speaker-lab files

notebooks/plots.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+
+#############
+#
+# Plotting utilities
+#
+#############
+
+import bob.io.image
+import bob.io.base
+import matplotlib.pyplot as pyplot
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import colors as mcolors
+import bob.measure
+import bob.measure.load
+import os
+
+
+def plot_with_subplots(image_a, image_b):
+    """
+    Plot two images side by side with matplotlib subplot
+    and set special titles for it
+    
+    **Parameters**
+
+      image_a:
+      
+      image_b:
+    """
+
+    if image_a.ndim == 3:
+        cmap=None
+    else:
+        cmap='gray'
+
+    figure = pyplot.figure(figsize=(16,8))
+    pyplot.subplot(121)
+    pyplot.axis('off')
+    pyplot.title('enroll')    
+    pyplot.imshow(bob.io.image.to_matplotlib(image_a), cmap=cmap)
+    
+    pyplot.subplot(122)
+    pyplot.axis('off')    
+    pyplot.title('probe')
+    pyplot.imshow(bob.io.image.to_matplotlib(image_b), cmap=cmap)
+     
+    pyplot.show()
+
+
+def plot_with_subplots_list(images):
+    """
+    Plot a list of images in subplots
+    
+    **Parameters**
+    
+      images: list      
+         List of loaded images
+    """
+
+    figure = pyplot.figure(figsize=(8,4))
+    for i,im in enumerate(images):
+
+        if im.ndim == 3:
+            cmap=None
+        else:
+            cmap='gray'
+
+        pyplot.subplot(1,len(images),i+1)
+        pyplot.axis('off')
+        pyplot.imshow(bob.io.image.to_matplotlib(im), cmap=cmap)
+
+    pyplot.show()
+
+
+def plot_hist(zero_effort_scores, genuine_scores, fmr_01=None, fmr_001=None):
+    """
+    Plot histogram distribution
+    
+    **Parameters**
+
+      zero_effort_scores: numpy.array      
+         Zeroth effort impostor scores
+      
+      genuine_scores: numpy.array
+         Genuine scores
+    """
+    bins = 100
+    figure = pyplot.figure(figsize=(16,8))
+
+    pyplot.suptitle("Score distribution")
+    if fmr_01 is None:
+        fmr_01 = bob.measure.far_threshold(zero_effort_scores, genuine_scores, 0.1)
+    if fmr_001 is None:
+        fmr_001 = bob.measure.far_threshold(zero_effort_scores, genuine_scores, 0.01)
+    
+    # DEV
+    pyplot.hist(zero_effort_scores, color="C0", bins=bins, alpha=0.8, label="Zero-effort impostors", density=True)
+    pyplot.hist(genuine_scores, color="C2", bins=bins, alpha=0.9, label="Genuines", density=True)
+
+    pyplot.axvline(x=fmr_01, color="red", linestyle="dashed", label="FMR=0.1")
+    pyplot.axvline(x=fmr_001, color="orange", linestyle="dashed", label="FMR=0.01")
+    
+    pyplot.legend(ncol=1, bbox_to_anchor=(0.7, 1.1))
+    
+    pyplot.show()
+    return fmr_01, fmr_001
+    
+    
+def plot_roc(zero_effort_scores, genuine_scores):
+    """
+    Plot ROC curve
+    
+    **Parameters**
+
+      zero_effort_scores: numpy.array      
+         Zeroth effort impostor scores
+      
+      genuine_scores: numpy.array
+         Genuine scores
+    """
+
+    # ROC curve
+    pyplot.figure(figsize=(16,8))    
+    bob.measure.plot.roc(zero_effort_scores, genuine_scores, npoints=100,  CAR=True)
+
+    pyplot.grid(True)
+    pyplot.title("Receiver Operating Characteristic (ROC) curve")
+    pyplot.xlabel("FMR")
+    pyplot.ylabel("100 - FNMR")
+    #pyplot.ylim((0,1.))
+    pyplot.show()
+    
+
+def plot_det(zero_effort_scores, genuine_scores):
+    """
+    Plot DET curve
+    
+    **Parameters**
+
+      zero_effort_scores: numpy.array      
+         Zeroth effort impostor scores
+      
+      genuine_scores: numpy.array
