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Merge branch 4.x
This commit is contained in:
Alexander Alekhin
@@ -23,7 +23,7 @@ In this tutorial, we first introduce how to obtain the custom OCR model, then ho
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After completing the model training, please use [transform_to_onnx.py](https://github.com/zihaomu/deep-text-recognition-benchmark/blob/master/transform_to_onnx.py) to convert the model into onnx format.
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#### Execute in webcam
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The Python version example code can be found at [here](https://github.com/opencv/opencv/blob/master/samples/dnn/text_detection.py).
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The Python version example code can be found at [here](https://github.com/opencv/opencv/blob/5.x/samples/dnn/text_detection.py).
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Example:
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@code{.bash}
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@@ -226,7 +226,7 @@ a centric one.
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@snippet dnn/edge_detection.py Register
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That's it! We've replaced an implemented OpenCV's layer to a custom one.
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You may find a full script in the [source code](https://github.com/opencv/opencv/tree/master/samples/dnn/edge_detection.py).
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You may find a full script in the [source code](https://github.com/opencv/opencv/tree/5.x/samples/dnn/edge_detection.py).
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<table border="0">
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<tr>
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@@ -12,7 +12,7 @@
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## Introduction
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In this section, we introduce the DNN-based module for face detection and face recognition. Models can be obtained in [Models](#Models). The usage of `FaceDetectorYN` and `FaceRecognizer` are presented in [Usage](#Usage).
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In this section, we introduce the DNN-based module for face detection and face recognition. Models can be obtained in [Models](#Models). The usage of `FaceDetectorYN` and `FaceRecognizerSF` are presented in [Usage](#Usage).
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## Models
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@@ -36,14 +36,34 @@ There are two models (ONNX format) pre-trained and required for this module:
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### DNNFaceDetector
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```cpp
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// Initialize FaceDetectorYN
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Ptr<FaceDetectorYN> faceDetector = FaceDetectorYN::create(onnx_path, "", image.size(), score_thresh, nms_thresh, top_k);
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@add_toggle_cpp
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/5.x/samples/dnn/face_detect.cpp)
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// Forward
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Mat faces;
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faceDetector->detect(image, faces);
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```
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- **Code at glance:**
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@include samples/dnn/face_detect.cpp
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@end_toggle
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@add_toggle_python
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/5.x/samples/dnn/face_detect.py)
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- **Code at glance:**
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@include samples/dnn/face_detect.py
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@end_toggle
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Explanation
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-----------
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@add_toggle_cpp
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@snippet dnn/face_detect.cpp initialize_FaceDetectorYN
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@snippet dnn/face_detect.cpp inference
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@end_toggle
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@add_toggle_python
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@snippet dnn/face_detect.py initialize_FaceDetectorYN
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@snippet dnn/face_detect.py inference
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@end_toggle
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The detection output `faces` is a two-dimension array of type CV_32F, whose rows are the detected face instances, columns are the location of a face and 5 facial landmarks. The format of each row is as follows:
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@@ -57,28 +77,25 @@ x1, y1, w, h, x_re, y_re, x_le, y_le, x_nt, y_nt, x_rcm, y_rcm, x_lcm, y_lcm
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Following Face Detection, run codes below to extract face feature from facial image.
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```cpp
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// Initialize FaceRecognizer with model path (cv::String)
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Ptr<FaceRecognizer> faceRecognizer = FaceRecognizer::create(model_path, "");
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@add_toggle_cpp
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@snippet dnn/face_detect.cpp initialize_FaceRecognizerSF
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@snippet dnn/face_detect.cpp facerecognizer
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@end_toggle
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// Aligning and cropping facial image through the first face of faces detected by dnn_face::DNNFaceDetector
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Mat aligned_face;
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faceRecognizer->alignCrop(image, faces.row(0), aligned_face);
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// Run feature extraction with given aligned_face (cv::Mat)
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Mat feature;
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faceRecognizer->feature(aligned_face, feature);
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feature = feature.clone();
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```
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@add_toggle_python
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@snippet dnn/face_detect.py initialize_FaceRecognizerSF
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@snippet dnn/face_detect.py facerecognizer
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@end_toggle
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After obtaining face features *feature1* and *feature2* of two facial images, run codes below to calculate the identity discrepancy between the two faces.
