Skip to content

NOT FOR MERGING: Fixes to pet detector tutorial #3490

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 12 commits into from
Closed

NOT FOR MERGING: Fixes to pet detector tutorial #3490

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
12 commits
Select commit Hold shift + click to select a range
199e254
annotates / fixes tutorial instructions
maxwang7 Feb 28, 2018
82857bd
fixes tf_example_decoder.py
maxwang7 Feb 28, 2018
5ffed73
adds dependencies
maxwang7 Feb 28, 2018
2d76dce
FOR DEMONSTRATION ONLY; NOT FOR PUSHING
maxwang7 Feb 28, 2018
b0e7db3
uses our own data
maxwang7 Mar 1, 2018
b849596
removes using coco metrics, evaluator is working
maxwang7 Mar 2, 2018
364e379
modifies inference tutorial to use our trained graph
maxwang7 Mar 6, 2018
3a897af
fixes syntax problems in ipython notebook, improves create_pet_tf_rec…
maxwang7 Mar 7, 2018
c4bf6d8
code for dealing with data
maxwang7 Mar 7, 2018
ba00f88
fixes config, ipynb code
maxwang7 Mar 12, 2018
e7ef8bd
fixes gitignore
maxwang7 Mar 12, 2018
7c3f094
2018-03-15-1340
maxwang7 Mar 15, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 2 additions & 0 deletions .gitignore
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -90,3 +90,5 @@ ENV/

# PyCharm
.idea/
research/demo_data/images
research/demo_data/annotations/xmls
20 changes: 20 additions & 0 deletions research/demo_data/annotations/process_labelImg_xml.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,20 @@
import os
import xml.etree.ElementTree as ET
import sys

def append_jpg_extension(directory):
# appends ".jpg" to all of the xml files inside the given directory at data["filename"]
for filename in os.listdir(directory):
if os.path.splitext(filename)[1] == ".xml":
tree = ET.parse(os.path.join(directory, filename))
root = tree.getroot()
filename_text = root[1].text
if os.path.splitext(filename_text)[1] != ".jpg":
root[1].text = filename_text + ".jpg"
tree.write(os.path.join(directory, filename))

def main():
append_jpg_extension(sys.argv[1])

