├── README.md └── faster_rcnn-master ├── .gitattributes ├── .gitignore ├── .gitmodules ├── 000456.jpg ├── 000542.jpg ├── 001150.jpg ├── 001763.jpg ├── 004545.jpg ├── 1 (1).jpg ├── 1 (11).jpg ├── 1 (2).jpg ├── 1 (3).JPG ├── 1 (4).JPG ├── 1 (5).JPG ├── CreateDatasets-master ├── Annotations │ ├── 000001.xml │ ├── 000002.xml │ ├── 000003.xml │ ├── 000004.xml │ ├── 000005.xml │ ├── 000006.xml │ ├── 000007.xml │ ├── 000008.xml │ ├── 000009.xml │ ├── 000010.xml │ ├── 000011.xml │ ├── 000012.xml │ ├── 000013.xml │ ├── 000014.xml │ ├── 000015.xml │ ├── 000016.xml │ ├── 000017.xml │ ├── 000018.xml │ ├── 000019.xml │ ├── 000020.xml │ ├── 000021.xml │ ├── 000022.xml │ ├── 000023.xml │ ├── 000024.xml │ ├── 000025.xml │ ├── 000026.xml │ ├── 000027.xml │ ├── 000028.xml │ ├── 000029.xml │ ├── 000030.xml │ ├── 000031.xml │ ├── 000032.xml │ ├── 000033.xml │ ├── 000034.xml │ ├── 000035.xml │ ├── 000036.xml │ ├── 000037.xml │ ├── 000038.xml │ ├── 000039.xml │ ├── 000040.xml │ ├── 000041.xml │ ├── 000042.xml │ ├── 000043.xml │ ├── 000044.xml │ ├── 000045.xml │ ├── 000046.xml │ ├── 000047.xml │ ├── 000048.xml │ ├── 000049.xml │ ├── 000050.xml │ ├── 000051.xml │ ├── 000052.xml │ ├── 000053.xml │ ├── 000054.xml │ ├── 000055.xml │ ├── 000056.xml │ ├── 000057.xml │ ├── 000058.xml │ ├── 000059.xml │ ├── 000060.xml │ └── tmp.xml ├── ImageSets │ └── Main │ │ ├── test.txt │ │ ├── train.txt │ │ ├── trainval.txt │ │ └── val.txt ├── JPEGImages │ ├── 000001.jpg │ ├── 000002.jpg │ ├── 000003.jpg │ ├── 000004.jpg │ ├── 000005.jpg │ ├── 000006.jpg │ ├── 000007.jpg │ ├── 000008.jpg │ ├── 000009.jpg │ ├── 000010.jpg │ ├── 000011.jpg │ ├── 000012.jpg │ ├── 000013.jpg │ ├── 000014.jpg │ ├── 000015.jpg │ ├── 000016.jpg │ ├── 000017.jpg │ ├── 000018.jpg │ ├── 000019.jpg │ ├── 000020.jpg │ ├── 000021.jpg │ ├── 000022.jpg │ ├── 000023.jpg │ ├── 000024.jpg │ ├── 000025.jpg │ ├── 000026.jpg │ ├── 000027.jpg │ ├── 000028.jpg │ ├── 000029.jpg │ ├── 000030.jpg │ ├── 000031.jpg │ ├── 000032.jpg │ ├── 000033.jpg │ ├── 000034.jpg │ ├── 000035.jpg │ ├── 000036.jpg │ ├── 000037.jpg │ ├── 000038.jpg │ ├── 000039.jpg │ ├── 000040.jpg │ ├── 000041.jpg │ ├── 000042.jpg │ ├── 000043.jpg │ ├── 000044.jpg │ ├── 000045.jpg │ ├── 000046.jpg │ ├── 000047.jpg │ ├── 000048.jpg │ ├── 000049.jpg │ ├── 000050.jpg │ ├── 000051.jpg │ ├── 000052.jpg │ ├── 000053.jpg │ ├── 000054.jpg │ ├── 000055.jpg │ ├── 000056.jpg │ ├── 000057.jpg │ ├── 000058.jpg │ ├── 000059.jpg │ ├── 000060.jpg │ ├── 000061.jpg │ ├── 000062.jpg │ ├── 000063.jpg │ ├── 000064.jpg │ ├── 000065.jpg │ ├── 000066.jpg │ ├── 000067.jpg │ ├── 000068.jpg │ ├── 000069.jpg │ ├── 000070.jpg │ ├── 000071.jpg │ ├── 000072.jpg │ ├── 000073.jpg │ ├── 000074.jpg │ ├── 000075.jpg │ ├── 000076.jpg │ ├── 000077.jpg │ └── output.txt ├── LabelPicture │ ├── label.exe │ ├── labelimg_ver1_api.dll │ ├── opencv_world300.dll │ ├── rename.py │ └── 使用说明.docx ├── README.md ├── create_imagesets.py ├── create_xml.py ├── createxml.m ├── img │ ├── apple.txt │ ├── banana.txt │ ├── bread.txt │ ├── bun.txt │ ├── coin.txt │ ├── doughnut.txt │ ├── egg.txt │ ├── fired_dough_twist.txt │ ├── grape.txt │ ├── lemon.txt │ ├── litchi.txt │ ├── mango.txt │ ├── mix.txt │ ├── mooncake.txt │ ├── orange.txt │ ├── output.txt │ ├── peach.txt │ ├── pear.txt │ ├── plum.txt │ ├── qiwi.txt │ ├── sachima.txt │ └── tomato.txt ├── rename_images.m └── rename_images.py ├── ECUSTFD_ORIGIN_IMAGE_TEST.m ├── ECUSTFD_TEST.m ├── LICENSE ├── README.md ├── RGB2Gray.cpp ├── RGB2Gray.mexw64 ├── bin └── nms_mex.mexw64 ├── density.xls ├── experiments ├── +Dataset │ ├── private │ │ ├── voc0712_devkit.m │ │ ├── voc2007_devkit.m │ │ └── voc2012_devkit.m │ ├── voc0712_trainval.m │ ├── voc0712_trainval_ss.m │ ├── voc0712plus_trainval.m │ ├── voc0712plus_trainval_ss.m │ ├── voc2007_test.m │ ├── voc2007_test_ss.m │ ├── voc2007_trainval.m │ ├── voc2007_trainval_ss.m │ ├── voc2012_test.m │ └── voc2012_trainval.m ├── +Faster_RCNN_Train │ ├── do_fast_rcnn_test.m │ ├── do_fast_rcnn_train.m │ ├── do_proposal_test.m │ ├── do_proposal_train.m │ ├── gather_rpn_fast_rcnn_models.m │ └── set_cache_folder.m ├── +Model │ ├── VGG16_for_Fast_RCNN_VOC0712.m │ ├── VGG16_for_Fast_RCNN_VOC2007.m │ ├── VGG16_for_Faster_RCNN_VOC0712.m │ ├── VGG16_for_Faster_RCNN_VOC0712plus.m │ ├── VGG16_for_Faster_RCNN_VOC2007.m │ ├── VGG16_for_Faster_RCNN_VOC2012.m │ ├── ZF_for_Fast_RCNN_VOC0712.m │ ├── ZF_for_Fast_RCNN_VOC2007.m │ ├── ZF_for_Faster_RCNN_VOC0712.m │ └── ZF_for_Faster_RCNN_VOC2007.m ├── script_fast_rcnn_VOC0712_VGG16.m ├── script_fast_rcnn_VOC0712_ZF.m ├── script_fast_rcnn_VOC2007_VGG16.m ├── script_fast_rcnn_VOC2007_ZF.m ├── script_faster_rcnn_VOC0712_VGG16.m ├── script_faster_rcnn_VOC0712_ZF.m ├── script_faster_rcnn_VOC0712plus_VGG16.m ├── script_faster_rcnn_VOC2007_VGG16.m ├── script_faster_rcnn_VOC2007_ZF.m ├── script_faster_rcnn_VOC2012_VGG16.m └── script_faster_rcnn_demo.m ├── faster_rcnn_build.m ├── faster_rcnn_rec.m ├── fetch_data ├── fetch_caffe_mex_windows_vs2013_cuda65.m ├── fetch_faster_rcnn_final_model.m ├── fetch_model_VGG16.m └── fetch_model_ZF.m ├── food_info.xls ├── functions ├── fast_rcnn │ ├── fast_rcnn_bbox_transform.m │ ├── fast_rcnn_bbox_transform_inv.m │ ├── fast_rcnn_config.m │ ├── fast_rcnn_conv_feat_detect.m │ ├── fast_rcnn_generate_sliding_windows.m │ ├── fast_rcnn_get_minibatch.m │ ├── fast_rcnn_im_detect.m │ ├── fast_rcnn_map_im_rois_to_feat_rois.m │ ├── fast_rcnn_prepare_image_roidb.m │ ├── fast_rcnn_test.m │ └── fast_rcnn_train.m ├── nms │ ├── nms.m │ ├── nms_gpu_mex.cu │ ├── nms_mex.cpp │ ├── nms_multiclass.m │ ├── nms_multiclass_mex.cpp │ └── nvmex.m └── rpn │ ├── proposal_calc_output_size.m │ ├── proposal_config.m │ ├── proposal_generate_anchors.m │ ├── proposal_generate_minibatch.m │ ├── proposal_im_detect.m │ ├── proposal_locate_anchors.m │ ├── proposal_prepare_image_roidb.m │ ├── proposal_test.m │ ├── proposal_train.m │ └── proposal_visual_debug.m ├── grabcut_mex.cpp ├── grabcut_mex.mexw64 ├── imdb ├── get_voc_opts.m ├── imdb_eval_voc.m ├── imdb_from_voc.m ├── roidb_from_proposal.m └── roidb_from_voc.m ├── load_proposal_detection_model.m ├── matlab2c.prj ├── nms_gpu_mex.mexw64 ├── nms_gpu_mex.o ├── nvmex.m ├── opencv_mex.m ├── proposal_prepare_anchors.m ├── result.xls ├── script_faster_rcnn_VOC2007_ZF.m ├── script_faster_rcnn_demo.m ├── show_calorie.m ├── show_segmentation_results.m ├── simple_faster_rcnn_rec.m ├── startup.m ├── utils ├── RectLTRB2LTWH.m ├── RectLTWH2LTRB.m ├── active_caffe_mex.m ├── auto_select_gpu.m ├── boxoverlap.m ├── im_list_to_blob.m ├── mkdir_if_missing.m ├── parse_rst.m ├── prep_im_for_blob.m ├── prep_im_for_blob_size.m ├── procid.m ├── seed_rand.m ├── showboxes.m ├── subsample_images.m ├── subsample_images_per_class.m ├── symbolic_link.m ├── tic_toc_print.m ├── vis_label.m ├── xVOCap.m ├── xVOChash_init.m └── xVOChash_lookup.m └── xls_results_analysis.m /faster_rcnn-master/.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | 4 | # Custom for Visual Studio 5 | *.cs diff=csharp 6 | 7 | # Standard to msysgit 8 | *.doc diff=astextplain 9 | *.DOC diff=astextplain 10 | *.docx diff=astextplain 11 | *.DOCX diff=astextplain 12 | *.dot diff=astextplain 13 | *.DOT diff=astextplain 14 | *.pdf diff=astextplain 15 | *.PDF diff=astextplain 16 | *.rtf diff=astextplain 17 | *.RTF diff=astextplain 18 | -------------------------------------------------------------------------------- /faster_rcnn-master/.gitignore: -------------------------------------------------------------------------------- 1 | # Windows image file caches 2 | Thumbs.db 3 | ehthumbs.db 4 | 5 | # Folder config file 6 | Desktop.ini 7 | 8 | # Recycle Bin used on file shares 9 | $RECYCLE.BIN/ 10 | 11 | # User Ingore 12 | models/ 13 | data/ 14 | datasets/ 15 | external/caffe/matlab 16 | output/ 17 | cachedir/ 18 | imdb/cache 19 | *.caffemodel 20 | *.mat 21 | 22 | # Windows Installer files 23 | *.cab 24 | *.msi 25 | *.msm 26 | *.msp 27 | 28 | # Windows shortcuts 29 | *.lnk 30 | 31 | # ========================= 32 | # Operating System Files 33 | # ========================= 34 | 35 | # OSX 36 | # ========================= 37 | 38 | .DS_Store 39 | .AppleDouble 40 | .LSOverride 41 | 42 | # Thumbnails 43 | ._* 44 | 45 | # Files that might appear on external disk 46 | .Spotlight-V100 47 | .Trashes 48 | 49 | # Directories potentially created on remote AFP share 50 | .AppleDB 51 | .AppleDesktop 52 | Network Trash Folder 53 | Temporary Items 54 | .apdisk 55 | -------------------------------------------------------------------------------- /faster_rcnn-master/.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "external/caffe"] 2 | path = external/caffe 3 | url = https://github.com/ShaoqingRen/caffe.git 4 | branch = faster-R-CNN 5 | -------------------------------------------------------------------------------- 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77 29 | 112 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000002.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000002.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 300 16 | 300 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 62 27 | 74 28 | 239 29 | 248 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000003.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000003.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 300 16 | 212 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 41 27 | 37 28 | 186 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VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 111 16 | 146 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 25 27 | 37 28 | 88 29 | 111 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000010.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000010.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 226 16 | 154 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 114 27 | 11 28 | 214 29 | 120 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000011.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000011.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 217 16 | 153 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 26 27 | 25 28 | 124 29 | 131 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000012.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000012.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 223 16 | 148 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 88 27 | 22 28 | 139 29 | 82 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000013.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000013.jpg 4 | 5 | My Database 6 | 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28 | 200 29 | 133 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000017.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000017.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 110 16 | 146 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 100 27 | 24 28 | 200 29 | 133 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000018.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000018.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 230 16 | 151 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 67 27 | 37 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92 | 93 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000020.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000020.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 219 16 | 146 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 5 27 | 19 28 | 105 29 | 119 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 104 39 | 14 40 | 196 41 | 109 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000021.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000021.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 228 16 | 148 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 10 27 | 68 28 | 75 29 | 124 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 61 39 | 18 40 | 123 41 | 75 42 | 43 | 44 | 45 | apple 46 | Unspecified 47 | 0 48 | 0 49 | 50 | 89 51 | 71 52 | 151 53 | 117 54 | 55 | 56 | 57 | apple 58 | Unspecified 59 | 0 60 | 0 61 | 62 | 151 63 | 60 64 | 206 65 | 111 66 | 67 | 68 | 69 | apple 70 | Unspecified 71 | 0 72 | 0 73 | 74 | 151 75 | 60 76 | 206 77 | 111 78 | 79 | 80 | 81 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000022.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000022.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 300 16 | 300 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 42 27 | 40 28 | 250 29 | 273 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000023.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000023.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 222 16 | 148 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 84 27 | 16 28 | 131 29 | 56 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 84 39 | 16 40 | 131 41 | 56 42 | 43 | 44 | 45 | apple 46 | Unspecified 47 | 0 48 | 0 49 | 50 | 83 51 | 88 52 | 129 53 | 142 54 | 55 | 56 | 57 | apple 58 | Unspecified 59 | 0 60 | 0 61 | 62 | 30 63 | 33 64 | 78 65 | 83 66 | 67 | 68 | 69 | apple 70 | Unspecified 71 | 0 72 | 0 73 | 74 | 30 75 | 33 76 | 78 77 | 83 78 | 79 | 80 | 81 | apple 82 | Unspecified 83 | 0 84 | 0 85 | 86 | 141 87 | 40 88 | 186 89 | 94 90 | 91 | 92 | 93 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000024.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000024.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 206 16 | 148 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 68 27 | 6 28 | 132 29 | 52 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 68 39 | 6 40 | 132 41 | 52 42 | 43 | 44 | 45 | apple 46 | Unspecified 47 | 0 48 | 0 49 | 50 | 72 51 | 99 52 | 127 53 | 150 54 | 55 | 56 | 57 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000025.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000025.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 280 16 | 300 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 8 27 | 8 28 | 289 29 | 294 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000026.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000026.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 275 16 | 300 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 9 27 | 14 28 | 269 29 | 286 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000028.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000028.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 300 16 | 205 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 107 27 | 82 28 | 7 29 | 5 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 107 39 | 82 40 | 7 41 | 5 42 | 43 | 44 | 45 | apple 46 | Unspecified 47 | 0 48 | 0 49 | 50 | 98 51 | 105 52 | 196 53 | 189 54 | 55 | 56 | 57 | apple 58 | Unspecified 59 | 0 60 | 0 61 | 62 | 98 63 | 105 64 | 196 65 | 189 66 | 67 | 68 | 69 | apple 70 | Unspecified 71 | 0 72 | 0 73 | 74 | 114 75 | 25 76 | 191 77 | 99 78 | 79 | 80 | 81 | apple 82 | Unspecified 83 | 0 84 | 0 85 | 86 | 202 87 | 2 88 | 299 89 | 85 90 | 91 | 92 | 93 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000029.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000029.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 221 16 | 146 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 49 27 | 24 28 | 134 29 | 110 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000030.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000030.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 300 16 | 300 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 45 27 | 45 28 | 254 29 | 256 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000031.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000031.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 218 16 | 135 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 87 27 | 28 28 | 184 29 | 120 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000032.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000032.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 205 16 | 146 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 91 27 | 26 28 | 174 29 | 117 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 37 39 | 21 40 | 102 41 | 89 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000033.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000033.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 218 16 | 137 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 91 27 | 29 28 | 189 29 | 128 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 28 39 | 15 40 | 108 41 | 97 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000034.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000034.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 216 16 | 144 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 30 27 | 53 28 | 72 29 | 98 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 77 39 | 62 40 | 112 41 | 101 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000035.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000035.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 212 16 | 141 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 39 27 | 18 28 | 130 29 | 106 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000036.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000036.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 210 16 | 140 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 39 27 | 18 28 | 130 29 | 106 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 54 39 | 74 40 | 97 41 | 120 42 | 43 | 44 | 45 | apple 46 | Unspecified 47 | 0 48 | 0 49 | 50 | 106 51 | 67 52 | 147 53 | 115 54 | 55 | 56 | 57 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000037.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000037.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 210 16 | 140 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 23 27 | 42 28 | 67 29 | 101 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 23 39 | 42 40 | 67 41 | 101 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000038.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000038.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 300 16 | 200 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 38 27 | 25 28 | 139 29 | 154 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000039.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000039.