def get_pd_masks_for_dir(indir,outdir):
filenames = [f for f in os.listdir(indir) if '.jpg' in f]
print('found {} files in {}'.format(len(filenames),indir))
for f in filenames:
print('sending img '+f)
full_imgname = os.path.join(indir,f)
im = cv2.imread(full_imgname)
start_time = time.time()
retval = paperdoll_parse_enqueue.paperdoll_enqueue(im)
print('waiting',end='')
while not retval.is_finished:
time.sleep(1)
print('.', end="")
sys.stdout.flush()
mask, labels = retval.result[:2]
end_time = time.time()
dt=end_time-start_time
dts.append(dt)
parse_name = f.split('.jpg')[0]+'_pdparse.bmp'
full_parse_name = os.path.join(outdir,parse_name)
cv2.imwrite(full_parse_name,mask)
print('labels:'+str(labels))
sorted_labels=sorted(labels.items(),key=operator.itemgetter(1))
print('sorted labels :'+str(sorted_labels))
labs_only = [i[0] for i in sorted_labels]
print('labsonly '+str(labs_only))
imutils.show_mask_with_labels(full_parse_name,labs_only,save_images=True)
aftermask = pipeline.after_pd_conclusions(mask, labels, face=None)
after_pd_conclusions_name = full_parse_name.split('_pdparse.bmp')[0]+'_pdconclusions.bmp'
cv2.imwrite(after_pd_conclusions_name,aftermask)
imutils.show_mask_with_labels(after_pd_conclusions_name,labs_only,save_images=True)
if retval is not None:
print('retval:' + str(retval.result)+' time:'+str(dt))
else:
print('no return val (None)')
def convert_and_save_results(mask,
label_names,
pose,
filename,
img,
url,
forwebtool=True):
'''
This saves the mask using the labelling fashionista_categories_augmented_zero_based
:param mask:
:param label_names:
:param pose:
:param filename:
:param img:
:param url:
:return:
'''
fashionista_ordered_categories = constants.fashionista_categories_augmented_zero_based #constants.fashionista_categories
h, w = mask.shape[0:2]
new_mask = np.ones(
(h, w, 3), dtype=np.uint8
) * 255 # anything left with 255 wasn't dealt with in the following conversion code
print('new mask size:' + str(new_mask.shape))
success = True #assume innocence until proven guilty
print('attempting convert and save, shapes:' + str(mask.shape) + ' new:' +
str(new_mask.shape))
for label in label_names: # need these in order
if label in fashionista_ordered_categories:
fashionista_index = fashionista_ordered_categories.index(
label) + 0 # number by 0=null, 55=skin , not 1=null,56=skin
pd_index = label_names[label]
pixlevel_v2_index = constants.fashionista_aug_zerobased_to_pixlevel_categories_v2[
fashionista_index]
pixlevel_index = constants.fashionista_aug_zerobased_to_pixlevel_categories_v4_for_web[
fashionista_index]
if pixlevel_index is None:
pixlevel_index = 0 #map unused categories (used in fashionista but not pixlevel v2) to background
# new_mask[mask==pd_index] = fashionista_index
# print('old index '+str(pd_index)+' for '+str(label)+': gets new index:'+str(fashionista_index)+':' + fashionista_ordered_categories[fashionista_index]+ ' and newer index '+str(pixlevel_v2_index)+':'+constants.pixlevel_categories_v2[pixlevel_v2_index])
new_mask[mask == pd_index] = pixlevel_index
else:
print('label ' + str(label) + ' not found in regular cats')
success = False
if 255 in new_mask:
print('didnt fully convert mask')
success = False
if success:
try: #write orig file
# conversion_utils.count_values(new_mask,labels=constants.pixlevel_categories_v2)
conversion_utils.count_values(
new_mask, labels=constants.pixlevel_categories_v4_for_web)
dir = constants.pd_output_savedir
Utils.ensure_dir(dir)
full_name = os.path.join(dir, filename)
