Added image_posterior (to create segmentation)

image_posterior.py

@@ -0,0 +1,73 @@
+"""Create a gif sampling from the posterior from an image.
+
+The file includes routines to create gifs of posterior samples for image
+explanations. To create the gif, we sample a number of draws from the posterior,
+plot the explanation and the image, and repeat this to stitch together a gif.
+
+The interpretation is that regions of the image that more frequency show up as
+green are more likely to positively impact the prediction. Similarly, regions that 
+more frequently show up as red are more likey to negatively impact the prediction.
+"""
+import os
+from os.path import exists, dirname
+import sys
+
+import imageio
+import matplotlib.pyplot as plt
+import numpy as np
+from skimage.segmentation import mark_boundaries
+import tempfile
+from tqdm import tqdm
+
+import lime.lime_tabular as baseline_lime_tabular
+import shap
+
+# Make sure we can get bayes explanations
+parent_dir = dirname(os.path.abspath(os.getcwd()))
+sys.path.append(parent_dir)
+
+from bayes.explanations import BayesLocalExplanations, explain_many
+from bayes.data_routines import get_dataset_by_name
+from bayes.models import *
+
+def fill_segmentation(values, segmentation, image, n_max=5):
+    max_segs = np.argsort(abs(values))[-n_max:]
+    out = np.zeros((224, 224))
+    c_image = np.zeros(image.shape)
+    for i in range(len(values)):
+        if i in max_segs:
+            out[segmentation == i] = 1 if values[i] > 0 else -1
+            c = 1 if values[i] > 0 else 0
+            c_image[segmentation == i, c] = np.max(image)
+    return c_image.astype(int), out.astype(int)
+
+def create_gif(explanation_blr, segments, image, n_images=20, n_max=5):
+    """Create the gif corresponding to the image explanation.
+
+    Arguments:
+        explanation_coefficients: The explanation blr object.
+        segments: The image segmentation.
+        image: The image for which to compute the explantion.
+        save_loc: The location to save the gif.
+        n_images: Number of images to create the gif with.
+        n_max: The number of superpixels to draw on the image.
+    """
+    draws = explanation_blr.draw_posterior_samples(n_images)
+    # Setup temporary directory to store paths in 
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        paths = []
+        for i, d in tqdm(enumerate(draws)):
+            c_image, filled_segs = fill_segmentation(d, segments, image, n_max=n_max)
+            plt.cla()
+            plt.axis('off')
+            plt.imshow(mark_boundaries(image, filled_segs))
+            plt.imshow(c_image, alpha=0.3)
+            paths.append(os.path.join(tmpdirname, f"{i}.png"))
+            plt.savefig(paths[-1])
+
+        # Save to gif
+        # https://stackoverflow.com/questions/61716066/creating-an-animation-out-of-matplotlib-pngs
+        print(f"Saving gif to {save_loc}")
+        ims = [imageio.imread(f) for f in paths]
+        return imageio.mimwrite(imageio.RETURN_BYTES, ims)
+