update

src/sfransen/Saliency/base.py

@@ -44,9 +44,9 @@ class SaliencyMap():
         with tf.GradientTape() as tape:
             tape.watch(image)
             preds = self.model(image)
-            print("get_gradients, size of preds",np.shape(preds))
+            # print("get_gradients, size of preds",np.shape(preds))
             top_class = preds[:]
-            print("get_gradients, size of top_class",np.shape(top_class))
+            # print("get_gradients, size of top_class",np.shape(top_class))
 
 
         grads = tape.gradient(top_class, image)

src/sfransen/Saliency/heatmap.py

@@ -0,0 +1,86 @@
+import os
+import numpy as np
+from PIL import  Image
+import matplotlib
+import matplotlib.pyplot as plt
+import scipy.ndimage as ndimage
+
+class HeatMap:
+    def __init__(self,image,heat_map,gaussian_std=10):
+        #if image is numpy array
+        if isinstance(image,np.ndarray):
+            height = image.shape[0]
+            width = image.shape[1]
+            self.image = image
+        else: 
+            #PIL open the image path, record the height and width
+            image = Image.open(image)
+            width, height = image.size
+            self.image = image
+        
+        #Convert numpy heat_map values into image formate for easy upscale
+        #Rezie the heat_map to the size of the input image
+        #Apply the gausian filter for smoothing
+        #Convert back to numpy
+        heatmap_image = Image.fromarray(heat_map*255)
+        heatmap_image_resized = heatmap_image.resize((width,height))
+        heatmap_image_resized = ndimage.gaussian_filter(heatmap_image_resized, 
+                                                        sigma=(gaussian_std, gaussian_std), 
+                                                        order=0)
+        heatmap_image_resized = np.asarray(heatmap_image_resized)
+        self.heat_map = heatmap_image_resized
+    
+    #Plot the figure
+    def plot(self,transparency=0.7,color_map='bwr',
+             show_axis=False, show_original=False, show_colorbar=False,width_pad=0):
+            
+        #If show_original is True, then subplot first figure as orginal image
+        #Set x,y to let the heatmap plot in the second subfigure, 
+        #otherwise heatmap will plot in the first sub figure
+        if show_original:
+            plt.subplot(1, 2, 1)
+            if not show_axis:
+                plt.axis('off')
+            plt.imshow(self.image,cmap='gray')
+            x,y=2,2
+        else:
+            x,y=1,1
+        
+        #Plot the heatmap
+        plt.subplot(1,x,y)
+        if not show_axis:
+            plt.axis('off')
+        plt.imshow(self.image,cmap='gray')
+        plt.imshow(self.heat_map/255, alpha=transparency, cmap=color_map)
+        if show_colorbar:
+            plt.colorbar()
+        plt.tight_layout(w_pad=width_pad)
+        plt.show()
+    
+    ###Save the figure
+    def save(self,filename,format='png',save_path=os.getcwd(),
+             transparency=0.7,color_map='bwr',width_pad = -10,
+             show_axis=False, show_original=False, show_colorbar=False, **kwargs):
+        if show_original:
+            plt.subplot(1, 2, 1)
+            if not show_axis:
+                plt.axis('off')
+            plt.imshow(self.image,cmap='gray')
+            x,y=2,2
+        else:
+            x,y=1,1
+        
+        #Plot the heatmap
+        plt.subplot(1,x,y)
+        if not show_axis:
+            plt.axis('off')
+        plt.imshow(self.image,cmap='gray')
+        plt.imshow(self.heat_map/255, alpha=transparency, cmap=color_map, caxis = [min(nonzeros(self.image)) max(nonzeros(self.image))])
+        if show_colorbar:
+            plt.colorbar()
+        plt.tight_layout(w_pad=width_pad)
+        plt.savefig(os.path.join(save_path,filename+'.'+format), 
+                    format=format, 
+                    bbox_inches='tight',
+                    pad_inches = 0, **kwargs)
+        print('{}.{} has been successfully saved to {}'.format(filename,format,save_path))

src/sfransen/Saliency/integrated_gradients.py

@@ -5,7 +5,7 @@ from sfransen.Saliency.base import SaliencyMap
 
 class IntegratedGradients(SaliencyMap):
 
-    def get_mask(self, image, baseline=None, num_steps=4):
+    def get_mask(self, image, baseline=None, num_steps=3):
         """Computes Integrated Gradients for a predicted label.
 
         Args:
@@ -38,7 +38,7 @@ class IntegratedGradients(SaliencyMap):
 
         grads = []
         for i, img in enumerate(interpolated_image):
-            print(f"interpolation step:",i," out of {num_steps}")
+            # print(f"interpolation step:",i,f" out of {num_steps}")
             img = tf.expand_dims(img, axis=0)
             grad = self.get_gradients(img)
             grads.append(grad[0])