Lecture05

Lectures/Lecture-04/Normalization.ipynb

@@ -329,7 +329,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.11.0"
   },
   "varInspector": {
    "cols": {

Lectures/Lecture-04/_toc.yml

@@ -1,5 +1,5 @@
 format: jb-article
 root: preface
 sections:
-- file: 04-BasicSegmentation
-- file: 04-BasicSegmentation_Part2
+  - file: 04-BasicSegmentation
+  - file: 04-BasicSegmentation_Part2

Lectures/Lecture-04/plotsupport.py

@@ -2,7 +2,7 @@
 from matplotlib.colors import ListedColormap
 import matplotlib.colors as colors
 from sklearn.metrics import confusion_matrix
-from seaborn import heatmap
+# from seaborn import heatmap
 import numpy as np
 
 def buildCMap(plots) :
@@ -13,6 +13,18 @@ def buildCMap(plots) :
 
     return ListedColormap(cmaplist)
 
+def heatmap(img, ax=None, cmap='inferno', fontsize=14, precision=3) :
+    if ax is None :
+        _,ax=plt.subplots(1)
+
+    ax.imshow(img,cmap=cmap,origin='upper')
+    ax.set(xticks=np.arange(0,img.shape[0]),yticks=np.arange(0,img.shape[0]))
+    props = dict(boxstyle='round', facecolor='lightgray', alpha=1) 
+    for i in range(img.shape[1]):
+        for j in range(img.shape[0]):
+            text = ax.text(j, i, "{0:0.{1}f}".format(img[i, j],precision),
+                           ha="center", va="center", fontsize=fontsize, color="k",bbox=props)
+
 def magnifyRegion(img,roi, figsize, cmap='gray',vmin=0,vmax=0,title='Original') :
     if vmin==vmax:
         vmin=img.min()
@@ -30,7 +42,7 @@ def magnifyRegion(img,roi, figsize, cmap='gray',vmin=0,vmax=0,title='Original')
     ax[1].set_title('Magnified ROI')
 
 
-def showHitMap(gt,pr,ax=None) :
+def showHitMap(gt,pr,ax=None, annot_kws = None) :
     if ax is None :
         fig, ax = plt.subplots(1,2,figsize=(12,4))
 
@@ -46,7 +58,8 @@ def showHitMap(gt,pr,ax=None) :
     ax[1].set_title('Hit map')
 
     cmat = confusion_matrix(gt.ravel(), pr.ravel(), normalize='all')
-    heatmap(cmat, annot=True,ax=ax[0]); ax[0].set_title('Confusion matrix');
+#     heatmap(cmat, annot=True,ax=ax[0], annot_kws=annot_kws); ax[0].set_title('Confusion matrix');
+    heatmap(cmat, ax=ax[0]); ax[0].set_title('Confusion matrix');
     ax[0].set_xticklabels(['Negative','Positive']);
     ax[0].set_yticklabels(['Negative','Positive']);
     ax[0].set_ylabel('Ground Truth')
@@ -87,7 +100,7 @@ def visualize_normalization(img, ob, dc,norm) :
     ax3.imshow(ob,clim=[mo-2*so,mo+2*so],cmap='gray')
     ax3.set_xticks([])
     ax3.set_yticks([])
-    ax3.set_title('Illumination')
+    ax3.set_title('Source profile')
 
 
     ax4.imshow(dc,clim=[md-2*sd,md+2*sd],cmap='gray')