示例#1
0
    def cropFaces(self, cascade=None):
        """
        **SUMMARY**

        This function helps in cropping a certain object in all of the images
        by using the provided haar cascade. A haar classifier is implemented 
        and images are cropped. This function also supports multiple faces in
        one single image.

        **PARAMETERS**

        cascade - haar cascade string

        **RETURNS**

        ImageSet

        **EXAMPLES**

        >>> imgs = ImageSet("some_directory/")
        >>> cascade = "face.xml"
        >>> faces = imgs.cropFaces(cascade)
        >>> faces.show()
        """
        if not cascade:
            cascade = CASCADE_PATH
        else:
            if not os.path.isfile(cascade):
                warnings.warn(
                    "The provided cascade does not exist. Using default cascade"
                )
                cascade = CASCADE_PATH
        classifier = cv2.CascadeClassifier(cascade)
        gray = [cv2.cvtColor(img, cv2.cv.CV_BGR2GRAY) for img in self]
        objects = [
            classifier.detectMultiScale(img,
                                        scaleFactor=1.1,
                                        minNeighbors=3,
                                        minSize=(10, 10),
                                        flags=cv2.cv.CV_HAAR_SCALE_IMAGE)
            for img in gray
        ]
        imgs = [[img[ob[1]:ob[1] + ob[3], ob[0]:ob[0] + ob[2]] for ob in obj]
                for img, obj in zip(self, objects)
                if isinstance(obj, np.ndarray)]
        imgs = concatenate(*imgs)
        return ImageSet(imgs=imgs)
示例#2
0
 def __init__(self):
     """
     Create a Face Recognizer Class using Fisher Face Recognizer. Uses
     OpenCV's FaceRecognizer class. Currently supports Fisher Faces.
     """
     self.supported = True
     self.model = None
     self.train_imgs = None
     self.train_labels = None
     self.csvfiles = []
     self.imageSize = None
     self.labels_dict = {}
     self.labels_set = []
     self.int_labels = []
     self.labels_dict_rev = {}
     if not hasattr(cv2, 'createFisherFaceRecognizer'):
         self.supported = False
         warnings.warn("Returning None. OpenCV >= 2.4.4 required.")
         return
     self.model = cv2.createFisherFaceRecognizer()
示例#3
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    def __init__(self, directory="", imgs=None):
        if os.path.isfile(directory):
            img = cv2.imread(directory)
            self.append(img)
            return
        if imgs:
            self.extend(imgs)
            return
        try:
            imagefiles = os.listdir(directory)

        except OSError as error:
            print "OS Error({0}): {1}" .format(error.errno, error.strerror)
            warnings.warn("encountered the above mentioned error. Returning Empty list.")
            return
        for imagefile in imagefiles:
            filename = os.path.join(directory, imagefile)
            img = cv2.imread(filename)
            if isinstance(img, np.ndarray):
                self.append(img)
示例#4
0
 def __init__(self):
     """
     Create a Face Recognizer Class using Fisher Face Recognizer. Uses
     OpenCV's FaceRecognizer class. Currently supports Fisher Faces.
     """
     self.supported = True
     self.model = None
     self.train_imgs = None
     self.train_labels = None
     self.csvfiles = []
     self.imageSize = None
     self.labels_dict = {}
     self.labels_set = []
     self.int_labels = []
     self.labels_dict_rev = {}
     if not hasattr(cv2, 'createFisherFaceRecognizer'):
         self.supported = False
         warnings.warn("Returning None. OpenCV >= 2.4.4 required.")
         return
     self.model = cv2.createFisherFaceRecognizer()
示例#5
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    def __init__(self, directory="", imgs=None):
        if os.path.isfile(directory):
            img = cv2.imread(directory)
            self.append(img)
            return
        if imgs:
            self.extend(imgs)
            return
        try:
            imagefiles = os.listdir(directory)

        except OSError as error:
            print "OS Error({0}): {1}".format(error.errno, error.strerror)
            warnings.warn(
                "encountered the above mentioned error. Returning Empty list.")
            return
        for imagefile in imagefiles:
            filename = os.path.join(directory, imagefile)
            img = cv2.imread(filename)
            if isinstance(img, np.ndarray):
                self.append(img)
示例#6
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    def load(self, filename):
        """
        **SUMMARY**

        Load the trainging data.

        **PARAMETERS**

        * *filename* - File where you want to load the data from.

        **RETURNS**

        Nothing. None.