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 194 16 | 140 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 120 27 | 6 28 | 167 29 | 50 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 120 39 | 6 40 | 167 41 | 50 42 | 43 | 44 | 45 | apple 46 | Unspecified 47 | 0 48 | 0 49 | 50 | 70 51 | 18 52 | 123 53 | 61 54 | 55 | 56 | 57 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000040.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000040.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 206 16 | 144 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 52 27 | 28 28 | 161 29 | 122 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000041.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000041.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 211 16 | 140 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 52 27 | 28 28 | 161 29 | 122 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000042.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000042.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 202 16 | 135 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 16 27 | 35 28 | 101 29 | 117 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 16 39 | 35 40 | 101 41 | 117 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000043.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000043.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 300 16 | 200 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 51 27 | 112 28 | 119 29 | 177 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 125 39 | 114 40 | 189 41 | 177 42 | 43 | 44 | 45 | apple 46 | Unspecified 47 | 0 48 | 0 49 | 50 | 196 51 | 115 52 | 259 53 | 176 54 | 55 | 56 | 57 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000044.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000044.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 300 16 | 200 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 28 27 | 42 28 | 141 29 | 160 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 28 39 | 42 40 | 141 41 | 160 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000045.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000045.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 300 16 | 300 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 144 27 | 23 28 | 274 29 | 148 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 12 39 | 67 40 | 138 41 | 196 42 | 43 | 44 | 45 | apple 46 | Unspecified 47 | 0 48 | 0 49 | 50 | 12 51 | 67 52 | 138 53 | 196 54 | 55 | 56 | 57 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000046.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000046.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 203 16 | 144 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 158 27 | 76 28 | 282 29 | 192 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000047.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000047.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 194 16 | 138 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 32 27 | 21 28 | 123 29 | 127 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 22 39 | 20 40 | 102 41 | 83 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000048.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000048.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 188 16 | 141 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 38 27 | 27 28 | 146 29 | 112 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000049.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000049.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 229 16 | 153 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 112 27 | 42 28 | 202 29 | 139 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000050.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000050.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 169 16 | 135 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 61 27 | 23 28 | 155 29 | 114 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000051.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000051.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 191 16 | 147 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 61 27 | 23 28 | 155 29 | 114 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000052.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000052.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 186 16 | 147 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 21 27 | 33 28 | 123 29 | 135 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000053.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000053.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 188 16 | 117 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 48 27 | 3 28 | 141 29 | 108 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000054.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000054.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 206 16 | 137 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 48 27 | 3 28 | 141 29 | 108 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 25 39 | 33 40 | 96 41 | 109 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000055.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000055.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 154 16 | 153 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 95 27 | 37 28 | 167 29 | 104 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000056.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000056.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 170 16 | 153 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 41 27 | 33 28 | 128 29 | 132 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000057.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000057.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 227 16 | 150 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 27 27 | 34 28 | 115 29 | 133 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 27 39 | 34 40 | 115 41 | 133 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000058.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000058.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 219 16 | 153 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 34 27 | 32 28 | 117 29 | 116 30 | 31 | 32 | 33 | apple 34 | Unspecified 35 | 0 36 | 0 37 | 38 | 34 39 | 32 40 | 117 41 | 116 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000059.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000059.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 154 16 | 153 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 62 27 | 42 28 | 139 29 | 113 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/000060.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000060.jpg 4 | 5 | My Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | xiaoxianyu 13 | 14 | 15 | 227 16 | 151 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 63 27 | 15 28 | 169 29 | 115 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/Annotations/tmp.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000060.jpg 4 | 5 | The VOC2007 Database 6 | VOC2007 7 | flickr 8 | NULL 9 | 10 | 11 | NULL 12 | Peic 13 | 14 | 15 | 227 16 | 151 17 | 3 18 | 19 | 0 20 | 21 | apple 22 | Unspecified 23 | 0 24 | 0 25 | 26 | 63 27 | 15 28 | 169 29 | 115 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/ImageSets/Main/test.txt: -------------------------------------------------------------------------------- 1 | 000001 2 | 000003 3 | 000005 4 | 000007 5 | 000009 6 | 000011 7 | 000013 8 | 000015 9 | 000017 10 | 000019 11 | 000021 12 | 000023 13 | 000025 14 | 000027 15 | 000029 16 | 000031 17 | 000033 18 | 000035 19 | 000037 20 | 000039 21 | 000041 22 | 000043 23 | 000045 24 | 000047 25 | 000049 26 | 000051 27 | 000053 28 | 000055 29 | 000057 30 | 000059 31 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/ImageSets/Main/train.txt: -------------------------------------------------------------------------------- 1 | 000002 2 | 000006 3 | 000010 4 | 000014 5 | 000018 6 | 000022 7 | 000026 8 | 000030 9 | 000034 10 | 000038 11 | 000042 12 | 000046 13 | 000050 14 | 000054 15 | 000058 16 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/ImageSets/Main/trainval.txt: -------------------------------------------------------------------------------- 1 | 000002 2 | 000004 3 | 000006 4 | 000008 5 | 000010 6 | 000012 7 | 000014 8 | 000016 9 | 000018 10 | 000020 11 | 000022 12 | 000024 13 | 000026 14 | 000028 15 | 000030 16 | 000032 17 | 000034 18 | 000036 19 | 000038 20 | 000040 21 | 000042 22 | 000044 23 | 000046 24 | 000048 25 | 000050 26 | 000052 27 | 000054 28 | 000056 29 | 000058 30 | 000060 31 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/ImageSets/Main/val.txt: 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print("args is not 2!") 12 | sys.exit(1); 13 | 14 | BASE_PATH = sys.argv[1]; 15 | print(BASE_PATH) 16 | 17 | # os.walk返回一个walk对象，是一个三元组 18 | for rootpath, dirnames, filenames in os.walk(BASE_PATH): 19 | name_number = 1 20 | name_length = 6 21 | for filename in filenames: 22 | #if(re.match("^(\d*).jpg", filename)): 23 | name_str = (name_length - len(str(name_number))) * '0' + str(name_number) + ".jpg" 24 | print name_str, '\n' 25 | name_number = name_number + 1 26 | image = Image.open(os.path.join(BASE_PATH, filename)) 27 | image.save(os.path.join(BASE_PATH, name_str), 'jpeg') 28 | 29 | 30 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/LabelPicture/使用说明.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/CreateDatasets-master/LabelPicture/使用说明.docx -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/README.md: -------------------------------------------------------------------------------- 1 | # CreateDatasets 2 | Create Images Datasets like VOC2007 3 | 4 | * Datasets created for deep learning -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/create_imagesets.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | 3 | import os 4 | 5 | __author__ = 'peic' 6 | 7 | _IMAGE_SETS_PATH = 'ImageSets' 8 | _MAin_PATH = 'ImageSets\\Main' 9 | _XML_FILE_PATH = 'Annotations' 10 | 11 | if __name__ == '__main__': 12 | 13 | if os.path.exists(_IMAGE_SETS_PATH): 14 | print('ImageSets dir is already exists') 15 | if os.path.exists(_MAin_PATH): 16 | print('Main dir is already in ImageSets') 17 | else: 18 | os.mkdir(_IMAGE_SETS_PATH) 19 | os.mkdir(_MAin_PATH) 20 | 21 | print _MAin_PATH 22 | 23 | # 测试集, 总数据的50% 24 | f_test = open(os.path.join(_MAin_PATH, 'test.txt'), 'w') 25 | # 训练和验证集, 除去测试的剩余50% 26 | f_trainval = open(os.path.join(_MAin_PATH, 'trainval.txt'), 'w') 27 | # trainval中训练部分, trainval的50% 28 | f_train = open(os.path.join(_MAin_PATH, 'train.txt'), 'w') 29 | # trainval中验证集, trainval的50% 30 | f_val = open(os.path.join(_MAin_PATH, 'val.txt'), 'w') 31 | # 遍历XML文件夹 32 | for root, dirs, files in os.walk(_XML_FILE_PATH): 33 | i = 1 34 | j = 1 35 | for file in files: 36 | print i,j 37 | if i % 2: 38 | # 作为测试集 39 | f_test.writelines(str(file).split('.')[0] + '\n') 40 | else: 41 | # 训练和验证集 42 | f_trainval.writelines(str(file).split('.')[0] + '\n') 43 | if j % 2: 44 | # 训练集 45 | f_train.writelines(str(file).split('.')[0] + '\n') 46 | else: 47 | # 验证集 48 | f_val.writelines(str(file).split('.')[0] + '\n') 49 | j += 1 50 | i += 1 51 | 52 | f_test.close() 53 | f_train.close() 54 | f_trainval.close() 55 | f_val.close() 56 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/createxml.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/CreateDatasets-master/createxml.m -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/bread.txt: -------------------------------------------------------------------------------- 1 | bread1.jpg bread 250 120 641 515 2 | bread10.jpg bread 30 24 166 114 3 | bread11.jpg bread 7 7 179 134 4 | bread12.jpg bread 12 19 99 115 5 | bread12.jpg bread 97 27 187 112 6 | bread13.jpg bread 29 16 165 123 7 | bread14.jpg bread 22 4 238 152 8 | bread15.jpg bread 22 4 238 152 9 | bread15.jpg bread 41 36 104 121 10 | bread16.jpg bread 8 32 237 131 11 | bread17.jpg bread 23 9 180 130 12 | bread18.jpg bread 36 31 199 122 13 | bread19.jpg bread 14 18 162 119 14 | bread2.jpg bread 234 113 643 533 15 | bread20.jpg bread 44 14 162 142 16 | bread21.jpg bread 14 31 179 115 17 | bread22.jpg bread 38 41 180 131 18 | bread23.jpg bread 20 5 147 114 19 | bread24.jpg bread 45 34 158 125 20 | bread25.jpg bread 4 26 128 125 21 | bread26.jpg bread 28 25 191 126 22 | bread27.jpg bread 12 8 108 141 23 | bread28.jpg bread 41 14 187 132 24 | bread29.jpg bread 41 14 187 132 25 | bread3.jpg bread 257 142 657 503 26 | bread30.jpg bread 21 18 196 106 27 | bread31.jpg bread 23 10 165 130 28 | bread32.jpg bread 24 16 175 134 29 | bread33.jpg bread 18 11 189 126 30 | bread34.jpg bread 18 11 189 126 31 | bread35.jpg bread 50 54 161 143 32 | bread35.jpg bread 76 6 197 78 33 | bread36.jpg bread 76 6 197 78 34 | bread37.jpg bread 32 23 175 126 35 | bread38.jpg bread 32 23 175 126 36 | bread39.jpg bread 29 11 174 119 37 | bread4.jpg bread 250 140 652 496 38 | bread40.jpg bread 17 7 134 126 39 | bread41.jpg bread 32 25 160 115 40 | bread42.jpg bread 17 14 165 118 41 | bread43.jpg bread 17 14 165 118 42 | bread44.jpg bread 24 12 131 131 43 | bread45.jpg bread 13 37 134 111 44 | bread46.jpg bread 37 1 190 143 45 | bread47.jpg bread 37 1 190 143 46 | bread48.jpg bread 34 32 152 114 47 | bread49.jpg bread 24 22 118 138 48 | bread5.jpg bread 20 33 210 123 49 | bread5.jpg bread 36 24 133 118 50 | bread50.jpg bread 130 21 226 118 51 | bread50.jpg bread 8 15 196 84 52 | bread51.jpg bread 6 81 190 141 53 | bread52.jpg bread 27 24 208 115 54 | bread53.jpg bread 36 11 169 115 55 | bread54.jpg bread 21 33 76 81 56 | bread54.jpg bread 91 20 143 69 57 | bread54.jpg bread 91 20 143 69 58 | bread54.jpg bread 155 32 210 78 59 | bread54.jpg bread 60 83 105 130 60 | bread55.jpg bread 60 83 105 130 61 | bread56.jpg bread 14 27 132 203 62 | bread57.jpg bread 58 21 103 220 63 | bread58.jpg bread 58 21 103 220 64 | bread58.jpg bread 14 15 130 90 65 | bread6.jpg bread 27 7 156 118 66 | bread7.jpg bread 27 7 156 118 67 | bread8.jpg bread 9 17 116 118 68 | bread8.jpg bread 9 17 116 118 69 | bread9.jpg bread 98 20 201 142 70 | bread9.jpg bread 98 20 201 142 71 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/bun.txt: -------------------------------------------------------------------------------- 1 | bun1.jpg bun 6 40 89 82 2 | bun1.jpg bun 66 8 117 65 3 | bun1.jpg bun 113 23 196 66 4 | bun1.jpg bun 119 66 203 120 5 | bun1.jpg bun 119 66 203 120 6 | bun10.jpg bun 55 16 128 62 7 | bun10.jpg bun 55 16 128 62 8 | bun10.jpg bun 14 50 63 96 9 | bun10.jpg bun 57 75 136 119 10 | bun11.jpg bun 117 48 184 92 11 | bun11.jpg bun 66 12 115 67 12 | bun11.jpg bun 109 34 160 106 13 | bun11.jpg bun 109 34 160 106 14 | bun12.jpg bun 77 141 216 263 15 | bun12.jpg bun 218 182 374 301 16 | bun12.jpg bun 294 129 451 249 17 | bun12.jpg bun 294 129 451 249 18 | bun12.jpg bun 206 39 347 141 19 | bun12.jpg bun 206 39 347 141 20 | bun13.jpg bun 29 66 173 196 21 | bun13.jpg bun 29 66 173 196 22 | bun13.jpg bun 396 149 531 254 23 | bun13.jpg bun 501 94 641 186 24 | bun13.jpg bun 501 94 641 186 25 | bun14.jpg bun 4 116 342 409 26 | bun14.jpg bun 353 100 695 391 27 | bun15.jpg bun 353 100 695 391 28 | bun16.jpg bun 61 86 354 307 29 | bun2.jpg bun 67 7 143 66 30 | bun2.jpg bun 67 7 143 66 31 | bun2.jpg bun 37 43 112 104 32 | bun2.jpg bun 116 56 197 109 33 | bun2.jpg bun 147 11 223 72 34 | bun2.jpg bun 158 90 229 149 35 | bun2.jpg bun 90 106 171 153 36 | bun2.jpg bun 10 110 101 153 37 | bun2.jpg bun 0 54 49 106 38 | bun3.jpg bun 5 15 81 52 39 | bun3.jpg bun 44 18 111 61 40 | bun3.jpg bun 45 52 120 95 41 | bun4.jpg bun 107 24 160 89 42 | bun4.jpg bun 28 19 103 76 43 | bun4.jpg bun 49 63 122 126 44 | bun4.jpg bun 101 14 164 69 45 | bun5.jpg bun 115 51 199 115 46 | bun5.jpg bun 27 6 88 69 47 | bun5.jpg bun 84 16 152 56 48 | bun6.jpg bun 55 55 143 107 49 | bun6.jpg bun 58 15 152 75 50 | bun6.jpg bun 24 65 86 125 51 | bun7.jpg bun 85 71 188 139 52 | bun7.jpg bun 50 92 116 149 53 | bun7.jpg bun 16 68 59 122 54 | bun7.jpg bun 31 38 69 69 55 | bun7.jpg bun 108 13 144 45 56 | bun7.jpg bun 142 31 185 59 57 | bun7.jpg bun 142 31 185 59 58 | bun7.jpg bun 145 57 208 105 59 | bun7.jpg bun 145 57 208 105 60 | bun7.jpg bun 97 72 170 124 61 | bun7.jpg bun 62 56 117 94 62 | bun8.jpg bun 34 3 95 48 63 | bun8.jpg bun 9 25 63 74 64 | bun8.jpg bun 9 25 63 74 65 | bun8.jpg bun 3 73 74 132 66 | bun8.jpg bun 70 43 133 97 67 | bun8.jpg bun 70 43 133 97 68 | bun8.jpg bun 140 36 211 90 69 | bun8.jpg bun 133 79 207 134 70 | bun9.jpg bun 33 23 90 70 71 | bun9.jpg bun 78 4 139 44 72 | bun9.jpg bun 78 4 139 44 73 | bun9.jpg bun 106 22 166 70 74 | bun9.jpg bun 106 22 166 70 75 | bun9.jpg bun 134 54 197 96 76 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/coin.txt: -------------------------------------------------------------------------------- 1 | coin1.jpg coin 35 9 204 145 2 | coin10.jpg coin 47 19 146 122 3 | coin11.jpg coin 45 18 160 132 4 | coin12.jpg coin 38 24 146 119 5 | coin13.jpg coin 30 21 166 146 6 | coin14.jpg coin 8 41 45 79 7 | coin14.jpg coin 7 79 43 122 8 | coin14.jpg coin 43 38 82 78 9 | coin14.jpg coin 42 75 80 115 10 | coin14.jpg coin 42 75 80 115 11 | coin14.jpg coin 80 38 120 78 12 | coin14.jpg coin 81 78 119 114 13 | coin14.jpg coin 120 75 154 113 14 | coin14.jpg coin 153 43 189 76 15 | coin14.jpg coin 159 77 194 113 16 | coin15.jpg coin 29 12 125 112 17 | coin16.jpg coin 29 12 125 112 18 | coin17.jpg coin 29 6 168 139 19 | coin18.jpg coin 1 9 96 101 20 | coin19.jpg coin 4 10 149 136 21 | coin2.jpg coin 61 33 159 129 22 | coin20.jpg coin 6 8 131 133 23 | coin21.jpg coin 77 22 151 98 24 | coin22.jpg coin 77 22 151 98 25 | coin24.jpg coin 13 26 95 106 26 | coin24.jpg coin 104 27 185 108 27 | coin25.jpg coin 73 26 188 138 28 | coin26.jpg coin 39 15 164 140 29 | coin27.jpg coin 30 23 68 68 30 | coin27.jpg coin 8 59 49 102 31 | coin27.jpg coin 8 59 49 102 32 | coin27.jpg coin 45 62 89 107 33 | coin27.jpg coin 108 31 149 68 34 | coin27.jpg coin 88 63 128 104 35 | coin27.jpg coin 124 63 167 105 36 | coin27.jpg coin 150 30 190 69 37 | coin27.jpg coin 170 66 206 106 38 | coin28.jpg coin 33 5 78 51 39 | coin28.jpg coin 32 50 75 99 40 | coin28.jpg coin 76 98 124 145 41 | coin28.jpg coin 81 53 123 96 