@ -16,7 +16,9 @@
import hashlib
import json
import os
from enum import Enum
import matplotlib . pyplot as plt
import matplotlib . patches as patches
import numpy as np
# import jsbeautifier
import mindspore . dataset as ds
@ -284,3 +286,124 @@ def config_get_set_num_parallel_workers(num_parallel_workers_new):
logger . info ( " num_parallel_workers: original = {} new = {} " . format ( num_parallel_workers_original ,
num_parallel_workers_new ) )
return num_parallel_workers_original
def visualize_with_bounding_boxes ( orig , aug , plot_rows = 3 ) :
"""
Take a list of un - augmented and augmented images with " annotation " bounding boxes
Plot images to compare test correct BBox augment functionality
: param orig : list of original images and bboxes ( without aug )
: param aug : list of augmented images and bboxes
: param plot_rows : number of rows on plot ( rows = samples on one plot )
: return : None
"""
def add_bounding_boxes ( ax , bboxes ) :
for bbox in bboxes :
rect = patches . Rectangle ( ( bbox [ 0 ] , bbox [ 1 ] ) ,
bbox [ 2 ] , bbox [ 3 ] ,
linewidth = 1 , edgecolor = ' r ' , facecolor = ' none ' )
# Add the patch to the Axes
ax . add_patch ( rect )
# Quick check to confirm correct input parameters
if not isinstance ( orig , list ) or not isinstance ( aug , list ) :
return
if len ( orig ) != len ( aug ) or not orig :
return
comp_set = int ( len ( orig ) / plot_rows )
orig , aug = np . array ( orig ) , np . array ( aug )
if len ( orig ) > plot_rows :
orig = np . split ( orig [ : comp_set * plot_rows ] , comp_set ) + [ orig [ comp_set * plot_rows : ] ]
aug = np . split ( aug [ : comp_set * plot_rows ] , comp_set ) + [ aug [ comp_set * plot_rows : ] ]
else :
orig = [ orig ]
aug = [ aug ]
for ix , allData in enumerate ( zip ( orig , aug ) ) :
base_ix = ix * plot_rows # will signal what base level we're on
sub_plot_count = 2 if ( len ( allData [ 0 ] ) < 2 ) else len ( allData [ 0 ] ) # if 1 image remains, create subplot for 2 to simplify axis selection
fig , axs = plt . subplots ( sub_plot_count , 2 )
fig . tight_layout ( pad = 1.5 )
for x , ( dataA , dataB ) in enumerate ( zip ( allData [ 0 ] , allData [ 1 ] ) ) :
cur_ix = base_ix + x
axs [ x , 0 ] . imshow ( dataA [ " image " ] )
add_bounding_boxes ( axs [ x , 0 ] , dataA [ " annotation " ] )
axs [ x , 0 ] . title . set_text ( " Original " + str ( cur_ix + 1 ) )
logger . info ( " Original ** \n {} : {} " . format ( str ( cur_ix + 1 ) , dataA [ " annotation " ] ) )
axs [ x , 1 ] . imshow ( dataB [ " image " ] )
add_bounding_boxes ( axs [ x , 1 ] , dataB [ " annotation " ] )
axs [ x , 1 ] . title . set_text ( " Augmented " + str ( cur_ix + 1 ) )
logger . info ( " Augmented ** \n {} : {} \n " . format ( str ( cur_ix + 1 ) , dataB [ " annotation " ] ) )
plt . show ( )
class InvalidBBoxType ( Enum ) :
"""
Defines Invalid Bounding Bbox types for test cases
"""
WidthOverflow = 1
HeightOverflow = 2
NegativeXY = 3
WrongShape = 4
def check_bad_bbox ( data , test_op , invalid_bbox_type , expected_error ) :
"""
: param data : de object detection pipeline
: param test_op : Augmentation Op to test on image
: param invalid_bbox_type : type of bad box
: param expected_error : error expected to get due to bad box
: return : None
"""
def add_bad_annotation ( img , bboxes , invalid_bbox_type_ ) :
"""
Used to generate erroneous bounding box examples on given img .
: param img : image where the bounding boxes are .
: param bboxes : in [ x_min , y_min , w , h , label , truncate , difficult ] format
: param box_type_ : type of bad box
: return : bboxes with bad examples added
"""
height = img . shape [ 0 ]
width = img . shape [ 1 ]
if invalid_bbox_type_ == InvalidBBoxType . WidthOverflow :
# use box that overflows on width
return img , np . array ( [ [ 0 , 0 , width + 1 , height , 0 , 0 , 0 ] ] ) . astype ( np . uint32 )
if invalid_bbox_type_ == InvalidBBoxType . HeightOverflow :
# use box that overflows on height
return img , np . array ( [ [ 0 , 0 , width , height + 1 , 0 , 0 , 0 ] ] ) . astype ( np . uint32 )
if invalid_bbox_type_ == InvalidBBoxType . NegativeXY :
# use box with negative xy
return img , np . array ( [ [ - 10 , - 10 , width , height , 0 , 0 , 0 ] ] ) . astype ( np . uint32 )
if invalid_bbox_type_ == InvalidBBoxType . WrongShape :
# use box that has incorrect shape
return img , np . array ( [ [ 0 , 0 , width - 1 ] ] ) . astype ( np . uint32 )
return img , bboxes
try :
# map to use selected invalid bounding box type
data = data . map ( input_columns = [ " image " , " annotation " ] ,
output_columns = [ " image " , " annotation " ] ,
columns_order = [ " image " , " annotation " ] ,
operations = lambda img , bboxes : add_bad_annotation ( img , bboxes , invalid_bbox_type ) )
# map to apply ops
data = data . map ( input_columns = [ " image " , " annotation " ] ,
output_columns = [ " image " , " annotation " ] ,
columns_order = [ " image " , " annotation " ] ,
operations = [ test_op ] ) # Add column for "annotation"
for _ , _ in enumerate ( data . create_dict_iterator ( ) ) :
break
except RuntimeError as error :
logger . info ( " Got an exception in DE: {} " . format ( str ( error ) ) )
assert expected_error in str ( error )
Danish Farid
5 years ago