novo fonte com o extrator de gabor

src/extraction/gabor.py

+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+#
+"""
+    Extract Gabor features.
+    
+    Hans G. Feichtinger, Thomas Strohmer: "Gabor Analysis and Algorithms", Birkhäuser, 1998
+    
+    Name: gabor.py
+    Author: Hemerson Pistori (pistori@ucdb.br)
+
+    Based on: https://github.com/riaanvddool/scikits-image/blob/master/doc/examples/plot_gabor.py
+
+"""
+
+import numpy as np
+
+from util.utils import ImageUtils
+from extractor import Extractor
+from scipy import ndimage as ndi
+from skimage import feature
+from skimage.filter import gabor_kernel
+from skimage.util import img_as_float
+
+
+
+class GABOR(Extractor):
+    """Implements GABOR feature extraction."""
+
+    
+    def __init__(self):
+        pass        
+        
+    
+    def run(self, image):
+        """Extract GABOR features.
+        
+        Parameters
+        ----------
+        image : opencv image
+            Image to be analyzed.
+        
+        Returns
+        -------
+        features : tuple
+            Returns a tuple containing a list of labels, type and values for each feature extracted.
+        """
+
+        def compute_feats(image, kernels):
+            feats = np.zeros((len(kernels), 2), dtype=np.double)
+            for k, kernel in enumerate(kernels): 
+                filtered = ndi.convolve(image, kernel, mode='wrap') 
+                feats[k, 0] = filtered.mean()
+                feats[k, 1] = filtered.var()
+
+                values.append(feats[k, 0])
+                values.append(feats[k, 1])
+            return feats 
+
+        
+        def power(image, kernel):
+                image = (image - image.mean()) / image.std()
+                return np.sqrt(ndi.convolve(image, np.real(kernel), mode='wrap')**2 +
+                            ndi.convolve(image, np.imag(kernel), mode='wrap')**2)
+
+
+
+        image_grayscale = ImageUtils.image_grayscale(image, bgr = True)
+        shrink =  (slice(0, None, 3), slice(0, None, 3))
+        image_float = img_as_float(self.image_grayscale)[shrink]
+
+
+        #Prepare filter bank kernels
+        labels = []
+        values = []
+        kernels = []
+        index = 0
+        for theta in range(4):
+            theta = theta / 4. * np.pi
+            for sigma in (1, 3):
+                for frequency in (0.05, 0.25):
+                    kernel = np.real(gabor_kernel(frequency, theta=theta,
+                                                  sigma_x=sigma, sigma_y=sigma))
+                    kernels.append(kernel)
+                    print ("Thet_%f_Sigma_%i_Frequencia_%.2f" % (theta, sigma, frequency))
+                    for stat in ("Mean", "Variance"):
+                        labels.append("Thet_%f_Sigma_%i_Frequencia_%.2f_%s" % (theta, sigma, frequency, stat))
+
+   
+        compute_feats(image_float,kernels)
+
+        types = ['numeric'] * len(labels)
+
+        return labels, types, values
+
+
+
+
+