@@ -351,7 +351,7 @@ def print_category_frame(obj_type, category, statistics, accuracy, path):
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objs = np .array (list (deepflatten (statistics )))
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detected = objs [objs < accuracy ]
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frame = {"category" : category , "detected " + obj_type : len (detected )/ max (1 , len (objs )),
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- "total detected " + obj_type : len (detected ), "total " + obj_type : len (detected ),
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+ "total detected " + obj_type : len (detected ), "total " + obj_type : len (objs ),
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"average error " + obj_type : np .mean (detected )}
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data_frame = pd .DataFrame (objs )
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data_frame .hist (bins = 500 )
@@ -677,7 +677,7 @@ def generate_dataset(args, synthetic_object, background_color=0):
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synthetic_object .transform_object (rotate_t )
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for transform in transforms :
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synthetic_object .transform_object (transform )
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- folder = '_' .join (synthetic_object .history )
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+ folder = '_' + '_' .join (synthetic_object .history )
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dictionary [folder ] = dictionary .get (folder , - 1 ) + 1
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if not os .path .exists (output + "/" + folder ):
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os .mkdir (output + "/" + folder )
@@ -775,7 +775,7 @@ def main():
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list_folders = next (os .walk (dataset_path ))[1 ]
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error_by_categories = {}
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for folder in list_folders :
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- if folder [0 ]. isdigit () :
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+ if folder [0 ] != '_' :
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continue
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configs = glob .glob (dataset_path + '/' + folder + '/*.json' )
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distances = {}
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