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```cpp
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// Calculating the discrepancy between two face features by using cosine distance.
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double cos_score = faceRecognizer->match(feature1, feature2, FaceRecognizer::DisType::COSINE);
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// Calculating the discrepancy between two face features by using normL2 distance.
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double L2_score = faceRecognizer->match(feature1, feature2, FaceRecognizer::DisType::NORM_L2);
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```
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@add_toggle_cpp
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@snippet dnn/face_detect.cpp match
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@end_toggle
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@add_toggle_python
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@snippet dnn/face_detect.py match
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@end_toggle
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For example, two faces have same identity if the cosine distance is greater than or equal to 0.363, or the normL2 distance is less than or equal to 1.128.
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@@ -22,7 +22,7 @@ We will demonstrate results of this example on the following picture.
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Source Code
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-----------
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We will be using snippets from the example application, that can be downloaded [here](https://github.com/opencv/opencv/blob/master/samples/dnn/classification.cpp).
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We will be using snippets from the example application, that can be downloaded [here](https://github.com/opencv/opencv/blob/5.x/samples/dnn/classification.cpp).
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@include dnn/classification.cpp
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@@ -30,11 +30,11 @@ Explanation
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-----------
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-# Firstly, download GoogLeNet model files:
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[bvlc_googlenet.prototxt ](https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/bvlc_googlenet.prototxt) and
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[bvlc_googlenet.prototxt ](https://github.com/opencv/opencv_extra/blob/5.x/testdata/dnn/bvlc_googlenet.prototxt) and
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[bvlc_googlenet.caffemodel](http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel)
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Also you need file with names of [ILSVRC2012](http://image-net.org/challenges/LSVRC/2012/browse-synsets) classes:
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[classification_classes_ILSVRC2012.txt](https://github.com/opencv/opencv/blob/master/samples/data/dnn/classification_classes_ILSVRC2012.txt).
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[classification_classes_ILSVRC2012.txt](https://github.com/opencv/opencv/blob/5.x/samples/data/dnn/classification_classes_ILSVRC2012.txt).
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Put these files into working dir of this program example.
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@@ -45,7 +45,7 @@ correspondingly. In example, for variable `x` in range `[0, 10)` directive
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`split: { x: 2 }` gives new ones `xo` in range `[0, 5)` and `xi` in range `[0, 2)`.
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Variable name `x` is no longer available in the same scheduling node.
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You can find scheduling examples at [opencv_extra/testdata/dnn](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn)
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You can find scheduling examples at [opencv_extra/testdata/dnn](https://github.com/opencv/opencv_extra/tree/5.x/testdata/dnn)
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and use it for schedule your networks.
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## Layers fusing
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+2
-2
@@ -30,7 +30,7 @@ source <env_dir_path>/bin/activate
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For OpenCV-Python building from source, follow the corresponding instructions from the @ref tutorial_py_table_of_contents_setup.
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Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
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Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/5.x/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
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The below line initiates requirements installation into the previously activated virtual environment:
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```console
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@@ -140,7 +140,7 @@ class CommonConfig:
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Thus, the converted ResNet-50 will be saved in ``dnn_model_runner/dnn_conversion/models``.
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### Inference Pipeline
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Now we can use ```models/resnet50.onnx``` for the inference pipeline using OpenCV C/C++ API. The implemented pipeline can be found in [samples/dnn/classification.cpp](https://github.com/opencv/opencv/blob/master/samples/dnn/classification.cpp).
