fix 4.x links

samples/dnn/README.md

@@ -7,7 +7,7 @@ Check [a wiki](https://github.com/opencv/opencv/wiki/Deep-Learning-in-OpenCV) fo
 If OpenCV is built with [Intel's Inference Engine support](https://github.com/opencv/opencv/wiki/Intel%27s-Deep-Learning-Inference-Engine-backend) you can use [Intel's pre-trained](https://github.com/opencv/open_model_zoo) models.
 
 There are different preprocessing parameters such mean subtraction or scale factors for different models.
-You may check the most popular models and their parameters at [models.yml](https://github.com/opencv/opencv/blob/master/samples/dnn/models.yml) configuration file. It might be also used for aliasing samples parameters. In example,
+You may check the most popular models and their parameters at [models.yml](https://github.com/opencv/opencv/blob/4.x/samples/dnn/models.yml) configuration file. It might be also used for aliasing samples parameters. In example,
 
 ```bash
 python object_detection.py opencv_fd --model /path/to/caffemodel --config /path/to/prototxt
@@ -27,7 +27,7 @@ You can download sample models using ```download_models.py```. For example, the
 python download_models.py --save_dir FaceDetector opencv_fd
 ```
 
-You can use default configuration files adopted for OpenCV from [here](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn).
+You can use default configuration files adopted for OpenCV from [here](https://github.com/opencv/opencv_extra/tree/4.x/testdata/dnn).
 
 You also can use the script to download necessary files from your code. Assume you have the following code inside ```your_script.py```:
 
@@ -50,14 +50,14 @@ python your_script.py
 **Note** that you can provide a directory using **save_dir** parameter or via **OPENCV_SAVE_DIR** environment variable.
 
 #### Face detection
-[An origin model](https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector)
+[An origin model](https://github.com/opencv/opencv/tree/4.x/samples/dnn/face_detector)
 with single precision floating point weights has been quantized using [TensorFlow framework](https://www.tensorflow.org/).
 To achieve the best accuracy run the model on BGR images resized to `300x300` applying mean subtraction
 of values `(104, 177, 123)` for each blue, green and red channels correspondingly.
 
 The following are accuracy metrics obtained using [COCO object detection evaluation
 tool](http://cocodataset.org/#detections-eval) on [FDDB dataset](http://vis-www.cs.umass.edu/fddb/)
-(see [script](https://github.com/opencv/opencv/blob/master/modules/dnn/misc/face_detector_accuracy.py))
+(see [script](https://github.com/opencv/opencv/blob/4.x/modules/dnn/misc/face_detector_accuracy.py))
 applying resize to `300x300` and keeping an origin images' sizes.
 ```
 AP - Average Precision                            | FP32/FP16 | UINT8          | FP32/FP16 | UINT8          |
@@ -79,6 +79,6 @@ AR @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.528     | 0.528          |
 
 ## References
 * [Models downloading script](https://github.com/opencv/opencv/samples/dnn/download_models.py)
-* [Configuration files adopted for OpenCV](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn)
+* [Configuration files adopted for OpenCV](https://github.com/opencv/opencv_extra/tree/4.x/testdata/dnn)
 * [How to import models from TensorFlow Object Detection API](https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API)
-* [Names of classes from different datasets](https://github.com/opencv/opencv/tree/master/samples/data/dnn)
+* [Names of classes from different datasets](https://github.com/opencv/opencv/tree/4.x/samples/data/dnn)

samples/dnn/js_face_recognition.html

@@ -69,7 +69,7 @@ function recognize(face) {
 
 function loadModels(callback) {
   var utils = new Utils('');
-  var proto = 'https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy_lowres.prototxt';
+  var proto = 'https://raw.githubusercontent.com/opencv/opencv/4.x/samples/dnn/face_detector/deploy_lowres.prototxt';
   var weights = 'https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel';
   var recognModel = 'https://raw.githubusercontent.com/pyannote/pyannote-data/master/openface.nn4.small2.v1.t7';
   utils.createFileFromUrl('face_detector.prototxt', proto, () => {

samples/dnn/openpose.cpp

@@ -3,11 +3,11 @@
 //
 //  it can be used for body pose detection, using either the COCO model(18 parts):
 //  http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel
-//  https://raw.githubusercontent.com/opencv/opencv_extra/master/testdata/dnn/openpose_pose_coco.prototxt
+//  https://raw.githubusercontent.com/opencv/opencv_extra/4.x/testdata/dnn/openpose_pose_coco.prototxt
 //
 //  or the MPI model(16 parts):
 //  http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/mpi/pose_iter_160000.caffemodel
-//  https://raw.githubusercontent.com/opencv/opencv_extra/master/testdata/dnn/openpose_pose_mpi_faster_4_stages.prototxt
+//  https://raw.githubusercontent.com/opencv/opencv_extra/4.x/testdata/dnn/openpose_pose_mpi_faster_4_stages.prototxt
 //
 //  (to simplify this sample, the body models are restricted to a single person.)
 //