# full_name = filename
print('writing output img to ' + str(full_name))
cv2.imwrite(full_name, img)
except:
print('fail in try 1, ' + sys.exc_info()[0])
try: #write rgb mask
# bmp_name = full_name.replace('.jpg','_pixv2.png')
bmp_name = full_name.replace('.jpg', '_pixv4.png')
print('writing mask bmp to ' + str(bmp_name))
cv2.imwrite(bmp_name, new_mask)
imutils.show_mask_with_labels(
new_mask,
labels=constants.pixlevel_categories_v4,
original_image=full_name,
save_images=True)
if socket.gethostname() != 'extremeli-evolution-1':
command_string = 'scp ' + bmp_name + ' [email protected]:/var/www/js-segment-annotator/data/pd_output/pd/'
subprocess.call(command_string, shell=True)
command_string = 'scp ' + full_name + ' [email protected]:/var/www/js-segment-annotator/data/pd_output/pd/'
subprocess.call(command_string, shell=True)
except:
print('fail in try 2, ' + str(sys.exc_info()[0]))
try: #write webtool mask
if forwebtool:
webtool_mask = copy.copy(new_mask)
webtool_mask[:, :,
0] = 0 #zero out the B,G for webtool - leave only R
webtool_mask[:, :,
1] = 0 #zero out the B,G for webtool - leave only R
# bmp_name=full_name.replace('.jpg','_pixv2_webtool.png')
bmp_name = full_name.replace('.jpg', '_pixv4_webtool.png')
print('writing mask bmp to ' + str(bmp_name))
cv2.imwrite(bmp_name, webtool_mask)
command_string = 'scp ' + bmp_name + ' [email protected]:/var/www/js-segment-annotator/data/pd_output/pd'
subprocess.call(command_string, shell=True)
except:
print('fail in try 3, ' + str(sys.exc_info()[0]))
try:
pose_name = full_name.strip('.jpg') + '.pose'
with open(pose_name, "w+") as outfile:
print('succesful open, attempting to write pose')
poselist = pose[0].tolist()
# json.dump([1,2,3], outfile, indent=4)
json.dump(poselist, outfile, indent=4)
if url is not None:
url_name = full_name.strip('.jpg') + '.url'
print('writing url to ' + str(url_name))
with open(url_name, "w+") as outfile2:
print('succesful open, attempting to write:' + str(url))
outfile2.write(url)
except:
print('fail in convert_and_save_results dude, bummer')
print(str(sys.exc_info()[0]))
return
else:
print(
'didnt fully convert mask, or unkown label in convert_and_save_results'
)
success = False
return
t3,im3 = cv2.threshold(result,180,255,cv2.THRESH_BINARY)
t4 = cv2.adaptiveThreshold(result,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
t5,im5 = cv2.threshold(result,128,255,cv2.THRESH_BINARY|cv2.THRESH_OTSU)
outmat[0:256,256*index_to_show:256*(index_to_show+1)] = result
outmat[256:256*2,256*index_to_show:256*(index_to_show+1)] = im1
outmat[256*2:256*3,256*index_to_show:256*(index_to_show+1)] = t4
outmat[256*3:256*4,256*index_to_show:256*(index_to_show+1)] = im5
cv2.imwrite('output.png',outmat)
# cv2.imshow('output layer'+str(index_to_show),result)
# cv2.waitKey(0)
cv2.imshow('output layers',outmat)
cv2.waitKey(0)
# labels=constants.ultimate_21
# imutils.show_mask_with_labels('output.png',labels,visual_output=True)
else:
url = 'http://diamondfilms.com.au/wp-content/uploads/2014/08/Fashion-Photography-Sydney-1.jpg'
url = 'http://pinmakeuptips.com/wp-content/uploads/2015/02/1.4.jpg'
result = infer_one(url,required_image_size=required_image_size)
cv2.imwrite('output.png',result)
labels=constants.ultimate_21
imutils.show_mask_with_labels('output.png',labels,visual_output=True)
# after_nn_result = pipeline.after_nn_conclusions(result,constants.ultimate_21_dict)
# cv2.imwrite('output_afternn.png',after_nn_result)
# labels=constants.ultimate_21