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Now we can use ```models/resnet50.onnx``` for the inference pipeline using OpenCV C/C++ API. The implemented pipeline can be found in [samples/dnn/classification.cpp](https://github.com/opencv/opencv/blob/5.x/samples/dnn/classification.cpp).
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After the build of samples (``BUILD_EXAMPLES`` flag value should be ``ON``), the appropriate ``example_dnn_classification`` executable file will be provided.
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To provide model inference we will use the below [squirrel photo](https://www.pexels.com/photo/brown-squirrel-eating-1564292) (under [CC0](https://www.pexels.com/terms-of-service/) license) corresponding to ImageNet class ID 335:
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+5
-5
@@ -31,7 +31,7 @@ source <env_dir_path>/bin/activate
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For OpenCV-Python building from source, follow the corresponding instructions from the @ref tutorial_py_table_of_contents_setup.
|
||||
|
||||
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
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Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/5.x/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
|
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The below line initiates requirements installation into the previously activated virtual environment:
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||||
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```console
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@@ -270,7 +270,7 @@ python -m dnn_model_runner.dnn_conversion.pytorch.classification.py_to_py_cls --
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Chosen from the list classification model will be read into OpenCV cv.dnn.Net object. Evaluation results of PyTorch and OpenCV models (accuracy, inference time, L1) will be written into the log file. Inference time values will be also depicted in a chart to generalize the obtained model information.
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Necessary evaluation configurations are defined in the [test_config.py](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) and can be modified in accordance with actual paths of data location:
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Necessary evaluation configurations are defined in the [test_config.py](https://github.com/opencv/opencv/tree/5.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) and can be modified in accordance with actual paths of data location:
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```python
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@dataclass
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@@ -308,7 +308,7 @@ python -m dnn_model_runner.dnn_conversion.pytorch.classification.py_to_py_cls --
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Here ``default_img_preprocess`` key defines whether you'd like to parametrize the model test process with some particular values or use the default values, for example, ``scale``, ``mean`` or ``std``.
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Test configuration is represented in [test_config.py](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestClsModuleConfig`` class:
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Test configuration is represented in [test_config.py](https://github.com/opencv/opencv/tree/5.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestClsModuleConfig`` class:
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```python
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@dataclass
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@@ -331,7 +331,7 @@ class TestClsModuleConfig:
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classes: str = os.path.join(cls_test_data_dir, "dnn", "classification_classes_ILSVRC2012.txt")
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```
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The default image preprocessing options are defined in [default_preprocess_config.py](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/default_preprocess_config.py). For instance:
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The default image preprocessing options are defined in [default_preprocess_config.py](https://github.com/opencv/opencv/tree/5.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/default_preprocess_config.py). For instance:
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```python
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BASE_IMG_SCALE_FACTOR = 1 / 255.0
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@@ -349,7 +349,7 @@ pytorch_resize_input_blob = {
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}
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```
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The basis of the model testing is represented in [samples/dnn/classification.py](https://github.com/opencv/opencv/blob/master/samples/dnn/classification.py). ``classification.py`` can be executed autonomously with provided converted model in ``--input`` and populated parameters for cv.dnn.blobFromImage.
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The basis of the model testing is represented in [samples/dnn/classification.py](https://github.com/opencv/opencv/blob/5.x/samples/dnn/classification.py). ``classification.py`` can be executed autonomously with provided converted model in ``--input`` and populated parameters for cv.dnn.blobFromImage.