        **EXAMPLES**

        >>> f = FaceRecognizer()
        >>> f.load("trainingdata.xml")
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f.predict(imgs)        
        """
        if not self.supported:
            warnings.warn("Fisher Recognizer is supported by OpenCV >= 2.4.4")
            return None

        self.model.load(filename)
        loadfile = open(filename, "r")
        for line in loadfile.readlines():
            if "cols" in line:
                match = re.search("(?<=\>)\w+", line)
                tsize = int(match.group(0))
                break
        loadfile.close()
        w = int(tsize ** 0.5)
        h = tsize / w
        while(w * h != tsize):
            w += 1
            h = tsize / w
        self.imageSize = (w, h)
示例#7
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    def load(self, filename):
        """
        **SUMMARY**

        Load the trainging data.

        **PARAMETERS**

        * *filename* - File where you want to load the data from.

        **RETURNS**

        Nothing. None.

        **EXAMPLES**

        >>> f = FaceRecognizer()
        >>> f.load("trainingdata.xml")
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f.predict(imgs)        
        """
        if not self.supported:
            warnings.warn("Fisher Recognizer is supported by OpenCV >= 2.4.4")
            return None

        self.model.load(filename)
        loadfile = open(filename, "r")
        for line in loadfile.readlines():
            if "cols" in line:
                match = re.search("(?<=\>)\w+", line)
                tsize = int(match.group(0))
                break
        loadfile.close()
        w = int(tsize**0.5)
        h = tsize / w
        while (w * h != tsize):
            w += 1
            h = tsize / w
        self.imageSize = (w, h)
示例#8
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    def save(self, filename):
        """
        **SUMMARY**

        Save the trainging data.

        **PARAMETERS**

        * *filename* - File where you want to save the data.

        **RETURNS**

        Nothing. None.

        **EXAMPLES**

        >>> f = FaceRecognizer()
        >>> imgs1 = ImageSet(path/to/images_of_type1)
        >>> labels1 = LabelSet("type1", imgs1)
        >>> imgs2 = ImageSet(path/to/images_of_type2)
        >>> labels2 = LabelSet("type2", imgs2)
        >>> imgs3 = ImageSet(path/to/images_of_type3)
        >>> labels3 = LabelSet("type3", imgs3)
        >>> imgs = concatenate(imgs1, imgs2, imgs3)
        >>> labels = concatenate(labels1, labels2, labels3)
        >>> f.train(imgs, labels)
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f.predict(imgs)

        #Save New Fisher Training Data
        >>> f.save("new_trainingdata.xml")
        """
        if not self.supported:
            warnings.warn("Fisher Recognizer is supported by OpenCV >= 2.4.4")
            return None

        self.model.save(filename)
示例#9
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    def save(self, filename):
        """
        **SUMMARY**

        Save the trainging data.

        **PARAMETERS**

        * *filename* - File where you want to save the data.

        **RETURNS**

        Nothing. None.

        **EXAMPLES**

        >>> f = FaceRecognizer()
        >>> imgs1 = ImageSet(path/to/images_of_type1)
        >>> labels1 = LabelSet("type1", imgs1)
        >>> imgs2 = ImageSet(path/to/images_of_type2)
        >>> labels2 = LabelSet("type2", imgs2)
        >>> imgs3 = ImageSet(path/to/images_of_type3)
        >>> labels3 = LabelSet("type3", imgs3)
        >>> imgs = concatenate(imgs1, imgs2, imgs3)
        >>> labels = concatenate(labels1, labels2, labels3)
        >>> f.train(imgs, labels)
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f.predict(imgs)

        #Save New Fisher Training Data
        >>> f.save("new_trainingdata.xml")
        """
        if not self.supported:
            warnings.warn("Fisher Recognizer is supported by OpenCV >= 2.4.4")
            return None

        self.model.save(filename)
示例#10
0
    def cropFaces(self, cascade=None):
        """
        **SUMMARY**

        This function helps in cropping a certain object in all of the images
        by using the provided haar cascade. A haar classifier is implemented 
        and images are cropped. This function also supports multiple faces in
        one single image.

        **PARAMETERS**

        cascade - haar cascade string

        **RETURNS**

        ImageSet

        **EXAMPLES**

        >>> imgs = ImageSet("some_directory/")
        >>> cascade = "face.xml"
        >>> faces = imgs.cropFaces(cascade)
        >>> faces.show()
        """
        if not cascade:
            cascade = CASCADE_PATH
        else:
            if not os.path.isfile(cascade):
                warnings.warn("The provided cascade does not exist. Using default cascade")
                cascade = CASCADE_PATH
        classifier = cv2.CascadeClassifier(cascade)
        gray = [cv2.cvtColor(img, cv2.cv.CV_BGR2GRAY) for img in self]
        objects = [classifier.detectMultiScale(img, scaleFactor=1.1, minNeighbors=3, minSize=(10, 10), flags = cv2.cv.CV_HAAR_SCALE_IMAGE) for img in gray]
        imgs = [[img[ob[1]:ob[1]+ob[3], ob[0]:ob[0]+ob[2]] for ob in obj] for img, obj in zip(self, objects) if isinstance(obj, np.ndarray)]
        imgs = concatenate(*imgs)
        return ImageSet(imgs=imgs)
示例#11
0
 def __init__(self, label, imageset):
     if not isinstance(imageset, collections.Iterable):
         warnings.warn("The provided ImageSet is not a list")
         return None
     labels = [label]*len(imageset)
     self.extend(labels)
示例#12
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    def train(self, images=None, labels=None, csvfile=None, delimiter=";"):
        """
        **SUMMARY**

        Train the face recognizer with images and labels.

