diff --git a/bob/ip/binseg/utils/FreeMono.ttf b/bob/ip/binseg/utils/FreeMono.ttf
new file mode 100644
index 0000000000000000000000000000000000000000..7485f9e4c84d5a372c81e11df2cd9f5e2eb2064a
Binary files /dev/null and b/bob/ip/binseg/utils/FreeMono.ttf differ
diff --git a/bob/ip/binseg/utils/plot.py b/bob/ip/binseg/utils/plot.py
index 594b97f06f2391c2524104f74d751b28a6ffa7db..f69df6e0473f6f4e8149354d78741ff98a2c809a 100644
--- a/bob/ip/binseg/utils/plot.py
+++ b/bob/ip/binseg/utils/plot.py
@@ -6,7 +6,7 @@ import os
import csv
import pandas as pd
import PIL
-from PIL import Image
+from PIL import Image,ImageFont, ImageDraw
import torchvision.transforms.functional as VF
import torch
@@ -198,10 +198,10 @@ def plot_overview(outputfolders,title):
def metricsviz(dataset
,output_path
- ,tp_color= (128,128,128)
- ,fp_color = (70, 240, 240)
- ,fn_color = (245, 130, 48)
- ):
+ ,tp_color= (0,255,0) # (128,128,128) Gray
+ ,fp_color = (0, 0, 255) # (70, 240, 240) Cyan
+ ,fn_color = (255, 0, 0) # (245, 130, 48) Orange
+ ,overlayed=True):
""" Visualizes true positives, false positives and false negatives
Default colors TP: Gray, FP: Cyan, FN: Orange
@@ -253,6 +253,16 @@ def metricsviz(dataset
# paste together
tp_pil_colored.paste(fp_pil_colored,mask=fp_pil)
tp_pil_colored.paste(fn_pil_colored,mask=fn_pil)
+
+ if overlayed:
+ tp_pil_colored = PIL.Image.blend(img, tp_pil_colored, 0.4)
+ img_metrics = pd.read_csv(os.path.join(output_path,'results',name+'.csv'))
+ f1 = img_metrics[' f1_score'].max()
+ # add f1-score
+ fnt_size = tp_pil_colored.size[1]//25
+ draw = ImageDraw.Draw(tp_pil_colored)
+ fnt = ImageFont.truetype('FreeMono.ttf', fnt_size)
+ draw.text((0, 0),"F1: {:.4f}".format(f1),(255,255,255),font=fnt)
# save to disk
overlayed_path = os.path.join(output_path,'tpfnfpviz')
@@ -277,8 +287,8 @@ def overlay(dataset, output_path):
gt = sample[2].byte() # byte tensor
# read metrics
- metrics = pd.read_csv(os.path.join(output_path,'results','Metrics.csv'))
- optimal_threshold = metrics['threshold'][metrics['f1_score'].idxmax()]
+ #metrics = pd.read_csv(os.path.join(output_path,'results',name+'.csv'))
+ #f1 = metrics[' f1_score'].max()
# read probability output
pred = Image.open(os.path.join(output_path,'images',name))
@@ -286,6 +296,11 @@ def overlay(dataset, output_path):
pred_green = PIL.ImageOps.colorize(pred, (0,0,0), (0,255,0))
overlayed = PIL.Image.blend(img, pred_green, 0.4)
+ # add f1-score
+ #fnt_size = overlayed.size[1]//25
+ #draw = ImageDraw.Draw(overlayed)
+ #fnt = ImageFont.truetype('FreeMono.ttf', fnt_size)
+ #draw.text((0, 0),"F1: {:.4f}".format(f1),(255,255,255),font=fnt)
# save to disk
overlayed_path = os.path.join(output_path,'overlayed')
if not os.path.exists(overlayed_path): os.makedirs(overlayed_path)
diff --git a/doc/covdresults.rst b/doc/covdresults.rst
index 9148f1ba2661d6ebdb4580b8fb6ea3f02c7fd3f1..680002d3c2975a454a6a6595de1af7b142ab41a4 100644
--- a/doc/covdresults.rst
+++ b/doc/covdresults.rst
@@ -11,27 +11,27 @@ F1 Scores
F1 score together with standard deviation across test images.
