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py-faster-rcnn-master

于 2020-12-11 发布 文件大小:654KB
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  图像检测的算法，Faster R-CNN算法，先对整张图像进行卷积计算，然后通过感兴趣区域池化层(RoI Pooling Layer)将选择性搜索算法推荐出来的候选区域和卷积网络计算出的特征映射图进行融合，得到候选区域对应的特征矢量，这种共享卷积计算的操作极大地减少了卷积计算的次数。而且这些特征矢量的维度统一，方便后续的分类工作。通过感兴趣区域池化层处理卷积特征，并将得到的特征送往两个并行计算任务进行训练，分类和定位回归。通过这些方法和改进的框架，Fast R-CNN 用更短的训练和测试时长，取得了比 R-CNN 更好的效果(Faster R-CNN algorithm first convolutes the whole image, then fuses the candidate regions recommended by the selective search algorithm and the feature mapping maps calculated by the convolution network through the RoI Pooling Layer to get the corresponding feature vectors of the candidate regions, which greatly reduces the number of convolution calculations. Moreover, the dimension of these feature vectors is unified, which facilitates the subsequent classification work. The convolution feature is processed by the pooling layer of the region of interest, and the obtained feature is sent to two parallel computing tasks for training, classification and positioning regression. Through these methods and improved framework, Fast R-CNN uses shorter training and testing time and achieves better results than R-CNN.)

文件列表：

py-faster-rcnn-master\.gitignore, 84 , 2018-12-17
py-faster-rcnn-master\.gitmodules, 131 , 2018-12-17
py-faster-rcnn-master\data\.gitignore, 70 , 2018-12-17
py-faster-rcnn-master\data\demo\000456.jpg, 105302 , 2018-12-17
py-faster-rcnn-master\data\demo\000542.jpg, 115536 , 2018-12-17
py-faster-rcnn-master\data\demo\001150.jpg, 88635 , 2018-12-17
py-faster-rcnn-master\data\demo\001763.jpg, 73424 , 2018-12-17
py-faster-rcnn-master\data\demo\004545.jpg, 123072 , 2018-12-17
py-faster-rcnn-master\data\pylintrc, 56 , 2018-12-17
py-faster-rcnn-master\data\README.md, 2516 , 2018-12-17
py-faster-rcnn-master\data\scripts\fetch_faster_rcnn_models.sh, 842 , 2018-12-17
py-faster-rcnn-master\data\scripts\fetch_imagenet_models.sh, 825 , 2018-12-17
py-faster-rcnn-master\data\scripts\fetch_selective_search_data.sh, 858 , 2018-12-17
py-faster-rcnn-master\experiments\cfgs\faster_rcnn_alt_opt.yml, 78 , 2018-12-17
py-faster-rcnn-master\experiments\cfgs\faster_rcnn_end2end.yml, 227 , 2018-12-17
py-faster-rcnn-master\experiments\logs\.gitignore, 7 , 2018-12-17
py-faster-rcnn-master\experiments\README.md, 185 , 2018-12-17
py-faster-rcnn-master\experiments\scripts\faster_rcnn_alt_opt.sh, 1509 , 2018-12-17
py-faster-rcnn-master\experiments\scripts\faster_rcnn_end2end.sh, 1781 , 2018-12-17
py-faster-rcnn-master\experiments\scripts\fast_rcnn.sh, 1448 , 2018-12-17
py-faster-rcnn-master\lib\datasets\coco.py, 16560 , 2018-12-17
py-faster-rcnn-master\lib\datasets\ds_utils.py, 1336 , 2018-12-17
py-faster-rcnn-master\lib\datasets\factory.py, 1403 , 2018-12-17
py-faster-rcnn-master\lib\datasets\imdb.py, 9811 , 2018-12-17
py-faster-rcnn-master\lib\datasets\pascal_voc.py, 14217 , 2018-12-17
py-faster-rcnn-master\lib\datasets\tools\mcg_munge.py, 1451 , 2018-12-17
py-faster-rcnn-master\lib\datasets\VOCdevkit-matlab-wrapper\get_voc_opts.m, 231 , 2018-12-17
py-faster-rcnn-master\lib\datasets\VOCdevkit-matlab-wrapper\voc_eval.m, 1332 , 2018-12-17
py-faster-rcnn-master\lib\datasets\VOCdevkit-matlab-wrapper\xVOCap.m, 258 , 2018-12-17
py-faster-rcnn-master\lib\datasets\voc_eval.py, 6938 , 2018-12-17
py-faster-rcnn-master\lib\datasets\__init__.py, 248 , 2018-12-17
py-faster-rcnn-master\lib\fast_rcnn\bbox_transform.py, 2540 , 2018-12-17
py-faster-rcnn-master\lib\fast_rcnn\config.py, 9213 , 2018-12-17
