if (response) {
        const ticket = JSON.parse(response);
        console.log(`ticket:`, ticket);
      }
    };

  submitForm(form: NgForm) {
    this.fileUploader.uploadAll();

    // NOTE: Upload multiple files in one request -> https://github.com/valor-software/ng2-file-upload/issues/671
  }

<div class="container">
  <h3>Support Form(ng2-file-upload)</h3>
  <form #form="ngForm" (submit)="submitForm(form)" novalidate>
    <div class="form-group" [class.has-error]="description.invalid && description.touched">
      <label class="control-label">Description</label>
      <input #description="ngModel" required type="text" class="form-control"
        name="description" [(ngModel)]="model.description">
      <div *ngIf="description.invalid && description.touched">
        <div class="alert alert-danger"  *ngIf="description.errors.required">
          description is required.
        </div>
      </div>
    </div>

    <div class="form-group">
      <label class="control-label">Screenshot(s)</label>
      <input required type="file" multiple ng2FileSelect [uploader]="fileUploader"
        class="form-control" name="screenshot">
    </div>

    <div style="margin-bottom: 10px" *ngIf="fileUploader.queue.length">
      <h3>Upload queue</h3>
      <p>Queue length: {{ fileUploader?.queue?.length }}</p>
      <table class="table">
        <thead>
          <tr>
            <th width="50%">Name</th>
            <th>Size</th>
            <th>Progress</th>
            <th>Status</th>
            <th>Actions</th>
          </tr>
        </thead>
        <tbody>
          <tr *ngFor="let item of fileUploader.queue">
            <td><strong>{{ item?.file?.name }}</strong></td>
            <td nowrap>{{ item?.file?.size/1024/1024 | number:'.2' }} MB</td>
            <td>
              <div class="progress" style="margin-bottom: 0;">
                <div class="progress-bar" role="progressbar" [ngStyle]="{ 'width': item.progress + '%' }"></div>
              </div>
            </td>
            <td class="text-center">
              <span *ngIf="item.isError"><i class="glyphicon glyphicon-remove"></i></span>
            </td>
            <td nowrap>
              <button type="button" class="btn btn-danger btn-xs" (click)="item.remove()">
                <span class="glyphicon glyphicon-trash"></span> Remove
              </button>
            </td>
          </tr>
        </tbody>
      </table>

      <div>
        <div>
          Queue progress:
          <div class="progress">
            <div class="progress-bar" role="progressbar" [ngStyle]="{ 'width': fileUploader.progress + '%' }"></div>
          </div>
        </div>
        <button type="button" class="btn btn-danger btn-s" (click)="fileUploader.clearQueue()"
          [disabled]="!fileUploader.queue.length">
          <span class="glyphicon glyphicon-trash"></span> Remove all
        </button>
      </div>
    </div>

              <div class="progress" style="margin-bottom: 0;">
                <div class="progress-bar" role="progressbar" [ngStyle]="{ 'width': item.progress + '%' }"></div>
              </div>

    <button class="btn btn-primary" [disabled]="form.invalid || !fileUploader.queue.length"
      type="submit">Submit</button>
  </form>

var srcImage = new Mat(@"..\..\Images\Test.jpg");
Cv2.ImShow("Source", srcImage);
Cv2.WaitKey(1); // do events
 
var grayImage = new Mat();
Cv2.CvtColor(srcImage, grayImage, ColorConversion.BgrToGray);
Cv2.EqualizeHist(grayImage, grayImage);

var cascade = new CascadeClassifier(@"..\..\Data\haarcascade_frontalface_alt.xml");
var nestedCascade = new CascadeClassifier(@"..\..\Data\haarcascade_eye_tree_eyeglasses.xml");
 
var faces = cascade.DetectMultiScale(
    image: grayImage,
    scaleFactor: 1.1,
    minNeighbors: 2,
    flags: HaarDetectionType.Zero | HaarDetectionType.ScaleImage,
    minSize: new Size(30, 30)
    );
 
Console.WriteLine("Detected faces: {0}", faces.Length);

var rnd = new Random();
var count = 1;
foreach (var faceRect in faces)
{
    var detectedFaceImage = new Mat(srcImage, faceRect);
    Cv2.ImShow(string.Format("Face {0}", count), detectedFaceImage);
    Cv2.WaitKey(1); // do events
 
    var color = Scalar.FromRgb(rnd.Next(0, 255), rnd.Next(0, 255), rnd.Next(0, 255));
    Cv2.Rectangle(srcImage, faceRect, color, 3);
  
    count++;
}
 
Cv2.ImShow("Haar Detection", srcImage);
Cv2.WaitKey(1); // do events

var rnd = new Random();
var count = 1;
foreach (var faceRect in faces)
{
    var detectedFaceImage = new Mat(srcImage, faceRect);
    Cv2.ImShow(string.Format("Face {0}", count), detectedFaceImage);
    Cv2.WaitKey(1); // do events
 
    var color = Scalar.FromRgb(rnd.Next(0, 255), rnd.Next(0, 255), rnd.Next(0, 255));
    Cv2.Rectangle(srcImage, faceRect, color, 3);
 
 
    var detectedFaceGrayImage = new Mat();
    Cv2.CvtColor(detectedFaceImage, detectedFaceGrayImage, ColorConversion.BgrToGray);
    var nestedObjects = nestedCascade.DetectMultiScale(
        image: detectedFaceGrayImage,
        scaleFactor: 1.1,
        minNeighbors: 2,
        flags: HaarDetectionType.Zero | HaarDetectionType.ScaleImage,
        minSize: new Size(30, 30)
        );
 
    Console.WriteLine("Nested Objects[{0}]: {1}", count, nestedObjects.Length);
 
    foreach (var nestedObject in nestedObjects)
    {
        var center = new Point
        {
            X = Cv.Round(nestedObject.X + nestedObject.Width * 0.5) + faceRect.Left,
            Y = Cv.Round(nestedObject.Y + nestedObject.Height * 0.5) + faceRect.Top
        };
        var radius = Cv.Round((nestedObject.Width + nestedObject.Height) * 0.25);
        Cv2.Circle(srcImage, center, radius, color, thickness: 3);
    }
 
    count++;
}
 
Cv2.ImShow("Haar Detection", srcImage);
Cv2.WaitKey(1); // do events