changing paper outline

2 files changed, 86 insertions, 0 deletions

diff --git a/data/face-frame-recognition-accuracy.py b/data/face-frame-recognition-accuracy.py
new file mode 100755
index 0000000..0d34cf9
--- /dev/null
+++ b/data/face-frame-recognition-accuracy.py
@@ -0,0 +1,63 @@
+#!/usr/bin/python
+import os
+import sys
+import pickle
+
+prun = 0
+runs = {}
+labels = {}
+users = pickle.load((open(sys.argv[3])))
+recs = map(lambda x:0,users)
+conf = {}
+thresh = 0.0
+try:
+    thresh = float(sys.argv[4])
+except:
+    pass
+
+for line in open(sys.argv[1]):
+    line = line.split(',')
+    try:
+        run = int(line[3])
+    except:
+        continue
+    user = line[1]
+    if run not in runs:
+        runs[run] = 0
+        labels[run] = users.index(user) + 1
+
+for line in open(sys.argv[2]):
+    line = line.split(',')
+    try:
+        run = int(line[3])
+    except:
+        continue
+    user = line[1]
+    rec = ' '.join(line[6].split('@')[0].split('_'))
+    if run != prun and prun > 0:
+        runs[prun] = recs.index(max(recs))+1
+        recs = map(lambda x:0,users)
+    recs[users.index(rec)] += 1
+    conf[run] = float(line[7])
+    prun = run
+
+t=0.0
+tp=0.0
+fp=0.0
+fn=0.0
+for (k,v) in runs.items():
+    #print v,labels[k]
+    if v == 0 or conf[k] < thresh:
+        fn += 1
+    elif v != labels[k]:
+        fp += 1
+    else:
+        tp += 1
+    t += 1
+#print runs[167],labels[167]
+#print tp,fp,fn
+#print("Precision: ",tp/(tp+fp))
+#print("False positives: ",fp/(tp+fp))
+#print("Recall: ",1.0-fn/t)
+print str(tp/(tp+fp))+","+str(1.0-fn/t)
+
diff --git a/data/face-pr.csv b/data/face-pr.csv
new file mode 100644
index 0000000..d595d13
--- /dev/null
+++ b/data/face-pr.csv
@@ -0,0 +1,23 @@
+0.292700597718,0.655321782178
+0.292700597718,0.655321782178
+0.29179496468,0.657355679702
+0.291432711465,0.658172778123
+0.290708205035,0.659190031153
+0.286904546278,0.664141414141
+0.280383988408,0.675064599483
+0.268973012135,0.693602693603
+0.250860351386,0.718411552347
+0.247600072451,0.725676664228
+0.243796413693,0.731797919762
+0.241260641188,0.734234234234
+0.237275855823,0.739694656489
+0.232204310813,0.751170046802
+0.230030791523,0.754330708661
+0.227857272233,0.757551669316
+0.205759826118,0.779049295775
+0.205759826118,0.779049295775
+0.205759826118,0.779049295775
+0.205759826118,0.779049295775
+0.199963774679,0.790760869565
+0.132946929904,0.893732970027
+0.0791523274769,0.929061784897