def read_ppm(self, rawfilename, filename):
# This function reads the ppm/jpg file and extracts the features if the
# features pkl file doesn't exist. It is also compatible for extension
# of the feauture vector and doesn't compute the already computed features
new_feature_string = []
updated_feature = 0
data = N.array([], dtype=int)
if os.path.exists(filename):
pkl_f = open(filename, 'r')
(data, labels, feature_string, width, height, winsize, nbins)= pickle.load(pkl_f)
self.winsize = winsize
self.nbins = nbins
new_feature_string = list(feature_string)
pkl_f.close()
if not new_feature_string.count('dsift'):
updated_feature = 1
(sift_features, labels, width, height) = self.extract_dsift(rawfilename, self.winsize, self.nbins)
if data.size:
data = scipy.concatenate((data.transpose(), sift_features.transpose()), 1).transpose()
else:
data = sift_features
new_feature_string.append('dsift')
if not new_feature_string.count('histogram'):
updated_feature = 1
(hist_features, labels, width, height) = self.extract_hist(rawfilename, self.winsize, self.nbins)
hist_features = hist_features/(self.winsize)
if data.size:
data = scipy.concatenate((data.transpose(), hist_features.transpose()), 1).transpose()
else:
data = hist_features
new_feature_string.append('histogram')
'''
if not new_feature_string.count('position'):
updated_feature = 1
position_features = []
for label in labels:
(y,x) = map(int, label.strip('()').split(','))
position_features.append([x,y])
position_features = N.array(position_features)
if data.size:
data = scipy.concatenate((data.transpose(), position_features), 1).transpose()
else:
data = position_features
new_feature_string.append('position')
'''
if updated_feature:
outf = open(filename, 'w')
pickle.dump((data, labels, new_feature_string, width, height, self.winsize, self.nbins),outf)
outf.close()
print 'Saved data to %s.' % filename
return (data, labels, new_feature_string, width, height, self.winsize, self.nbins)
def si_read_ppm(self, rawfilename, filename):
# This function reads the ppm/jpg file and extracts the features if the
# features pkl file doesn't exist. It is also compatible for extension
# of the feauture vector and doesn't compute the already computed features
new_feature_string = []
updated_feature = 0
data = N.array([], dtype=int)
if os.path.exists(filename):
pkl_f = open(filename, 'r')
(data, labels, feature_string, width, height, winsize, nbins)= pickle.load(pkl_f)
self.winsize = winsize
self.nbins = nbins
new_feature_string = list(feature_string)
pkl_f.close()
if not new_feature_string.count('sift'):
updated_feature = 1
(sift_features, labels, width, height) = self.extract_sift(rawfilename, self.winsize, self.nbins)
if data.size:
data = scipy.concatenate((data.transpose(), sift_features.transpose()), 1).transpose()
else:
data = sift_features
new_feature_string.append('sift')
if updated_feature:
outf = open(filename, 'w')
pickle.dump((data, labels, new_feature_string, width, height, self.winsize, self.nbins),outf)
outf.close()
print 'Saved data to %s.' % filename
return (data, labels, new_feature_string, width, height, self.winsize, self.nbins)
def runAOD(img):
model = 'AOD_Net.caffemodel'
net = caffe.Net('DeployT.prototxt', model, caffe.TEST)
batchdata = []
data = img / 255.0
data = data.transpose((2, 0, 1))
batchdata.append(data)
net.blobs['data'].data[...] = batchdata
net.forward()
data = net.blobs['sum'].data[0]
data = data.transpose((1, 2, 0))
data = data * 255.0
return data.astype(np.uint8)
def plot_image(path):
fig = go.Figure();
# plt.figure(figsize=(12, 12));
# path = image_fetcher.fetch('data/cells.tif')
# path = image_fetcher.fetch('data/cells.tif')
try:
if path != None:
path = path
except:
print(f"Default image:")