42 | coin28.jpg coin 82 3 126 51 43 | coin28.jpg coin 127 6 175 54 44 | coin28.jpg coin 127 6 175 54 45 | coin3.jpg coin 11 48 110 126 46 | coin3.jpg coin 111 47 201 131 47 | coin4.jpg coin 25 6 156 134 48 | coin5.jpg coin 25 6 156 134 49 | coin6.jpg coin 39 17 152 130 50 | coin7.jpg coin 39 17 152 130 51 | coin8.jpg coin 41 7 169 139 52 | coin18.jpg coin 3 9 97 101 53 | coin18.jpg coin 107 7 202 98 54 | coin9.jpg coin 15 7 73 67 55 | coin9.jpg coin 4 67 62 129 56 | coin9.jpg coin 4 67 62 129 57 | coin9.jpg coin 74 5 132 67 58 | coin9.jpg coin 136 16 189 76 59 | coin9.jpg coin 125 82 176 136 60 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/doughnut.txt: -------------------------------------------------------------------------------- 1 | doughnut1.jpg doughnut 110 111 347 365 2 | doughnut10.jpg doughnut 23 27 68 73 3 | doughnut10.jpg doughnut 48 82 95 124 4 | doughnut10.jpg doughnut 78 23 132 68 5 | doughnut10.jpg doughnut 146 21 191 70 6 | doughnut10.jpg doughnut 119 81 166 129 7 | doughnut11.jpg doughnut 119 81 166 129 8 | doughnut12.jpg doughnut 82 52 151 117 9 | doughnut12.jpg doughnut 82 52 151 117 10 | doughnut12.jpg doughnut 131 8 196 69 11 | doughnut13.jpg doughnut 154 76 211 137 12 | doughnut13.jpg doughnut 34 20 144 119 13 | doughnut14.jpg doughnut 123 18 190 82 14 | doughnut15.jpg doughnut 34 24 131 101 15 | doughnut15.jpg doughnut 110 2 196 62 16 | doughnut16.jpg doughnut 31 25 166 140 17 | doughnut17.jpg doughnut 48 19 141 116 18 | doughnut18.jpg doughnut 38 33 274 172 19 | doughnut19.jpg doughnut 38 19 143 100 20 | doughnut2.jpg doughnut 211 148 361 245 21 | doughnut2.jpg doughnut 211 148 361 245 22 | doughnut2.jpg doughnut 105 168 232 237 23 | doughnut2.jpg doughnut 361 192 504 286 24 | doughnut2.jpg doughnut 261 241 430 335 25 | doughnut20.jpg doughnut 31 9 154 129 26 | doughnut21.jpg doughnut 32 20 174 128 27 | doughnut22.jpg doughnut 32 20 174 128 28 | doughnut23.jpg doughnut 28 13 145 123 29 | doughnut24.jpg doughnut 14 6 127 126 30 | doughnut25.jpg doughnut 14 6 127 126 31 | doughnut25.jpg doughnut 43 17 181 67 32 | doughnut25.jpg doughnut 183 35 232 97 33 | doughnut26.jpg doughnut 183 35 232 97 34 | doughnut26.jpg doughnut 11 43 105 109 35 | doughnut27.jpg doughnut 103 35 198 103 36 | doughnut28.jpg doughnut 12 8 211 133 37 | doughnut29.jpg doughnut 14 13 207 133 38 | doughnut3.jpg doughnut 61 178 287 398 39 | doughnut30.jpg doughnut 21 16 177 120 40 | doughnut31.jpg doughnut 21 16 177 120 41 | doughnut4.jpg doughnut 8 10 403 406 42 | doughnut4.jpg doughnut 338 31 635 393 43 | doughnut5.jpg doughnut 79 300 389 606 44 | doughnut5.jpg doughnut 79 300 389 606 45 | doughnut6.jpg doughnut 190 230 437 459 46 | doughnut6.jpg doughnut 2 2 95 68 47 | doughnut6.jpg doughnut 54 42 181 126 48 | doughnut7.jpg doughnut 141 1 232 49 49 | doughnut7.jpg doughnut 124 25 192 106 50 | doughnut7.jpg doughnut 40 57 124 135 51 | doughnut8.jpg doughnut 92 21 150 65 52 | doughnut8.jpg doughnut 75 7 126 57 53 | doughnut8.jpg doughnut 133 6 180 55 54 | doughnut9.jpg doughnut 14 11 137 130 55 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/egg.txt: -------------------------------------------------------------------------------- 1 | egg1.jpg egg 239 234 408 379 2 | egg10.jpg egg 23 3 133 144 3 | egg11.jpg egg 5 7 113 144 4 | egg12.jpg egg 5 7 113 144 5 | egg13.jpg egg 37 26 201 142 6 | egg14.jpg egg 18 21 74 78 7 | egg14.jpg egg 101 54 150 115 8 | egg14.jpg egg 101 54 150 115 9 | egg14.jpg egg 148 64 216 122 10 | egg15.jpg egg 4 6 167 131 11 | egg16.jpg egg 4 6 167 131 12 | egg16.jpg egg 65 92 148 138 13 | egg16.jpg egg 65 92 148 138 14 | egg17.jpg egg 50 21 106 85 15 | egg17.jpg egg 96 37 169 62 16 | egg18.jpg egg 97 33 177 105 17 | egg19.jpg egg 46 27 159 113 18 | egg2.jpg egg 266 200 459 363 19 | egg20.jpg egg 266 200 459 363 20 | egg21.jpg egg 34 8 110 125 21 | egg22.jpg egg 26 27 86 112 22 | egg23.jpg egg 11 58 77 127 23 | egg23.jpg egg 64 32 127 76 24 | egg23.jpg egg 108 7 182 62 25 | egg23.jpg egg 75 59 138 137 26 | egg23.jpg egg 141 55 211 116 27 | egg24.jpg egg 141 55 211 116 28 | egg25.jpg egg 12 9 105 140 29 | egg26.jpg egg 17 11 97 114 30 | egg27.jpg egg 17 11 97 114 31 | egg28.jpg egg 21 39 86 112 32 | egg29.jpg egg 49 17 132 127 33 | egg3.jpg egg 211 188 407 357 34 | egg30.jpg egg 17 10 179 129 35 | egg31.jpg egg 48 9 121 112 36 | egg32.jpg egg 26 31 73 111 37 | egg32.jpg egg 17 36 78 110 38 | egg33.jpg egg 23 9 136 118 39 | egg34.jpg egg 24 9 131 150 40 | egg35.jpg egg 24 9 131 150 41 | egg4.jpg egg 27 11 122 134 42 | egg5.jpg egg 343 344 465 483 43 | egg6.jpg egg 17 50 131 137 44 | egg7.jpg egg 46 11 133 128 45 | egg8.jpg egg 46 11 133 128 46 | egg9.jpg egg 34 18 143 145 47 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/fired_dough_twist.txt: -------------------------------------------------------------------------------- 1 | fired_dough_twist1.jpg fired_dough_twist 68 23 133 149 2 | fired_dough_twist1.jpg fired_dough_twist 68 23 133 149 3 | fired_dough_twist10.jpg fired_dough_twist 1 59 195 99 4 | fired_dough_twist11.jpg fired_dough_twist 6 101 98 154 5 | fired_dough_twist12.jpg fired_dough_twist 6 101 98 154 6 | fired_dough_twist13.jpg fired_dough_twist 78 61 156 124 7 | fired_dough_twist14.jpg fired_dough_twist 49 13 95 123 8 | fired_dough_twist14.jpg fired_dough_twist 49 13 95 123 9 | fired_dough_twist15.jpg fired_dough_twist 24 58 87 102 10 | fired_dough_twist15.jpg fired_dough_twist 24 58 87 102 11 | fired_dough_twist15.jpg fired_dough_twist 59 47 127 92 12 | fired_dough_twist16.jpg fired_dough_twist 93 20 154 66 13 | fired_dough_twist17.jpg fired_dough_twist 10 16 183 111 14 | fired_dough_twist18.jpg fired_dough_twist 20 50 151 141 15 | fired_dough_twist18.jpg fired_dough_twist 51 88 121 118 16 | fired_dough_twist18.jpg fired_dough_twist 52 72 153 139 17 | fired_dough_twist19.jpg fired_dough_twist 14 24 42 62 18 | fired_dough_twist2.jpg fired_dough_twist 9 11 160 125 19 | fired_dough_twist2.jpg fired_dough_twist 78 22 162 70 20 | fired_dough_twist2.jpg fired_dough_twist 78 22 162 70 21 | fired_dough_twist2.jpg fired_dough_twist 93 48 144 113 22 | fired_dough_twist3.jpg fired_dough_twist 141 53 207 116 23 | fired_dough_twist4.jpg fired_dough_twist 8 49 156 108 24 | fired_dough_twist5.jpg fired_dough_twist 7 40 191 120 25 | fired_dough_twist6.jpg fired_dough_twist 54 51 166 89 26 | fired_dough_twist6.jpg fired_dough_twist 72 69 203 138 27 | fired_dough_twist7.jpg fired_dough_twist 18 16 118 48 28 | fired_dough_twist7.jpg fired_dough_twist 18 51 124 76 29 | fired_dough_twist7.jpg fired_dough_twist 18 51 124 76 30 | fired_dough_twist7.jpg tomato 132 58 155 80 31 | fired_dough_twist7.jpg tomato 132 58 155 80 32 | fired_dough_twist7.jpg tomato 156 60 177 82 33 | fired_dough_twist7.jpg tomato 149 79 176 98 34 | fired_dough_twist8.jpg fired_dough_twist 68 16 117 114 35 | fired_dough_twist8.jpg fired_dough_twist 31 51 63 118 36 | fired_dough_twist8.jpg fired_dough_twist 15 63 71 119 37 | fired_dough_twist9.jpg fired_dough_twist 41 19 98 104 38 | fired_dough_twist9.jpg fired_dough_twist 41 19 98 104 39 | fired_dough_twist9.jpg fired_dough_twist 100 6 208 39 40 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/grape.txt: -------------------------------------------------------------------------------- 1 | grape1.jpg grape 354 125 743 420 2 | grape10.jpg grape 17 8 85 137 3 | grape10.jpg grape 79 53 124 134 4 | grape10.jpg grape 79 53 124 134 5 | grape11.jpg grape 51 21 135 147 6 | grape12.jpg grape 51 21 135 147 7 | grape13.jpg grape 57 7 132 138 8 | grape13.jpg grape 87 20 160 117 9 | grape13.jpg grape 87 20 160 117 10 | grape14.jpg grape 27 42 145 110 11 | grape15.jpg grape 9 46 113 108 12 | grape15.jpg grape 112 52 178 130 13 | grape16.jpg grape 13 16 74 54 14 | grape16.jpg grape 9 64 78 105 15 | grape16.jpg grape 12 111 80 145 16 | grape16.jpg grape 12 111 80 145 17 | grape16.jpg grape 85 17 161 47 18 | grape16.jpg grape 95 58 164 98 19 | grape17.jpg grape 102 111 159 151 20 | grape18.jpg grape 40 20 209 137 21 | grape18.jpg grape 12 41 58 126 22 | grape19.jpg grape 58 26 112 125 23 | grape2.jpg grape 361 119 746 428 24 | grape20.jpg grape 93 23 182 137 25 | grape21.jpg grape 93 23 182 137 26 | grape21.jpg grape 38 9 104 122 27 | grape21.jpg grape 113 14 179 137 28 | grape21.jpg grape 6 61 60 124 29 | grape22.jpg grape 6 61 60 124 30 | grape23.jpg grape 31 12 177 122 31 | grape23.jpg grape 57 22 126 115 32 | grape23.jpg grape 30 49 72 137 33 | grape24.jpg grape 142 58 191 149 34 | grape24.jpg grape 13 24 91 132 35 | grape25.jpg grape 117 29 192 134 36 | grape26.jpg grape 104 26 197 136 37 | grape3.jpg grape 169 10 686 341 38 | grape5.jpg grape 109 24 188 141 39 | grape6.jpg grape 33 43 202 145 40 | grape7.jpg grape 22 16 103 94 41 | grape7.jpg grape 22 16 103 94 42 | grape8.jpg grape 86 37 185 131 43 | grape8.jpg grape 16 37 113 127 44 | grape9.jpg grape 108 38 187 123 45 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/lemon.txt: -------------------------------------------------------------------------------- 1 | lemon1.jpg lemon 46 70 1178 1027 2 | lemon10.jpg lemon 27 8 197 122 3 | lemon11.jpg lemon 27 8 197 122 4 | lemon12.jpg lemon 1 4 201 144 5 | lemon13.jpg lemon 12 11 179 132 6 | lemon14.jpg lemon 8 21 69 99 7 | lemon14.jpg lemon 69 17 133 102 8 | lemon14.jpg lemon 137 22 192 103 9 | lemon15.jpg lemon 7 13 170 128 10 | lemon16.jpg lemon 7 13 170 128 11 | lemon17.jpg lemon 35 25 159 110 12 | lemon18.jpg lemon 35 25 159 110 13 | lemon19.jpg lemon 7 16 164 127 14 | lemon19.jpg lemon 15 71 80 134 15 | lemon19.jpg lemon 15 71 80 134 16 | lemon19.jpg lemon 81 59 150 131 17 | lemon2.jpg lemon 81 59 150 131 18 | lemon20.jpg lemon 37 9 147 71 19 | lemon20.jpg lemon 101 46 178 128 20 | lemon21.jpg lemon 101 46 178 128 21 | lemon22.jpg lemon 104 32 179 108 22 | lemon23.jpg lemon 52 38 159 106 23 | lemon24.jpg lemon 52 38 159 106 24 | lemon25.jpg lemon 46 39 161 115 25 | lemon25.jpg lemon 86 58 144 98 26 | lemon25.jpg lemon 86 58 144 98 27 | lemon26.jpg lemon 131 42 185 87 28 | lemon27.jpg lemon 57 37 134 115 29 | lemon28.jpg lemon 3 71 43 109 30 | lemon28.jpg lemon 3 71 43 109 31 | lemon28.jpg lemon 36 94 74 131 32 | lemon28.jpg lemon 139 51 168 90 33 | lemon29.jpg lemon 139 51 168 90 34 | lemon29.jpg lemon 3 46 85 115 35 | lemon3.jpg lemon 86 36 155 112 36 | lemon30.jpg lemon 38 15 175 148 37 | lemon4.jpg lemon 9 5 65 80 38 | lemon4.jpg lemon 62 21 142 104 39 | lemon5.jpg lemon 14 52 90 136 40 | lemon5.jpg lemon 14 52 90 136 41 | lemon5.jpg lemon 41 27 128 91 42 | lemon5.jpg lemon 41 27 128 91 43 | lemon6.jpg lemon 17 23 119 129 44 | lemon7.jpg lemon 19 37 121 108 45 | lemon8.jpg lemon 1 13 115 116 46 | lemon9.jpg lemon 1 13 115 116 47 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/litchi.txt: -------------------------------------------------------------------------------- 1 | litchi1.jpg litchi 24 48 96 116 2 | litchi1.jpg litchi 24 48 96 116 3 | litchi10.jpg litchi 96 45 164 119 4 | litchi10.jpg litchi 60 17 116 68 5 | litchi11.jpg litchi 125 24 190 82 6 | litchi11.jpg litchi 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litchi6.jpg litchi 31 32 127 94 34 | litchi7.jpg litchi 31 32 127 94 35 | litchi8.jpg litchi 4 25 94 119 36 | litchi8.jpg litchi 4 25 94 119 37 | litchi9.jpg litchi 51 40 135 121 38 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/mango.txt: -------------------------------------------------------------------------------- 1 | mango1.jpg mango 107 53 279 280 2 | mango1.jpg mango 107 53 279 280 3 | mango1.jpg mango 177 191 401 399 4 | mango10.jpg mango 281 92 494 275 5 | mango11.jpg mango 19 38 175 128 6 | mango12.jpg mango 19 38 175 128 7 | mango12.jpg mango 85 59 201 128 8 | mango13.jpg mango 46 41 179 117 9 | mango14.jpg mango 11 32 119 95 10 | mango15.jpg mango 18 65 113 109 11 | mango15.jpg mango 114 56 204 108 12 | mango16.jpg mango 14 18 210 124 13 | mango17.jpg mango 14 18 210 124 14 | mango18.jpg mango 40 11 161 85 15 | mango19.jpg mango 15 32 59 78 16 | mango19.jpg mango 15 32 59 78 17 | mango19.jpg mango 27 80 86 126 18 | mango19.jpg mango 44 14 88 58 19 | mango19.jpg mango 89 3 124 41 20 | mango19.jpg mango 59 41 171 83 21 | mango19.jpg mango 118 63 167 120 22 | mango2.jpg mango 83 95 119 137 23 | mango20.jpg mango 33 23 548 375 24 | mango21.jpg mango 10 35 123 123 25 | mango21.jpg mango 10 35 123 123 26 | mango22.jpg mango 85 13 200 94 27 | mango23.jpg mango 55 14 155 132 28 | mango24.jpg mango 36 21 102 108 29 | mango24.jpg mango 36 21 102 108 30 | mango25.jpg mango 99 25 153 124 31 | mango26.jpg mango 24 27 87 142 32 | mango26.jpg mango 24 27 87 142 33 | mango27.jpg mango 94 3 171 133 34 | mango27.jpg mango 12 13 101 58 35 | mango27.jpg mango 10 51 112 119 36 | mango27.jpg mango 92 10 173 70 37 | mango28.jpg mango 114 56 201 123 38 | mango3.jpg mango 66 138 310 280 39 | mango3.jpg mango 66 138 310 280 40 | mango3.jpg mango 236 87 430 201 41 | mango4.jpg mango 314 184 542 314 42 | mango4.jpg mango 86 20 256 205 43 | mango4.jpg mango 246 94 570 327 44 | mango5.jpg mango 341 2 756 329 45 | mango5.jpg mango 140 302 541 615 46 | mango5.jpg mango 594 274 1027 644 47 | mango6.jpg mango 135 123 672 456 48 | mango6.jpg mango 135 123 672 456 49 | mango7.jpg mango 522 99 927 353 50 | mango7.jpg mango 141 129 457 346 51 | mango8.jpg mango 27 37 106 125 52 | mango8.jpg mango 27 37 106 125 53 | mango9.jpg mango 2 16 144 141 54 | mango9.jpg mango 123 28 232 143 55 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/mix.txt: -------------------------------------------------------------------------------- 1 | mix13.JPG tomato 167 246 415 487 2 | mix13.JPG orange 444 150 708 440 3 | mix14.JPG orange 331 65 584 344 4 | mix15.JPG tomato 305 135 489 318 5 | mix15.JPG tomato 184 328 409 534 6 | mix15.JPG orange 462 300 701 541 7 | mix1.jpg qiwi 93 26 213 118 8 | mix10.jpg lemon 59 23 94 47 9 | mix10.jpg lemon 45 41 76 72 10 | mix10.jpg lemon 45 41 76 72 11 | mix11.jpg apple 43 29 83 70 12 | mix11.jpg apple 86 44 127 85 13 | mix11.jpg apple 122 35 160 73 14 | mix11.jpg apple 122 35 160 73 15 | mix12.jpg grape 60 11 122 77 16 | mix12.jpg orange 41 72 92 120 17 | mix12.jpg orange 120 48 168 84 18 | mix12.jpg peach 93 79 141 119 19 | mix12.jpg peach 93 79 141 119 20 | mix2.jpg grape 34 13 189 121 21 | mix3.jpg apple 40 33 143 126 22 | mix3.jpg apple 40 33 143 126 23 | mix4.jpg banana 68 26 188 71 24 | mix4.jpg orange 54 47 116 103 25 | mix4.jpg apple 119 60 176 117 26 | mix5.jpg orange 53 9 93 50 27 | mix5.jpg orange 117 19 191 83 28 | mix5.jpg apple 123 80 162 120 29 | mix5.jpg apple 88 64 129 94 30 | mix5.jpg lemon 22 74 55 114 31 | mix5.jpg banana 31 36 127 75 32 | mix6.jpg apple 80 94 134 138 33 | mix6.jpg pear 114 64 162 110 34 | mix7.jpg grape 17 8 97 70 35 | mix7.jpg apple 103 19 142 58 36 | mix7.jpg apple 144 18 190 61 37 | mix7.jpg pear 120 73 148 136 38 | mix7.jpg pear 120 73 148 136 39 | mix7.jpg pear 149 83 188 141 40 | mix7.jpg plum 19 92 56 120 41 | mix7.jpg plum 19 92 56 120 42 | mix8.jpg banana 58 3 144 51 43 | mix8.jpg lemon 155 42 188 76 44 | mix8.jpg lemon 155 42 188 76 45 | mix8.jpg lemon 107 32 151 61 46 | mix8.jpg apple 120 67 162 111 47 | mix8.jpg apple 120 67 162 111 48 | mix8.jpg apple 49 57 81 89 49 | mix9.jpg apple 109 8 143 45 50 | mix9.jpg apple 109 8 143 45 51 | mix9.jpg apple 53 60 83 89 52 | mix9.jpg apple 53 60 83 89 53 | mix9.jpg pear 40 39 84 68 54 | mix9.jpg pear 81 40 122 78 55 | mix9.jpg pear 81 40 122 78 56 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/mooncake.txt: -------------------------------------------------------------------------------- 1 | mooncake1.jpg mooncake 47 26 165 135 2 | mooncake10.jpg mooncake 28 33 93 110 3 | mooncake10.jpg mooncake 28 33 93 110 4 | mooncake10.jpg mooncake 78 7 163 76 5 | mooncake11.jpg mooncake 77 42 142 102 6 | mooncake12.jpg mooncake 77 42 142 102 7 | mooncake12.jpg mooncake 97 88 174 154 8 | mooncake12.jpg mooncake 112 17 170 90 9 | mooncake12.jpg mooncake 101 27 172 85 10 | mooncake12.jpg mooncake 54 0 117 54 11 | mooncake13.jpg mooncake 108 35 147 67 12 | mooncake13.jpg mooncake 85 59 127 92 13 | mooncake13.jpg mooncake 48 92 94 130 14 | mooncake14.jpg mooncake 67 31 164 127 15 | mooncake14.jpg mooncake 22 14 103 86 16 | mooncake15.jpg mooncake 57 20 159 125 17 | mooncake16.jpg mooncake 19 13 142 139 18 | mooncake2.jpg mooncake 57 4 154 64 19 | mooncake2.jpg mooncake 4 55 115 148 20 | mooncake2.jpg mooncake 4 55 115 148 21 | mooncake3.jpg mooncake 119 37 232 138 22 | mooncake4.jpg mooncake 50 29 156 131 23 | mooncake5.jpg mooncake 56 16 173 131 24 | mooncake6.jpg mooncake 0 1 39 45 25 | mooncake6.jpg mooncake 0 1 39 45 26 | mooncake6.jpg mooncake 3 46 43 83 27 | mooncake6.jpg mooncake 2 90 40 131 28 | mooncake6.jpg mooncake 40 87 82 129 29 | mooncake6.jpg mooncake 86 45 126 88 30 | mooncake6.jpg mooncake 88 3 126 41 31 | mooncake6.jpg mooncake 88 3 126 41 32 | mooncake6.jpg mooncake 42 3 84 41 33 | mooncake7.jpg mooncake 48 44 85 86 34 | mooncake7.jpg mooncake 13 26 105 122 35 | mooncake8.jpg mooncake 32 56 119 133 36 | mooncake8.jpg mooncake 32 56 119 133 37 | mooncake9.jpg mooncake 9 10 89 90 38 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/orange.txt: -------------------------------------------------------------------------------- 1 | orange1.jpg orange 284 13 767 499 2 | orange10.jpg orange 16 59 81 129 3 | orange10.jpg orange 84 45 145 111 4 | orange10.jpg orange 84 45 145 111 5 | orange11.jpg orange 141 59 210 129 6 | orange12.jpg orange 6 5 124 126 7 | orange13.jpg orange 11 7 142 134 8 | orange14.jpg orange 16 6 128 137 9 | orange15.jpg orange 16 6 128 137 10 | orange16.jpg orange 6 12 129 132 11 | orange17.jpg orange 8 14 115 126 12 | orange18.jpg orange 45 10 168 136 13 | orange19.jpg orange 17 57 72 127 14 | orange19.jpg orange 17 57 72 127 15 | orange19.jpg orange 80 46 132 110 16 | orange2.jpg orange 294 36 767 510 17 | orange20.jpg orange 25 4 161 141 18 | orange21.jpg orange 55 31 141 114 19 | orange22.jpg orange 39 40 95 100 20 | orange22.jpg orange 39 40 95 100 21 | orange23.jpg orange 56 6 148 118 22 | orange24.jpg orange 46 9 158 120 23 | orange25.jpg orange 80 13 150 75 24 | orange26.jpg orange 31 9 153 127 25 | orange27.jpg orange 34 19 152 118 26 | orange28.jpg orange 34 19 152 118 27 | orange29.jpg orange 59 4 159 115 28 | orange3.jpg orange 212 56 709 544 29 | orange30.jpg orange 