Mask_RCNN
Mask_RCNN copied to clipboard
matterport
Error while loading my own dataset(images)
hi every body i made a dataset but i think I made this dataset incorrect Because when i want to train it i get take this error:
Loading weights /home/farzad/mask_rcnn_coco.h5
2018-10-21 10:13:52.335212: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
Traceback (most recent call last):
File "custom.py", line 363, in
@lovedanang You mean the Json file? this is my Json file: {"_via_settings":{"ui":{"annotation_editor_height":25,"annotation_editor_fontsize":0.8,"leftsidebar_width":18,"image_grid":{"img_height":80,"rshape_fill":"none","rshape_fill_opacity":0.3,"rshape_stroke":"yellow","rshape_stroke_width":2,"show_region_shape":true,"show_image_policy":"all"},"image":{"region_label":"region_id","region_label_font":"10px Sans"}},"core":{"buffer_size":18,"filepath":{},"default_filepath":""},"project":{"name":"via_project_22Oct2018_9h4m"}},"_via_img_metadata":{"image1.jpg22699":{"filename":"image1.jpg","size":22699,"regions":[{"shape_attributes":{"name":"polyline","all_points_x":[23,56,96,130,174,209,235,263,276,269,231,170,132,68,31,13,13,24],"all_points_y":[131,77,44,30,28,37,54,101,176,212,251,269,274,257,230,195,147,132]},"region_attributes":{"head":"head"}}],"file_attributes":{}},"image2.jpg55195":{"filename":"image2.jpg","size":55195,"regions":[{"shape_attributes":{"name":"polyline","all_points_x":[375,343,310,317,344,416,459,495,505,490,435,375],"all_points_y":[28,49,137,236,287,302,284,206,112,53,22,27]},"region_attributes":{"head":"head"}},{"shape_attributes":{"name":"polyline","all_points_x":[98,137,191,231,246,247,223,198,139,98,61,24,53,98],"all_points_y":[28,26,35,64,134,188,246,289,313,307,268,164,78,27]},"region_attributes":{"head":"head"}}],"file_attributes":{}},"image3.jpg10744":{"filename":"image3.jpg","size":10744,"regions":[{"shape_attributes":{"name":"polyline","all_points_x":[150,90,67,74,113,153,208,236,225,148],"all_points_y":[22,48,95,150,184,204,185,129,60,22]},"region_attributes":{"head":"head"}}],"file_attributes":{}},"image4.jpg30982":{"filename":"image4.jpg","size":30982,"regions":[{"shape_attributes":{"name":"polyline","all_points_x":[151,91,61,77,126,186,235,250,208,151],"all_points_y":[115,150,210,278,308,308,278,215,126,113]},"region_attributes":{"head":"head"}}],"file_attributes":{}},"image5.jpg52968":{"filename":"image5.jpg","size":52968,"regions":[{"shape_attributes":{"name":"polyline","all_points_x":[387,250,216,220,254,278,425,470,527,521,463,387],"all_points_y":[89,157,224,305,369,388,388,357,253,146,84,87]},"region_attributes":{"head":"head"}}],"file_attributes":{}},"image6.jpg56672":{"filename":"image6.jpg","size":56672,"regions":[{"shape_attributes":{"name":"polyline","all_points_x":[349,279,258,314,428,528,539,455,349,350],"all_points_y":[85,127,271,347,364,300,185,109,83,82]},"region_attributes":{"head":"head"}}],"file_attributes":{}},"image7.jpg73111":{"filename":"image7.jpg","size":73111,"regions":[{"shape_attributes":{"name":"polyline","all_points_x":[391,310,252,224,243,379,475,565,604,591,508,392,393],"all_points_y":[122,153,226,399,453,538,553,504,417,275,141,121,120]},"region_attributes":{"head":"head"}}],"file_attributes":{}}},"_via_attributes":{"region":{"head":{"type":"image","description":"","options":{"head":""},"default_options":{"head":true}}},"file":{}}}
but this is the original json file:
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You JSON does not have the same formatting as the required one. You need only one part of your JSON: the "_via_img_metadata". If you load your JSON into a variable names var, just pass var["_via_img_metadata"] and it should be fine.
