def mws_segmentation(self, affs):
image_shape = affs.shape[1:]
number_of_nodes = affs.shape[-1] * affs.shape[-2]
offsets = [[-1, 0],
[0, -1],
[-3, 0],
[0, -3],
[-9, 0],
[0, -9],
[-27, 0],
[0, -27]]
api.log('creating gridgraph')
graph = MWSGridGraph(image_shape)
api.log('create local edges')
uvs, costs = graph.compute_nh_and_weights(1 - affs[:2],
offsets[:2])
api.log('create mutex edges')
mutex_uvs, mutex_costs = graph.compute_nh_and_weights(affs[2:],
offsets[2:],
[10, 10],
True)
api.log('running mws')
segmentation = compute_mws_clustering(number_of_nodes,
uvs,
mutex_uvs,
costs,
mutex_costs)
return segmentation.reshape(image_shape)
def setup(self):
api.log('initialized')
def run(self, ctx):
raw_file = "/home/swolf/local/data/hackathon2019/slice0.png"
model_file = "/home/swolf/local/data/models/isbi_2d.pytorch"
raw_img = self.load_image(raw_file)
# model = self.load_model(model_file)
model = None
api.log('predicting')
affinities = self.predict(model, raw_img)
api.log('running mws')
segmentation = self.mws_segmentation(affinities)
api.log('mws done')
segmentation = (segmentation % 255).astype(np.uint8)
blend_image = segmentation[:512, :512]
api.log('uniques' + str(np.unique(blend_image)))
api.log("blend_shape ", blend_image.shape)
name_plot = "/home/swolf/pictures/tmp.png"
imsave(name_plot, blend_image)
with open(name_plot, 'rb') as f:
data = f.read()
api.log("QQQQQQ" + str(type(data)))
result = base64.b64encode(data).decode('ascii')
imgurl = 'data:image/png;base64,' + result
api.createWindow(type = 'imjoy/image', w=12, h=15,data = {"src": imgurl})
async def finish_config_callback(self, callback_config):
print("callback_config:", callback_config)
if self.config_win is not None:
self.config_win.close()
api.showMessage('network configured.')
with open(os.path.join(self.work_dir, "anet-config.json"), "w") as f:
f.write(json.dumps(callback_config))
self.config_json.update(callback_config)
# await self.get_data_by_config(config=self.config_json)
api.showStatus("generating mask from the annotation file ...")
self.get_mask_by_json(config=self.config_json)
api.showStatus("Masks generated, now start training...")
self._opt = self.get_opt(self.config_json, work_dir=self.work_dir)
self.initialize(self._opt)
api.log("self._opt.work_dir:" + self._opt.work_dir)
api.log("self._opt.input_channels:" + str(self._opt.input_channels))
api.log("self._opt.target_channels:" + str(self._opt.target_channels))
api.log("self._opt.input_nc:" + str(self._opt.input_nc))
api.log("self._opt.target_nc:" + str(self._opt.target_nc))
config = my_config()
api.log("config.name:" + config.name)
api.log("config.epochs:" + str(config.epochs))
api.log("config.steps:" + str(config.steps))
api.log("config.batchsize:" + str(config.batchsize))
await self.train_2(config)