opencv中的dnn模块_opencvdnn资源-CSDN文库

共37个文件

py：17个

cpp：6个

prototxt：4个

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OpenCV（开源计算机视觉库）中的DNN（Deep Neural Network）模块是用于处理深度学习模型的一个强大工具。这个模块使得开发者能够直接在OpenCV中利用预训练的深度学习网络进行图像识别、目标检测、图像分割等多种任务。DNN模块支持多种深度学习框架的模型，如TensorFlow、Caffe、ONNX等。一、DNN模块概述 OpenCV的DNN模块是为了简化深度学习模型的集成而设计的，它允许开发者加载预训练的神经网络模型，并在图像数据上运行前向传播。通过这个模块，你可以利用GPU或者CPU来加速计算，实现高效的应用程序。二、目标检测案例在OpenCV的DNN模块中，目标检测是一个常见的应用场景。例如，可以使用SSD（Single Shot Multibox Detector）、YOLO（You Only Look Once）或者 Faster R-CNN 等模型进行实时目标检测。这些模型可以识别并定位图像中的多个对象，返回每个对象的类别和边界框坐标。三、模型转换：PB到PBTXT 在OpenCV中使用TensorFlow模型时，通常会遇到模型文件为`.pb`格式，这是TensorFlow的二进制模型文件。然而，为了读取模型结构，我们需要`.pbtxt`文本文件。转换过程涉及解析`.pb`文件并将其结构导出为`.pbtxt`，这样可以清晰地查看网络层和参数。在OpenCV中，可以使用`tf.train.write_graph()`函数将.pb模型转换为.pbtxt，以便OpenCV的DNN模块能够理解模型的结构。四、DNN模块支持的框架 OpenCV的DNN模块不仅支持TensorFlow，还支持其他流行的深度学习框架，如Caffe和ONNX。对于Caffe模型，可以使用`cv::dnn::readNetFromCaffe()`加载模型；对于TensorFlow模型，可以使用`cv::dnn::readNetFromTensorflow()`；而对于ONNX模型，可以使用`cv::dnn::readNetFromONNX()`。五、使用DNN模块进行推理加载模型后，可以通过`cv::dnn::Net::forward()`方法进行前向传播，将输入图像送入网络，获取预测结果。该方法接受一个blob（多维数组）作为输入，输出同样是一个blob，包含了网络的预测输出。六、性能优化 OpenCV的DNN模块支持在GPU上执行计算，以提高推理速度。要启用GPU加速，需要确保已安装NVIDIA CUDA库，并在编译OpenCV时开启CUDA支持。此外，还可以调整批处理大小、内存复用策略等参数来优化性能。七、应用实例 DNN模块在实际应用中广泛应用于自动驾驶、无人机视觉、安防监控、医疗影像分析等领域。例如，在自动驾驶中，车辆可以利用DNN模块实时检测行人、交通标志和障碍物，以确保安全行驶。总结，OpenCV的DNN模块提供了一种简单易用的方式，将深度学习模型集成到计算机视觉项目中。无论你是要进行目标检测、图像分类还是其他复杂的任务，DNN模块都能为你提供必要的工具和支持。通过与各种深度学习框架的兼容性，OpenCV DNN使得开发者能够轻松地在不同环境中迁移和部署模型。