if __name__ == "__main__":
main()
241 changes: 241 additions & 0 deletions research/demo_data/annotations/trainval.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,241 @@
2018-03-01T02-19-54-064624_exo183CGE-574231519870794064624_343 1 1 1
2018-03-01T02-19-54-564622_exo183CGE-574231519870794564622_344 1 1 1
2018-03-01T02-19-55-064628_exo183CGE-574231519870795064628_345 1 1 1
2018-03-01T02-19-55-564699_exo183CGE-574231519870795564699_346 1 1 1
2018-03-01T02-19-56-064632_exo183CGE-574231519870796064632_347 1 1 1
2018-03-01T02-19-56-564691_exo183CGE-574231519870796564691_348 1 1 1
2018-03-01T02-19-57-064796_exo183CGE-574231519870797064796_349 1 1 1
2018-03-01T02-19-57-564672_exo183CGE-574231519870797564672_350 1 1 1
2018-03-01T02-19-58-064745_exo183CGE-574231519870798064745_351 1 1 1
2018-03-01T02-19-58-564756_exo183CGE-574231519870798564756_352 1 1 1
2018-03-01T02-19-59-064701_exo183CGE-574231519870799064701_353 1 1 1
2018-03-01T02-19-59-564733_exo183CGE-574231519870799564733_354 1 1 1
2018-03-01T02-20-00-064774_exo183CGE-574231519870800064774_355 1 1 1
2018-03-01T02-20-00-564740_exo183CGE-574231519870800564740_356 1 1 1
2018-03-01T02-20-01-064768_exo183CGE-574231519870801064768_357 1 1 1
2018-03-01T02-20-01-564808_exo183CGE-574231519870801564808_358 1 1 1
2018-03-01T02-20-02-064754_exo183CGE-574231519870802064754_359 1 1 1
2018-03-01T02-20-02-564773_exo183CGE-574231519870802564773_360 1 1 1
2018-03-01T02-20-03-064855_exo183CGE-574231519870803064855_361 1 1 1
2018-03-01T02-20-03-564855_exo183CGE-574231519870803564855_362 1 1 1
2018-03-01T02-20-04-064815_exo183CGE-574231519870804064815_363 1 1 1
2018-03-01T02-20-04-564831_exo183CGE-574231519870804564831_364 1 1 1
2018-03-01T02-20-05-064895_exo183CGE-574231519870805064895_365 1 1 1
2018-03-01T02-20-05-564836_exo183CGE-574231519870805564836_366 1 1 1
2018-03-01T02-20-06-064877_exo183CGE-574231519870806064877_367 1 1 1
2018-03-01T02-20-06-564919_exo183CGE-574231519870806564919_368 1 1 1
2018-03-01T02-20-07-064847_exo183CGE-574231519870807064847_369 1 1 1
2018-03-01T02-20-07-564879_exo183CGE-574231519870807564879_370 1 1 1
2018-03-01T02-20-08-064918_exo183CGE-574231519870808064918_371 1 1 1
2018-03-01T02-20-08-564898_exo183CGE-574231519870808564898_372 1 1 1
2018-03-01T02-20-09-064942_exo183CGE-574231519870809064942_373 1 1 1
2018-03-01T02-20-09-564893_exo183CGE-574231519870809564893_374 1 1 1
2018-03-01T02-20-10-064959_exo183CGE-574231519870810064959_375 1 1 1
2018-03-01T02-20-10-564996_exo183CGE-574231519870810564996_376 1 1 1
2018-03-01T02-20-11-064989_exo183CGE-574231519870811064989_377 1 1 1
2018-03-01T02-20-11-564928_exo183CGE-574231519870811564928_378 1 1 1
2018-03-01T02-20-12-064983_exo183CGE-574231519870812064983_379 1 1 1
2018-03-01T02-20-12-564965_exo183CGE-574231519870812564965_380 1 1 1
2018-03-01T02-20-13-064988_exo183CGE-574231519870813064988_381 1 1 1
2018-03-01T02-20-13-565022_exo183CGE-574231519870813565022_382 1 1 1
2018-03-01T02-20-14-065044_exo183CGE-574231519870814065044_383 1 1 1
2018-03-01T02-20-14-565062_exo183CGE-574231519870814565062_384 1 1 1
2018-03-01T02-20-15-065014_exo183CGE-574231519870815065014_385 1 1 1
2018-03-01T02-20-15-565031_exo183CGE-574231519870815565031_386 1 1 1
2018-03-01T02-20-16-065036_exo183CGE-574231519870816065036_387 1 1 1
2018-03-01T02-20-16-565072_exo183CGE-574231519870816565072_388 1 1 1
2018-03-01T02-20-17-065102_exo183CGE-574231519870817065102_389 1 1 1
2018-03-01T02-20-17-565048_exo183CGE-574231519870817565048_390 1 1 1
2018-03-01T02-20-18-065103_exo183CGE-574231519870818065103_391 1 1 1
2018-03-01T02-20-18-565071_exo183CGE-574231519870818565071_392 1 1 1
2018-03-01T02-20-19-065122_exo183CGE-574231519870819065122_393 1 1 1
2018-03-01T02-20-19-565149_exo183CGE-574231519870819565149_394 1 1 1
2018-03-01T02-20-20-065160_exo183CGE-574231519870820065160_395 1 1 1
2018-03-01T02-20-20-565128_exo183CGE-574231519870820565128_396 1 1 1
2018-03-01T02-20-21-065182_exo183CGE-574231519870821065182_397 1 1 1
2018-03-01T02-20-21-565172_exo183CGE-574231519870821565172_398 1 1 1
2018-03-01T02-20-22-065140_exo183CGE-574231519870822065140_399 1 1 1
2018-03-01T02-20-22-565203_exo183CGE-574231519870822565203_400 1 1 1
2018-03-01T02-20-23-065219_exo183CGE-574231519870823065219_401 1 1 1
2018-03-01T02-20-23-565171_exo183CGE-574231519870823565171_402 1 1 1
2018-03-01T02-20-24-065229_exo183CGE-574231519870824065229_403 1 1 1
2018-03-01T02-20-24-565235_exo183CGE-574231519870824565235_404 1 1 1
2018-03-01T02-20-25-065244_exo183CGE-574231519870825065244_405 1 1 1
2018-03-01T02-20-25-565191_exo183CGE-574231519870825565191_406 1 1 1
2018-03-01T02-20-26-065249_exo183CGE-574231519870826065249_407 1 1 1
2018-03-01T02-20-26-565256_exo183CGE-574231519870826565256_408 1 1 1
2018-03-01T02-20-27-065224_exo183CGE-574231519870827065224_409 1 1 1
2018-03-01T02-20-27-565273_exo183CGE-574231519870827565273_410 1 1 1
