Update utility for FaceRecognize Moudle in SimpleCV.

FaceRecognize_update,py

    def update(self, images=None, labels=None, csvfile=None, delimiter=";"):
        """
        **SUMMARY**

        Update the training set of the face recognizer.

        **PARAMETERS**

        * *images*    - A list of Images or ImageSet. All the images must be of
                        same size.
        * *labels*    - A list of labels(int) corresponding to the image in
                        images. There must be at least two different labels.
        * *csvfile*   - You can also provide a csv file with image filenames
                        and labels instead of providing labels and images
                        separately.
        * *delimiter* - The delimiter used in csv files.

        **RETURNS**

        Nothing. None.

        **EXAMPLES**

        >>> f = FaceRecognizer()
        >>> imgs1 = ImageSet(path/to/images_of_type1)
        >>> labels1 = ["type1"]*len(imgs1)
        >>> imgs2 = ImageSet(path/to/images_of_type2)
        >>> labels2 = ["type2"]*len(imgs2)
        >>> imgs3 = ImageSet(path/to/images_of_type3)
        >>> labels3 = ["type3"]*len(imgs3)
        >>> imgs = imgs1 + imgs2 + imgs3
        >>> labels = labels1 + labels2 + labels3
        >>> f.train(imgs, labels)
        >>> img = Image("some_image_of_any_of_the_above_type")
        >>> print f.predict(img)

        >>> imgs4 = ImageSet(path/to/some_more_images_of_type1)
        >>> labels4 = ["type1"]*len(imgs4)
        >>> imgs5 = ImageSet(path/to/some_more_images_of_type2)
        >>> labels5 = ["type2"]*len(imgs4)

        >>> newimgs = imgs4 + imgs5
        >>> newlabels = labels4 + labels5
        >>> f.update(newimgs, newlabels)

        >>> img1 = Image("some_image_of_any_of_the_above_type")
        >>> print f.predict(img1)

        Save New Fisher Training Data
        >>> f.save("new_trainingdata.xml")

        Load Fisher Training Data to update.
        >>> f1 = FaceRecognizer()
        >>> f1.load("trainingdata.xml")
        >>> img = Image("some_image_of_any_of_the_above_type")
        >>> print f1.predict(img)

        >>> f1.load("new_trainingdata.xml")
        >>> img1 = Image("some_image_of_any_of_the_above_type")
        >>> print f1.predict(img1)

        #Now Use CSV files for update
        >>> f1.update(csvfile="CSV_file_name", delimiter=";")
        >>> img2 = Image("some_image_of_any_of_the_above_type")
        >>> print f1.predict(img2)
        """

        if not self.supported:
            warnings.warn("Fisher Recognizer is supported by OpenCV >= 2.4.4")
            return None

        if csvfile:
            images = []
            labels = []
            import csv
            try:
                f = open(csvfile, "rb")
            except IOError:
                warnings.warn("No such file found. Update not inititated.")
                return None

            self.csvfiles.append(csvfiles)
            filereader = csv.reader(csvfile, delimiter=delimiter)
            for row in filereader:
                images.append(Image(row[0]))
                labels.append(row[1])

        labels_set = list(set(labels))
        i = 0
        for label in labels_set:
            self.labels_dict.update({label: i})
            self.labels_dict_rev.update({i: label})
            i += 1
        self.labels_set = list(set(self.labels_set + labels_set))
        if isinstance(labels, type(None)):
            warnings.warn("Labels not provided. Update not inititated.")
            return None

        if len(images) != len(labels):
            warnings.warn("Mismatch in number of labels and number of" /
                          "training images. Update not initiated.")
            return None

        w, h = self.imageSize
        images = [img if img.size() == self.imageSize else img.resize(w, h)
                  for img in images]

        int_labels = [self.labels_dict[key] for key in labels]
        self.int_labels += int_labels
        self.train_labels += labels
        labels = np.array(int_labels)
        self.train_imgs += images
        cv2imgs = [img.getGrayNumpyCv2() for img in images]

        # self.model.update(cv2imgs, labels) #  not yet supported
        self.model.update(cv2imgs, labels)

        # Not yet supported
        # self.eigenValues = self.model.getMat("eigenValues")
        # self.eigenVectors = self.model.getMat("eigenVectors")
        # self.mean = self.model.getMat("mean")