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dnn.zip （37个子文件）

dnn

tf_text_graph_common.py 10KB

classification.py 4KB

tf_text_graph_mask_rcnn.py 10KB

openpose.cpp 6KB

colorization.py 3KB

mobilenet_ssd_accuracy.py 5KB

openpose.py 5KB

custom_layers.hpp 11KB

models.yml 3KB

classification.cpp 5KB

object_detection.py 10KB

face_detector

test.prototxt 30KB

train.prototxt 32KB

download_weights.py 3KB

weights.meta4 837B

solver.prototxt 492B

how_to_train_face_detector.txt 4KB

deploy.prototxt 29KB

opencv_face_detector.pbtxt 36KB

tf_text_graph_ssd.py 12KB

CMakeLists.txt 703B

mask_rcnn.py 5KB

text_detection.py 6KB

fast_neural_style.py 2KB

shrink_tf_graph_weights.py 2KB

README.md 5KB

js_face_recognition.html 7KB

text_detection.cpp 6KB

object_detection.cpp 10KB

__pycache__

tf_text_graph_common.cpython-35.pyc 9KB

tf_text_graph_faster_rcnn.py 11KB

colorization.cpp 7KB

common.hpp 4KB

edge_detection.py 3KB

segmentation.py 5KB

segmentation.cpp 8KB

common.py 5KB

# OpenCV deep learning module samples ## Model Zoo ### Object detection | Model | Scale | Size WxH| Mean subtraction | Channels order | |---------------|-------|-----------|--------------------|-------| | [MobileNet-SSD, Caffe](https://github.com/chuanqi305/MobileNet-SSD/) | `0.00784 (2/255)` | `300x300` | `127.5 127.5 127.5` | BGR | | [OpenCV face detector](https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector) | `1.0` | `300x300` | `104 177 123` | BGR | | [SSDs from TensorFlow](https://github.com/tensorflow/models/tree/master/research/object_detection/) | `0.00784 (2/255)` | `300x300` | `127.5 127.5 127.5` | RGB | | [YOLO](https://pjreddie.com/darknet/yolo/) | `0.00392 (1/255)` | `416x416` | `0 0 0` | RGB | | [VGG16-SSD](https://github.com/weiliu89/caffe/tree/ssd) | `1.0` | `300x300` | `104 117 123` | BGR | | [Faster-RCNN](https://github.com/rbgirshick/py-faster-rcnn) | `1.0` | `800x600` | `102.9801 115.9465 122.7717` | BGR | | [R-FCN](https://github.com/YuwenXiong/py-R-FCN) | `1.0` | `800x600` | `102.9801 115.9465 122.7717` | BGR | | [Faster-RCNN, ResNet backbone](https://github.com/tensorflow/models/tree/master/research/object_detection/) | `1.0` | `300x300` | `103.939 116.779 123.68` | RGB | | [Faster-RCNN, InceptionV2 backbone](https://github.com/tensorflow/models/tree/master/research/object_detection/) | `0.00784 (2/255)` | `300x300` | `127.5 127.5 127.5` | RGB | #### Face detection [An origin model](https://github.com/opencv/opencv/tree/master/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)) applying resize to `300x300` and keeping an origin images' sizes. ``` AP - Average Precision | FP32/FP16 | UINT8 | FP32/FP16 | UINT8 | AR - Average Recall | 300x300 | 300x300 | any size | any size | --------------------------------------------------|-----------|----------------|-----------|----------------| AP @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.408 | 0.408 | 0.378 | 0.328 (-0.050) | AP @[ IoU=0.50 | area= all | maxDets=100 ] | 0.849 | 0.849 | 0.797 | 0.790 (-0.007) | AP @[ IoU=0.75 | area= all | maxDets=100 ] | 0.251 | 0.251 | 0.208 | 0.140 (-0.068) | AP @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.050 | 0.051 (+0.001) | 0.107 | 0.070 (-0.037) | AP @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.381 | 0.379 (-0.002) | 0.380 | 0.368 (-0.012) | AP @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.455 | 0.455 | 0.412 | 0.337 (-0.075) | AR @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] | 0.299 | 0.299 | 0.279 | 0.246 (-0.033) | AR @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] | 0.482 | 0.482 | 0.476 | 0.436 (-0.040) | AR @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.496 | 0.496 | 0.491 | 0.451 (-0.040) | AR @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.189 | 0.193 (+0.004) | 0.284 | 0.232 (-0.052) | AR @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.481 | 0.480 (-0.001) | 0.470 | 0.458 (-0.012) | AR @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.528 | 0.528 | 0.520 | 0.462 (-0.058) | ``` ### Classification | Model | Scale | Size WxH| Mean subtraction | Channels order | |---------------|-------|-----------|--------------------|-------| | GoogLeNet | `1.0` | `224x224` | `104 117 123` | BGR | | [SqueezeNet](https://github.com/DeepScale/SqueezeNet) | `1.0` | `227x227` | `0 0 0` | BGR | ### Semantic segmentation | Model | Scale | Size WxH| Mean subtraction | Channels order | |---------------|-------|-----------|--------------------|-------| | [ENet](https://github.com/e-lab/ENet-training) | `0.00392 (1/255)` | `1024x512` | `0 0 0` | RGB | | FCN8s | `1.0` | `500x500` | `0 0 0` | BGR | ## References * [Models downloading script](https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/download_models.py) * [Configuration files adopted for OpenCV](https://github.com/opencv/opencv_extra/tree/master/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)

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