def __getitem__(self, index):
item = self.items[index].copy()
bunch = item.pop('images')
if self.channels_mode == 'six':
channels = []
for i, filename in sorted(bunch, key=itemgetter(0)):
img = self.open_fn(filename)
img = img if self.tr is None else self.tr(img)
channels.append(img)
sample = torch.cat(channels, dim=0)
else:
filenames = [filename for _, filename in bunch]
t = rio.load_images_as_tensor(filenames)
arr = rio.convert_tensor_to_rgb(t)
img = PIL.Image.fromarray(arr.astype(np.uint8))
sample = img if self.tr is None else self.tr(img)
# y = self.targets[index]
y = item[self.targets_key]
if self.drop_meta:
item = dict(features=sample, targets=y)
else:
item.update(dict(features=sample, targets=y))
if self.onehot:
y_enc = get_one_hot(y,
smoothing=self.label_smoothing,
num_classes=self.num_classes)
sample['targets_one_hot'] = y_enc
return item
def plot_rgb(data, figsize=10):
"""
convert 6-channels to RGB (3-channel)
and visualize the figure.
"""
dirs = data[0].split("/")
base_plates = [dirs.index(i) for i in dirs if i.startswith("Plate")][0]
pre = "_".join(dirs[base_plates - 1:base_plates + 1])
suf = "_".join(os.path.basename(data[0]).split("_")[:2])
imgname = "_".join((pre, suf))
plt.figure(figsize=(figsize, figsize))
img_6channel = rio.load_images_as_tensor(data)
img_as_rgb = rio.convert_tensor_to_rgb(img_6channel)
plt.title(imgname)
plt.imshow(img_as_rgb)
def plot_cell_sirna_rgb(filtered_data, figsize=20):
"""
plot all data in a row (Actually mutliple rows)
:param filtered_data: dataframe, after processed with 'filter_data_with_cell_sirna'
:param figsize: column size
:return:
"""
data_num = len(filtered_data)
row_num = math.ceil((data_num // 4))
plt.figure(figsize=(figsize, figsize * row_num * 0.25))
for i in range(data_num):
plt.subplot(row_num, 4, i + 1)
img_paths = filtered_data.iloc[i, -6:].tolist()
img_6channel = rio.load_images_as_tensor(img_paths)
img_as_rgb = rio.convert_tensor_to_rgb(img_6channel)
plt.title(filtered_data.iat[i, 0])
plt.imshow(img_as_rgb)
def upload():
print('--> /upload')
if 'POST' == request.method:
HEPG2 = request.form.get('radio-choice-h-2a')
HVUEC = request.form.get('radio-choice-h-2b')
RPE = request.form.get('radio-choice-h-2c')
U2OS = request.form.get('radio-choice-h-2d')
cell_type = ''
if HEPG2:
cell_type = 'HEPG2'
elif HVUEC:
cell_type = 'HVUEC'
elif RPE:
cell_type = 'RPE'
else:
cell_type = 'U2OS'
experiment_on = request.form.get('experiment-no')
plate_no = request.form.get('plate-no')
positive_control = request.form.get('well-radio-choice-h-2')
negative_control = request.form.get('well-radio-choice-h-2b')
treatment = request.form.get('well-radio-choice-h-2c')
well_type = ''
if positive_control:
well_type = 'positive_control'
elif negative_control:
well_type = 'negative_control'
else:
well_type = 'treatment'
print(HEPG2, HVUEC, RPE, U2OS)
print(experiment_on)
files = []
rgb_file_name = ''
for file in request.files.getlist('files'):
if file and allowed_file(file.filename):
filename = cell_type + '-' + experiment_on \
+ '-Plate' + plate_no \
+ '-' + secure_filename(file.filename)
print('-->', file.filename, '==>', filename)
t = filename.split('_')
rgb_file_name = t[0] + '_rgb.png'
files.append(filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
# load
channel_paths = [
os.path.join(app.config['UPLOAD_FOLDER'], f) for f in files
]
tensor = rio.load_images_as_tensor(channel_paths)
#
rgb = rio.convert_tensor_to_rgb(tensor)
rgb = np.array(rgb, dtype=np.uint8)
matplotlib.image.imsave(
os.path.join(app.config['UPLOAD_FOLDER'], rgb_file_name), rgb)
# predict:
tensor = np.expand_dims(tensor, axis=0)
predictions = pred({'feature': tensor})
print(predictions)
id = str(uuid.uuid1())
submit_time = isoformat(datetime.datetime.now())
# save file to database
record_file = File(id, session['email'], cell_type, experiment_on,
plate_no, well_type, ','.join(files), rgb_file_name,
submit_time, '%d' % predictions['classes'][0])
db.session.add(record_file)
db.session.commit()
return redirect('file_list.html')