        **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 = LabelSet("type1", imgs1)
        >>> imgs2 = ImageSet(path/to/images_of_type2)
        >>> labels2 = LabelSet("type2", imgs2)
        >>> imgs3 = ImageSet(path/to/images_of_type3)
        >>> labels3 = LabelSet("type3", imgs3)
        >>> imgs = concatenate(imgs1, imgs2, imgs3)
        >>> labels = concatenate(labels1, labels2, labels3)
        >>> f.train(imgs, labels)
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f.predict(imgs)

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

        Load Fisher Training Data and directly use without trainging
        >>> f1 = FaceRecognizer()
        >>> f1.load("trainingdata.xml")
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f1.predict(imgs)

        Use CSV files for training
        >>> f = FaceRecognizer()
        >>> f.train(csvfile="CSV_file_name", delimiter=";")
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f.predict(imgs)
        """

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

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

        if isinstance(labels, type(None)):
            warnings.warn("Labels not provided. Training not inititated.")
            return None

        self.labels_set = list(set(labels))
        i = 0
        for label in self.labels_set:
            self.labels_dict.update({label: i})
            self.labels_dict_rev.update({i: label})
            i += 1

        if len(self.labels_set) < 2:
            warnings.warn("At least two classes/labels are required"
                          "for training. Training not inititated.")
            return None

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

        self.imageSize = images[0].shape[:2]
        h, w = self.imageSize
        images = [img if img.shape[:2] == self.imageSize 
                 else cv2.resize(img, (w, h)) for img in images]

        self.int_labels = [self.labels_dict[key] for key in labels]
        self.train_labels = labels
        labels = np.array(self.int_labels)
        self.train_imgs = images
        cv2imgs = [cv2.cvtColor(img, cv2.cv.CV_BGR2GRAY) for img in images]

        self.model.train(cv2imgs, labels)
示例#13
0
    def predict(self, imgs):
        """
        **SUMMARY**

        Predict the class of the image using trained face recognizer.

        **PARAMETERS**

        * *image*    -  Image.The images must be of the same size as provided
                        in training.

        **RETURNS**

        * *label* - Class of the image which it belongs to.

        **EXAMPLES**

        >>> f = FaceRecognizer()
        >>> imgs1 = ImageSet(path/to/images_of_type1)
        >>> labels1 = LabelSet("type1", imgs1)
        >>> imgs2 = ImageSet(path/to/images_of_type2)
        >>> labels2 = LabelSet("type2", imgs2)
        >>> imgs3 = ImageSet(path/to/images_of_type3)
        >>> labels3 = LabelSet("type3", imgs3)
        >>> imgs = concatenate(imgs1, imgs2, imgs3)
        >>> labels = concatenate(labels1, labels2, labels3)
        >>> f.train(imgs, labels)
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f.predict(imgs)

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

        Load Fisher Training Data and directly use without trainging
        >>> f1 = FaceRecognizer()
        >>> f1.load("trainingdata.xml")
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f1.predict(imgs)

        Use CSV files for training
        >>> f = FaceRecognizer()
        >>> f.train(csvfile="CSV_file_name", delimiter=";")
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f.predict(imgs)
        """
        if not self.supported:
            warnings.warn("Fisher Recognizer is supported by OpenCV >= 2.4.4")
            return None
        h, w = self.imageSize
        images = [img if img.shape[:2] == self.imageSize
                 else cv2.resize(img, (w, h)) for img in imgs]

        if isinstance(imgs, np.ndarray):
            if imgs.shape[:2] != self.imageSize:
                image = cv2.resize(imgs, (w, h))
            cv2img = cv2.cvtColor(image, cv2.cv.CV_BGR2GRAY)
            label, confidence = self.model.predict(cv2img)
            retLabel = self.labels_dict_rev.get(label)
            if not retLabel:
                retLabel = label
            return (retLabel, confidence)

        retVal = []
        for image in images:
            cv2img = cv2.cvtColor(image, cv2.cv.CV_BGR2GRAY)
            label, confidence = self.model.predict(cv2img)
            retLabel = self.labels_dict_rev.get(label)
            if not retLabel:
                retLabel = label
            retVal.append((retLabel, confidence))
        return retVal
示例#14
0
 def __init__(self, label, imageset):
     if not isinstance(imageset, collections.Iterable):
         warnings.warn("The provided ImageSet is not a list")
         return None
     labels = [label] * len(imageset)
     self.extend(labels)
示例#15
0
    def train(self, images=None, labels=None, csvfile=None, delimiter=";"):
        """
        **SUMMARY**

        Train the face recognizer with images and labels.