py-faster-rcnn-master\lib\fast_rcnn\nms_wrapper.py, 642 , 2018-12-17
py-faster-rcnn-master\lib\fast_rcnn\test.py, 11120 , 2018-12-17
py-faster-rcnn-master\lib\fast_rcnn\train.py, 6076 , 2018-12-17
py-faster-rcnn-master\lib\fast_rcnn\__init__.py, 248 , 2018-12-17
py-faster-rcnn-master\lib\Makefile, 56 , 2018-12-17
py-faster-rcnn-master\lib\nms\.gitignore, 15 , 2018-12-17
py-faster-rcnn-master\lib\nms\cpu_nms.pyx, 2241 , 2018-12-17
py-faster-rcnn-master\lib\nms\gpu_nms.hpp, 146 , 2018-12-17
py-faster-rcnn-master\lib\nms\gpu_nms.pyx, 1110 , 2018-12-17
py-faster-rcnn-master\lib\nms\nms_kernel.cu, 5064 , 2018-12-17
py-faster-rcnn-master\lib\nms\py_cpu_nms.py, 1051 , 2018-12-17
py-faster-rcnn-master\lib\nms\__init__.py, 0 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\coco.py, 14881 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\cocoeval.py, 19735 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\license.txt, 1533 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\mask.py, 4058 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\maskApi.c, 7704 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\maskApi.h, 1928 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\UPSTREAM_REV, 80 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\_mask.pyx, 10709 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\__init__.py, 21 , 2018-12-17
py-faster-rcnn-master\lib\roi_data_layer\layer.py, 7450 , 2018-12-17
py-faster-rcnn-master\lib\roi_data_layer\minibatch.py, 8169 , 2018-12-17
py-faster-rcnn-master\lib\roi_data_layer\roidb.py, 5611 , 2018-12-17
py-faster-rcnn-master\lib\roi_data_layer\__init__.py, 248 , 2018-12-17
py-faster-rcnn-master\lib\rpn\anchor_target_layer.py, 11344 , 2018-12-17
py-faster-rcnn-master\lib\rpn\generate.py, 3894 , 2018-12-17
py-faster-rcnn-master\lib\rpn\generate_anchors.py, 3110 , 2018-12-17
py-faster-rcnn-master\lib\rpn\proposal_layer.py, 6803 , 2018-12-17
py-faster-rcnn-master\lib\rpn\proposal_target_layer.py, 7495 , 2018-12-17
py-faster-rcnn-master\lib\rpn\README.md, 780 , 2018-12-17
py-faster-rcnn-master\lib\rpn\__init__.py, 262 , 2018-12-17
py-faster-rcnn-master\lib\setup.py, 5665 , 2018-12-17
py-faster-rcnn-master\lib\transform\torch_image_transform_layer.py, 2000 , 2018-12-17
py-faster-rcnn-master\lib\transform\__init__.py, 0 , 2018-12-17
py-faster-rcnn-master\lib\utils\.gitignore, 9 , 2018-12-17
py-faster-rcnn-master\lib\utils\bbox.pyx, 1756 , 2018-12-17
py-faster-rcnn-master\lib\utils\blob.py, 1625 , 2018-12-17
py-faster-rcnn-master\lib\utils\timer.py, 948 , 2018-12-17
py-faster-rcnn-master\lib\utils\__init__.py, 248 , 2018-12-17
py-faster-rcnn-master\LICENSE, 3745 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG16\faster_rcnn_end2end\solver.prototxt, 387 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG16\faster_rcnn_end2end\test.prototxt, 8754 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG16\faster_rcnn_end2end\train.prototxt, 9840 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG16\fast_rcnn\solver.prototxt, 395 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG16\fast_rcnn\test.prototxt, 6774 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG16\fast_rcnn\train.prototxt, 6625 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG_CNN_M_1024\faster_rcnn_end2end\solver.prototxt, 392 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG_CNN_M_1024\faster_rcnn_end2end\test.prototxt, 6973 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG_CNN_M_1024\faster_rcnn_end2end\train(1).prototxt, 7282 