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|
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To reproduce from scratch the described in "Model Conversion Pipeline" OpenCV steps with ``dnn_model_runner`` execute the below line:
|
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|
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|
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@@ -28,7 +28,7 @@ source <env_dir_path>/bin/activate
|
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|
||||
For OpenCV-Python building from source, follow the corresponding instructions from the @ref tutorial_py_table_of_contents_setup.
|
||||
|
||||
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
|
||||
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/5.x/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
|
||||
The below line initiates requirements installation into the previously activated virtual environment:
|
||||
|
||||
```console
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@@ -273,7 +273,7 @@ python -m dnn_model_runner.dnn_conversion.tf.classification.py_to_py_cls --model
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Chosen from the list classification model will be read into OpenCV ``cv.dnn_Net`` object. Evaluation results of TF and OpenCV models (accuracy, inference time, L1) will be written into the log file. Inference time values will be also depicted in a chart to generalize the obtained model information.
|
||||
|
||||
Necessary evaluation configurations are defined in the [test_config.py](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) and can be modified in accordance with actual paths of data location::
|
||||
Necessary evaluation configurations are defined in the [test_config.py](https://github.com/opencv/opencv/tree/5.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) and can be modified in accordance with actual paths of data location::
|
||||
|
||||
```python
|
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@dataclass
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@@ -312,7 +312,7 @@ python -m dnn_model_runner.dnn_conversion.tf.classification.py_to_py_cls --model
|
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|
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Here ``default_img_preprocess`` key defines whether you'd like to parametrize the model test process with some particular values or use the default values, for example, ``scale``, ``mean`` or ``std``.
|
||||
|
||||
Test configuration is represented in [test_config.py](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestClsModuleConfig`` class:
|
||||
Test configuration is represented in [test_config.py](https://github.com/opencv/opencv/tree/5.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestClsModuleConfig`` class:
|
||||
|
||||
```python
|
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@dataclass
|
||||
@@ -347,7 +347,7 @@ tf_input_blob = {
|
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}
|
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```
|
||||
|
||||
The basis of the model testing is represented in [samples/dnn/classification.py](https://github.com/opencv/opencv/blob/master/samples/dnn/classification.py). ``classification.py`` can be executed autonomously with provided converted model in ``--input`` and populated parameters for cv.dnn.blobFromImage.
|
||||
The basis of the model testing is represented in [samples/dnn/classification.py](https://github.com/opencv/opencv/blob/5.x/samples/dnn/classification.py). ``classification.py`` can be executed autonomously with provided converted model in ``--input`` and populated parameters for cv.dnn.blobFromImage.
|
||||
|
||||
To reproduce from scratch the described in "Model Conversion Pipeline" OpenCV steps with ``dnn_model_runner`` execute the below line:
|
||||
|
||||
|
||||
@@ -27,7 +27,7 @@ source <env_dir_path>/bin/activate
|
||||
|
||||
For OpenCV-Python building from source, follow the corresponding instructions from the @ref tutorial_py_table_of_contents_setup.
|
||||
|
||||
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
|
||||
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/5.x/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
|
||||
The below line initiates requirements installation into the previously activated virtual environment:
|
||||
|
||||
```console
|
||||
@@ -98,7 +98,7 @@ To provide model inference we will use the below [double-decker bus photo](https
|
||||
To initiate the test process we need to provide an appropriate model configuration. We will use [``ssd_mobilenet_v1_coco.config``](https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_mobilenet_v1_coco.config) from [TensorFlow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection#tensorflow-object-detection-api).
|
||||
TensorFlow Object Detection API framework contains helpful mechanisms for object detection model manipulations.
|
||||
|
||||
We will use this configuration to provide a text graph representation. To generate ``.pbtxt`` we will use the corresponding [``samples/dnn/tf_text_graph_ssd.py``](https://github.com/opencv/opencv/blob/master/samples/dnn/tf_text_graph_ssd.py) script:
|
||||
We will use this configuration to provide a text graph representation. To generate ``.pbtxt`` we will use the corresponding [``samples/dnn/tf_text_graph_ssd.py``](https://github.com/opencv/opencv/blob/5.x/samples/dnn/tf_text_graph_ssd.py) script:
|
||||
|
||||
```console
|
||||
python tf_text_graph_ssd.py --input ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb --config ssd_mobilenet_v1_coco_2017_11_17/ssd_mobilenet_v1_coco.config --output ssd_mobilenet_v1_coco_2017_11_17.pbtxt
|
||||
@@ -106,7 +106,7 @@ python tf_text_graph_ssd.py --input ssd_mobilenet_v1_coco_2017_11_17/frozen_infe
|
||||
|
||||
After successful execution ``ssd_mobilenet_v1_coco_2017_11_17.pbtxt`` will be created.