2 31 126 152 30 | orange31.jpg orange 2 31 126 152 31 | orange32.jpg orange 10 28 135 156 32 | orange33.jpg orange 4 17 109 118 33 | orange34.jpg orange 17 30 131 135 34 | orange35.jpg orange 7 5 139 140 35 | orange36.jpg orange 17 63 131 168 36 | orange37.jpg orange 17 18 118 109 37 | orange37.jpg orange 15 106 126 210 38 | orange38.jpg orange 64 25 152 115 39 | orange39.jpg orange 64 20 150 119 40 | orange4.jpg orange 319 63 719 471 41 | orange40.jpg orange 319 63 719 471 42 | orange41.jpg orange 63 17 138 106 43 | orange42.jpg orange 15 21 122 122 44 | orange5.jpg orange 15 13 137 140 45 | orange5.jpg orange 76 64 139 132 46 | orange5.jpg orange 76 64 139 132 47 | orange5.jpg orange 117 12 179 77 48 | orange6.jpg orange 138 68 204 134 49 | orange7.jpg orange 67 21 168 128 50 | orange8.jpg orange 67 21 168 128 51 | orange9.jpg orange 37 16 106 75 52 | orange9.jpg orange 37 16 106 75 53 | orange9.jpg orange 110 19 193 98 54 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/peach.txt: -------------------------------------------------------------------------------- 1 | peach1.jpg peach 48 46 94 103 2 | peach1.jpg peach 83 71 133 133 3 | peach1.jpg peach 83 71 133 133 4 | peach1.jpg peach 160 56 206 109 5 | peach1.jpg peach 160 56 206 109 6 | peach10.jpg peach 25 15 81 78 7 | peach10.jpg peach 54 69 121 131 8 | peach10.jpg peach 54 69 121 131 9 | peach11.jpg peach 6 10 116 121 10 | peach11.jpg peach 6 10 116 121 11 | peach12.jpg peach 26 27 86 76 12 | peach12.jpg peach 80 17 123 64 13 | peach12.jpg peach 80 17 123 64 14 | peach12.jpg peach 45 76 98 130 15 | peach12.jpg peach 154 62 196 107 16 | peach12.jpg peach 45 4 87 32 17 | peach13.jpg peach 40 25 130 113 18 | peach14.jpg peach 50 45 126 132 19 | peach14.jpg peach 1 6 69 104 20 | peach14.jpg peach 1 6 69 104 21 | peach14.jpg peach 74 4 153 59 22 | peach2.jpg peach 49 5 230 205 23 | peach3.jpg peach 61 20 140 114 24 | peach4.jpg peach 20 38 131 137 25 | peach4.jpg peach 20 38 131 137 26 | peach5.jpg peach 65 47 137 124 27 | peach6.jpg peach 11 54 82 111 28 | peach6.jpg peach 11 54 82 111 29 | peach6.jpg peach 90 39 151 103 30 | peach7.jpg peach 151 47 211 110 31 | peach7.jpg peach 4 26 71 95 32 | peach7.jpg peach 117 64 192 141 33 | peach7.jpg peach 117 64 192 141 34 | peach7.jpg peach 80 0 139 66 35 | peach8.jpg peach 39 6 168 127 36 | peach9.jpg peach 29 5 175 140 37 | peach9.jpg peach 26 50 95 122 38 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/pear.txt: -------------------------------------------------------------------------------- 1 | pear1.jpg pear 13 7 244 223 2 | pear10.jpg pear 10 23 73 102 3 | pear10.jpg pear 10 23 73 102 4 | pear11.jpg pear 38 24 146 108 5 | pear12.jpg pear 9 22 101 104 6 | pear13.jpg pear 7 14 67 95 7 | pear13.jpg pear 74 15 135 90 8 | pear14.jpg pear 15 43 67 113 9 | pear14.jpg pear 66 31 124 113 10 | pear15.jpg pear 45 33 120 121 11 | pear15.jpg pear 114 47 181 117 12 | pear16.jpg pear 15 27 103 144 13 | pear17.jpg pear 36 32 103 115 14 | pear17.jpg pear 36 32 103 115 15 | pear18.jpg pear 28 40 83 108 16 | pear18.jpg pear 28 40 83 108 17 | pear18.jpg pear 71 42 149 119 18 | pear19.jpg pear 139 46 193 110 19 | pear19.jpg pear 23 65 95 138 20 | pear2.jpg pear 101 53 171 132 21 | pear2.jpg pear 11 44 123 142 22 | pear2.jpg pear 131 3 226 41 23 | pear20.jpg pear 131 3 226 41 24 | pear21.jpg pear 41 38 89 111 25 | pear21.jpg pear 104 45 167 124 26 | pear22.jpg pear 7 45 81 121 27 | pear22.jpg pear 7 45 81 121 28 | pear23.jpg pear 27 42 75 106 29 | pear23.jpg pear 62 58 134 120 30 | pear24.jpg pear 15 26 86 120 31 | pear25.jpg pear 19 44 120 135 32 | pear25.jpg pear 95 26 190 98 33 | pear26.jpg pear 12 20 107 121 34 | pear26.jpg pear 12 20 107 121 35 | pear27.jpg pear 5 46 91 136 36 | pear27.jpg pear 34 7 114 62 37 | pear27.jpg pear 34 7 114 62 38 | pear28.jpg pear 5 39 64 98 39 | pear28.jpg pear 5 39 64 98 40 | pear28.jpg pear 61 43 117 112 41 | pear29.jpg pear 74 16 138 75 42 | pear29.jpg pear 41 65 102 124 43 | pear29.jpg pear 41 65 102 124 44 | pear3.jpg pear 64 9 179 133 45 | pear30.jpg pear 33 39 103 130 46 | pear31.jpg pear 4 20 88 109 47 | pear31.jpg pear 4 20 88 109 48 | pear32.jpg pear 90 21 182 113 49 | pear32.jpg pear 22 38 98 118 50 | pear33.jpg pear 103 35 184 109 51 | pear33.jpg pear 22 20 98 66 52 | pear33.jpg pear 6 53 76 126 53 | pear34.jpg pear 16 58 88 126 54 | pear34.jpg pear 16 58 88 126 55 | pear35.jpg pear 16 18 85 94 56 | pear35.jpg pear 16 18 85 94 57 | pear36.jpg pear 7 15 61 69 58 | pear36.jpg pear 20 52 109 139 59 | pear36.jpg pear 94 20 152 78 60 | pear36.jpg pear 94 20 152 78 61 | pear37.jpg pear 14 69 116 166 62 | pear38.jpg pear 12 12 70 74 63 | pear38.jpg pear 2 12 68 78 64 | pear38.jpg pear 74 5 140 74 65 | pear39.jpg pear 27 51 106 148 66 | pear4.jpg pear 17 29 101 124 67 | pear4.jpg pear 17 29 101 124 68 | pear5.jpg pear 41 16 147 126 69 | pear6.jpg pear 41 16 147 126 70 | pear7.jpg pear 23 26 109 134 71 | pear7.jpg pear 11 30 108 139 72 | pear8.jpg pear 38 108 130 204 73 | pear9.jpg pear 43 16 156 126 74 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/plum.txt: -------------------------------------------------------------------------------- 1 | plum1.jpg plum 43 24 188 148 2 | plum10.jpg plum 43 24 188 148 3 | plum11.jpg plum 17 43 83 112 4 | plum12.jpg plum 30 43 114 126 5 | plum12.jpg plum 30 43 114 126 6 | plum13.jpg plum 105 35 181 98 7 | plum13.jpg plum 98 74 161 137 8 | plum14.jpg plum 30 61 66 101 9 | plum14.jpg plum 56 47 92 76 10 | plum14.jpg plum 56 47 92 76 11 | plum14.jpg plum 88 49 123 81 12 | plum14.jpg plum 132 55 166 87 13 | plum14.jpg plum 70 82 105 112 14 | plum14.jpg plum 106 91 140 118 15 | plum15.jpg plum 8 35 99 139 16 | plum15.jpg plum 8 35 99 139 17 | plum16.jpg plum 98 17 192 106 18 | plum16.jpg plum 9 56 89 122 19 | plum16.jpg plum 9 56 89 122 20 | plum17.jpg plum 21 19 109 128 21 | plum17.jpg plum 21 19 109 128 22 | plum18.jpg plum 8 38 71 107 23 | plum18.jpg plum 72 33 141 109 24 | plum19.jpg plum 8 38 83 112 25 | plum19.jpg plum 8 38 83 112 26 | plum19.jpg plum 140 48 221 119 27 | plum2.jpg plum 28 20 113 100 28 | plum2.jpg plum 28 20 113 100 29 | plum20.jpg plum 15 10 123 128 30 | plum21.jpg plum 55 43 142 118 31 | plum22.jpg plum 17 65 87 135 32 | plum23.jpg plum 23 16 151 136 33 | plum24.jpg plum 23 16 151 136 34 | plum24.jpg plum 9 66 92 138 35 | plum24.jpg plum 96 55 193 142 36 | plum25.jpg plum 45 4 87 37 37 | plum25.jpg plum 45 4 87 37 38 | plum25.jpg plum 9 72 48 110 39 | plum25.jpg plum 48 38 91 71 40 | plum25.jpg plum 88 26 132 62 41 | plum25.jpg plum 84 66 133 105 42 | plum25.jpg plum 84 66 133 105 43 | plum25.jpg plum 125 54 168 92 44 | plum26.jpg plum 150 86 197 135 45 | plum3.jpg plum 35 30 148 130 46 | plum3.jpg plum 43 64 125 147 47 | plum3.jpg plum 43 64 125 147 48 | plum4.jpg plum 13 17 87 126 49 | plum4.jpg plum 7 20 98 135 50 | plum5.jpg plum 83 42 155 120 51 | plum5.jpg plum 83 42 155 120 52 | plum6.jpg plum 34 24 98 94 53 | plum6.jpg plum 160 62 223 118 54 | plum6.jpg plum 41 32 91 86 55 | plum6.jpg plum 104 18 163 62 56 | plum6.jpg plum 72 6 120 41 57 | plum7.jpg plum 42 8 92 51 58 | plum7.jpg plum 5 43 54 94 59 | plum7.jpg plum 82 56 135 106 60 | plum7.jpg plum 82 56 135 106 61 | plum8.jpg plum 115 11 170 60 62 | plum9.jpg plum 26 8 149 130 63 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/sachima.txt: -------------------------------------------------------------------------------- 1 | sachima1.jpg sachima 10 159 353 316 2 | sachima1.jpg sachima 20 317 350 474 3 | sachima1.jpg sachima 20 317 350 474 4 | sachima10.jpg sachima 21 51 150 135 5 | sachima11.jpg sachima 34 26 179 122 6 | sachima12.jpg sachima 16 27 106 129 7 | sachima12.jpg sachima 103 3 210 122 8 | sachima13.jpg sachima 103 3 210 122 9 | sachima14.jpg sachima 23 50 144 136 10 | sachima14.jpg sachima 18 13 132 67 11 | sachima15.jpg sachima 18 13 132 67 12 | sachima15.jpg sachima 4 62 114 137 13 | sachima15.jpg sachima 4 62 114 137 14 | sachima16.jpg sachima 7 55 76 134 15 | sachima16.jpg sachima 49 36 147 115 16 | sachima17.jpg sachima 34 37 98 113 17 | sachima17.jpg sachima 34 37 98 113 18 | sachima18.jpg sachima 95 37 160 108 19 | sachima18.jpg sachima 8 83 94 146 20 | sachima18.jpg sachima 8 83 94 146 21 | sachima19.jpg sachima 19 12 96 113 22 | sachima19.jpg sachima 90 36 135 112 23 | sachima2.jpg sachima 7 117 300 400 24 | sachima2.jpg sachima 249 10 636 370 25 | sachima20.jpg sachima 249 10 636 370 26 | sachima21.jpg sachima 9 49 144 136 27 | sachima21.jpg sachima 6 34 140 125 28 | sachima22.jpg sachima 15 12 98 82 29 | sachima22.jpg sachima 4 82 87 136 30 | sachima22.jpg sachima 68 50 147 133 31 | sachima23.jpg sachima 89 18 173 108 32 | sachima23.jpg sachima 31 38 113 112 33 | sachima24.jpg sachima 11 26 99 98 34 | sachima24.jpg sachima 11 26 99 98 35 | sachima25.jpg sachima 1 23 148 108 36 | sachima26.jpg sachima 4 14 172 138 37 | sachima3.jpg sachima 253 121 811 642 38 | sachima4.jpg sachima 253 121 811 642 39 | sachima5.jpg sachima 32 6 178 132 40 | sachima5.jpg sachima 58 7 173 80 41 | sachima5.jpg sachima 19 52 129 131 42 | sachima6.jpg sachima 119 53 197 116 43 | sachima6.jpg sachima 24 84 139 145 44 | sachima7.jpg sachima 24 84 139 145 45 | sachima8.jpg sachima 69 68 174 141 46 | sachima9.jpg sachima 25 32 187 123 47 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/img/tomato.txt: -------------------------------------------------------------------------------- 1 | tomato1.jpg tomato 461 29 621 204 2 | tomato1.jpg tomato 461 29 621 204 3 | tomato10.jpg tomato 421 194 613 384 4 | tomato10.jpg tomato 4 50 72 107 5 | tomato10.jpg tomato 40 87 117 143 6 | tomato10.jpg tomato 40 87 117 143 7 | tomato10.jpg tomato 81 43 167 98 8 | tomato10.jpg tomato 81 43 167 98 9 | tomato10.jpg tomato 170 39 242 94 10 | tomato11.jpg tomato 178 44 248 96 11 | tomato11.jpg tomato 65 17 136 78 12 | tomato12.jpg tomato 6 11 159 148 13 | tomato12.jpg tomato 123 14 210 101 14 | tomato13.jpg tomato 8 19 71 74 15 | tomato13.jpg tomato 30 50 121 128 16 | tomato13.jpg tomato 30 50 121 128 17 | tomato14.jpg tomato 25 28 133 125 18 | tomato15.jpg tomato 32 5 184 154 19 | tomato16.jpg tomato 7 41 111 135 20 | tomato16.jpg tomato 7 41 111 135 21 | tomato17.jpg tomato 10 7 112 105 22 | tomato18.jpg tomato 10 7 112 105 23 | tomato19.jpg tomato 18 19 127 117 24 | tomato2.jpg tomato 220 151 399 311 25 | tomato2.jpg tomato 220 151 399 311 26 | tomato20.jpg tomato 2 4 66 62 27 | tomato20.jpg tomato 71 12 111 52 28 | tomato20.jpg tomato 71 12 111 52 29 | tomato20.jpg tomato 10 93 49 127 30 | tomato21.jpg tomato 14 10 124 129 31 | tomato22.jpg tomato 28 3 160 149 32 | tomato23.jpg tomato 28 3 160 149 33 | tomato24.jpg tomato 42 14 161 129 34 | tomato25.jpg tomato 42 14 161 129 35 | tomato26.jpg tomato 1 9 75 83 36 | tomato26.jpg tomato 78 23 141 79 37 | tomato26.jpg tomato 106 80 171 144 38 | tomato26.jpg tomato 35 70 105 137 39 | tomato27.jpg tomato 59 21 170 129 40 | tomato28.jpg tomato 59 21 170 129 41 | tomato29.jpg tomato 19 5 174 146 42 | tomato3.jpg tomato 353 73 514 246 43 | tomato3.jpg tomato 205 261 392 434 44 | tomato30.jpg tomato 11 11 117 125 45 | tomato30.jpg tomato 11 11 117 125 46 | tomato31.jpg tomato 36 43 132 142 47 | tomato32.jpg tomato 30 11 156 126 48 | tomato33.jpg tomato 36 28 146 111 49 | tomato34.jpg tomato 7 11 148 132 50 | tomato35.jpg tomato 16 28 138 127 51 | tomato36.jpg tomato 16 28 138 127 52 | tomato37.jpg tomato 40 32 118 106 53 | tomato38.jpg tomato 40 32 118 106 54 | tomato39.jpg tomato 16 60 139 184 55 | tomato4.jpg tomato 417 66 581 246 56 | tomato4.jpg tomato 291 294 483 473 57 | tomato40.jpg tomato 291 294 483 473 58 | tomato41.jpg tomato 43 12 160 112 59 | tomato42.jpg tomato 39 105 133 194 60 | tomato5.jpg tomato 11 35 116 155 61 | tomato6.jpg tomato 272 142 758 650 62 | tomato7.jpg tomato 272 142 758 650 63 | tomato8.jpg tomato 20 18 145 122 64 | tomato9.jpg tomato 18 43 108 124 65 | -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/rename_images.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/CreateDatasets-master/rename_images.m -------------------------------------------------------------------------------- /faster_rcnn-master/CreateDatasets-master/rename_images.py: -------------------------------------------------------------------------------- 1 | # _*_ coding:utf-8 _*_ 2 | # 3 | from PIL import Image 4 | import os 5 | import re 6 | import sys 7 | 8 | if __name__ == "__main__": 9 | 10 | if(len(sys.argv) != 2): 11 | print("args is not 2!") 12 | sys.exit(1); 13 | 14 | BASE_PATH = sys.argv[1]; 15 | print(BASE_PATH) 16 | 17 | # os.walk返回一个walk对象，是一个三元组 18 | for rootpath, dirnames, filenames in os.walk(BASE_PATH): 19 | name_number = 1 20 | name_length = 6 21 | for filename in filenames: 22 | #if(re.match("^(\d*).jpg", filename)): 23 | name_str = (name_length - len(str(name_number))) * '0' + str(name_number) + ".jpg" 24 | print name_str, '\n' 25 | name_number = name_number + 1 26 | image = Image.open(os.path.join(BASE_PATH, filename)) 27 | image.save(os.path.join(BASE_PATH, name_str), 'jpeg') 28 | 29 | 30 | -------------------------------------------------------------------------------- /faster_rcnn-master/ECUSTFD_ORIGIN_IMAGE_TEST.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/ECUSTFD_ORIGIN_IMAGE_TEST.m -------------------------------------------------------------------------------- /faster_rcnn-master/ECUSTFD_TEST.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/ECUSTFD_TEST.m -------------------------------------------------------------------------------- /faster_rcnn-master/RGB2Gray.cpp: -------------------------------------------------------------------------------- 1 | // Interface: convert an image to gray and return to Matlab 2 | // Author : zouxy 3 | // Date : 2014-03-05 4 | // HomePage : http://blog.csdn.net/zouxy09 5 | // Email : zouxy09@qq.com 6 | 7 | #include "opencv2/opencv.hpp" 8 | #include "mex.h" 9 | 10 | using namespace cv; 11 | 12 | /******************************************************* 13 | Usage: [imageMatrix] = RGB2Gray('imageFile.jpeg'); 14 | Input: 15 | a image file 16 | OutPut: 17 | a matrix of image which can be read by Matlab 18 | 19 | **********************************************************/ 20 | 21 | 22 | void exit_with_help() 23 | { 24 | mexPrintf( 25 | "Usage: [imageMatrix] = DenseTrack('imageFile.jpg');\n" 26 | ); 27 | } 28 | 29 | static void fake_answer(mxArray *plhs[]) 30 | { 31 | plhs[0] = mxCreateDoubleMatrix(0, 0, mxREAL); 32 | } 33 | 34 | void RGB2Gray(char *filename, mxArray *plhs[]) 35 | { 36 | // read the image 37 | Mat image = imread(filename); 38 | if(image.empty()) { 39 | mexPrintf("can't open input file %s\n", filename); 40 | fake_answer(plhs); 41 | return; 42 | } 43 | 44 | // convert it to gray format 45 | Mat gray; 46 | if (image.channels() == 3) 47 | cvtColor(image, gray, CV_RGB2GRAY); 48 | else 49 | image.copyTo(gray); 50 | 51 | // convert the result to Matlab-supported format for returning 52 | int rows = gray.rows; 53 | int cols = gray.cols; 54 | plhs[0] = mxCreateDoubleMatrix(rows, cols, mxREAL); 55 | double *imgMat; 56 | imgMat = mxGetPr(plhs[0]); 57 | for (int i = 0; i < rows; i++) 58 | for (int j = 0; j < cols; j++) 59 | *(imgMat + i + j * rows) = (double)gray.at(i, j); 60 | 61 | return; 62 | } 63 | 64 | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) 65 | { 66 | if(nrhs == 1) 67 | { 68 | char filename[256]; 69 | mxGetString(prhs[0], filename, mxGetN(prhs[0]) + 1); 70 | if(filename == NULL) 71 | { 72 | mexPrintf("Error: filename is NULL\n"); 73 | exit_with_help(); 74 | return; 75 | } 76 | 77 | RGB2Gray(filename, plhs); 78 | } 79 | else 80 | { 81 | exit_with_help(); 82 | fake_answer(plhs); 83 | return; 84 | } 85 | } -------------------------------------------------------------------------------- /faster_rcnn-master/RGB2Gray.mexw64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/RGB2Gray.mexw64 -------------------------------------------------------------------------------- /faster_rcnn-master/bin/nms_mex.mexw64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/bin/nms_mex.mexw64 -------------------------------------------------------------------------------- /faster_rcnn-master/density.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/density.xls -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Dataset/private/voc0712_devkit.m: -------------------------------------------------------------------------------- 1 | function path = voc0712_devkit() 2 | path = './datasets/VOCdevkit0712'; 3 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Dataset/private/voc2007_devkit.m: -------------------------------------------------------------------------------- 1 | function path = voc2007_devkit() 2 | path = './datasets/VOCdevkit2007'; 3 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Dataset/private/voc2012_devkit.m: -------------------------------------------------------------------------------- 1 | function path = voc2012_devkit() 2 | path = './datasets/VOCdevkit2012'; 3 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Dataset/voc0712_trainval.m: -------------------------------------------------------------------------------- 1 | function dataset = voc0712_trainval(dataset, usage, use_flip) 2 | % Pascal voc 0712 trainval set 3 | % set opts.imdb_train opts.roidb_train 4 | % or set opts.imdb_test opts.roidb_train 5 | 6 | % change to point to your devkit install 7 | devkit2007 = voc2007_devkit(); 8 | devkit2012 = voc2012_devkit(); 9 | 10 | switch usage 11 | case {'train'} 12 | dataset.imdb_train = { imdb_from_voc(devkit2007, 'trainval', '2007', use_flip), ... 13 | imdb_from_voc(devkit2012, 'trainval', '2012', use_flip)}; 14 | dataset.roidb_train = cellfun(@(x) x.roidb_func(x), dataset.imdb_train, 'UniformOutput', false); 15 | case {'test'} 16 | error('only supports one source test currently'); 17 | otherwise 18 | error('usage = ''train'' or ''test'''); 19 | end 20 | 21 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Dataset/voc0712_trainval_ss.m: -------------------------------------------------------------------------------- 1 | function dataset = voc0712_trainval_ss(dataset, usage, use_flip) 2 | % Pascal voc 0712 trainval set with selective search 3 | % set opts.imdb_train opts.roidb_train 4 | % or set opts.imdb_test opts.roidb_train 5 | 6 | % change to point to your devkit install 7 | devkit2007 = voc2007_devkit(); 8 | devkit2012 = voc2012_devkit(); 9 | 10 | switch usage 11 | case {'train'} 12 | dataset.imdb_train = { imdb_from_voc(devkit2007, 'trainval', '2007', use_flip), ... 