def didClickSubmitButton(self, event):
print(self.imageFilePath)
img = img_as_ubyte(io.imread(CLUSTER_IMAGE_FILENAME))
roi_img = spectral_roi.extract_roi(img, gui_checkbox_handlers.getSelectedClusters())
roi_img_filename = "{}.png".format(Helper.generate_random_id())
io.imsave(roi_img_filename, roi_img)
Display.show_image(roi_img, roi_img_filename)
def run_with_cnn(image_filename="../Wheat_Images/004.jpg", ser_filename=None):
'''
Estimates the number of grains in a given image using a
Convolutional neural network.
Args:
image_filename: The path to the image from which a grain count
is to be obtained.
ser_filename: path to serialized list of isub-images already extracted
from the image from which a grain count is to be obtained.
Returns:
count: An estimate of the number of grains in the provided image.
'''
global img_data
# Chop image up into sub-images and serilaise or just load serialised data if
# it already exists.
if (ser_filename == None and image_filename == "../Wheat_Images/004.jpg"):
ser_filename = "../Wheat_Images/xxx_004.data"
if (Helper.unserialize(ser_filename) == None):
img = img_as_ubyte(io.imread(image_filename))
roi_img = spectral_roi.extract_roi(img, [1])
Helper.block_proc(roi_img, (20, 20), blockfunc)
#Helper.serialize(ser_filename, img_data)
else:
img_data = Helper.unserialize(ser_filename)
# classify
r = CNN.classify(img_data[0],
model_file=None,
featureRepresentation='glcm',
shouldSaveResult=True)
# Count number of '1s' in the result and return
count = r.tolist().count(1)
print("COUNT: {}".format(count))
return count
def run_with_mlp(image_filename="../Wheat_Images/004.jpg", ser_filename=None):
'''
Estimates the number of grains in a given image using a
Multilayer Perceptron neural network.
Args:
image_filename: The path to the image from which a grain count
is to be obtained.
ser_filename: path to serialized list of isub-images already extracted
from the image from which a grain count is to be obtained.
Returns:
count: An estimate of the number of grains in the provided image.
'''
global img_data
# Chop image up into sub-images and serilaise or just load serialised data if
# it already exists.
if(ser_filename == None and image_filename == "../Wheat_Images/004.jpg"):
ser_filename = "../Wheat_Images/xxx_004.data"
if(Helper.unserialize(ser_filename) == None):
img = img_as_ubyte(io.imread(image_filename))
roi_img = spectral_roi.extract_roi(img, [1])
Helper.block_proc(roi_img, (20,20), blockfunc)
#Helper.serialize(ser_filename, img_data)
else:
img_data = Helper.unserialize(ser_filename)
# classify
#MLP.build_model('glcm', iters=30, glcm_isMultidirectional=True)
r = MLP.classify(img_data, featureRepresentation='glcm', shouldSaveResult=True)
# Count number of '1s' in the result and return
count = r.tolist().count(1)
print("COUNT: {}".format(count))
return count