2018-03-01T02-20-28-065271_exo183CGE-574231519870828065271_411 1 1 1
2018-03-01T02-20-28-565255_exo183CGE-574231519870828565255_412 1 1 1
2018-03-01T02-20-29-065282_exo183CGE-574231519870829065282_413 1 1 1
2018-03-01T02-20-29-565309_exo183CGE-574231519870829565309_414 1 1 1
2018-03-01T02-20-30-065285_exo183CGE-574231519870830065285_415 1 1 1
2018-03-01T02-20-30-565285_exo183CGE-574231519870830565285_416 1 1 1
2018-03-01T02-20-31-065289_exo183CGE-574231519870831065289_417 1 1 1
2018-03-01T02-20-31-565340_exo183CGE-574231519870831565340_418 1 1 1
2018-03-01T02-20-32-065309_exo183CGE-574231519870832065309_419 1 1 1
2018-03-01T02-20-32-565352_exo183CGE-574231519870832565352_420 1 1 1
2018-03-01T02-20-33-065333_exo183CGE-574231519870833065333_421 1 1 1
2018-03-01T02-20-33-565377_exo183CGE-574231519870833565377_422 1 1 1
2018-03-01T02-20-34-065411_exo183CGE-574231519870834065411_423 1 1 1
2018-03-01T02-20-34-565354_exo183CGE-574231519870834565354_424 1 1 1
2018-03-01T02-20-35-065369_exo183CGE-574231519870835065369_425 1 1 1
2018-03-01T02-20-35-565392_exo183CGE-574231519870835565392_426 1 1 1
2018-03-01T02-20-36-065566_exo183CGE-574231519870836065566_427 1 1 1
2018-03-01T02-20-36-565397_exo183CGE-574231519870836565397_428 1 1 1
2018-03-01T02-20-37-065426_exo183CGE-574231519870837065426_429 1 1 1
2018-03-01T02-20-37-565440_exo183CGE-574231519870837565440_430 1 1 1
2018-03-01T02-20-38-065449_exo183CGE-574231519870838065449_431 1 1 1
2018-03-01T02-20-38-565443_exo183CGE-574231519870838565443_432 1 1 1
2018-03-01T02-43-58-991936_exo183CGE-574231519872238991936_3284 1 1 1
2018-03-01T02-43-59-491969_exo183CGE-574231519872239491969_3285 1 1 1
2018-03-01T02-43-59-991939_exo183CGE-574231519872239991939_3286 1 1 1
2018-03-01T02-44-00-491920_exo183CGE-574231519872240491920_3287 1 1 1
2018-03-01T02-44-00-991922_exo183CGE-574231519872240991922_3288 1 1 1
2018-03-01T02-44-01-991943_exo183CGE-574231519872241991943_3289 1 1 1
2018-03-01T02-44-02-491911_exo183CGE-574231519872242491911_3290 1 1 1
2018-03-01T02-44-02-991954_exo183CGE-574231519872242991954_3291 1 1 1
2018-03-01T02-44-03-328612_exo183CGE-574231519872243328612_3292 1 1 1
2018-03-01T02-44-03-578604_exo183CGE-574231519872243578604_3293 1 1 1
2018-03-01T02-44-03-828574_exo183CGE-574231519872243828574_3294 1 1 1
2018-03-01T02-44-04-078620_exo183CGE-574231519872244078620_3295 1 1 1
2018-03-01T02-44-04-328614_exo183CGE-574231519872244328614_3296 1 1 1
2018-03-01T02-44-04-828627_exo183CGE-574231519872244828627_3297 1 1 1
2018-03-01T02-44-05-078602_exo183CGE-574231519872245078602_3298 1 1 1
2018-03-01T02-44-05-578580_exo183CGE-574231519872245578580_3299 1 1 1
2018-03-01T02-44-05-828602_exo183CGE-574231519872245828602_3300 1 1 1
2018-03-01T02-44-06-328584_exo183CGE-574231519872246328584_3301 1 1 1
2018-03-01T02-44-06-578637_exo183CGE-574231519872246578637_3302 1 1 1
2018-03-01T02-44-06-828600_exo183CGE-574231519872246828600_3303 1 1 1
2018-03-01T02-44-07-078562_exo183CGE-574231519872247078562_3304 1 1 1
2018-03-01T02-44-07-578597_exo183CGE-574231519872247578597_3305 1 1 1
2018-03-01T02-44-07-828576_exo183CGE-574231519872247828576_3306 1 1 1
2018-03-01T02-45-15-828707_exo183CGE-574231519872315828707_3437 1 1 1
2018-03-01T02-45-16-328639_exo183CGE-574231519872316328639_3438 1 1 1
2018-03-01T02-45-16-828816_exo183CGE-574231519872316828816_3439 1 1 1
exo183CGETR-629001520289499305166_31 1 1 1
exo183CGETR-629001520289512303963_44 1 1 1
exo183CGETR-629001520289523267341_54 1 1 1
exo183CGETR-629001520289529650440_62 1 1 1
exo183CGETR-629001520289533117658_66 1 1 1
exo183CGETR-629001520382157009968_97 1 1 1
exo183CGETR-629001520382165511131_110 1 1 1
exo183CGETR-629001520382175012266_121 1 1 1
exo183CGETR-629001520382181513120_130 1 1 1
exo183CGETR-629001520382185511644_136 1 1 1
exo183CGETR-629001520382195505420_149 1 1 1
exo183CGETR-629001520382202501289_160 1 1 1
exo183CGETR-629001520382203500843_161 1 1 1
exo183CGETR-629001520382208498179_168 1 1 1
exo183CGETR-629001520382214994634_176 1 1 1
exo183CGETR-629001520382227988423_191 1 1 1
exo183CGETR-629001520382232986214_199 1 1 1
exo183CGETR-629001520382242482248_211 1 1 1
exo183CGETR-629001520382245980854_215 1 1 1
exo183CGETR-629001520382256476960_230 1 1 1
exo183CGETR-629001520382262474905_237 1 1 1
exo183CGETR-629001520382333457218_61 1 1 1
exo183CGETR-629001520382418447057_200 1 1 1
exo183CGETR-629001520382423946721_210 1 1 1
exo183CGETR-629001520382430946280_220 1 1 1
exo183CGETR-629001520382438945749_231 1 1 1
exo183CGETR-629001520382448445290_245 1 1 1
exo183CGETR-629001520382519443311_340 1 1 1
exo183CGETR-629001520382523943155_348 1 1 1
exo183CGETR-629001520382533443040_365 1 1 1
exo183CGETR-629001520382539943016_375 1 1 1
exo183CGETR-629001520382547443067_386 1 1 1
exo183CGETR-629001520382559443033_404 1 1 1
exo183CGETR-629001520382564943026_412 1 1 1