+         Genuine scores
+    """
+
+    pyplot.figure(figsize=(16, 8))
+    
+    bob.measure.plot.det(zero_effort_scores, genuine_scores, npoints=100)
+    bob.measure.plot.det_axis([0.01, 99, 0.01, 99])
+    
+    pyplot.grid(True)
+    pyplot.title("Detection Error Tradeoff (DET)")
+    pyplot.xlabel("FMR(%)")
+    pyplot.ylabel("FNMR(%)")
+    pyplot.show()    
+
+
+def compute_fnmrs(zero_effort_scores, genuine_scores, one_minus=True):
+    """
+    Compute the False Non Match Rate at FMR 0.1 and 0.01
+    
+    **Parameters**
+
+      zero_effort_scores: numpy.array      
+         Zeroth effort impostor scores
+      
+      genuine_scores: numpy.array
+         Genuine scores
+         
+      one_minus: bool
+         one_minus = True returns 1-FNMR
+    """
+
+    _,frr_01 = bob.measure.farfrr(zero_effort_scores, genuine_scores, bob.measure.far_threshold(zero_effort_scores, genuine_scores, 0.1))
+    _,frr_001 = bob.measure.farfrr(zero_effort_scores, genuine_scores, bob.measure.far_threshold(zero_effort_scores, genuine_scores, 0.01))
+    
+    if one_minus:
+        return round(1-frr_01,2)*100, round(1-frr_001, 2)*100
+    else:
+        return round(frr_01, 2)*100, round(frr_001, 2)*100
+    
+
+def plot_multiroc(file_names, labels, base_path="./scores"):
+    """
+    Plot ROC curves from several systems
+    
+    **Parameters**
+
+      zero_effort_scores: numpy.array      
+         Zeroth effort impostor scores
+      
+      genuine_scores: numpy.array
+         Genuine scores
+    """
+
+    # ROC curve
+    pyplot.figure(figsize=(16,8))
+    
+    for f, l in zip(file_names, labels):
+        zero_effort_scores, genuine_scores = bob.measure.load.split(os.path.join(base_path, f))
+        bob.measure.plot.roc(zero_effort_scores, genuine_scores, npoints=100,  CAR=True, label=l)
+
+    pyplot.grid(True)
+    pyplot.title("Receiver Operating Characteristic (ROC) curve")
+    pyplot.xlabel("FMR")
+    pyplot.ylabel("100 - FNMR")
+    #pyplot.ylim((0,1.))
+    pyplot.legend()
+    pyplot.show()
+
+
+def plot_multidet(file_names, labels, base_path="./scores"):
+    """
+    Plot DET curves from several systems
+    
+    **Parameters**
+
+      zero_effort_scores: numpy.array      
+         Zeroth effort impostor scores
+      
+      genuine_scores: numpy.array
+         Genuine scores
+    """
+
+    # ROC curve
+    pyplot.figure(figsize=(16,8))
+    
+    for f, l in zip(file_names, labels):
+        zero_effort_scores, genuine_scores = bob.measure.load.split(os.path.join(base_path, f))
+        bob.measure.plot.det(zero_effort_scores, genuine_scores, npoints=100, label=l)
+
+    bob.measure.plot.det_axis([0.01, 99, 0.01, 99])
+    
+    pyplot.grid(True)
+    pyplot.title("Detection Error Tradeoff (DET)")
+    pyplot.xlabel("FMR(%)")
+    pyplot.ylabel("FNMR(%)")
+    pyplot.legend()
+    pyplot.show()    
+

notebooks/speaker_lib.py

+from pathlib import Path
+import bob.bio.gmm
+import numpy
+
+# we assume that the file is in the 4-column format devised for bob-score-files.
+
+def load_scores(score_filename):
+    my_file = Path(score_filename)
+    assert my_file.is_file(), "File %s does not exist. Quitting." %score_filename
+    
+    dsf = open(score_filename, "r")
+    x = dsf.readlines()
+    dsf.close()
+    
+    gen_scores = [float(line.split()[3]) for line in x if line.split()[0] == line.split()[1]]
+    zei_scores = [float(line.split()[3]) for line in x if line.split()[0] != line.split()[1]]
+    
+    return numpy.array(zei_scores), numpy.array(gen_scores)