def browse_images(caller):
root.withdraw()
files_address = askopenfilenames()
try:
# change image to binary thens store in array
raws = [(Image.open(imageAddress)) for imageAddress in files_address]
# Clipping
images = []
for image in raws:
images.extend(futil.image_clip(image))
for image in images:
caller.put_image(image)
except Exception:
showerror('Failed', 'One file or more is not an image')
finally:
root.deiconify()
continue
items.append(os.path.join(subdir, f))
traintype = {}
traincolor = {}
with open('data_type.json') as ft, open('data_color.json') as fc:
traintype = json.load(ft)
traincolor = json.load(fc)
for item in items:
img = Image.open(item)
img = futil.image_cleanup(img, 20)
container = {}
imgs = futil.image_clip(img)
for obj in imgs:
shape = fshape.feature_shape_extract(obj)
color = fcolor.feature_color_extract(obj)
texture = ftexture.feature_texture_extract(obj)
# Get Color
colorval = {}
colorsum = []
for key, val in traincolor.iteritems():
colorval[key] = []
for feature in val:
mean = (feature[1][0][0] - color[1][0][0]) ** 2 + (feature[1][0][1] - color[1][0][1]) ** 2 + (feature[1][0][2] - color[1][0][2]) ** 2
std = (feature[1][1][0] - color[1][1][0]) ** 2 + (feature[1][1][1] - color[1][1][1]) ** 2 + (feature[1][1][2] - color[1][1][2]) ** 2
skew = (feature[1][2][0] - color[1][2][0]) ** 2 + (feature[1][2][1] - color[1][2][1]) ** 2 + (feature[1][2][2] - color[1][2][2]) ** 2
colorval[key].append(mean + std + skew)