exo183CGETR-629001520382569443079_419 1 1 1
exo183CGETR-629001520382578443065_435 1 1 1
exo183CGETR-629001520382585443070_448 1 1 1
exo183CGETR-629001520382594443232_461 1 1 1
exo183CGETR-629001520382596943268_465 1 1 1
exo183CGETR-629001520382601443346_471 1 1 1
exo183CGETR-629001520382603943318_475 1 1 1
exo183CGETR-629001520382606943427_481 1 1 1
exo183CGETR-629001520382609443403_485 1 1 1
exo183CGETR-629001520382611443390_489 1 1 1
exo183CGETR-629001520545633619991_39 1 1 1
exo183CGETR-629001520545642119430_51 1 1 1
exo183CGETR-629001520545649119629_62 1 1 1
exo183CGETR-629001520545656120128_70 1 1 1
exo183CGETR-629001520545656620176_71 1 1 1
exo183CGETR-629001520545668121499_87 - Copy 1 1 1
exo183CGETR-629001520545679621567_98 - Copy 1 1 1
exo183CGETR-629001520545688122498_111 - Copy 1 1 1
exo183CGETR-629001520546262146854_973 - Copy 1 1 1
exo183CGETR-629001520546268146710_981 - Copy 1 1 1
exo183CGETR-629001520546274646741_991 1 1 1
exo183CGETR-629001520546281646886_1002 1 1 1
exo183CGETR-629001520546289647310_1012 1 1 1
exo183CGETR-629001520546999158528_259 1 1 1
exo183CGETR-629001520547006657170_272 1 1 1
exo183CGETR-629001520547013657104_282 1 1 1
exo183CGETR-629001520547019657131_292 1 1 1
exo183CGETR-629001520547021157139_295 1 1 1
exo183CGETR-629001520547027657028_307 1 1 1
exo183CGETR-629001520547032657151_315 1 1 1
exo183CGETR-629001520547035157249_319 1 1 1
exo183CGETR-629001520547038657279_325 1 1 1
exo183CGETR-629001520547047656882_339 1 1 1
exo183CGETR-629001520547055656740_353 1 1 1
exo183CGETR-629001520547063156949_367 1 1 1
exo183CGETR-629001520547069157047_377 1 1 1
exo183CGETR-629001520547709659781_49 1 1 1
exo183CGETR-629001520547716658603_60 1 1 1
exo183CGETR-629001520547722157928_69 1 1 1
exo183CGETR-629001520547730657041_82 1 1 1
exo183CGETR-629001520547737656247_90 1 1 1
exo183CGETR-629001520547751155166_104 1 1 1
exo183CGETR-629001520547763654243_118 1 1 1
exo183CGETR-629001520547776153115_132 1 1 1
exo183CGETR-629001520547780652626_137 1 1 1
exo183CGETR-629001520547782652492_141 1 1 1
exo183CGETR-629001520547791651630_154 1 1 1
exo183CGETR-629001520547807150203_176 1 1 1
exo183CGETR-629001520547808650127_178 1 1 1
exo304MGETR-638221520044541587365_2392 1 1 1
exo304MGETR-638221520044542087308_2393 1 1 1
exo304MGETR-638221520044542587396_2394 1 1 1
exo304MGETR-638221520044543087332_2395 1 1 1
exo304MGETR-638221520044543587325_2396 1 1 1
exo304MGETR-638221520044544087347_2397 1 1 1
exo304MGETR-638221520044544587296_2398 1 1 1
exo304MGETR-638221520044545087375_2399 1 1 1
exo304MGETR-638221520044545587328_2400 1 1 1
exo304MGETR-638221520044546087307_2401 1 1 1
exo304MGETR-638221520044546587337_2402 1 1 1
exo304MGETR-638221520044547587326_2403 1 1 1
exo304MGETR-638221520044548087328_2404 1 1 1
exo304MGETR-638221520044548587330_2405 1 1 1
exo304MGETR-638221520044549087301_2406 1 1 1
exo304MGETR-638221520044549587367_2407 1 1 1
exo304MGETR-638221520044550087334_2408 1 1 1
exo304MGETR-638221520044550587373_2409 1 1 1
exo304MGETR-638221520044551087354_2410 1 1 1
exo304MGETR-638221520044551587368_2411 1 1 1
exo304MGETR-638221520044552087352_2412 1 1 1
exo304MGETR-638221520044552587332_2413 1 1 1
exo304MGETR-638221520044553087361_2414 1 1 1
exo304MGETR-638221520044553587348_2415 1 1 1
exo304MGETR-638221520044554087314_2416 1 1 1
exo304MGETR-638221520044554587319_2417 1 1 1
exo304MGETR-638221520044555087294_2418 1 1 1
exo304MGETR-638221520047497166908_8233 1 1 1
exo304MGETR-638221520047499166659_8237 1 1 1
exo304MGETR-638221520047500666425_8240 1 1 1
exo304MGETR-638221520047502166274_8243 1 1 1
exo304MGETR-638221520047504166056_8247 1 1 1
exo304MGETR-638221520047505665772_8250 1 1 1
exo304MGETR-638221520047507165593_8253 1 1 1
exo304MGETR-638221520047508665446_8256 1 1 1
exo304MGETR-638221520047510165196_8259 1 1 1
ext-1520280592537-exo183CGETR-629001520289496803415_29-0-1520289497607927938 1 1 1
ext-1520379505933-exo183CGETR-629001520382153009540_94-1-1520382153778460076 1 1 1
ext-1520379505933-exo183CGETR-629001520382157510059_98-0-1520382158502503707 1 1 1
ext-1520379505933-exo183CGETR-629001520382189009358_139-1-1520382189782750164 1 1 1
ext-1520542201255-exo183CGETR-629001520547744655744_97-0-1520547745411355328 1 1 1
ext-1520542201255-exo183CGETR-629001520547775153168_131-0-1520547776131305715 1 1 1
Binary file added research/demo_data/images/Person_1.jpg
View file
Open in desktop
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Binary file added research/demo_data/images/Person_1.png
View file
Open in desktop
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Binary file added research/demo_data/images/demo_data_001.png
View file
Open in desktop
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
9 changes: 9 additions & 0 deletions research/demo_data/label_map.pbtxt
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,9 @@
item {
id: 1
name: 'person'
}

