update results

classification.py

@@ -34,7 +34,7 @@ def compute_glcm(image_path, ispath=True):
         img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
     else:
         img = image_path
-    # compute the GLCM properties. Distance = 2, and 4 angles: 0, 45, 90, 135
+    # compute the GLCM properties. Distance = 3, and 4 angles: 0, 45, 90, 135
     glcm = graycomatrix(img, [3], [0, np.pi/4, np.pi/2, 3*np.pi/4], 256, symmetric=True, normed=True)
     # extract the properties
     contrast = graycoprops(glcm, 'contrast')
@@ -42,7 +42,6 @@ def compute_glcm(image_path, ispath=True):
     energy = graycoprops(glcm, 'energy')
     homogeneity = graycoprops(glcm, 'homogeneity')    
     # return the feature vector
-    # return [contrast[0][0], correlation[0][0], energy[0][0], homogeneity[0][0]]
     # Flatten the arrays first
     contrast_flat = contrast.flatten()
     correlation_flat = correlation.flatten()
@@ -239,7 +238,7 @@ if __name__ == '__main__':
         # Evaluate each classifier on the tesing set.
         # Compare the results.
         # Save the results to a CSV file.
-        results = pd.DataFrame({'GLCM': [accuracy_score(y_test_glcm, y_pred_glcm)], 'LBP': [accuracy_score(y_test_lbp, y_pred_lbp)]})
+        results = pd.DataFrame({'GLCM_accuracy': [accuracy_score(y_test_glcm, y_pred_glcm)], 'LBP_accuracy': [accuracy_score(y_test_lbp, y_pred_lbp)]})
         # Add the precision to the results
         results['GLCM_precision'] = precision_score(y_test_glcm, y_pred_glcm, average='weighted')
         results['LBP_precision'] = precision_score(y_test_lbp, y_pred_lbp, average='weighted')

results.csv

@@ -1,2 +1,2 @@
-GLCM,LBP,GLCM_precision,LBP_precision
+GLCM_accuracy,LBP_accuracy,GLCM_precision,LBP_precision
 0.9333333333333333,0.9333333333333333,0.9411764705882353,0.9411764705882353