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG_CNN_M_1024\fast_rcnn\solver.prototxt, 398 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG_CNN_M_1024\fast_rcnn\test.prototxt, 4037 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG_CNN_M_1024\fast_rcnn\train.prototxt, 4051 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\faster_rcnn_test.pt, 6263 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\rpn_test.pt, 5305 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage1_fast_rcnn_solver30k40k.pt, 390 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage1_fast_rcnn_train.pt, 8241 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage1_rpn_solver60k80k.pt, 378 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage1_rpn_train.pt, 8062 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage2_fast_rcnn_solver30k40k.pt, 390 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage2_fast_rcnn_train.pt, 8337 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage2_rpn_solver60k80k.pt, 378 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage2_rpn_train.pt, 8126 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_end2end\solver.prototxt, 407 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_end2end\test.prototxt, 8945 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_end2end\train.prototxt, 10209 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\fast_rcnn\solver.prototxt, 400 , 2018-12-17

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    给出了图像融合中三种经典的融合算法：加权融合、PCA融合和IHS融合。程序中有比较详细的说明，特别适合初学图像融合者使用(Image fusion are given the convergence of three classical algorithms: weighted fusion, PCA fusion and IHS fusion. Procedures are a more detailed explanation, especially for beginners to use the image fusion)
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    2009-12-16 00:58:58下载
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    积分：1
  • SHAKE_PROOF
    手持式摄像机在使用时常常会受到使用者有意无意抖动的影响,从而影响成像效果，造成录制视频的不稳定及跳动问题，尤其是在使用者在一场景中特写或者跟踪某一具体目标时，使用者通常不能准确定位到或者估计出运动目标的位置，从而造成目标在视频中位置的不稳定，造成视频的主观效果变得不理想。 为了解决这一问题，我们需要设计一种算法来识别这种无意义的运动并设法通过补偿的方式来使得场景中的目标物体保持位置稳定的状态。 手持式摄像机捕获的视频通常都会受到抖动的影响，这严重的影响视频的主观效果。 这里提出的算法可以可靠的用于数字视频的去抖动。这个算法通过识别这些意外的抖动并且利用运动补偿的方法来获得一个较好的视频输出。 这个系统可以分为三个模块：（1）运动估计模块；（2）抖动识别模块；（3）运动补偿模块。 注：代码在matlab 7.7.0(R2008b)运行通过 [1] F Vella, A Castorina, M Mancuso. Digital image stabilization by adaptive block motion vectors filtering, IEEE Transactions on Consumer Electronics, 2002(Handheld cameras are often used by the user intentionally or unintentionally, the influence of jitter affect the imaging results, resulting in instability and beat the problem of recording video, and especially in the user in a scene close-up or tracking a specific target,users often can not accurately navigate to estimate the location of the moving target, resulting in the subjective effects of target location of the instability in the video, resulting in video not ideal. In order to solve this problem, we need to design an algorithm to identify such a meaningless exercise and try to make the object in the scene to keep the position stable state by way of compensation. The handheld camera captured video usually subject to the influence of jitter, the subjective effect of which seriously affect the video. The algorithm proposed here can be reliably used for digital video to jitter. This algorithm by identifying the jitter of these accidents and the use of motion compensation m)
    2012-05-30 12:03:28下载
    积分：1
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