13 | imdb_from_voc(devkit2012, 'trainval', '2012', use_flip)}; 14 | dataset.roidb_train = cellfun(@(x) x.roidb_func(x, 'with_selective_search', true), dataset.imdb_train, 'UniformOutput', false); 15 | case {'test'} 16 | error('only supports one source test currently'); 17 | otherwise 18 | error('usage = ''train'' or ''test'''); 19 | end 20 | 21 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Dataset/voc0712plus_trainval.m: -------------------------------------------------------------------------------- 1 | function dataset = voc0712plus_trainval(dataset, usage, use_flip) 2 | % Pascal voc 0712 trainval set 3 | % set opts.imdb_train opts.roidb_train 4 | % or set opts.imdb_test opts.roidb_train 5 | 6 | % change to point to your devkit install 7 | devkit2007 = voc2007_devkit(); 8 | devkit2012 = voc2012_devkit(); 9 | 10 | switch usage 11 | case {'train'} 12 | dataset.imdb_train = { imdb_from_voc(devkit2012, 'trainval', '2012', use_flip), ... 13 | imdb_from_voc(devkit2007, 'trainval', '2007', use_flip), ... 14 | imdb_from_voc(devkit2007, 'test', '2007', use_flip)}; 15 | dataset.roidb_train = cellfun(@(x) x.roidb_func(x), dataset.imdb_train, 'UniformOutput', false); 16 | case {'test'} 17 | error('only supports one source test currently'); 18 | otherwise 19 | error('usage = ''train'' or ''test'''); 20 | end 21 | 22 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Dataset/voc0712plus_trainval_ss.m: -------------------------------------------------------------------------------- 1 | function dataset = voc0712plus_trainval_ss(dataset, usage, use_flip) 2 | % Pascal voc 0712 trainval set with selective search 3 | % set opts.imdb_train opts.roidb_train 4 | % or set opts.imdb_test opts.roidb_train 5 | 6 | % change to point to your devkit install 7 | devkit2007 = voc2007_devkit(); 8 | devkit2012 = voc2012_devkit(); 9 | 10 | switch usage 11 | case {'train'} 12 | dataset.imdb_train = { imdb_from_voc(devkit2012, 'trainval', '2012', use_flip), ... 13 | imdb_from_voc(devkit2007, 'trainval', '2007', use_flip), ... 14 | imdb_from_voc(devkit2007, 'test', '2007', use_flip)}; 15 | dataset.roidb_train = cellfun(@(x) x.roidb_func(x, 'with_selective_search', true), dataset.imdb_train, 'UniformOutput', false); 16 | case {'test'} 17 | error('only supports one source test currently'); 18 | otherwise 19 | error('usage = ''train'' or ''test'''); 20 | end 21 | 22 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Dataset/voc2007_test.m: -------------------------------------------------------------------------------- 1 | function dataset = voc2007_test(dataset, usage, use_flip) 2 | % Pascal voc 2007 test set 3 | % set opts.imdb_train opts.roidb_train 4 | % or set opts.imdb_test opts.roidb_train 5 | 6 | % change to point to your devkit install 7 | devkit = voc2007_devkit(); 8 | 9 | switch usage 10 | case {'train'} 11 | dataset.imdb_train = { imdb_from_voc(devkit, 'test', '2007', use_flip) }; 12 | dataset.roidb_train = cellfun(@(x) x.roidb_func(x), dataset.imdb_train, 'UniformOutput', false); 13 | case {'test'} 14 | dataset.imdb_test = imdb_from_voc(devkit, 'test', '2007', use_flip) ; 15 | dataset.roidb_test = dataset.imdb_test.roidb_func(dataset.imdb_test); 16 | otherwise 17 | error('usage = ''train'' or ''test'''); 18 | end 19 | 20 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Dataset/voc2007_test_ss.m: -------------------------------------------------------------------------------- 1 | function dataset = voc2007_test_ss(dataset, usage, use_flip) 2 | % Pascal voc 2007 test set with selective search 3 | % set opts.imdb_train opts.roidb_train 4 | % or set opts.imdb_test opts.roidb_train 5 | 6 | % change to point to your devkit install 7 | devkit = voc2007_devkit(); 8 | 9 | switch usage 10 | case {'train'} 11 | dataset.imdb_train = { imdb_from_voc(devkit, 'test', '2007', use_flip) }; 12 | dataset.roidb_train = cellfun(@(x) x.roidb_func(x, 'with_selective_search', true), dataset.imdb_train, 'UniformOutput', false); 13 | case {'test'} 14 | dataset.imdb_test = imdb_from_voc(devkit, 'test', '2007', use_flip) ; 15 | dataset.roidb_test = dataset.imdb_test.roidb_func(dataset.imdb_test, 'with_selective_search', true); 16 | otherwise 17 | error('usage = ''train'' or ''test'''); 18 | end 19 | 20 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Dataset/voc2007_trainval.m: -------------------------------------------------------------------------------- 1 | function dataset = voc2007_trainval(dataset, usage, use_flip) 2 | % Pascal voc 2007 trainval set 3 | % set opts.imdb_train opts.roidb_train 4 | % or set opts.imdb_test opts.roidb_train 5 | 6 | % change to point to your devkit install 7 | devkit = voc2007_devkit(); 8 | 9 | switch usage 10 | case {'train'} 11 | dataset.imdb_train = { imdb_from_voc(devkit, 'trainval', '2007', use_flip) }; 12 | dataset.roidb_train = cellfun(@(x) x.roidb_func(x), dataset.imdb_train, 'UniformOutput', false); 13 | case {'test'} 14 | dataset.imdb_test = imdb_from_voc(devkit, 'trainval', '2007', use_flip) ; 15 | dataset.roidb_test = dataset.imdb_test.roidb_func(dataset.imdb_test); 16 | otherwise 17 | error('usage = ''train'' or ''test'''); 18 | end 19 | 20 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Dataset/voc2007_trainval_ss.m: -------------------------------------------------------------------------------- 1 | function dataset = voc2007_trainval_ss(dataset, usage, use_flip) 2 | % Pascal voc 2007 trainval set with selective search 3 | % set opts.imdb_train opts.roidb_train 4 | % or set opts.imdb_test opts.roidb_train 5 | 6 | % change to point to your devkit install 7 | devkit = voc2007_devkit(); 8 | 9 | switch usage 10 | case {'train'} 11 | dataset.imdb_train = { imdb_from_voc(devkit, 'trainval', '2007', use_flip) }; 12 | dataset.roidb_train = cellfun(@(x) x.roidb_func(x, 'with_selective_search', true), dataset.imdb_train, 'UniformOutput', false); 13 | case {'test'} 14 | dataset.imdb_test = imdb_from_voc(devkit, 'trainval', '2007', use_flip) ; 15 | dataset.roidb_test = dataset.imdb_test.roidb_func(dataset.imdb_test, 'with_selective_search', true); 16 | otherwise 17 | error('usage = ''train'' or ''test'''); 18 | end 19 | 20 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Dataset/voc2012_test.m: -------------------------------------------------------------------------------- 1 | function dataset = voc2012_test(dataset, usage, use_flip) 2 | % Pascal voc 2012 test set 3 | % set opts.imdb_train opts.roidb_train 4 | % or set opts.imdb_test opts.roidb_train 5 | 6 | % change to point to your devkit install 7 | devkit = voc2012_devkit(); 8 | 9 | switch usage 10 | case {'train'} 11 | dataset.imdb_train = { imdb_from_voc(devkit, 'test', '2012', use_flip) }; 12 | dataset.roidb_train = cellfun(@(x) x.roidb_func(x), dataset.imdb_train, 'UniformOutput', false); 13 | case {'test'} 14 | dataset.imdb_test = imdb_from_voc(devkit, 'test', '2012', use_flip) ; 15 | dataset.roidb_test = dataset.imdb_test.roidb_func(dataset.imdb_test); 16 | otherwise 17 | error('usage = ''train'' or ''test'''); 18 | end 19 | 20 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Dataset/voc2012_trainval.m: -------------------------------------------------------------------------------- 1 | function dataset = voc2012_trainval(dataset, usage, use_flip) 2 | % Pascal voc 2012 trainval set 3 | % set opts.imdb_train opts.roidb_train 4 | % or set opts.imdb_test opts.roidb_train 5 | 6 | % change to point to your devkit install 7 | devkit = voc2012_devkit(); 8 | 9 | switch usage 10 | case {'train'} 11 | dataset.imdb_train = { imdb_from_voc(devkit, 'trainval', '2012', use_flip) }; 12 | dataset.roidb_train = cellfun(@(x) x.roidb_func(x), dataset.imdb_train, 'UniformOutput', false); 13 | case {'test'} 14 | dataset.imdb_test = imdb_from_voc(devkit, 'trainval', '2012', use_flip) ; 15 | dataset.roidb_test = dataset.imdb_test.roidb_func(dataset.imdb_test); 16 | otherwise 17 | error('usage = ''train'' or ''test'''); 18 | end 19 | 20 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Faster_RCNN_Train/do_fast_rcnn_test.m: -------------------------------------------------------------------------------- 1 | function mAP = do_fast_rcnn_test(conf, model_stage, imdb, roidb, ignore_cache) 2 | if ~exist('ignore_cache', 'var') 3 | ignore_cache = false; 4 | end 5 | 6 | mAP = fast_rcnn_test(conf, imdb, roidb, ... 7 | 'net_def_file', model_stage.test_net_def_file, ... 8 | 'net_file', model_stage.output_model_file, ... 9 | 'cache_name', model_stage.cache_name, ... 10 | 'ignore_cache', ignore_cache); 11 | end 12 | -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Faster_RCNN_Train/do_fast_rcnn_train.m: -------------------------------------------------------------------------------- 1 | function model_stage = do_fast_rcnn_train(conf, dataset, model_stage, do_val) 2 | if ~do_val 3 | dataset.imdb_test = struct(); 4 | dataset.roidb_test = struct(); 5 | end 6 | 7 | model_stage.output_model_file = fast_rcnn_train(conf, dataset.imdb_train, dataset.roidb_train, ... 8 | 'do_val', do_val, ... 9 | 'imdb_val', dataset.imdb_test, ... 10 | 'roidb_val', dataset.roidb_test, ... 11 | 'solver_def_file', model_stage.solver_def_file, ... 12 | 'net_file', model_stage.init_net_file, ... 13 | 'cache_name', model_stage.cache_name); 14 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Faster_RCNN_Train/do_proposal_test.m: -------------------------------------------------------------------------------- 1 | function roidb_new = do_proposal_test(conf, model_stage, imdb, roidb) 2 | aboxes = proposal_test(conf, imdb, ... 3 | 'net_def_file', model_stage.test_net_def_file, ... 4 | 'net_file', model_stage.output_model_file, ... 5 | 'cache_name', model_stage.cache_name); 6 | 7 | aboxes = boxes_filter(aboxes, model_stage.nms.per_nms_topN, model_stage.nms.nms_overlap_thres, model_stage.nms.after_nms_topN, conf.use_gpu); 8 | 9 | roidb_regions = make_roidb_regions(aboxes, imdb.image_ids); 10 | 11 | roidb_new = roidb_from_proposal(imdb, roidb, roidb_regions, ... 12 | 'keep_raw_proposal', false); 13 | end 14 | 15 | function aboxes = boxes_filter(aboxes, per_nms_topN, nms_overlap_thres, after_nms_topN, use_gpu) 16 | % to speed up nms 17 | if per_nms_topN > 0 18 | aboxes = cellfun(@(x) x(1:min(length(x), per_nms_topN), :), aboxes, 'UniformOutput', false); 19 | end 20 | % do nms 21 | if nms_overlap_thres > 0 && nms_overlap_thres < 1 22 | if use_gpu 23 | for i = 1:length(aboxes) 24 | aboxes{i} = aboxes{i}(nms(aboxes{i}, nms_overlap_thres, use_gpu), :); 25 | end 26 | else 27 | parfor i = 1:length(aboxes) 28 | aboxes{i} = aboxes{i}(nms(aboxes{i}, nms_overlap_thres), :); 29 | end 30 | end 31 | end 32 | aver_boxes_num = mean(cellfun(@(x) size(x, 1), aboxes, 'UniformOutput', true)); 33 | fprintf('aver_boxes_num = %d, select top %d\n', round(aver_boxes_num), after_nms_topN); 34 | if after_nms_topN > 0 35 | aboxes = cellfun(@(x) x(1:min(length(x), after_nms_topN), :), aboxes, 'UniformOutput', false); 36 | end 37 | end 38 | 39 | function regions = make_roidb_regions(aboxes, images) 40 | regions.boxes = aboxes; 41 | regions.images = images; 42 | end 43 | -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Faster_RCNN_Train/do_proposal_train.m: -------------------------------------------------------------------------------- 1 | function model_stage = do_proposal_train(conf, dataset, model_stage, do_val) 2 | if ~do_val 3 | dataset.imdb_test = struct(); 4 | dataset.roidb_test = struct(); 5 | end 6 | 7 | model_stage.output_model_file = proposal_train(conf, dataset.imdb_train, dataset.roidb_train, ... 8 | 'do_val', do_val, ... 9 | 'imdb_val', dataset.imdb_test, ... 10 | 'roidb_val', dataset.roidb_test, ... 11 | 'solver_def_file', model_stage.solver_def_file, ... 12 | 'net_file', model_stage.init_net_file, ... 13 | 'cache_name', model_stage.cache_name); 14 | end 15 | -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Faster_RCNN_Train/set_cache_folder.m: -------------------------------------------------------------------------------- 1 | function model = set_cache_folder(cache_base_proposal, cache_base_fast_rcnn, model) 2 | % model = set_cache_folder(cache_base_proposal, cache_base_fast_rcnn, model) 3 | % -------------------------------------------------------- 4 | % Faster R-CNN 5 | % Copyright (c) 2015, Shaoqing Ren 6 | % Licensed under The MIT License [see LICENSE for details] 7 | % -------------------------------------------------------- 8 | 9 | model.stage1_rpn.cache_name = [cache_base_proposal, '_stage1_rpn']; 10 | 11 | model.stage1_fast_rcnn.cache_name = ... 12 | [cache_base_proposal, ... 13 | strrep(sprintf('_top%d_nms%g_top%d', model.stage1_rpn.nms.per_nms_topN, ... 14 | model.stage1_rpn.nms.nms_overlap_thres, model.stage1_rpn.nms.after_nms_topN), '.', '_'), ... 15 | cache_base_fast_rcnn, '_stage1_fast_rcnn']; 16 | 17 | model.stage2_rpn.cache_name = ... 18 | [cache_base_proposal, ... 19 | strrep(sprintf('_top%d_nms%g_top%d', model.stage2_rpn.nms.per_nms_topN, ... 20 | model.stage2_rpn.nms.nms_overlap_thres, model.stage2_rpn.nms.after_nms_topN), '.', '_'), ... 21 | cache_base_fast_rcnn, '_stage2_rpn']; 22 | 23 | model.stage2_fast_rcnn.cache_name = ... 24 | [cache_base_proposal, ... 25 | strrep(sprintf('_top%d_nms%g_top%d', model.stage2_rpn.nms.per_nms_topN, ... 26 | model.stage2_rpn.nms.nms_overlap_thres, model.stage2_rpn.nms.after_nms_topN), '.', '_'), ... 27 | cache_base_fast_rcnn, '_stage2_fast_rcnn']; 28 | 29 | model.final_model.cache_name = [cache_base_proposal, cache_base_fast_rcnn]; 30 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Model/VGG16_for_Fast_RCNN_VOC0712.m: -------------------------------------------------------------------------------- 1 | function model = VGG16_for_Fast_RCNN_VOC0712(model) 2 | % VGG 16layers (only finetuned from conv3_1) 3 | 4 | model.solver_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'vgg_16layers_conv3_1', 'solver_30k60k.prototxt'); 5 | model.test_net_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'vgg_16layers_conv3_1', 'test.prototxt'); 6 | 7 | model.net_file = fullfile(pwd, 'models', 'pre_trained_models', 'vgg_16layers', 'vgg16.caffemodel'); 8 | model.mean_image = fullfile(pwd, 'models', 'pre_trained_models', 'vgg_16layers', 'mean_image'); 9 | 10 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Model/VGG16_for_Fast_RCNN_VOC2007.m: -------------------------------------------------------------------------------- 1 | function model = VGG16_for_Fast_RCNN_VOC2007(model) 2 | % VGG 16layers (only finetuned from conv3_1) 3 | 4 | model.solver_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'vgg_16layers_conv3_1', 'solver_30k40k.prototxt'); 5 | model.test_net_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'vgg_16layers_conv3_1', 'test.prototxt'); 6 | 7 | model.net_file = fullfile(pwd, 'models', 'pre_trained_models', 'vgg_16layers', 'vgg16.caffemodel'); 8 | model.mean_image = fullfile(pwd, 'models', 'pre_trained_models', 'vgg_16layers', 'mean_image'); 9 | 10 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Model/ZF_for_Fast_RCNN_VOC0712.m: -------------------------------------------------------------------------------- 1 | function model = ZF_for_Fast_RCNN_VOC0712(model) 2 | 3 | model.solver_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'ZF', 'solver_30k60k.prototxt'); 4 | model.test_net_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'ZF', 'test.prototxt'); 5 | 6 | model.net_file = fullfile(pwd, 'models', 'pre_trained_models', 'ZF', 'ZF.caffemodel'); 7 | model.mean_image = fullfile(pwd, 'models', 'pre_trained_models', 'ZF', 'mean_image'); 8 | 9 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Model/ZF_for_Fast_RCNN_VOC2007.m: -------------------------------------------------------------------------------- 1 | function model = ZF_for_Fast_RCNN_VOC2007(model) 2 | 3 | model.solver_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'ZF', 'solver_30k40k.prototxt'); 4 | model.test_net_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'ZF', 'test.prototxt'); 5 | 6 | model.net_file = fullfile(pwd, 'models', 'pre_trained_models', 'ZF', 'ZF.caffemodel'); 7 | model.mean_image = fullfile(pwd, 'models', 'pre_trained_models', 'ZF', 'mean_image'); 8 | 9 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Model/ZF_for_Faster_RCNN_VOC0712.m: -------------------------------------------------------------------------------- 1 | function model = ZF_for_Faster_RCNN_VOC0712(model) 2 | 3 | model.mean_image = fullfile(pwd, 'models', 'pre_trained_models', 'ZF', 'mean_image'); 4 | model.pre_trained_net_file = fullfile(pwd, 'models', 'pre_trained_models', 'ZF', 'ZF.caffemodel'); 5 | % Stride in input image pixels at the last conv layer 6 | model.feat_stride = 16; 7 | 8 | %% stage 1 rpn, inited from pre-trained network 9 | model.stage1_rpn.solver_def_file = fullfile(pwd, 'models', 'rpn_prototxts', 'ZF', 'solver_60k80k.prototxt'); 10 | model.stage1_rpn.test_net_def_file = fullfile(pwd, 'models', 'rpn_prototxts', 'ZF', 'test.prototxt'); 11 | model.stage1_rpn.init_net_file = model.pre_trained_net_file; 12 | 13 | % rpn test setting 14 | model.stage1_rpn.nms.per_nms_topN = -1; 15 | model.stage1_rpn.nms.nms_overlap_thres = 0.7; 16 | model.stage1_rpn.nms.after_nms_topN = 2000; 17 | 18 | %% stage 1 fast rcnn, inited from pre-trained network 19 | model.stage1_fast_rcnn.solver_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'ZF', 'solver_30k60k.prototxt'); 20 | model.stage1_fast_rcnn.test_net_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'ZF', 'test.prototxt'); 21 | model.stage1_fast_rcnn.init_net_file = model.pre_trained_net_file; 22 | 23 | %% stage 2 rpn, only finetune fc layers 24 | model.stage2_rpn.solver_def_file = fullfile(pwd, 'models', 'rpn_prototxts', 'ZF_fc6', 'solver_60k80k.prototxt'); 25 | model.stage2_rpn.test_net_def_file = fullfile(pwd, 'models', 'rpn_prototxts', 'ZF_fc6', 'test.prototxt'); 26 | 27 | % rpn test setting 28 | model.stage2_rpn.nms.per_nms_topN = -1; 29 | model.stage2_rpn.nms.nms_overlap_thres = 0.7; 30 | model.stage2_rpn.nms.after_nms_topN = 2000; 31 | 32 | %% stage 2 fast rcnn, only finetune fc layers 33 | model.stage2_fast_rcnn.solver_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'ZF_fc6', 