#!/usr/bin/env python
# coding: utf-8

# # Drawing annotations using stackivew
# In this notebook we use `stackview.annotate` to create a label image with manual annotations.

# In[1]:


import stackview

import numpy as np
from skimage.data import cells3d


# The cells3d dataset from scikit-image serves as example image. One of the two channels shown nuclei.

# In[2]:


image = cells3d()[:,1]

stackview.insight(image)


# Before annotating the nuclei, we create an empty label image of type unsigned 32-bit integer. This type is necessary so that stackview views the image correctly as labels. Compatible types are: `int32`,`uint32`,`int64`,`uint64`.

# In[3]:


labels = np.zeros(image.shape).astype(np.uint32)


# The function `stackview.annotate` creates a user interface where we can draw labels on top of the original image. Click and drag for drawing. Hold the `ALT` key for erasing.

# In[4]:


stackview.annotate(image, labels)


# You can also annotate in a different plane than the Z-plane by providing an axis (0=z,1=y,2=x).

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stackview.annotate(image, labels, axis=1)


# Once annotations are drawn, we can view them as usual using the `insight()` function.

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stackview.insight(labels)


# Just for fun we use the [apoc](https://github.com/haesleinhuepf/apoc) library to create an instance segmentation of nuclei from our annotation.

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import apoc

segmenter = apoc.ObjectSegmenter(positive_class_identifier=1)
features = apoc.PredefinedFeatureSet.small_quick.value

segmenter.train(image=image, ground_truth=labels, features=features)
result = segmenter.predict(image)

stackview.insight(result[30])


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