Also, you could just modify your JSON to keep only the "_via_img_metadata" part, like that :
{ "image1.jpg22699": { "filename": "image1.jpg", "size": 22699, "regions": [ { "shape_attributes": { "name": "polyline", "all_points_x": [ 23, 56, 96, 130, 174, 209, 235, 263, 276, 269, 231, 170, 132, 68, 31, 13, 13, 24 ], "all_points_y": [ 131, 77, 44, 30, 28, 37, 54, 101, 176, 212, 251, 269, 274, 257, 230, 195, 147, 132 ] }, "region_attributes": { "head": "head" } } ], "file_attributes": {} }, "image2.jpg55195": { "filename": "image2.jpg", "size": 55195, "regions": [ { "shape_attributes": { "name": "polyline", "all_points_x": [ 375, 343, 310, 317, 344, 416, 459, 495, 505, 490, 435, 375 ], "all_points_y": [ 28, 49, 137, 236, 287, 302, 284, 206, 112, 53, 22, 27 ] }, "region_attributes": { "head": "head" } }, { "shape_attributes": { "name": "polyline", "all_points_x": [ 98, 137, 191, 231, 246, 247, 223, 198, 139, 98, 61, 24, 53, 98 ], "all_points_y": [ 28, 26, 35, 64, 134, 188, 246, 289, 313, 307, 268, 164, 78, 27 ] }, "region_attributes": { "head": "head" } } ], "file_attributes": {} }, "image3.jpg10744": { "filename": "image3.jpg", "size": 10744, "regions": [ { "shape_attributes": { "name": "polyline", "all_points_x": [ 150, 90, 67, 74, 113, 153, 208, 236, 225, 148 ], "all_points_y": [ 22, 48, 95, 150, 184, 204, 185, 129, 60, 22 ] }, "region_attributes": { "head": "head" } } ], "file_attributes": {} }, "image4.jpg30982": { "filename": "image4.jpg", "size": 30982, "regions": [ { "shape_attributes": { "name": "polyline", "all_points_x": [ 151, 91, 61, 77, 126, 186, 235, 250, 208, 151 ], "all_points_y": [ 115, 150, 210, 278, 308, 308, 278, 215, 126, 113 ] }, "region_attributes": { "head": "head" } } ], "file_attributes": {} }, "image5.jpg52968": { "filename": "image5.jpg", "size": 52968, "regions": [ { "shape_attributes": { "name": "polyline", "all_points_x": [ 387, 250, 216, 220, 254, 278, 425, 470, 527, 521, 463, 387 ], "all_points_y": [ 89, 157, 224, 305, 369, 388, 388, 357, 253, 146, 84, 87 ] }, "region_attributes": { "head": "head" } } ], "file_attributes": {} }, "image6.jpg56672": { "filename": "image6.jpg", "size": 56672, "regions": [ { "shape_attributes": { "name": "polyline", "all_points_x": [ 349, 279, 258, 314, 428, 528, 539, 455, 349, 350 ], "all_points_y": [ 85, 127, 271, 347, 364, 300, 185, 109, 83, 82 ] }, "region_attributes": { "head": "head" } } ], "file_attributes": {} }, "image7.jpg73111": { "filename": "image7.jpg", "size": 73111, "regions": [ { "shape_attributes": { "name": "polyline", "all_points_x": [ 391, 310, 252, 224, 243, 379, 475, 565, 604, 591, 508, 392, 393 ], "all_points_y": [ 122, 153, 226, 399, 453, 538, 553, 504, 417, 275, 141, 121, 120 ] }, "region_attributes": { "head": "head" } } ], "file_attributes": {} } }
@stygian2a thanks my dear one another question when i run this python3 custom.py train --dataset=/home/farzad/Deep-Learning/mask_rcnn_damage_detection --weights=coco after train how can i save the weights.h5?
@amin3511 you can add callbacks in model.train() or modify callbacks in model.py (line 2347-2353).
@lovedanang Can you explain more? i use this model.save_weights('my_model_weights.h5') but i get error
@amin3511 you can do like this.
this code will save your best weights. Because the model you have define by model = modellib.MaskRCNN(mode="training", config=config, model_dir="logs/") it wasn't defined by keras. if you want to save weights after training, you should modify train() function in model.py. Add self.keras_model.save_weights(...) below self.keras_model.fit_generator(....).
@stygian2a , im trying to modify my JSON file. i created annotations with VIA 2.0.7 and got ERRROR: KeyError: 'regions'. Please, can you help me? many thx.