'solver_30k60k.prototxt'); 34 | model.stage2_fast_rcnn.test_net_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'ZF_fc6', 'test.prototxt'); 35 | 36 | %% final test 37 | model.final_test.nms.per_nms_topN = 6000; % to speed up nms 38 | model.final_test.nms.nms_overlap_thres = 0.7; 39 | model.final_test.nms.after_nms_topN = 300; 40 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/+Model/ZF_for_Faster_RCNN_VOC2007.m: -------------------------------------------------------------------------------- 1 | function model = ZF_for_Faster_RCNN_VOC2007(model) 2 | 3 | model.mean_image = fullfile(pwd, 'models', 'pre_trained_models', 'ZF', 'mean_image'); 4 | model.pre_trained_net_file = fullfile(pwd, 'models', 'pre_trained_models', 'ZF', 'ZF.caffemodel'); 5 | % Stride in input image pixels at the last conv layer 6 | model.feat_stride = 16; 7 | 8 | %% stage 1 rpn, inited from pre-trained network 9 | model.stage1_rpn.solver_def_file = fullfile(pwd, 'models', 'rpn_prototxts', 'ZF', 'solver_60k80k.prototxt'); 10 | model.stage1_rpn.test_net_def_file = fullfile(pwd, 'models', 'rpn_prototxts', 'ZF', 'test.prototxt'); 11 | model.stage1_rpn.init_net_file = model.pre_trained_net_file; 12 | 13 | % rpn test setting 14 | model.stage1_rpn.nms.per_nms_topN = -1; 15 | model.stage1_rpn.nms.nms_overlap_thres = 0.7; 16 | model.stage1_rpn.nms.after_nms_topN = 2000; 17 | 18 | %% stage 1 fast rcnn, inited from pre-trained network 19 | model.stage1_fast_rcnn.solver_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'ZF', 'solver_30k40k.prototxt'); 20 | model.stage1_fast_rcnn.test_net_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'ZF', 'test.prototxt'); 21 | model.stage1_fast_rcnn.init_net_file = model.pre_trained_net_file; 22 | 23 | %% stage 2 rpn, only finetune fc layers 24 | model.stage2_rpn.solver_def_file = fullfile(pwd, 'models', 'rpn_prototxts', 'ZF_fc6', 'solver_60k80k.prototxt'); 25 | model.stage2_rpn.test_net_def_file = fullfile(pwd, 'models', 'rpn_prototxts', 'ZF_fc6', 'test.prototxt'); 26 | 27 | % rpn test setting 28 | model.stage2_rpn.nms.per_nms_topN = -1; 29 | model.stage2_rpn.nms.nms_overlap_thres = 0.7; 30 | model.stage2_rpn.nms.after_nms_topN = 2000; 31 | 32 | %% stage 2 fast rcnn, only finetune fc layers 33 | model.stage2_fast_rcnn.solver_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'ZF_fc6', 'solver_30k40k.prototxt'); 34 | model.stage2_fast_rcnn.test_net_def_file = fullfile(pwd, 'models', 'fast_rcnn_prototxts', 'ZF_fc6', 'test.prototxt'); 35 | 36 | %% final test 37 | model.final_test.nms.per_nms_topN = 6000; % to speed up nms 38 | model.final_test.nms.nms_overlap_thres = 0.7; 39 | model.final_test.nms.after_nms_topN = 300; 40 | end -------------------------------------------------------------------------------- /faster_rcnn-master/experiments/script_fast_rcnn_VOC0712_ZF.m: -------------------------------------------------------------------------------- 1 | function script_fast_rcnn_VOC0712_ZF() 2 | % script_fast_rcnn_VOC0712_ZF() 3 | % Fast rcnn training and testing with Zeiler & Fergus model 4 | % -------------------------------------------------------- 5 | % Fast R-CNN 6 | % Reimplementation based on Python Fast R-CNN (https://github.com/rbgirshick/fast-rcnn) 7 | % Copyright (c) 2015, Shaoqing Ren 8 | % Licensed under The MIT License [see LICENSE for details] 9 | % -------------------------------------------------------- 10 | 11 | clc; 12 | clear mex; 13 | clear is_valid_handle; % to clear init_key 14 | run(fullfile(fileparts(fileparts(mfilename('fullpath'))), 'startup')); 15 | %% -------------------- CONFIG -------------------- 16 | opts.caffe_version = 'caffe_faster_rcnn'; 17 | opts.gpu_id = auto_select_gpu; 18 | active_caffe_mex(opts.gpu_id, opts.caffe_version); 19 | 20 | % model 21 | model = Model.ZF_for_Fast_RCNN_VOC0712(); 22 | % cache name 23 | opts.cache_name = 'fast_rcnn_VOC0712_ZF'; 24 | % config 25 | conf = fast_rcnn_config('image_means', model.mean_image); 26 | % train/test data 27 | dataset = []; 28 | dataset = Dataset.voc0712_trainval_ss(dataset, 'train', conf.use_flipped); 29 | dataset = Dataset.voc2007_test_ss(dataset, 'test', false); 30 | 31 | % do validation, or not 32 | opts.do_val = true; 33 | 34 | %% -------------------- TRAINING -------------------- 35 | 36 | opts.fast_rcnn_model = fast_rcnn_train(conf, dataset.imdb_train, dataset.roidb_train, ... 37 | 'do_val', opts.do_val, ... 38 | 'imdb_val', dataset.imdb_test, ... 39 | 'roidb_val', dataset.roidb_test, ... 40 | 'solver_def_file', model.solver_def_file, ... 41 | 'net_file', model.net_file, ... 42 | 'cache_name', opts.cache_name); 43 | assert(exist(opts.fast_rcnn_model, 'file') ~= 0, 'not found trained model'); 44 | 45 | 46 | %% -------------------- TESTING -------------------- 47 | fast_rcnn_test(conf, dataset.imdb_test, dataset.roidb_test, ... 48 | 'net_def_file', model.test_net_def_file, ... 49 | 'net_file', opts.fast_rcnn_model, ... 50 | 'cache_name', opts.cache_name); 51 | 52 | 53 | end 54 | -------------------------------------------------------------------------------- /faster_rcnn-master/faster_rcnn_build.m: -------------------------------------------------------------------------------- 1 | function faster_rcnn_build() 2 | % faster_rcnn_build() 3 | % -------------------------------------------------------- 4 | % Faster R-CNN 5 | % Copyright (c) 2015, Shaoqing Ren 6 | % Licensed under The MIT License [see LICENSE for details] 7 | % -------------------------------------------------------- 8 | 9 | % Compile nms_mex 10 | if ~exist('nms_mex', 'file') 11 | fprintf('Compiling nms_mex\n'); 12 | 13 | mex -O -outdir bin ... 14 | CXXFLAGS="\$CXXFLAGS -std=c++11" ... 15 | -largeArrayDims ... 16 | functions/nms/nms_mex.cpp ... 17 | -output nms_mex; 18 | end 19 | 20 | if ~exist('nms_gpu_mex', 'file') 21 | fprintf('Compiling nms_gpu_mex\n'); 22 | addpath(fullfile(pwd, 'functions', 'nms')); 23 | nvmex('functions/nms/nms_gpu_mex.cu', 'bin'); 24 | delete('nms_gpu_mex.o'); 25 | end 26 | 27 | 28 | -------------------------------------------------------------------------------- /faster_rcnn-master/faster_rcnn_rec.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/faster_rcnn_rec.m -------------------------------------------------------------------------------- /faster_rcnn-master/fetch_data/fetch_caffe_mex_windows_vs2013_cuda65.m: -------------------------------------------------------------------------------- 1 | 2 | cur_dir = pwd; 3 | cd(fileparts(mfilename('fullpath'))); 4 | 5 | try 6 | fprintf('Downloading caffe_mex...\n'); 7 | urlwrite('https://onedrive.live.com/download?resid=36FEC490FBC32F1A!111&authkey=!AFVWFGTbViiX5tg&ithint=file%2czip', ... 8 | 'caffe_mex.zip'); 9 | 10 | fprintf('Unzipping...\n'); 11 | unzip('caffe_mex.zip', '..'); 12 | 13 | fprintf('Done.\n'); 14 | delete('caffe_mex.zip'); 15 | catch 16 | fprintf('Error in downloading, please try links in README.md https://github.com/ShaoqingRen/faster_rcnn'); 17 | end 18 | 19 | cd(cur_dir); 20 | -------------------------------------------------------------------------------- /faster_rcnn-master/fetch_data/fetch_faster_rcnn_final_model.m: -------------------------------------------------------------------------------- 1 | 2 | cur_dir = pwd; 3 | cd(fileparts(mfilename('fullpath'))); 4 | 5 | try 6 | fprintf('Downloading faster_rcnn_final_model...\n'); 7 | urlwrite('https://onedrive.live.com/download?resid=D7AF52BADBA8A4BC!114&authkey=!AERHoxZ-iAx_j34&ithint=file%2czip', ... 8 | 'faster_rcnn_final_model.zip'); 9 | 10 | 11 | fprintf('Unzipping...\n'); 12 | unzip('faster_rcnn_final_model.zip', '..'); 13 | 14 | fprintf('Done.\n'); 15 | delete('faster_rcnn_final_model.zip'); 16 | catch 17 | fprintf('Error in downloading, please try links in README.md https://github.com/ShaoqingRen/faster_rcnn'); 18 | end 19 | 20 | cd(cur_dir); 21 | -------------------------------------------------------------------------------- /faster_rcnn-master/fetch_data/fetch_model_VGG16.m: -------------------------------------------------------------------------------- 1 | 2 | cur_dir = pwd; 3 | cd(fileparts(mfilename('fullpath'))); 4 | 5 | try 6 | fprintf('Downloading model_VGG16...\n'); 7 | urlwrite('https://onedrive.live.com/download?resid=36FEC490FBC32F1A!114&authkey=!AE8uV9B07dREbhM&ithint=file%2czip', ... 8 | 'model_VGG16.zip'); 9 | 10 | fprintf('Unzipping...\n'); 11 | unzip('model_VGG16.zip', '..'); 12 | 13 | fprintf('Done.\n'); 14 | delete('model_VGG16.zip'); 15 | catch 16 | fprintf('Error in downloading, please try links in README.md https://github.com/ShaoqingRen/faster_rcnn'); 17 | end 18 | 19 | cd(cur_dir); 20 | -------------------------------------------------------------------------------- /faster_rcnn-master/fetch_data/fetch_model_ZF.m: -------------------------------------------------------------------------------- 1 | 2 | cur_dir = pwd; 3 | cd(fileparts(mfilename('fullpath'))); 4 | 5 | try 6 | fprintf('Downloading model_ZF...\n'); 7 | urlwrite('https://onedrive.live.com/download?resid=36FEC490FBC32F1A!113&authkey=!AIzdm0sD_SmhUQ4&ithint=file%2czip', ... 8 | 'model_ZF.zip'); 9 | 10 | fprintf('Unzipping...\n'); 11 | unzip('model_ZF.zip', '..'); 12 | 13 | fprintf('Done.\n'); 14 | delete('model_ZF.zip'); 15 | catch 16 | fprintf('Error in downloading, please try links in README.md https://github.com/ShaoqingRen/faster_rcnn'); 17 | end 18 | 19 | cd(cur_dir); 20 | -------------------------------------------------------------------------------- /faster_rcnn-master/food_info.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/food_info.xls -------------------------------------------------------------------------------- /faster_rcnn-master/functions/fast_rcnn/fast_rcnn_bbox_transform.m: -------------------------------------------------------------------------------- 1 | function [regression_label] = fast_rcnn_bbox_transform(ex_boxes, gt_boxes) 2 | % [regression_label] = fast_rcnn_bbox_transform(ex_boxes, gt_boxes) 3 | % -------------------------------------------------------- 4 | % Fast R-CNN 5 | % Reimplementation based on Python Fast R-CNN (https://github.com/rbgirshick/fast-rcnn) 6 | % Copyright (c) 2015, Shaoqing Ren 7 | % Licensed under The MIT License [see LICENSE for details] 8 | % -------------------------------------------------------- 9 | 10 | ex_widths = ex_boxes(:, 3) - ex_boxes(:, 1) + 1; 11 | ex_heights = ex_boxes(:, 4) - ex_boxes(:, 2) + 1; 12 | ex_ctr_x = ex_boxes(:, 1) + 0.5 * (ex_widths - 1); 13 | ex_ctr_y = ex_boxes(:, 2) + 0.5 * (ex_heights - 1); 14 | 15 | gt_widths = gt_boxes(:, 3) - gt_boxes(:, 1) + 1; 16 | gt_heights = gt_boxes(:, 4) - gt_boxes(:, 2) + 1; 17 | gt_ctr_x = gt_boxes(:, 1) + 0.5 * (gt_widths - 1); 18 | gt_ctr_y = gt_boxes(:, 2) + 0.5 * (gt_heights - 1); 19 | 20 | targets_dx = (gt_ctr_x - ex_ctr_x) ./ (ex_widths+eps); 21 | targets_dy = (gt_ctr_y - ex_ctr_y) ./ (ex_heights+eps); 22 | targets_dw = log(gt_widths ./ ex_widths); 23 | targets_dh = log(gt_heights ./ ex_heights); 24 | 25 | regression_label = [targets_dx, targets_dy, targets_dw, targets_dh]; 26 | end -------------------------------------------------------------------------------- /faster_rcnn-master/functions/fast_rcnn/fast_rcnn_bbox_transform_inv.m: -------------------------------------------------------------------------------- 1 | function [pred_boxes] = fast_rcnn_bbox_transform_inv(boxes, box_deltas) 2 | % [pred_boxes] = fast_rcnn_bbox_transform_inv(boxes, box_deltas) 3 | % -------------------------------------------------------- 4 | % Fast R-CNN 5 | % Reimplementation based on Python Fast R-CNN (https://github.com/rbgirshick/fast-rcnn) 6 | % Copyright (c) 2015, Shaoqing Ren 7 | % Licensed under The MIT License [see LICENSE for details] 8 | % -------------------------------------------------------- 9 | 10 | src_w = double(boxes(:, 3) - boxes(:, 1) + 1); 11 | src_h = double(boxes(:, 4) - boxes(:, 2) + 1); 12 | src_ctr_x = double(boxes(:, 1) + 0.5*(src_w-1)); 13 | src_ctr_y = double(boxes(:, 2) + 0.5*(src_h-1)); 14 | 15 | dst_ctr_x = double(box_deltas(:, 1:4:end)); 16 | dst_ctr_y = double(box_deltas(:, 2:4:end)); 17 | dst_scl_x = double(box_deltas(:, 3:4:end)); 18 | dst_scl_y = double(box_deltas(:, 4:4:end)); 19 | 20 | pred_ctr_x = bsxfun(@plus, bsxfun(@times, dst_ctr_x, src_w), src_ctr_x); 21 | pred_ctr_y = bsxfun(@plus, bsxfun(@times, dst_ctr_y, src_h), src_ctr_y); 22 | pred_w = bsxfun(@times, exp(dst_scl_x), src_w); 23 | pred_h = bsxfun(@times, exp(dst_scl_y), src_h); 24 | pred_boxes = zeros(size(box_deltas), 'single'); 25 | pred_boxes(:, 1:4:end) = pred_ctr_x - 0.5*(pred_w-1); 26 | pred_boxes(:, 2:4:end) = pred_ctr_y - 0.5*(pred_h-1); 27 | pred_boxes(:, 3:4:end) = pred_ctr_x + 0.5*(pred_w-1); 28 | pred_boxes(:, 4:4:end) = pred_ctr_y + 0.5*(pred_h-1); 29 | end -------------------------------------------------------------------------------- /faster_rcnn-master/functions/fast_rcnn/fast_rcnn_generate_sliding_windows.m: -------------------------------------------------------------------------------- 1 | function roidb = fast_rcnn_generate_sliding_windows(conf, imdb, roidb, roipool_in_size) 2 | % [pred_boxes, scores] = fast_rcnn_conv_feat_detect(conf, im, conv_feat, boxes, max_rois_num_in_gpu, net_idx) 3 | % -------------------------------------------------------- 4 | % Fast R-CNN 5 | % Reimplementation based on Python Fast R-CNN (https://github.com/rbgirshick/fast-rcnn) 6 | % Copyright (c) 2015, Shaoqing Ren 7 | % Licensed under The MIT License [see LICENSE for details] 8 | % -------------------------------------------------------- 9 | 10 | regions.images = imdb.image_ids; 11 | 12 | im_sizes = imdb.sizes; 13 | regions.boxes = cellfun(@(x) generate_sliding_windows_one_image(conf, x, roipool_in_size), num2cell(im_sizes, 2), 'UniformOutput', false); 14 | 15 | roidb = roidb_from_proposal(imdb, roidb, regions); 16 | end 17 | 18 | function boxes = generate_sliding_windows_one_image(conf, im_size, roipool_in_size) 19 | im_scale = prep_im_for_blob_size(im_size, conf.scales, conf.max_size); 20 | im_size = round(im_size * im_scale); 21 | 22 | x1 = 1:conf.feat_stride:im_size(2); 23 | y1 = 1:conf.feat_stride:im_size(1); 24 | [x1, y1] = meshgrid(x1, y1); 25 | x1 = x1(:); 26 | y1 = y1(:); 27 | x2 = x1 + roipool_in_size * conf.feat_stride - 1; 28 | y2 = y1 + roipool_in_size * conf.feat_stride - 1; 29 | 30 | boxes = [x1, y1, x2, y2]; 31 | boxes = filter_boxes(im_size, boxes); 32 | 33 | boxes = bsxfun(@times, boxes-1, 1/im_scale) + 1; 34 | end 35 | 36 | function boxes = filter_boxes(im_size, boxes) 37 | valid_ind = boxes(:, 1) >= 1 & boxes(:, 1) <= im_size(2) & ... 38 | boxes(:, 2) >= 1 & boxes(:, 2) <= im_size(1) & ... 39 | boxes(:, 3) >= 1 & boxes(:, 3) <= im_size(2) & ... 40 | boxes(:, 4) >= 1 & boxes(:, 4) <= im_size(1); 41 | 42 | boxes = boxes(valid_ind, :); 43 | end -------------------------------------------------------------------------------- /faster_rcnn-master/functions/fast_rcnn/fast_rcnn_map_im_rois_to_feat_rois.m: -------------------------------------------------------------------------------- 1 | function [feat_rois] = fast_rcnn_map_im_rois_to_feat_rois(conf, im_rois, im_scale_factor) 2 | % [feat_rois] = fast_rcnn_map_im_rois_to_feat_rois(conf, im_rois, im_scale_factor) 3 | % -------------------------------------------------------- 4 | % Fast R-CNN 5 | % Reimplementation based on Python Fast R-CNN (https://github.com/rbgirshick/fast-rcnn) 6 | % Copyright (c) 2015, Shaoqing Ren 7 | % Licensed under The MIT License [see LICENSE for details] 8 | % -------------------------------------------------------- 9 | 10 | %% Map a ROI in image-pixel coordinates to a ROI in feature coordinates. 11 | % in matlab's index (start from 1) 12 | 13 | feat_rois = round((im_rois-1) * im_scale_factor) + 1; 14 | 15 | %feat_rois = round((im_rois-1) * im_scale_factor / single(conf.feat_stride)) + 1; 16 | 17 | end -------------------------------------------------------------------------------- /faster_rcnn-master/functions/nms/nms.m: -------------------------------------------------------------------------------- 1 | function pick = nms(boxes, overlap, use_gpu) 2 | % top = nms(boxes, overlap) 3 | % Non-maximum suppression. (FAST VERSION) 4 | % Greedily select high-scoring detections and skip detections 5 | % that are significantly covered by a previously selected 6 | % detection. 7 | % 8 | % NOTE: This is adapted from Pedro Felzenszwalb's version (nms.m), 9 | % but an inner loop has been eliminated to significantly speed it 10 | % up in the case of a large number of boxes 11 | 12 | % Copyright (C) 2011-12 by Tomasz Malisiewicz 13 | % All rights reserved. 14 | % 15 | % This file is part of the Exemplar-SVM library and is made 16 | % available under the terms of the MIT license (see COPYING file). 17 | % Project homepage: https://github.com/quantombone/exemplarsvm 18 | 19 | 20 | if isempty(boxes) 21 | pick = []; 22 | return; 23 | end 24 | 25 | if ~exist('use_gpu', 'var') 26 | use_gpu = false; 27 | end 28 | 29 | if use_gpu 30 | s = boxes(:, end); 31 | if ~issorted(s(end:-1:1)) 32 | [~, I] = sort(s, 'descend'); 33 | boxes = boxes(I, :); 34 | pick = nms_gpu_mex(single(boxes)', double(overlap)); 35 | pick = I(pick); 36 | else 37 | pick = nms_gpu_mex(single(boxes)', double(overlap)); 38 | end 39 | return; 40 | end 41 | 42 | if size(boxes, 1) < 1000000 43 | pick = nms_mex(double(boxes), double(overlap)); 44 | return; 45 | end 46 | 47 | x1 = boxes(:,1); 48 | y1 = boxes(:,2); 49 | x2 = boxes(:,3); 50 | y2 = boxes(:,4); 51 | s = boxes(:,end); 52 | 53 | area = (x2-x1+1) .* (y2-y1+1); 54 | [vals, I] = sort(s); 55 | 56 | pick = s*0; 57 | counter = 1; 58 | while ~isempty(I) 59 | last = length(I); 60 | i = I(last); 61 | pick(counter) = i; 62 | counter = counter + 1; 63 | 64 | xx1 = max(x1(i), x1(I(1:last-1))); 65 | yy1 = max(y1(i), y1(I(1:last-1))); 66 | xx2 = min(x2(i), x2(I(1:last-1))); 67 | yy2 = min(y2(i), y2(I(1:last-1))); 68 | 69 | w = max(0.0, xx2-xx1+1); 70 | h = max(0.0, yy2-yy1+1); 71 | 72 | inter = w.*h; 73 | o = inter ./ (area(i) + area(I(1:last-1)) - inter); 74 | 75 | I = I(find(o<=overlap)); 76 | end 77 | 78 | pick = pick(1:(counter-1)); 79 | -------------------------------------------------------------------------------- /faster_rcnn-master/functions/nms/nms_multiclass.m: -------------------------------------------------------------------------------- 1 | function picks = nms_multiclass(boxes, overlap) 2 | % top = nms(boxes, overlap) 3 | % Non-maximum suppression. (FAST VERSION) 4 | % Greedily select high-scoring detections and skip detections 5 | % that are significantly covered by a previously selected 6 | % detection. 