item {
id: 2
name: 'car'
}
Binary file added research/demo_data/output/lattice_train_with_masks.record
View file
Open in desktop
Binary file not shown.
Empty file added research/demo_data/output/lattice_val_with_masks.record
View file
Open in desktop
Empty file.
6 changes: 4 additions & 2 deletions research/object_detection/data_decoders/tf_example_decoder.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -106,8 +106,7 @@ def __init__(self,
slim_example_decoder.Image(
image_key='image/encoded',
format_key='image/format',
channels=3,
dct_method=dct_method),
channels=3),
fields.InputDataFields.source_id: (
slim_example_decoder.Tensor('image/source_id')),
fields.InputDataFields.key: (
Expand Down Expand Up @@ -160,10 +159,13 @@ def __init__(self,
default_value=-1)
# If the label_map_proto is provided, try to use it in conjunction with
# the class text, and fall back to a materialized ID.
"""
label_handler = slim_example_decoder.BackupHandler(
slim_example_decoder.LookupTensor(
'image/object/class/text', table, default_value=''),
slim_example_decoder.Tensor('image/object/class/label'))
"""
label_handler = slim_example_decoder.Tensor('image/object/class/label')
else:
label_handler = slim_example_decoder.Tensor('image/object/class/label')
self.items_to_handlers[
Expand Down
34 changes: 18 additions & 16 deletions research/object_detection/dataset_tools/create_pet_tf_record.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -32,12 +32,13 @@
import os
import random
import re
import math