Hi @stygian2a @flit86 Could you please share which part of your JSON did you change to match the formatting required by the code?
I am trying to utilize the Mask RCNN to identify buildings from images.
My JSON file is the following: {"_via_settings":{"ui":{"annotation_editor_height":25,"annotation_editor_fontsize":0.8,"leftsidebar_width":18,"image_grid":{"img_height":80,"rshape_fill":"none","rshape_fill_opacity":0.3,"rshape_stroke":"yellow","rshape_stroke_width":2,"show_region_shape":true,"show_image_policy":"all"},"image":{"region_label":"via_region_id","region_color":"via_default_region_color","region_label_font":"10px Sans","on_image_annotation_editor_placement":"NEAR_REGION"}},"core":{"buffer_size":18,"filepath":{},"default_filepath":""},"project":{"name":"train"}},
"_via_img_metadata":{"Z1.jpg59410":{"filename":"Z1.jpg","size":59410,"regions":[{"shape_attributes":{"name":"polygon","all_points_x":[82,77,82,155,163,160,143,142,133,133,117,115],"all_points_y":[206,249,256,271,229,217,213,209,207,200,197,205]},"region_attributes":{"dbuildings":"dbuilding"}},{"shape_attributes":{"name":"polygon","all_points_x":[213,212,182,178,213,221,227,234,243,243,265,271,273,269,265,258,262,235,232],"all_points_y":[25,40,37,69,75,79,78,82,81,70,73,58,38,36,31,35,11,7,24]},"region_attributes":{"dbuildings":"dbuilding"}},{"shape_attributes":{"name":"polygon","all_points_x":[193,234,239,230,179,189],"all_points_y":[219,225,234,299,299,226]},"region_attributes":{"dbuildings":"dbuilding"}},{"shape_attributes":{"name":"polygon","all_points_x":[248,289,294,288,237],"all_points_y":[229,235,243,298,299]},"region_attributes":{"dbuildings":"dbuilding"}}],
"file_attributes":{}},"Z2.jpg62702":{"filename":"Z2.jpg","size":62702,"regions":[{"shape_attributes":{"name":"polygon","all_points_x":[143,193,198,199,199,203,203,255,257,239,237,233,231,230,191,192,171,151,149,161,163,149],"all_points_y":[54,50,55,73,76,76,74,70,117,115,128,129,134,136,137,142,141,145,131,131,126,127]},"region_attributes":{"dbuildings":"dbuilding"}}],
"file_attributes":{}},"Z3.jpg60052":{"filename":"Z3.jpg","size":60052,"regions":[{"shape_attributes":{"name":"polygon","all_points_x":[164,169,187,189,240,237,288,283,203,202,165],"all_points_y":[95,146,146,166,166,143,139,89,96,90,93]},"region_attributes":{"dbuildings":"dbuilding"}},{"shape_attributes":{"name":"polygon","all_points_x":[45,50,50,148,148,143,103,103],"all_points_y":[104,130,156,150,129,94,96,101]},"region_attributes":{"dbuildings":"dbuilding"}}],
"file_attributes":{}},"Z4.jpg64861":{"filename":"Z4.jpg","size":64861,"regions":[{"shape_attributes":{"name":"polygon","all_points_x":[68,70,62,62,65,66,77,77,113,110,125,128,123],"all_points_y":[157,174,175,185,183,197,197,206,204,181,179,165,153]},"region_attributes":{"dbuildings":"dbuilding"}},{"shape_attributes":{"name":"polygon","all_points_x":[87,89,80,81,108,124,124,127,127,131,131,110,107,95],"all_points_y":[235,253,253,271,269,269,261,260,253,253,235,235,230,230]},"region_attributes":{"dbuildings":"dbuilding"}},{"shape_attributes":{"name":"polygon","all_points_x":[229,236,252,273,271,262,262,267,266,258,259],"all_points_y":[87,155,153,151,126,126,120,120,105,105,88]},"region_attributes":{"dbuildings":"dbuilding"}},{"shape_attributes":{"name":"polygon","all_points_x":[237,238,247,250,265,279,274],"all_points_y":[163,193,194,227,227,225,161]},"region_attributes":{"dbuildings":"dbuilding"}},{"shape_attributes":{"name":"polygon","all_points_x":[222,225,213,215,227,228,258,258,268,268,256,255],"all_points_y":[14,37,42,55,55,65,65,55,55,35,33,13]},"region_attributes":{"dbuildings":"dbuilding"}}],
