final submission

README.md

@@ -23,6 +23,6 @@ or you can use conda to create a virtual environment and install packages using
         data/001/3/2.png
         data/001/test.png
     and so on...
-    3- run: python src/main.py
-    4- check results at results.txt and timing at time.txt
+    3- run: `python src/main.py`
+    4- check results at `results.txt` and timing at `time.txt`
         

results.txt

@@ -1,10 +1,10 @@
-2
-3
-3
-3
-3
-3
-3
-3
-3
-3
+1
+1
+1
+1
+3
+1
+1
+1
+1
+1

src/main.py

@@ -10,9 +10,8 @@
     # evaluations.eval_perfomance_lbph_svm(MODE, VERBOSE)
     # evaluations.eval_ada()
     # evaluations.eval_pca(VERBOSE)
-    # while True:
-    #     evaluations.seperable()
-
+    # evaluations.final_eval()
+
     VERBOSE = False
     MODE = 'deliver'
     data_path = 'data/'
@@ -23,15 +22,18 @@
     if os.path.exists('time.txt'):
         os.remove('time.txt')
 
+    results_file = open('results.txt',"a+")
+    time_file = open('time.txt',"a+")
+
     for test_folder in test_folders:
         res,time = pipeline.pipe(feature='cslbcop', clf='svm',_verbose=VERBOSE, _mode=MODE, test_folder=test_folder)
         time = "{:.2f}".format(time)
+        results_file.write(str(res))
+        results_file.write('\n')
+        results_file.flush()
+        time_file.write(str(time))
+        time_file.write('\n')
+        time_file.flush()
 
-        f = open('results.txt',"a+")
-        f.write(str(res))
-        f.write('\n')
-        f.close()
-        f = open('time.txt',"a+")
-        f.write(str(time))
-        f.write('\n')
-        f.close()
+    results_file.close()
+    time_file.close()

src/pipeline.py

@@ -61,10 +61,10 @@ def step_1(images, VERBOSE=False, test_label=None, feat = 'cslbcop'):
             coeffs = features.waveletTransform(img,'db4')
             cA ,(cH,cV,cD) = coeffs
 
-            hist_of_line = []
+            hist_of_line = None
             if feat == 'cslbcop':
                 hist_of_line = features.CSLBCoP(cA)
-            elif feat == 'lpbh':
+            elif feat == 'lbph':
                 hist_of_line = features.LPBH(cA,1,8)
 
             #hist_of_line_horizontal = features.LPBH(cH,1,8)

time.txt

@@ -1,10 +1,10 @@
+2.74
+2.11
 1.94
+2.10
 1.95
-1.89
-1.66
-1.90
-2.00
-2.08
-2.17
-2.00
-1.87
+2.11
+2.33
+1.98
+2.29
+2.21