-+-------------------+--------+-----------------+---------+
-| F1 score | DRIU | DRIU BN | M2U-Net |
-+-------------------+--------+-----------------+---------+
-| COVD-DRIVE | 0.7896 | 0.8000 (0.0182) | 0.7906 |
-+-------------------+--------+-----------------+---------+
-| COVD-DRIVE SSL | 0.7870 | 0.8020 (0.0179) | 0.7938 |
-+-------------------+--------+-----------------+---------+
-| COVD-STARE | 0.7979 | 0.8129 (0.0986) | 0.8120 |
-+-------------------+--------+-----------------+---------+
-| COVD-STARE SSL | 0.8062 | 0.8221 (0.0784) | 0.8222 |
-+-------------------+--------+-----------------+---------+
-| COVD-CHASEDB1 | 0.7979 | 0.7923 (0.0240) | 0.7898 |
-+-------------------+--------+-----------------+---------+
-| COVD-CHASEDB1 SSL | 0.7976 | 0.7992 (0.0235) | 0.8000 |
-+-------------------+--------+-----------------+---------+
-| COVD-HRF | 0.8013 | 0.8027 (0.0452) | 0.8036 |
-+-------------------+--------+-----------------+---------+
-| COVD-HRF SSL | 0.8002 | | 0.7999 |
-+-------------------+--------+-----------------+---------+
-| COVD-IOSTAR | 0.7934 | 0.7763 (0.0311) | 0.7953 |
-+-------------------+--------+-----------------+---------+
-| COVD-IOSTAR SSL | 0.7995 | 0.7904 (0.0215) | 0.7868 |
-+-------------------+--------+-----------------+---------+
++-------------------+-----------------+-----------------+-----------------+
+| F1 score | DRIU | DRIU BN | M2U-Net |
++-------------------+-----------------+-----------------+-----------------+
+| COVD-DRIVE | 0.7896 (0.0178) | 0.8000 (0.0182) | 0.7906 (0.0179) |
++-------------------+-----------------+-----------------+-----------------+
+| COVD-DRIVE SSL | 0.7870 (0.0176) | 0.8020 (0.0179) | 0.7938 (0.0142) |
++-------------------+-----------------+-----------------+-----------------+
+| COVD-STARE | 0.7979 (0.1254) | 0.8129 (0.0986) | 0.8120 (0.0457) |
++-------------------+-----------------+-----------------+-----------------+
+| COVD-STARE SSL | 0.8062 (0.1033) | 0.8221 (0.0784) | 0.8222 (0.0441) |
++-------------------+-----------------+-----------------+-----------------+
+| COVD-CHASEDB1 | 0.7979 (0.0284) | 0.7923 (0.0240) | 0.7898 (0.0236) |
++-------------------+-----------------+-----------------+-----------------+
+| COVD-CHASEDB1 SSL | 0.7976 (0.0242) | 0.7992 (0.0235) | 0.8000 (0.0268) |
++-------------------+-----------------+-----------------+-----------------+
+| COVD-HRF | 0.8013 (0.0436) | 0.8027 (0.0452) | 0.8036 (0.0442) |
++-------------------+-----------------+-----------------+-----------------+
+| COVD-HRF SSL | 0.8002 (0.0421) | | 0.7987 (0.0436) |
++-------------------+-----------------+-----------------+-----------------+
+| COVD-IOSTAR | 0.7934 (0.0206) | 0.7763 (0.0311) | 0.7953 (0.0152) |
++-------------------+-----------------+-----------------+-----------------+
+| COVD-IOSTAR SSL | 0.7995 (0.0174) | 0.7904 (0.0215) | 0.7868 (0.0182) |
++-------------------+-----------------+-----------------+-----------------+