path = 'ะก:/Users/Admin/Documents/JN/NN/COVID_detection_picture/MRI/sick_7c7160149aec1ebf15b28166f5458c49.nii'
# print(type(path))
data = nib.load(path);
print(type(data))
data = data.get_data();
print(type(data[0]), data[0].shape)
data = data.transpose(2,1,0)
print(type(data), data.shape)
data = ndimage.zoom(data, (1, 1, 1), order = 1)
print(data.shape)
img = data[:]
fig = px.imshow(img,
animation_frame = 0,
binary_string = True,
labels = dict(animation_frame = "slice"),
width = 512,
height = 512,
# title = "NIFTI Detection scanner"
)
# fig.show()
return fig
def si_read_ppm(self, rawfilename, filename):
# This function reads the ppm/jpg file and extracts the features if the
# features pkl file doesn't exist. It is also compatible for extension
# of the feauture vector and doesn't compute the already computed features
new_feature_string = []
updated_feature = 0
data = N.array([], dtype=int)
if os.path.exists(filename):
pkl_f = open(filename, 'r')
(data, labels, feature_string, width, height, winsize,
nbins) = pickle.load(pkl_f)
self.winsize = winsize
self.nbins = nbins
new_feature_string = list(feature_string)
pkl_f.close()
if not new_feature_string.count('sift'):
updated_feature = 1
(sift_features, labels, width,
height) = self.extract_sift(rawfilename, self.winsize, self.nbins)
if data.size:
data = scipy.concatenate(
(data.transpose(), sift_features.transpose()),
1).transpose()
else:
data = sift_features
new_feature_string.append('sift')
if updated_feature:
outf = open(filename, 'w')
pickle.dump((data, labels, new_feature_string, width, height,
self.winsize, self.nbins), outf)
outf.close()
print 'Saved data to %s.' % filename
return (data, labels, new_feature_string, width, height, self.winsize,
self.nbins)
def read_ppm(self, rawfilename, filename):
# This function reads the ppm/jpg file and extracts the features if the
# features pkl file doesn't exist. It is also compatible for extension
# of the feauture vector and doesn't compute the already computed features
new_feature_string = []
updated_feature = 0
data = N.array([], dtype=int)
if os.path.exists(filename):
pkl_f = open(filename, 'r')
(data, labels, feature_string, width, height, winsize,
nbins) = pickle.load(pkl_f)
self.winsize = winsize
self.nbins = nbins
new_feature_string = list(feature_string)
pkl_f.close()
if not new_feature_string.count('dsift'):
updated_feature = 1
(sift_features, labels, width,
height) = self.extract_dsift(rawfilename, self.winsize,
self.nbins)
if data.size:
data = scipy.concatenate(
(data.transpose(), sift_features.transpose()),
1).transpose()
else:
data = sift_features
new_feature_string.append('dsift')
if not new_feature_string.count('histogram'):
updated_feature = 1
(hist_features, labels, width,
height) = self.extract_hist(rawfilename, self.winsize, self.nbins)
hist_features = hist_features / (self.winsize)
if data.size:
data = scipy.concatenate(
(data.transpose(), hist_features.transpose()),
1).transpose()
else:
data = hist_features
new_feature_string.append('histogram')
'''
if not new_feature_string.count('position'):
updated_feature = 1
position_features = []
for label in labels:
(y,x) = map(int, label.strip('()').split(','))
position_features.append([x,y])
position_features = N.array(position_features)
if data.size:
data = scipy.concatenate((data.transpose(), position_features), 1).transpose()
else:
data = position_features
new_feature_string.append('position')
'''
if updated_feature:
outf = open(filename, 'w')
pickle.dump((data, labels, new_feature_string, width, height,
self.winsize, self.nbins), outf)
outf.close()
print 'Saved data to %s.' % filename
return (data, labels, new_feature_string, width, height, self.winsize,
self.nbins)