7 | % 8 | % NOTE: This is adapted from Pedro Felzenszwalb's version (nms.m), 9 | % but an inner loop has been eliminated to significantly speed it 10 | % up in the case of a large number of boxes 11 | 12 | % Copyright (C) 2011-12 by Tomasz Malisiewicz 13 | % All rights reserved. 14 | % 15 | % This file is part of the Exemplar-SVM library and is made 16 | % available under the terms of the MIT license (see COPYING file). 17 | % Project homepage: https://github.com/quantombone/exemplarsvm 18 | 19 | 20 | if isempty(boxes) 21 | picks = {}; 22 | return; 23 | end 24 | 25 | if size(boxes, 1) < 10000 26 | picks = nms_multiclass_mex(double(boxes), double(overlap)); 27 | return; 28 | end 29 | 30 | x1 = boxes(:,1); 31 | y1 = boxes(:,2); 32 | x2 = boxes(:,3); 33 | y2 = boxes(:,4); 34 | 35 | area = (x2-x1+1) .* (y2-y1+1); 36 | 37 | picks = cell(size(boxes, 2)-4, 1); 38 | for iS = 5:size(boxes, 2) 39 | s = boxes(:,iS); 40 | [~, I] = sort(s); 41 | 42 | pick = s*0; 43 | counter = 1; 44 | while ~isempty(I) 45 | last = length(I); 46 | i = I(last); 47 | pick(counter) = i; 48 | counter = counter + 1; 49 | 50 | xx1 = max(x1(i), x1(I(1:last-1))); 51 | yy1 = max(y1(i), y1(I(1:last-1))); 52 | xx2 = min(x2(i), x2(I(1:last-1))); 53 | yy2 = min(y2(i), y2(I(1:last-1))); 54 | 55 | w = max(0.0, xx2-xx1+1); 56 | h = max(0.0, yy2-yy1+1); 57 | 58 | inter = w.*h; 59 | o = inter ./ (area(i) + area(I(1:last-1)) - inter); 60 | 61 | I = I(o<=overlap); 62 | end 63 | 64 | pick = pick(1:(counter-1)); 65 | picks{iS-4} = pick; 66 | end 67 | -------------------------------------------------------------------------------- /faster_rcnn-master/functions/nms/nvmex.m: -------------------------------------------------------------------------------- 1 | function nvmex(cuFileName, outDir) 2 | %NVMEX Compiles and links a CUDA file for MATLAB usage 3 | % NVMEX(FILENAME) will create a MEX-File (also with the name FILENAME) by 4 | % invoking the CUDA compiler, nvcc, and then linking with the MEX 5 | % function in MATLAB. 6 | 7 | if ispc % Windows 8 | Host_Compiler_Location = '-ccbin "C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin\x86_amd64"'; 9 | CUDA_INC_Location = ['"' getenv('CUDA_PATH') '\include"']; 10 | CUDA_SAMPLES_Location =['"' getenv('NVCUDASAMPLES6_5_ROOT') '\common\inc"']; 11 | PIC_Option = ''; 12 | if ( strcmp(computer('arch'),'win32') ==1) 13 | machine_str = ' --machine 32 '; 14 | CUDA_LIB_Location = ['"' getenv('CUDA_PATH') '\lib\Win32"']; 15 | elseif ( strcmp(computer('arch'),'win64') ==1) 16 | machine_str = ' --machine 64 '; 17 | CUDA_LIB_Location = ['"' getenv('CUDA_PATH') '\lib\x64"']; 18 | end 19 | NVCC = 'nvcc'; 20 | else % Mac and Linux (assuming gcc is on the path) 21 | CUDA_INC_Location = '/usr/local/cuda/include'; 22 | CUDA_SAMPLES_Location = '/usr/local/cuda/samples/common/inc'; 23 | Host_Compiler_Location = ' '; 24 | PIC_Option = ' --compiler-options -fPIC '; 25 | machine_str = []; 26 | CUDA_LIB_Location = '/usr/local/cuda/lib64'; 27 | NVCC = '/usr/local/cuda/bin/nvcc'; 28 | end 29 | % !!! End of things to modify !!! 30 | [~, filename] = fileparts(cuFileName); 31 | nvccCommandLine = [ ... 32 | NVCC ' --compile ' Host_Compiler_Location ' ' ... 33 | '-o ' filename '.o ' ... 34 | machine_str PIC_Option ... 35 | ' -I' '"' matlabroot '/extern/include "' ... 36 | ' -I' CUDA_INC_Location ' -I' CUDA_SAMPLES_Location ... 37 | ' "' cuFileName '" ' 38 | ]; 39 | mexCommandLine = ['mex ' '-outdir ' outDir ' ' filename '.o' ' -L' CUDA_LIB_Location ' -lcudart']; 40 | disp(nvccCommandLine); 41 | warning off; 42 | status = system(nvccCommandLine); 43 | warning on; 44 | if status < 0 45 | error 'Error invoking nvcc'; 46 | end 47 | disp(mexCommandLine); 48 | eval(mexCommandLine); 49 | end 50 | -------------------------------------------------------------------------------- /faster_rcnn-master/functions/rpn/proposal_calc_output_size.m: -------------------------------------------------------------------------------- 1 | function [output_width_map, output_height_map] = proposal_calc_output_size(conf, test_net_def_file) 2 | % [output_width_map, output_height_map] = proposal_calc_output_size(conf, test_net_def_file) 3 | % -------------------------------------------------------- 4 | % Faster R-CNN 5 | % Copyright (c) 2015, Shaoqing Ren 6 | % Licensed under The MIT License [see LICENSE for details] 7 | % -------------------------------------------------------- 8 | 9 | % caffe.init_log(fullfile(pwd, 'caffe_log')); 10 | caffe_net = caffe.Net(test_net_def_file, 'test'); 11 | 12 | % set gpu/cpu 13 | if conf.use_gpu 14 | caffe.set_mode_gpu(); 15 | else 16 | caffe.set_mode_cpu(); 17 | end 18 | 19 | input = 100:conf.max_size; 20 | output_w = nan(size(input)); 21 | output_h = nan(size(input)); 22 | for i = 1:length(input) 23 | s = input(i); 24 | im_blob = single(zeros(s, s, 3, 1)); 25 | net_inputs = {im_blob}; 26 | 27 | % Reshape net's input blobs 28 | caffe_net.reshape_as_input(net_inputs); 29 | caffe_net.forward(net_inputs); 30 | 31 | cls_score = caffe_net.blobs('proposal_cls_score').get_data(); 32 | output_w(i) = size(cls_score, 1); 33 | output_h(i) = size(cls_score, 2); 34 | end 35 | 36 | output_width_map = containers.Map(input, output_w); 37 | output_height_map = containers.Map(input, output_h); 38 | 39 | caffe.reset_all(); 40 | end -------------------------------------------------------------------------------- /faster_rcnn-master/functions/rpn/proposal_locate_anchors.m: -------------------------------------------------------------------------------- 1 | function [anchors, im_scales] = proposal_locate_anchors(conf, im_size, target_scale, feature_map_size) 2 | % [anchors, im_scales] = proposal_locate_anchors(conf, im_size, target_scale, feature_map_size) 3 | % -------------------------------------------------------- 4 | % Faster R-CNN 5 | % Copyright (c) 2015, Shaoqing Ren 6 | % Licensed under The MIT License [see LICENSE for details] 7 | % -------------------------------------------------------- 8 | % generate anchors for each scale 9 | 10 | % only for fcn 11 | if ~exist('feature_map_size', 'var') 12 | feature_map_size = []; 13 | end 14 | 15 | func = @proposal_locate_anchors_single_scale; 16 | 17 | if exist('target_scale', 'var') 18 | [anchors, im_scales] = func(im_size, conf, target_scale, feature_map_size); 19 | else 20 | [anchors, im_scales] = arrayfun(@(x) func(im_size, conf, x, feature_map_size), ... 21 | conf.scales, 'UniformOutput', false); 22 | end 23 | 24 | end 25 | 26 | function [anchors, im_scale] = proposal_locate_anchors_single_scale(im_size, conf, target_scale, feature_map_size) 27 | if isempty(feature_map_size) 28 | im_scale = prep_im_for_blob_size(im_size, target_scale, conf.max_size); 29 | img_size = round(im_size * im_scale); 30 | output_size = cell2mat([conf.output_height_map.values({img_size(1)}), conf.output_width_map.values({img_size(2)})]); 31 | else 32 | im_scale = prep_im_for_blob_size(im_size, target_scale, conf.max_size); 33 | output_size = feature_map_size; 34 | end 35 | 36 | shift_x = [0:(output_size(2)-1)] * conf.feat_stride; 37 | shift_y = [0:(output_size(1)-1)] * conf.feat_stride; 38 | [shift_x, shift_y] = meshgrid(shift_x, shift_y); 39 | 40 | % concat anchors as [channel, height, width], where channel is the fastest dimension. 41 | anchors = reshape(bsxfun(@plus, permute(conf.anchors, [1, 3, 2]), ... 42 | permute([shift_x(:), shift_y(:), shift_x(:), shift_y(:)], [3, 1, 2])), [], 4); 43 | 44 | % equals to 45 | % anchors = arrayfun(@(x, y) single(bsxfun(@plus, conf.anchors, [x, y, x, y])), shift_x, shift_y, 'UniformOutput', false); 46 | % anchors = reshape(anchors, [], 1); 47 | % anchors = cat(1, anchors{:}); 48 | 49 | end -------------------------------------------------------------------------------- /faster_rcnn-master/grabcut_mex.cpp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/grabcut_mex.cpp -------------------------------------------------------------------------------- /faster_rcnn-master/grabcut_mex.mexw64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/grabcut_mex.mexw64 -------------------------------------------------------------------------------- /faster_rcnn-master/imdb/get_voc_opts.m: -------------------------------------------------------------------------------- 1 | function VOCopts = get_voc_opts(path) 2 | 3 | tmp = pwd; 4 | cd(path); 5 | try 6 | addpath('VOCcode'); 7 | VOCinit; 8 | catch 9 | rmpath('VOCcode'); 10 | cd(tmp); 11 | error(sprintf('VOCcode directory not found under %s', path)); 12 | end 13 | rmpath('VOCcode'); 14 | cd(tmp); 15 | -------------------------------------------------------------------------------- /faster_rcnn-master/imdb/roidb_from_proposal.m: -------------------------------------------------------------------------------- 1 | function roidb = roidb_from_proposal(imdb, roidb, regions, varargin) 2 | % roidb = roidb_from_proposal(imdb, roidb, regions, varargin)s 3 | % -------------------------------------------------------- 4 | % Faster R-CNN 5 | % Copyright (c) 2015, Shaoqing Ren 6 | % Licensed under The MIT License [see LICENSE for details] 7 | % -------------------------------------------------------- 8 | 9 | ip = inputParser; 10 | ip.addRequired('imdb', @isstruct); 11 | ip.addRequired('roidb', @isstruct); 12 | ip.addRequired('regions', @isstruct); 13 | ip.addParamValue('keep_raw_proposal', true, @islogical); 14 | ip.parse(imdb, roidb, regions, varargin{:}); 15 | opts = ip.Results; 16 | 17 | assert(strcmp(opts.roidb.name, opts.imdb.name)); 18 | rois = opts.roidb.rois; 19 | 20 | if ~opts.keep_raw_proposal 21 | % remove proposal boxes in roidb 22 | for i = 1:length(rois) 23 | is_gt = rois(i).gt; 24 | rois(i).gt = rois(i).gt(is_gt, :); 25 | rois(i).overlap = rois(i).overlap(is_gt, :); 26 | rois(i).boxes = rois(i).boxes(is_gt, :); 27 | rois(i).class = rois(i).class(is_gt, :); 28 | end 29 | end 30 | 31 | % add new proposal boxes 32 | for i = 1:length(rois) 33 | [~, image_name1] = fileparts(imdb.image_ids{i}); 34 | [~, image_name2] = fileparts(opts.regions.images{i}); 35 | assert(strcmp(image_name1, image_name2)); 36 | 37 | boxes = opts.regions.boxes{i}(:, 1:4); 38 | is_gt = rois(i).gt; 39 | gt_boxes = rois(i).boxes(is_gt, :); 40 | gt_classes = rois(i).class(is_gt, :); 41 | all_boxes = cat(1, rois(i).boxes, boxes); 42 | 43 | num_gt_boxes = size(gt_boxes, 1); 44 | num_boxes = size(boxes, 1); 45 | 46 | rois(i).gt = cat(1, rois(i).gt, false(num_boxes, 1)); 47 | rois(i).overlap = cat(1, rois(i).overlap, zeros(num_boxes, size(rois(i).overlap, 2))); 48 | rois(i).boxes = cat(1, rois(i).boxes, boxes); 49 | rois(i).class = cat(1, rois(i).class, zeros(num_boxes, 1)); 50 | for j = 1:num_gt_boxes 51 | rois(i).overlap(:, gt_classes(j)) = ... 52 | max(full(rois(i).overlap(:, gt_classes(j))), boxoverlap(all_boxes, gt_boxes(j, :))); 53 | end 54 | end 55 | 56 | roidb.rois = rois; 57 | 58 | end -------------------------------------------------------------------------------- /faster_rcnn-master/load_proposal_detection_model.m: -------------------------------------------------------------------------------- 1 | function proposal_detection_model = load_proposal_detection_model(model_dir) 2 | ld = load(fullfile(model_dir, 'model')); 3 | proposal_detection_model = ld.proposal_detection_model; 4 | clear ld; 5 | 6 | proposal_detection_model.proposal_net_def ... 7 | = fullfile(model_dir, proposal_detection_model.proposal_net_def); 8 | proposal_detection_model.proposal_net ... 9 | = fullfile(model_dir, proposal_detection_model.proposal_net); 10 | proposal_detection_model.detection_net_def ... 11 | = fullfile(model_dir, proposal_detection_model.detection_net_def); 12 | proposal_detection_model.detection_net ... 13 | = fullfile(model_dir, proposal_detection_model.detection_net); 14 | 15 | end 16 | -------------------------------------------------------------------------------- /faster_rcnn-master/nms_gpu_mex.mexw64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/nms_gpu_mex.mexw64 -------------------------------------------------------------------------------- /faster_rcnn-master/nms_gpu_mex.o: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/nms_gpu_mex.o -------------------------------------------------------------------------------- /faster_rcnn-master/nvmex.m: -------------------------------------------------------------------------------- 1 | function nvmex(cuFileName) 2 | %NVMEX Compiles and links a CUDA file for MATLAB usage 3 | % NVMEX(FILENAME) will create a MEX-File (also with the name FILENAME) by 4 | % invoking the CUDA compiler, nvcc, and then linking with the MEX 5 | % function in MATLAB. 6 | 7 | 8 | if ispc % Windows 9 | 10 | Host_Compiler_Location = '-ccbin "E:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin\x86_amd64"'; 11 | CUDA_INC_Location = ['"' getenv('CUDA_PATH') '\include"']; 12 | CUDA_SAMPLES_Location =['"' getenv('NVCUDASAMPLES5_5_ROOT') '\common\inc"']; 13 | PIC_Option = ''; 14 | if ( strcmp(computer('arch'),'win32') ==1) 15 | machine_str = ' --machine 32 '; 16 | CUDA_LIB_Location = ['"' getenv('CUDA_PATH') '\lib\Win32"']; 17 | elseif ( strcmp(computer('arch'),'win64') ==1) 18 | machine_str = ' --machine 64 '; 19 | CUDA_LIB_Location = ['"' getenv('CUDA_PATH') '\lib\x64"']; 20 | end 21 | else % Mac and Linux (assuming gcc is on the path) 22 | 23 | CUDA_INC_Location = '/usr/local/cuda/include'; 24 | CUDA_SAMPLES_Location = '/usr/local/cuda/samples/common/inc'; 25 | Host_Compiler_Location = ' '; 26 | PIC_Option = ' --compiler-options -fPIC '; 27 | machine_str = []; 28 | if ( strcmp(computer('arch'),'win32') ==1) 29 | CUDA_LIB_Location = '/usr/local/cuda/lib'; 30 | elseif ( strcmp(computer('arch'),'win64') ==1) 31 | CUDA_LIB_Location = '/usr/local/cuda/lib64'; 32 | end 33 | end 34 | % !!! End of things to modify !!! 35 | [~, filename] = fileparts(cuFileName); 36 | nvccCommandLine = [ ... 37 | 'nvcc --compile ' Host_Compiler_Location ' ' ... 38 | '-o ' filename '.o ' ... 39 | machine_str PIC_Option ... 40 | ' -I' '"' matlabroot '/extern/include "' ... 41 | ' -I' CUDA_INC_Location ' -I' CUDA_SAMPLES_Location ... 42 | ' "' cuFileName '" ' 43 | ]; 44 | mexCommandLine = ['mex ' filename '.o' ' -L' CUDA_LIB_Location ' -lcudart']; 45 | disp(nvccCommandLine); 46 | warning off; 47 | status = system(nvccCommandLine); 48 | warning on; 49 | if status < 0 50 | error 'Error invoking nvcc'; 51 | end 52 | disp(mexCommandLine); 53 | eval(mexCommandLine); 54 | end 55 | -------------------------------------------------------------------------------- /faster_rcnn-master/proposal_prepare_anchors.m: -------------------------------------------------------------------------------- 1 | function [anchors, output_width_map, output_height_map] = proposal_prepare_anchors(conf, cache_name, test_net_def_file) 2 | [output_width_map, output_height_map] ... 3 | = proposal_calc_output_size(conf, test_net_def_file); 4 | anchors = proposal_generate_anchors(cache_name, ... 5 | 'scales', 2.^[3:5]); 6 | end -------------------------------------------------------------------------------- /faster_rcnn-master/result.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/result.xls -------------------------------------------------------------------------------- /faster_rcnn-master/script_faster_rcnn_VOC2007_ZF.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/script_faster_rcnn_VOC2007_ZF.m -------------------------------------------------------------------------------- /faster_rcnn-master/show_segmentation_results.m: -------------------------------------------------------------------------------- 1 | top=imread('top.jpg'); 2 | top_mask=imread('top_mask.jpg'); 3 | side=imread('side.jpg'); 4 | side_mask=imread('side_mask.jpg'); 5 | for i=1:size(top_mask,1) 6 | for j=1:size(top_mask,2) 7 | if top_mask(i,j)~=3 8 | top(i,j,:)=[0 0 0]; 9 | end 10 | end 11 | end 12 | figure 13 | imshow(top) 14 | for i=1:size(side_mask,1) 15 | for j=1:size(side_mask,2) 16 | if side_mask(i,j)~=3 17 | side(i,j,:)=[0 0 0]; 18 | end 19 | end 20 | end 21 | figure 22 | imshow(side) -------------------------------------------------------------------------------- /faster_rcnn-master/simple_faster_rcnn_rec.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/simple_faster_rcnn_rec.m -------------------------------------------------------------------------------- /faster_rcnn-master/startup.m: -------------------------------------------------------------------------------- 1 | function startup() 2 | % startup() 3 | % -------------------------------------------------------- 4 | % Faster R-CNN 5 | % Copyright (c) 2015, Shaoqing Ren 6 | % Licensed under The MIT License [see LICENSE for details] 7 | % -------------------------------------------------------- 8 | 9 | curdir = fileparts(mfilename('fullpath')); 10 | addpath(genpath(fullfile(curdir, 'utils'))); 11 | addpath(genpath(fullfile(curdir, 'functions'))); 12 | addpath(genpath(fullfile(curdir, 'bin'))); 13 | addpath(genpath(fullfile(curdir, 'experiments'))); 14 | addpath(genpath(fullfile(curdir, 'imdb'))); 15 | 16 | mkdir_if_missing(fullfile(curdir, 'datasets')); 17 | 18 | mkdir_if_missing(fullfile(curdir, 'external')); 19 | 20 | caffe_path = fullfile(curdir, 'external', 'caffe', 'matlab'); 21 | if exist(caffe_path, 'dir') == 0 22 | error('matcaffe is missing from external/caffe/matlab; See README.md'); 23 | end 24 | addpath(genpath(caffe_path)); 25 | 26 | mkdir_if_missing(fullfile(curdir, 'imdb', 'cache')); 27 | 28 | mkdir_if_missing(fullfile(curdir, 'output')); 29 | 30 | mkdir_if_missing(fullfile(curdir, 'models')); 31 | 32 | fprintf('fast_rcnn startup done\n'); 33 | end 34 | -------------------------------------------------------------------------------- /faster_rcnn-master/utils/RectLTRB2LTWH.m: -------------------------------------------------------------------------------- 1 | function [ rectsLTWH ] = RectLTRB2LTWH( rectsLTRB ) 2 | %rects (l, t, r, b) to (l, t, w, h) 3 | 4 | rectsLTWH = [rectsLTRB(:, 1), rectsLTRB(:, 2), rectsLTRB(:, 3)-rectsLTRB(:,1)+1, rectsLTRB(:,4)-rectsLTRB(:,2)+1]; 5 | end 6 | 7 | -------------------------------------------------------------------------------- /faster_rcnn-master/utils/RectLTWH2LTRB.m: -------------------------------------------------------------------------------- 1 | function [ rectsLTRB ] = RectLTWH2LTRB(rectsLTWH) 2 | %rects (l, t, r, b) to (l, t, w, h) 3 | 4 | rectsLTRB = [rectsLTWH(:, 1), rectsLTWH(:, 2), rectsLTWH(:, 1)+rectsLTWH(:,3)-1, rectsLTWH(:,2)+rectsLTWH(:,4)-1]; 5 | end 6 | 7 | -------------------------------------------------------------------------------- /faster_rcnn-master/utils/active_caffe_mex.m: -------------------------------------------------------------------------------- 1 | function active_caffe_mex(gpu_id, caffe_version) 2 | % active_caffe_mex(gpu_id, caffe_version) 3 | % -------------------------------------------------------- 4 | % Faster R-CNN 5 | % Copyright (c) 2015, Shaoqing Ren 6 | % Licensed under The MIT License [see LICENSE for details] 7 | % -------------------------------------------------------- 8 | 9 | % set gpu in matlab 10 | gpuDevice(gpu_id); 11 | 12 | if ~exist('caffe_version', 'var') || isempty(caffe_version) 13 | caffe_version = 'caffe'; 14 | end 15 | cur_dir = pwd; 16 | caffe_dir = fullfile(pwd, 'external', 'caffe', 'matlab', caffe_version); 17 | 18 | if ~exist(caffe_dir, 'dir') 19 | warning('Specified caffe folder (%s) is not exist, change to default one (%s)', ... 