def webfile_prep(fwebname):
robo.whereami(sys._getframe().f_code.co_name)
try:
webfile_prepped = open(fwebname, "r")
return webfile_prepped
except:
print cfg.color.magenta
print "Hmm, Unable to load WEBSTAGRAM response: "+fwebname
print "(usually, the tag has no images. so check the txt file, and also give it a look online.)"
print cfg.color.white
robo.goodbye()
sys.exit(1) #shouldnt get here, but for safety
def webfile_prep(fwebname):
robo.whereami(sys._getframe().f_code.co_name)
with open(fwebname, "r") as webfile_local:
try:
webfile_prepped = json.load(webfile_local)
return webfile_prepped
except:
print cfg.color.magenta
print "Hmm, Unable to load JSON from IMGUR API response."
print "(usually, the tag has no images. so check the txt file above, and give it a look online.)"
print cfg.color.white
robo.goodbye()
sys.exit(1) #shouldnt get here, but for safety
def getwebfile(webfileurl):
robo.whereami(sys._getframe().f_code.co_name)
print "get data from:", webfileurl
try:
webfile = urlopen(webfileurl)
return webfile
except:
print cfg.color.magenta
print "doh, didnt get file from "+webfileurl+"!"
print "usually this is random. good next steps:"
print "1. maybe check the url in a browser or\n2. wait like 15 seconds and try again."
print cfg.color.white
robo.goodbye()
sys.exit(1) #shouldnt get here, but for safety
def classify_image(testimg, model_data):
robo.whereami(sys._getframe().f_code.co_name)
basetag = model_data["basetag"]
model_type = model_data["model_type"]
model_dir = model_data["model_dir"]
path_to_retrainedgraph = cfg.path_to_trainingsumms + cfg.dd + basetag + cfg.dd + model_dir + cfg.dd + cfg.retrainedgraph_file
path_to_labels = cfg.path_to_trainingsumms + cfg.dd + basetag + cfg.dd + model_dir + cfg.dd + cfg.retrainedlabels_file
# build a command
if model_type == cfg.mobile_model:
testimgcommand = "python ../scripts/label_image.py \
--graph='" + path_to_retrainedgraph + "' \
--labels='" + path_to_labels + "' \
--input_height=224 \
--input_width=224 \
--input_mean=128 \
--input_std=128 \
--image=" + testimg
elif model_type == cfg.inception_model:
testimgcommand = "python ../scripts/label_image.py \
--graph='" + path_to_retrainedgraph + "' \
--labels='" + path_to_labels + "' \
--input_height=299 \
--input_width=299 \
--input_mean=128 \
--input_std=128 \
--input_layer='Mul' \
--image=" + testimg
else:
robo.goodbye("woops no classification model! Program stopping...")
print "image: " + testimg
# use the tensorflow label_image script
try:
imagelabel_raw = subprocess.check_output(testimgcommand, shell=True)
except Exception:
# just remove file for now (rather than store for later analysis)
# because they are broken images, not misclassified
os.remove(testimg)
imagelabel_raw = False
return imagelabel_raw
def getwebfile(webfileurl):
robo.whereami(sys._getframe().f_code.co_name)
print "API for:", webfileurl
imgur_client_id = 'Client-ID ' + os.environ.get('IMGURAPI_ID')
req = Request(webfileurl)
req.add_header('Authorization', imgur_client_id)
try:
webfile = urlopen(req)
return webfile
except:
print cfg.color.magenta
print "doh, probably 'urllib2.HTTPError: HTTP Error 500: Internal Server Error'"
print "(something wrong with imgur API. happens all the time. try again in a min.)"
print cfg.color.white
robo.goodbye()
sys.exit(1) #shouldnt get here, but for safety
def imgurapi_clientid_confirm():
robo.whereami(sys._getframe().f_code.co_name)
try:
imgur_client_id = 'Client-ID ' + os.environ.get('IMGURAPI_ID')
return imgur_client_id
except:
print cfg.color.magenta + '''
Whelp! no Imgur API Client-ID found in environment variables.
(and thus, no ability to download images from imgur.com...) '''
print cfg.color.white + '''
SOLUTIONS:
1. Change 'scrapesite_default' in config file, or
2. Set yourself up an API key at: https://apidocs.imgur.com/
then take 10 seconds to add it to your environment (on mac) at:
http://osxdaily.com/2015/07/28/set-enviornment-variables-mac-os-x/'''
robo.goodbye()
sys.exit(1) # for safety
def main(model_data):
robo.whereami(sys._getframe().f_code.co_name)
basetag = model_data["basetag"]
model_type = model_data["model_type"]
path_to_sortedimgs_basetag = cfg.path_to_testimgs + cfg.dd + basetag + cfg.dd + cfg.sorted_dirname
if makesortedlabels_dirs(model_data) == False:
robo.goodbye(
"Dirs for final output NOT created or available. Check your permissions. Stopping program..."
)
#get images
images_list = robo.getimagelist_fromdir(cfg.path_to_testimgs + cfg.dd +
basetag)
#call the labeling function
testimages_dict = {}
for testimage in images_list:
timestart = time.strftime("%H%M%S")
##### call tensorflow label_image script
imagelabel_raw = classify_image(testimage, model_data)
#######################################
# process the label
if imagelabel_raw:
imagelabel_processed = process_imagelabel_for_final(imagelabel_raw)
testimage_clean = testimage.replace(
cfg.path_to_testimgs + cfg.dd + basetag + cfg.dd, "")
#process the classifiedimages
proc_classed_images_list = {}
proc_result = processclassifiedimages(testimage_clean,
imagelabel_processed,
basetag)
# save to dict for later analysis
timeend = time.strftime("%H%M%S")
timespent = float(timeend) - float(timestart)
testimages_dict[proc_result[0]] = (proc_result[1], proc_result[2],
timespent)
#make a CLASSIFICATION log file
filetitle = cfg.imagelog_prefix + model_type + "_" + logtime + cfg.imagelog_suffix
path_to_thisfile = path_to_sortedimgs_basetag + cfg.dd + filetitle
robo.createfilefromdict(path_to_thisfile, testimages_dict)
#make a MODEL INFO log file
thatfile = cfg.imagelog_prefix + cfg.modeldatalog_name + logtime + cfg.imagelog_suffix
path_to_thatfile = path_to_sortedimgs_basetag + cfg.dd + thatfile
robo.createfilefromdict(path_to_thatfile, model_data)
#AFTER -- send a message
if cfg.twilio_active == True:
num_of_testimages = len(testimages_dict)
sms_msg = "roboclassified and moved " + str(
num_of_testimages
) + " imgs! now, do a manual QA sortcheck (and/or re-sort) for best longterm success. boop boop."
sms_status = robo.sendsms(sms_msg)
print "exiting robo_classify..."
return
sys.exit(1) #shouldnt get here, but just in case...
#AFTER -- send a message
if cfg.twilio_active == True:
num_of_testimages = len(testimages_dict)
sms_msg = "roboclassified and moved " + str(
num_of_testimages
) + " imgs! now, do a manual QA sortcheck (and/or re-sort) for best longterm success. boop boop."
sms_status = robo.sendsms(sms_msg)
print "exiting robo_classify..."
return
sys.exit(1) #shouldnt get here, but just in case...
#################################
#################################
# boilerplate kicker offer (yes thats a tech term!)
if __name__ == '__main__':
try:
model_data
except:
print
print "This script not callable directly, as it needs data from " + cfg.download_script + " passed to it."
print "maybe it's time read the help? copy/paste this:"
print "\tpython roboflow.py --help"
robo.goodbye()
#go get 'er done
main(model_data)