from lxml import etree
import numpy as np
import PIL.Image
import tensorflow as tf

import sys; print sys.path
from object_detection.utils import dataset_util
from object_detection.utils import label_map_util

Expand Down Expand Up @@ -110,18 +111,18 @@ def dict_to_tf_example(data,
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()

with tf.gfile.GFile(mask_path, 'rb') as fid:
encoded_mask_png = fid.read()
encoded_png_io = io.BytesIO(encoded_mask_png)
mask = PIL.Image.open(encoded_png_io)
if mask.format != 'PNG':
raise ValueError('Mask format not PNG')
# with tf.gfile.GFile(mask_path, 'rb') as fid:
# encoded_mask_png = fid.read()
# encoded_png_io = io.BytesIO(encoded_mask_png)
# mask = PIL.Image.open(encoded_png_io)
# if mask.format != 'PNG':
# raise ValueError('Mask format not PNG')

mask_np = np.asarray(mask)
nonbackground_indices_x = np.any(mask_np != 2, axis=0)
nonbackground_indices_y = np.any(mask_np != 2, axis=1)
nonzero_x_indices = np.where(nonbackground_indices_x)
nonzero_y_indices = np.where(nonbackground_indices_y)
# mask_np = np.asarray(mask)
# nonbackground_indices_x = np.any(mask_np != 2, axis=0)
# nonbackground_indices_y = np.any(mask_np != 2, axis=1)
# nonzero_x_indices = np.where(nonbackground_indices_x)
# nonzero_y_indices = np.where(nonbackground_indices_y)

width = int(data['size']['width'])
height = int(data['size']['height'])
Expand Down Expand Up @@ -157,7 +158,8 @@ def dict_to_tf_example(data,
ymins.append(ymin / height)
xmaxs.append(xmax / width)
ymaxs.append(ymax / height)
class_name = get_class_name_from_filename(data['filename'])
class_name = obj['name']
# class_name = get_class_name_from_filename(data['filename']) # Really? This is how you do it? In the future will need to be able to have multiple different kinds of classes in an image
classes_text.append(class_name.encode('utf8'))
classes.append(label_map_dict[class_name])
truncated.append(int(obj['truncated']))
Expand Down Expand Up @@ -272,7 +274,7 @@ def main(_):
random.seed(42)
random.shuffle(examples_list)
num_examples = len(examples_list)
num_train = int(0.7 * num_examples)
num_train = int(math.ceil(0.7 * num_examples))
train_examples = examples_list[:num_train]
val_examples = examples_list[num_train:]
logging.info('%d training and %d validation examples.',
Expand All @@ -282,9 +284,9 @@ def main(_):
val_output_path = os.path.join(FLAGS.output_dir, 'pet_val.record')
if FLAGS.faces_only:
train_output_path = os.path.join(FLAGS.output_dir,
'pet_train_with_masks.record')
'lattice_train_with_masks.record')
val_output_path = os.path.join(FLAGS.output_dir,
'pet_val_with_masks.record')
'lattice_val_with_masks.record')
create_tf_record(
train_output_path,
label_map_dict,
Expand Down
Loading