|
||||
|
||||
Before we run ``object_detection.py``, let's have a look at the default values for the SSD MobileNetV1 test process configuration. They are located in [``models.yml``](https://github.com/opencv/opencv/blob/master/samples/dnn/models.yml):
|
||||
Before we run ``object_detection.py``, let's have a look at the default values for the SSD MobileNetV1 test process configuration. They are located in [``models.yml``](https://github.com/opencv/opencv/blob/5.x/samples/dnn/models.yml):
|
||||
|
||||
```yml
|
||||
ssd_tf:
|
||||
|
||||
+2
-2
@@ -258,7 +258,7 @@ python -m dnn_model_runner.dnn_conversion.pytorch.segmentation.py_to_py_segm --m
|
||||
|
||||
Chosen from the list segmentation model will be read into OpenCV ``cv.dnn_Net`` object. Evaluation results of PyTorch and OpenCV models (pixel accuracy, mean IoU, inference time) will be written into the log file. Inference time values will be also depicted in a chart to generalize the obtained model information.
|
||||
|
||||
Necessary evaluation configurations are defined in the [``test_config.py``](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py):
|
||||
Necessary evaluation configurations are defined in the [``test_config.py``](https://github.com/opencv/opencv/tree/5.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py):
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
@@ -290,7 +290,7 @@ python -m dnn_model_runner.dnn_conversion.pytorch.segmentation.py_to_py_segm --m
|
||||
|
||||
Here ``default_img_preprocess`` key defines whether you'd like to parametrize the model test process with some particular values or use the default values, for example, ``scale``, ``mean`` or ``std``.
|
||||
|
||||
Test configuration is represented in [``test_config.py``](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestSegmModuleConfig`` class:
|
||||
Test configuration is represented in [``test_config.py``](https://github.com/opencv/opencv/tree/5.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestSegmModuleConfig`` class:
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
|
||||
+2
-2
@@ -338,7 +338,7 @@ python -m dnn_model_runner.dnn_conversion.tf.segmentation.py_to_py_segm
|
||||
|
||||
The model will be read into OpenCV ``cv.dnn_Net`` object. Evaluation results of TF and OpenCV models (pixel accuracy, mean IoU, inference time) will be written into the log file. Inference time values will be also depicted in a chart to generalize the obtained model information.
|
||||
|
||||
Necessary evaluation configurations are defined in the [``test_config.py``](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py):
|
||||
Necessary evaluation configurations are defined in the [``test_config.py``](https://github.com/opencv/opencv/tree/5.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py):
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
@@ -364,7 +364,7 @@ python -m dnn_model_runner.dnn_conversion.tf.segmentation.py_to_py_segm --test T
|
||||
|
||||
Here ``default_img_preprocess`` key defines whether you'd like to parametrize the model test process with some particular values or use the default values, for example, ``scale``, ``mean`` or ``std``.
|
||||
|
||||
Test configuration is represented in [``test_config.py``](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestSegmModuleConfig`` class:
|
||||
Test configuration is represented in [``test_config.py``](https://github.com/opencv/opencv/tree/5.x/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestSegmModuleConfig`` class:
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
|
||||
@@ -102,7 +102,7 @@ recommended parameter setting: -inputHeight=736, -inputWidth=1280;
|
||||
description: This model is trained on ICDAR2015, so it can only detect English text instances.