20 | caffe_dir, fullfile(pwd, 'external', 'caffe', 'matlab')); 21 | caffe_dir = fullfile(pwd, 'external', 'caffe', 'matlab'); 22 | end 23 | 24 | addpath(genpath(caffe_dir)); 25 | cd(caffe_dir); 26 | caffe.set_device(gpu_id-1); 27 | cd(cur_dir); 28 | end 29 | -------------------------------------------------------------------------------- /faster_rcnn-master/utils/auto_select_gpu.m: -------------------------------------------------------------------------------- 1 | function gpu_id = auto_select_gpu() 2 | % gpu_id = auto_select_gpu() 3 | % Select the gpu which has the maximum free memory 4 | % -------------------------------------------------------- 5 | % Faster R-CNN 6 | % Copyright (c) 2015, Shaoqing Ren 7 | % Licensed under The MIT License [see LICENSE for details] 8 | % -------------------------------------------------------- 9 | 10 | % deselects all GPU devices 11 | gpuDevice([]); 12 | 13 | maxFreeMemory = 0; 14 | for i = 1:gpuDeviceCount 15 | g = gpuDevice(i); 16 | freeMemory = g.FreeMemory(); 17 | fprintf('GPU %d: free memory %d\n', i, freeMemory); 18 | if freeMemory > maxFreeMemory 19 | maxFreeMemory = freeMemory; 20 | gpu_id = i; 21 | end 22 | end 23 | fprintf('Use GPU %d\n', gpu_id); 24 | 25 | % deselects all GPU devices 26 | gpuDevice([]); 27 | end 28 | -------------------------------------------------------------------------------- /faster_rcnn-master/utils/boxoverlap.m: -------------------------------------------------------------------------------- 1 | function o = boxoverlap(a, b) 2 | % Compute the symmetric intersection over union overlap between a set of 3 | % bounding boxes in a and a single bounding box in b. 4 | % 5 | % a a matrix where each row specifies a bounding box 6 | % b a matrix where each row specifies a bounding box 7 | 8 | % AUTORIGHTS 9 | % ------------------------------------------------------- 10 | % Copyright (C) 2011-2012 Ross Girshick 11 | % Copyright (C) 2008, 2009, 2010 Pedro Felzenszwalb, Ross Girshick 12 | % 13 | % This file is part of the voc-releaseX code 14 | % (http://people.cs.uchicago.edu/~rbg/latent/) 15 | % and is available under the terms of an MIT-like license 16 | % provided in COPYING. Please retain this notice and 17 | % COPYING if you use this file (or a portion of it) in 18 | % your project. 19 | % ------------------------------------------------------- 20 | 21 | o = cell(1, size(b, 1)); 22 | for i = 1:size(b, 1) 23 | x1 = max(a(:,1), b(i,1)); 24 | y1 = max(a(:,2), b(i,2)); 25 | x2 = min(a(:,3), b(i,3)); 26 | y2 = min(a(:,4), b(i,4)); 27 | 28 | w = x2-x1+1; 29 | h = y2-y1+1; 30 | inter = w.*h; 31 | aarea = (a(:,3)-a(:,1)+1) .* (a(:,4)-a(:,2)+1); 32 | barea = (b(i,3)-b(i,1)+1) * (b(i,4)-b(i,2)+1); 33 | % intersection over union overlap 34 | o{i} = inter ./ (aarea+barea-inter); 35 | % set invalid entries to 0 overlap 36 | o{i}(w <= 0) = 0; 37 | o{i}(h <= 0) = 0; 38 | end 39 | 40 | o = cell2mat(o); 41 | -------------------------------------------------------------------------------- /faster_rcnn-master/utils/im_list_to_blob.m: -------------------------------------------------------------------------------- 1 | function blob = im_list_to_blob(ims) 2 | max_shape = max(cell2mat(cellfun(@size, ims(:), 'UniformOutput', false)), [], 1); 3 | assert(all(cellfun(@(x) size(x, 3), ims, 'UniformOutput', true) == 3)); 4 | num_images = length(ims); 5 | blob = zeros(max_shape(1), max_shape(2), 3, num_images, 'single'); 6 | 7 | for i = 1:length(ims) 8 | im = ims{i}; 9 | blob(1:size(im, 1), 1:size(im, 2), :, i) = im; 10 | end 11 | end -------------------------------------------------------------------------------- /faster_rcnn-master/utils/mkdir_if_missing.m: -------------------------------------------------------------------------------- 1 | function made = mkdir_if_missing(path) 2 | made = false; 3 | if exist(path, 'dir') == 0 4 | mkdir(path); 5 | made = true; 6 | end 7 | -------------------------------------------------------------------------------- /faster_rcnn-master/utils/parse_rst.m: -------------------------------------------------------------------------------- 1 | function results = parse_rst(results, rst) 2 | % results = parse_rst(results, rst) 3 | % -------------------------------------------------------- 4 | % Faster R-CNN 5 | % Copyright (c) 2015, Shaoqing Ren 6 | % Licensed under The MIT License [see LICENSE for details] 7 | % -------------------------------------------------------- 8 | 9 | if isempty(results) 10 | for i = 1:length(rst) 11 | results.(rst(i).blob_name).data = []; 12 | end 13 | end 14 | 15 | for i = 1:length(rst) 16 | results.(rst(i).blob_name).data = [results.(rst(i).blob_name).data; rst(i).data(:)]; 17 | end 18 | end -------------------------------------------------------------------------------- /faster_rcnn-master/utils/prep_im_for_blob.m: -------------------------------------------------------------------------------- 1 | function [im, im_scale] = prep_im_for_blob(im, im_means, target_size, max_size) 2 | im = single(im); 3 | 4 | if ~isa(im, 'gpuArray') 5 | try 6 | im = bsxfun(@minus, im, im_means); 7 | catch 8 | im_means = imresize(im_means, [size(im, 1), size(im, 2)], 'bilinear', 'antialiasing', false); 9 | im = bsxfun(@minus, im, im_means); 10 | end 11 | im_scale = prep_im_for_blob_size(size(im), target_size, max_size); 12 | 13 | target_size = round([size(im, 1), size(im, 2)] * im_scale); 14 | im = imresize(im, target_size, 'bilinear', 'antialiasing', false); 15 | else 16 | % for im as gpuArray 17 | try 18 | im = bsxfun(@minus, im, im_means); 19 | catch 20 | im_means_scale = max(double(size(im, 1)) / size(im_means, 1), double(size(im, 2)) / size(im_means, 2)); 21 | im_means = imresize(im_means, im_means_scale); 22 | y_start = floor((size(im_means, 1) - size(im, 1)) / 2) + 1; 23 | x_start = floor((size(im_means, 2) - size(im, 2)) / 2) + 1; 24 | im_means = im_means(y_start:(y_start+size(im, 1)-1), x_start:(x_start+size(im, 2)-1)); 25 | im = bsxfun(@minus, im, im_means); 26 | end 27 | 28 | im_scale = prep_im_for_blob_size(size(im), target_size, max_size); 29 | im = imresize(im, im_scale); 30 | end 31 | end -------------------------------------------------------------------------------- /faster_rcnn-master/utils/prep_im_for_blob_size.m: -------------------------------------------------------------------------------- 1 | function im_scale = prep_im_for_blob_size(im_size, target_size, max_size) 2 | 3 | im_size_min = min(im_size(1:2)); 4 | im_size_max = max(im_size(1:2)); 5 | im_scale = double(target_size) / im_size_min; 6 | 7 | % Prevent the biggest axis from being more than MAX_SIZE 8 | if round(im_scale * im_size_max) > max_size 9 | im_scale = double(max_size) / double(im_size_max); 10 | end 11 | end -------------------------------------------------------------------------------- /faster_rcnn-master/utils/procid.m: -------------------------------------------------------------------------------- 1 | function s = procid() 2 | % Returns a string identifying the process. 3 | 4 | % AUTORIGHTS 5 | % ------------------------------------------------------- 6 | % Copyright (C) 2009-2012 Ross Girshick 7 | % 8 | % This file is part of the voc-releaseX code 9 | % (http://people.cs.uchicago.edu/~rbg/latent/) 10 | % and is available under the terms of an MIT-like license 11 | % provided in COPYING. Please retain this notice and 12 | % COPYING if you use this file (or a portion of it) in 13 | % your project. 14 | % ------------------------------------------------------- 15 | 16 | d = pwd(); 17 | i = strfind(d, filesep); 18 | d = d(i(end)+1:end); 19 | s = d; 20 | -------------------------------------------------------------------------------- /faster_rcnn-master/utils/seed_rand.m: -------------------------------------------------------------------------------- 1 | function prev_rng = seed_rand(seed) 2 | % seed_rand - Set random number generator to a fixed seed. 3 | % prev_rng = seed_rand(seed) 4 | % 5 | % Strategic use ensures that results are reproducible. 6 | % 7 | % To restore the previous rng after calling this do: 8 | % rng(prev_rng); 9 | 10 | % AUTORIGHTS 11 | % --------------------------------------------------------- 12 | % Copyright (c) 2014, Ross Girshick 13 | % 14 | % This file is part of the R-CNN code and is available 15 | % under the terms of the Simplified BSD License provided in 16 | % LICENSE. Please retain this notice and LICENSE if you use 17 | % this file (or any portion of it) in your project. 18 | % --------------------------------------------------------- 19 | 20 | if nargin < 1 21 | % This value works best for me. 22 | seed = 3; 23 | % Just kidding, of course ;-). 24 | end 25 | 26 | prev_rng = rng; 27 | rng(seed, 'twister') 28 | -------------------------------------------------------------------------------- /faster_rcnn-master/utils/subsample_images.m: -------------------------------------------------------------------------------- 1 | function [imdbs, roidbs] = subsample_images(imdbs, roidbs, max_num_neg_images, seed) 2 | 3 | if ~exist('seed', 'var') 4 | seed = 6; 5 | end 6 | 7 | % class_num = cellfun(@(x) length(x.class_ids), imdbs, 'UniformOutput', true); 8 | % assert(length(unique(class_num)) == 1); 9 | % class_num = unique(class_num); 10 | 11 | rois = cellfun(@(x) x.rois(:), roidbs, 'UniformOutput', false); 12 | rois_combine = cell2mat(rois(:)); 13 | 14 | % fix the random seed for repeatability 15 | prev_rng = seed_rand(seed); 16 | inds = randperm(length(rois_combine), max_num_neg_images); 17 | inds = sort(inds); 18 | 19 | img_idx_start = 1; 20 | for i = 1:length(imdbs) 21 | imdb_img_num = length(imdbs{i}.image_ids); 22 | img_idx_end = img_idx_start + imdb_img_num - 1; 23 | inds_start = find(inds >= img_idx_start, 1, 'first'); 24 | inds_end = find(inds <= img_idx_end, 1, 'last'); 25 | 26 | inds_sub = inds(inds_start:inds_end); 27 | inds_sub = inds_sub - img_idx_start + 1; 28 | 29 | imdbs{i}.image_ids = imdbs{i}.image_ids(inds_sub); 30 | imdbs{i}.sizes = imdbs{i}.sizes(inds_sub, :); 31 | if isfield(imdbs{i}, 'image_dir') 32 | imdbs{i}.image_at = @(x) ... 33 | sprintf('%s/%s.%s', imdbs{i}.image_dir, imdbs{i}.image_ids{x}, imdbs{i}.extension); 34 | else 35 | imdbs{i}.image_at = @(x) ... 36 | sprintf('%s/%s.%s', imdbs{i}.imagedir, imdbs{i}.image_ids{x}, imdbs{i}.extension); 37 | end 38 | roidbs{i}.rois = roidbs{i}.rois(inds_sub); 39 | 40 | img_idx_start = img_idx_start + imdb_img_num; 41 | end 42 | 43 | % restore previous rng 44 | rng(prev_rng); 45 | 46 | end -------------------------------------------------------------------------------- /faster_rcnn-master/utils/subsample_images_per_class.m: -------------------------------------------------------------------------------- 1 | function [imdbs, roidbs] = subsample_images_per_class(imdbs, roidbs, max_per_class_image_num, seed) 2 | 3 | if ~exist('seed', 'var') 4 | seed = 6; 5 | end 6 | 7 | class_num = cellfun(@(x) length(x.class_ids), imdbs, 'UniformOutput', true); 8 | assert(length(unique(class_num)) == 1); 9 | class_num = unique(class_num); 10 | 11 | rois = cellfun(@(x) x.rois, roidbs, 'UniformOutput', false); 12 | rois_combine = cell2mat(rois(:)); 13 | rois_combine_class = arrayfun(@(x) x.class, rois_combine, 'UniformOutput', false); 14 | 15 | %% select images with max_image_num 16 | 17 | % fix the random seed for repeatability 18 | prev_rng = seed_rand(seed); 19 | inds = cell(class_num, 1); 20 | rois_combine_length = length(rois_combine); 21 | valid_idxs = cell(class_num, 1); 22 | parfor i = 1:class_num 23 | valid_idxs{i} = cellfun(@(x) any(x == i), rois_combine_class, 'UniformOutput', false); 24 | valid_idxs{i} = cell2mat(valid_idxs{i}); 25 | end 26 | 27 | for i = 1:class_num 28 | valid_num = sum(valid_idxs{i}); 29 | 30 | num = min(valid_num, max_per_class_image_num); 31 | inds{i} = 1:rois_combine_length; 32 | inds{i} = inds{i}(valid_idxs{i}); 33 | inds{i} = inds{i}(randperm(length(inds{i}), num)); 34 | end 35 | 36 | inds = cell2mat(inds')'; 37 | inds = unique(inds); 38 | 39 | % restore previous rng 40 | rng(prev_rng); 41 | 42 | img_idx_start = 1; 43 | for i = 1:length(imdbs) 44 | imdb_img_num = length(imdbs{i}.image_ids); 45 | img_idx_end = img_idx_start + imdb_img_num - 1; 46 | inds_start = find(inds >= img_idx_start, 1, 'first'); 47 | inds_end = find(inds <= img_idx_end, 1, 'last'); 48 | 49 | inds_sub = inds(inds_start:inds_end); 50 | inds_sub = inds_sub - img_idx_start + 1; 51 | 52 | imdbs{i}.image_ids = imdbs{i}.image_ids(inds_sub); 53 | imdbs{i}.sizes = imdbs{i}.sizes(inds_sub, :); 54 | if isfield(imdbs{i}, 'image_dir') 55 | imdbs{i}.image_at = @(x) ... 56 | sprintf('%s/%s.%s', imdbs{i}.image_dir, imdbs{i}.image_ids{x}, imdbs{i}.extension); 57 | else 58 | imdbs{i}.image_at = @(x) ... 59 | sprintf('%s/%s.%s', imdbs{i}.imagedir, imdbs{i}.image_ids{x}, imdbs{i}.extension); 60 | end 61 | roidbs{i}.rois = roidbs{i}.rois(inds_sub); 62 | 63 | img_idx_start = img_idx_start + imdb_img_num; 64 | end 65 | 66 | 67 | -------------------------------------------------------------------------------- /faster_rcnn-master/utils/symbolic_link.m: -------------------------------------------------------------------------------- 1 | function symbolic_link(link, target) 2 | % symbolic_link(link, target) 3 | % -------------------------------------------------------- 4 | % Faster R-CNN 5 | % Copyright (c) 2015, Shaoqing Ren 6 | % Licensed under The MIT License [see LICENSE for details] 7 | % -------------------------------------------------------- 8 | 9 | if ispc() 10 | system(sprintf('mklink /J %s %s', link, target)); 11 | else 12 | system(sprintf('ln -s %s %s', link, target)); 13 | end 14 | 15 | end 16 | -------------------------------------------------------------------------------- /faster_rcnn-master/utils/tic_toc_print.m: -------------------------------------------------------------------------------- 1 | function tic_toc_print(fmt, varargin) 2 | % Print only after 1 second has passed since the last print. 3 | % Arguments are the same as for fprintf. 4 | 5 | % AUTORIGHTS 6 | % ------------------------------------------------------- 7 | % Copyright (C) 2009-2012 Ross Girshick 8 | % 9 | % This file is part of the voc-releaseX code 10 | % (http://people.cs.uchicago.edu/~rbg/latent/) 11 | % and is available under the terms of an MIT-like license 12 | % provided in COPYING. Please retain this notice and 13 | % COPYING if you use this file (or a portion of it) in 14 | % your project. 15 | % ------------------------------------------------------- 16 | 17 | persistent th; 18 | 19 | if isempty(th) 20 | th = tic(); 21 | end 22 | 23 | if toc(th) > 1 24 | fprintf(fmt, varargin{:}); 25 | drawnow; 26 | th = tic(); 27 | end 28 | -------------------------------------------------------------------------------- /faster_rcnn-master/utils/vis_label.m: -------------------------------------------------------------------------------- 1 | function vis_label(imdb, roidb) 2 | 3 | rois = roidb.rois; 4 | for iIM = 1:length(rois) 5 | im = imread(imdb.image_at(iIM)); 6 | boxes = arrayfun(@(x) rois(iIM).boxes(rois(iIM).class == x, :), 1:length(imdb.classes), 'UniformOutput', false); 7 | legends = imdb.classes; 8 | showboxes(im, boxes, legends); 9 | pause; 10 | end 11 | end 12 | -------------------------------------------------------------------------------- /faster_rcnn-master/utils/xVOCap.m: -------------------------------------------------------------------------------- 1 | function ap = xVOCap(rec,prec) 2 | % From the PASCAL VOC 2011 devkit 3 | 4 | mrec=[0 ; rec ; 1]; 5 | mpre=[0 ; prec ; 0]; 6 | for i=numel(mpre)-1:-1:1 7 | mpre(i)=max(mpre(i),mpre(i+1)); 8 | end 9 | i=find(mrec(2:end)~=mrec(1:end-1))+1; 10 | ap=sum((mrec(i)-mrec(i-1)).*mpre(i)); 11 | -------------------------------------------------------------------------------- /faster_rcnn-master/utils/xVOChash_init.m: -------------------------------------------------------------------------------- 1 | function hash = xVOChash_init(strs) 2 | % From the PASCAL VOC 2011 devkit 3 | 4 | hsize=4999; 5 | hash.key=cell(hsize,1); 6 | hash.val=cell(hsize,1); 7 | 8 | for i=1:numel(strs) 9 | s=strs{i}; 10 | h=mod(str2double(s([4 6:end])),hsize)+1; 11 | j=numel(hash.key{h})+1; 12 | hash.key{h}{j}=strs{i}; 13 | hash.val{h}(j)=i; 14 | end 15 | 16 | -------------------------------------------------------------------------------- /faster_rcnn-master/utils/xVOChash_lookup.m: -------------------------------------------------------------------------------- 1 | function ind = xVOChash_lookup(hash,s) 2 | % From the PASCAL VOC 2011 devkit 3 | 4 | hsize=numel(hash.key); 5 | h=mod(str2double(s([4 6:end])),hsize)+1; 6 | ind=hash.val{h}(strmatch(s,hash.key{h},'exact')); 7 | -------------------------------------------------------------------------------- /faster_rcnn-master/xls_results_analysis.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Liang-yc/CalorieEstimation/0b56fe6fa44f36ce4a1c428e249f6e9cfb51fdc2/faster_rcnn-master/xls_results_analysis.m --------------------------------------------------------------------------------