        **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 = LabelSet("type1", imgs1)
        >>> imgs2 = ImageSet(path/to/images_of_type2)
        >>> labels2 = LabelSet("type2", imgs2)
        >>> imgs3 = ImageSet(path/to/images_of_type3)
        >>> labels3 = LabelSet("type3", imgs3)
        >>> imgs = concatenate(imgs1, imgs2, imgs3)
        >>> labels = concatenate(labels1, labels2, labels3)
        >>> f.train(imgs, labels)
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f.predict(imgs)

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

        Load Fisher Training Data and directly use without trainging
        >>> f1 = FaceRecognizer()
        >>> f1.load("trainingdata.xml")
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f1.predict(imgs)

        Use CSV files for training
        >>> f = FaceRecognizer()
        >>> f.train(csvfile="CSV_file_name", delimiter=";")
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f.predict(imgs)
        """

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

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

        if isinstance(labels, type(None)):
            warnings.warn("Labels not provided. Training not inititated.")
            return None

        self.labels_set = list(set(labels))
        i = 0
        for label in self.labels_set:
            self.labels_dict.update({label: i})
            self.labels_dict_rev.update({i: label})
            i += 1

        if len(self.labels_set) < 2:
            warnings.warn("At least two classes/labels are required"
                          "for training. Training not inititated.")
            return None

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

        self.imageSize = images[0].shape[:2]
        h, w = self.imageSize
        images = [
            img if img.shape[:2] == self.imageSize else cv2.resize(
                img, (w, h)) for img in images
        ]

        self.int_labels = [self.labels_dict[key] for key in labels]
        self.train_labels = labels
        labels = np.array(self.int_labels)
        self.train_imgs = images
        cv2imgs = [cv2.cvtColor(img, cv2.cv.CV_BGR2GRAY) for img in images]

        self.model.train(cv2imgs, labels)
示例#16
0
    def predict(self, imgs):
        """
        **SUMMARY**

        Predict the class of the image using trained face recognizer.

        **PARAMETERS**

        * *image*    -  Image.The images must be of the same size as provided
                        in training.

        **RETURNS**

        * *label* - Class of the image which it belongs to.

        **EXAMPLES**

        >>> f = FaceRecognizer()
        >>> imgs1 = ImageSet(path/to/images_of_type1)
        >>> labels1 = LabelSet("type1", imgs1)
        >>> imgs2 = ImageSet(path/to/images_of_type2)
        >>> labels2 = LabelSet("type2", imgs2)
        >>> imgs3 = ImageSet(path/to/images_of_type3)
        >>> labels3 = LabelSet("type3", imgs3)
        >>> imgs = concatenate(imgs1, imgs2, imgs3)
        >>> labels = concatenate(labels1, labels2, labels3)
        >>> f.train(imgs, labels)
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f.predict(imgs)

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

        Load Fisher Training Data and directly use without trainging
        >>> f1 = FaceRecognizer()
        >>> f1.load("trainingdata.xml")
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f1.predict(imgs)

        Use CSV files for training
        >>> f = FaceRecognizer()
        >>> f.train(csvfile="CSV_file_name", delimiter=";")
        >>> imgs = ImageSet("path/to/testing_images")
        >>> print f.predict(imgs)
        """
        if not self.supported:
            warnings.warn("Fisher Recognizer is supported by OpenCV >= 2.4.4")
            return None
        h, w = self.imageSize
        images = [
            img if img.shape[:2] == self.imageSize else cv2.resize(
                img, (w, h)) for img in imgs
        ]

        if isinstance(imgs, np.ndarray):
            if imgs.shape[:2] != self.imageSize:
                image = cv2.resize(imgs, (w, h))
            cv2img = cv2.cvtColor(image, cv2.cv.CV_BGR2GRAY)
            label, confidence = self.model.predict(cv2img)
            retLabel = self.labels_dict_rev.get(label)
            if not retLabel:
                retLabel = label
            return (retLabel, confidence)

        retVal = []
        for image in images:
            cv2img = cv2.cvtColor(image, cv2.cv.CV_BGR2GRAY)
            label, confidence = self.model.predict(cv2img)
            retLabel = self.labels_dict_rev.get(label)
            if not retLabel:
                retLabel = label
            retVal.append((retLabel, confidence))
        return retVal