|
||||
|
||||
- DB_IC15_resnet18.onnx:
|
||||
url: https://drive.google.com/uc?export=dowload&id=1sZszH3pEt8hliyBlTmB-iulxHP1dCQWV
|
||||
url: https://drive.google.com/uc?export=dowload&id=1vY_KsDZZZb_svd5RT6pjyI8BS1nPbBSX
|
||||
sha: 19543ce09b2efd35f49705c235cc46d0e22df30b
|
||||
recommended parameter setting: -inputHeight=736, -inputWidth=1280;
|
||||
description: This model is trained on ICDAR2015, so it can only detect English text instances.
|
||||
@@ -114,7 +114,7 @@ recommended parameter setting: -inputHeight=736, -inputWidth=736;
|
||||
description: This model is trained on MSRA-TD500, so it can detect both English and Chinese text instances.
|
||||
|
||||
- DB_TD500_resnet18.onnx:
|
||||
url: https://drive.google.com/uc?export=dowload&id=1vY_KsDZZZb_svd5RT6pjyI8BS1nPbBSX
|
||||
url: https://drive.google.com/uc?export=dowload&id=1sZszH3pEt8hliyBlTmB-iulxHP1dCQWV
|
||||
sha: 8a3700bdc13e00336a815fc7afff5dcc1ce08546
|
||||
recommended parameter setting: -inputHeight=736, -inputWidth=736;
|
||||
description: This model is trained on MSRA-TD500, so it can detect both English and Chinese text instances.
|
||||
@@ -287,15 +287,15 @@ Output Examples:
|
||||
|
||||
|
||||
## Source Code
|
||||
The [source code](https://github.com/opencv/opencv/blob/master/modules/dnn/src/model.cpp)
|
||||
The [source code](https://github.com/opencv/opencv/blob/5.x/modules/dnn/src/model.cpp)
|
||||
of these APIs can be found in the DNN module.
|
||||
|
||||
## Detailed Sample
|
||||
For more information, please refer to:
|
||||
- [samples/dnn/scene_text_recognition.cpp](https://github.com/opencv/opencv/blob/master/samples/dnn/scene_text_recognition.cpp)
|
||||
- [samples/dnn/scene_text_detection.cpp](https://github.com/opencv/opencv/blob/master/samples/dnn/scene_text_detection.cpp)
|
||||
- [samples/dnn/text_detection.cpp](https://github.com/opencv/opencv/blob/master/samples/dnn/text_detection.cpp)
|
||||
- [samples/dnn/scene_text_spotting.cpp](https://github.com/opencv/opencv/blob/master/samples/dnn/scene_text_spotting.cpp)
|
||||
- [samples/dnn/scene_text_recognition.cpp](https://github.com/opencv/opencv/blob/5.x/samples/dnn/scene_text_recognition.cpp)
|
||||
- [samples/dnn/scene_text_detection.cpp](https://github.com/opencv/opencv/blob/5.x/samples/dnn/scene_text_detection.cpp)
|
||||
- [samples/dnn/text_detection.cpp](https://github.com/opencv/opencv/blob/5.x/samples/dnn/text_detection.cpp)
|
||||
- [samples/dnn/scene_text_spotting.cpp](https://github.com/opencv/opencv/blob/5.x/samples/dnn/scene_text_spotting.cpp)
|
||||
|
||||
#### Test with an image
|
||||
Examples:
|
||||
|
||||
@@ -29,8 +29,8 @@ Source Code
|
||||
-----------
|
||||
|
||||
Use a universal sample for object detection models written
|
||||
[in C++](https://github.com/opencv/opencv/blob/master/samples/dnn/object_detection.cpp) and
|
||||
[in Python](https://github.com/opencv/opencv/blob/master/samples/dnn/object_detection.py) languages
|
||||
[in C++](https://github.com/opencv/opencv/blob/5.x/samples/dnn/object_detection.cpp) and
|
||||
[in Python](https://github.com/opencv/opencv/blob/5.x/samples/dnn/object_detection.py) languages
|
||||
|
||||
Usage examples
|
||||
--------------
|
||||
|
||||
Reference in New Issue
Block a user