"file_attributes":{}},"Z5.jpg68200":{"filename":"Z5.jpg","size":68200,"regions":[{"shape_attributes":{"name":"polygon","all_points_x":[209,217,282,275,295,293,257,257,249,250,259,259,250,250],"all_points_y":[59,133,128,62,60,13,18,21,23,39,39,55,58,55]},"region_attributes":{"dbuildings":"dbuilding"}},{"shape_attributes":{"name":"polygon","all_points_x":[94,127,133,133,160,158,154,93,91],"all_points_y":[180,180,185,175,181,197,210,210,185]},"region_attributes":{"dbuildings":"dbuilding"}},{"shape_attributes":{"name":"polygon","all_points_x":[29,99,99,97,95,101,107,104,24,21,32],"all_points_y":[21,16,24,35,35,68,71,81,83,54,53]},"region_attributes":{"dbuildings":"dbuilding"}}],
"file_attributes":{}},"Z6.jpg64795":{"filename":"Z6.jpg","size":64795,"regions":[{"shape_attributes":{"name":"polygon","all_points_x":[50,51,127,127,89,89,70,70],"all_points_y":[41,72,73,41,41,29,30,41]},"region_attributes":{"dbuildings":"dbuilding"}},{"shape_attributes":{"name":"polygon","all_points_x":[191,193,193,243,244,239,239,230,230,212,213],"all_points_y":[137,186,207,209,184,183,169,169,148,142,137]},"region_attributes":{"dbuildings":"dbuilding"}},{"shape_attributes":{"name":"polygon","all_points_x":[29,31,49,63,57,42,23],"all_points_y":[193,193,194,197,259,257,255]},"region_attributes":{"dbuildings":"dbuilding"}},{"shape_attributes":{"name":"polygon","all_points_x":[131,170,170,199,199,201,203,130],"all_points_y":[237,235,224,223,230,235,271,275]},"region_attributes":{"dbuildings":"dbuilding"}},{"shape_attributes":{"name":"polygon","all_points_x":[102,153,147,146,131,131,121,119,110,109,102,90],"all_points_y":[139,127,91,85,87,90,91,96,96,104,106,109]},"region_attributes":{"dbuildings":"dbuilding"}}],"file_attributes":{}}},"_via_attributes":{"region":{"dbuildings":{"type":"text","description":"","default_value":""}},"file":{}}}
This is the error that keeps popping up:
2019-07-08 19:04:21.815598: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set. If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU. To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.
Traceback (most recent call last):
File "dbuilding.py", line 358, in
train(model)
File "dbuilding.py", line 175, in train
dataset_train.load_dbuilding(args.dataset, "train")
File "dbuilding.py", line 109, in load_dbuilding
annotations = [a for a in annotations if a['regions']]
File "dbuilding.py", line 109, in
annotations = [a for a in annotations if a['regions']]
KeyError: 'regions'
Would really appreciate it if you could provide a detailed explanation of the changes that you made and also suggest the changes that I should proceed with!
Dear amin3511,
I want to use Mask-RCNN to train on my custom dataset (just one class).
First of all, I use Labelme to create groundtruth for each image. Each image has a .json file as its ground-truth.
After that, I don't know what should I do...
I want to know how to convert my groundtruth file to a format that Mask-rcnn can use. Do you have any tools ?
Looking forward to hearing from you, thanks in advance.
With best regards, Ming
Hi folks, i am trying to run annotated images which i labeled through VGG annotator and then try to get the json through colab directory and google drive directory. But it always give me no such file directory. Any suggestions ??
modify your JSON to keep only the "_via_img_metadata" part
Sorry, how do you have this format ?
I was using VGG Image Annotator 2.0.11 to annotate my dataset and I had the same issue. I fixed it in custom.py like this :
annotations1 = json.load(open(os.path.join(dataset_dir, "via_region_data.json")))
annotations2 = list(annotations1.values())
annotations3 = list(annotations2[1].values()) # need to adapt to our dataset
# annotations. Skip unannotated images.
annotations = [a for a in annotations3 if a['regions']]
It's not very gracefull but it works !
