import cv2
import numpy as np
import operator

RESIZED_IMAGE_WIDTH = 20
RESIZED_IMAGE_HEIGHT = 30
MIN_CONTOUR_AREA = 100


class ImageClassifier:

    def __init__(self):
        self.knn = cv2.KNearest()

    def train(self, label_path, training_set_path):

        labels = np.loadtxt(label_path, np.float32)  # Labels
        labels = labels.reshape((labels.size, 1))
        training_set = np.loadtxt(training_set_path, np.float32)   # Training set

        self.knn.train(training_set, labels)

    def recognize(self, image):

        final_string = ""
        preprocessed_image = self._preprocess_image(image)
        valid_contours = self._find_valid_contours(preprocessed_image.copy())

        for contour in valid_contours:
            cv2.rectangle(image,
                          (contour.x, contour.y),
                          (contour.x + contour.width, contour.y + contour.height),
                          (0, 255, 0), 2)

            crop = preprocessed_image[contour.y: contour.y + contour.height, contour.x: contour.x + contour.width]
            resized_crop = cv2.resize(crop, (RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT))
            flattened_crop = resized_crop.reshape((1, RESIZED_IMAGE_WIDTH * RESIZED_IMAGE_HEIGHT))
            float_array = np.float32(flattened_crop)
            value, npa_results, neigh_resp, dists = self.knn.find_nearest(float_array, k=1)
            current_char = str(chr(int(npa_results[0][0])))
            final_string = final_string + current_char

        return final_string, image

    def _find_valid_contours(self, image):

        image_contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        contours = [_ContourWrapper(contour) for contour in image_contours]
        valid_contours = [contour for contour in contours if contour.area > MIN_CONTOUR_AREA]

        valid_contours.sort(key=operator.attrgetter("x"))

        return valid_contours

    def _preprocess_image(self, image):

        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
        dilated_image = cv2.dilate(image, kernel, iterations=1)
        eroded_image = cv2.erode(dilated_image, kernel, iterations=1)

        grayscale_image = cv2.cvtColor(eroded_image, cv2.COLOR_BGR2GRAY)
        blurred_image = cv2.GaussianBlur(grayscale_image, (5, 5), 0)

        thresholded_image = cv2.adaptiveThreshold(blurred_image, 255,
                                                  cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)

        return thresholded_image


class _ContourWrapper:

    def __init__(self, contour):

        self.contour = contour
        self.bounding_rect = cv2.boundingRect(contour)
        self.x, self.y, self.width, self.height = self.bounding_rect
        self.area = cv2.contourArea(contour)

f422d61189b43f301acde7b887737e8d6ec30fc7
<mask token>


class ImageClassifier:
    <mask token>
    <mask token>
    <mask token>
    <mask token>
    <mask token>


class _ContourWrapper:

    def __init__(self, contour):
        self.contour = contour
        self.bounding_rect = cv2.boundingRect(contour)
        self.x, self.y, self.width, self.height = self.bounding_rect
        self.area = cv2.contourArea(contour)

<mask token>


class ImageClassifier:

    def __init__(self):
        self.knn = cv2.KNearest()

    def train(self, label_path, training_set_path):
        labels = np.loadtxt(label_path, np.float32)
        labels = labels.reshape((labels.size, 1))
        training_set = np.loadtxt(training_set_path, np.float32)
        self.knn.train(training_set, labels)

    def recognize(self, image):
        final_string = ''
        preprocessed_image = self._preprocess_image(image)
        valid_contours = self._find_valid_contours(preprocessed_image.copy())
        for contour in valid_contours:
            cv2.rectangle(image, (contour.x, contour.y), (contour.x +
                contour.width, contour.y + contour.height), (0, 255, 0), 2)
            crop = preprocessed_image[contour.y:contour.y + contour.height,
                contour.x:contour.x + contour.width]
            resized_crop = cv2.resize(crop, (RESIZED_IMAGE_WIDTH,
                RESIZED_IMAGE_HEIGHT))
            flattened_crop = resized_crop.reshape((1, RESIZED_IMAGE_WIDTH *
                RESIZED_IMAGE_HEIGHT))
            float_array = np.float32(flattened_crop)
            value, npa_results, neigh_resp, dists = self.knn.find_nearest(
                float_array, k=1)
            current_char = str(chr(int(npa_results[0][0])))
            final_string = final_string + current_char
        return final_string, image

    def _find_valid_contours(self, image):
        image_contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.
            CHAIN_APPROX_SIMPLE)
        contours = [_ContourWrapper(contour) for contour in image_contours]
        valid_contours = [contour for contour in contours if contour.area >
            MIN_CONTOUR_AREA]
        valid_contours.sort(key=operator.attrgetter('x'))
        return valid_contours

    def _preprocess_image(self, image):
        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
        dilated_image = cv2.dilate(image, kernel, iterations=1)
        eroded_image = cv2.erode(dilated_image, kernel, iterations=1)
        grayscale_image = cv2.cvtColor(eroded_image, cv2.COLOR_BGR2GRAY)
        blurred_image = cv2.GaussianBlur(grayscale_image, (5, 5), 0)
        thresholded_image = cv2.adaptiveThreshold(blurred_image, 255, cv2.
            ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
        return thresholded_image


class _ContourWrapper:

    def __init__(self, contour):
        self.contour = contour
        self.bounding_rect = cv2.boundingRect(contour)
        self.x, self.y, self.width, self.height = self.bounding_rect
        self.area = cv2.contourArea(contour)

<mask token>
RESIZED_IMAGE_WIDTH = 20
RESIZED_IMAGE_HEIGHT = 30
MIN_CONTOUR_AREA = 100


class ImageClassifier:

    def __init__(self):
        self.knn = cv2.KNearest()

    def train(self, label_path, training_set_path):
        labels = np.loadtxt(label_path, np.float32)
        labels = labels.reshape((labels.size, 1))
        training_set = np.loadtxt(training_set_path, np.float32)
        self.knn.train(training_set, labels)

    def recognize(self, image):
        final_string = ''
        preprocessed_image = self._preprocess_image(image)
        valid_contours = self._find_valid_contours(preprocessed_image.copy())
        for contour in valid_contours:
            cv2.rectangle(image, (contour.x, contour.y), (contour.x +
                contour.width, contour.y + contour.height), (0, 255, 0), 2)
            crop = preprocessed_image[contour.y:contour.y + contour.height,
                contour.x:contour.x + contour.width]
            resized_crop = cv2.resize(crop, (RESIZED_IMAGE_WIDTH,
                RESIZED_IMAGE_HEIGHT))
            flattened_crop = resized_crop.reshape((1, RESIZED_IMAGE_WIDTH *
                RESIZED_IMAGE_HEIGHT))
            float_array = np.float32(flattened_crop)
            value, npa_results, neigh_resp, dists = self.knn.find_nearest(
                float_array, k=1)
            current_char = str(chr(int(npa_results[0][0])))
            final_string = final_string + current_char
        return final_string, image

    def _find_valid_contours(self, image):
        image_contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.
            CHAIN_APPROX_SIMPLE)
        contours = [_ContourWrapper(contour) for contour in image_contours]
        valid_contours = [contour for contour in contours if contour.area >
            MIN_CONTOUR_AREA]
        valid_contours.sort(key=operator.attrgetter('x'))
        return valid_contours

    def _preprocess_image(self, image):
        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
        dilated_image = cv2.dilate(image, kernel, iterations=1)
        eroded_image = cv2.erode(dilated_image, kernel, iterations=1)
        grayscale_image = cv2.cvtColor(eroded_image, cv2.COLOR_BGR2GRAY)
        blurred_image = cv2.GaussianBlur(grayscale_image, (5, 5), 0)
        thresholded_image = cv2.adaptiveThreshold(blurred_image, 255, cv2.
            ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
        return thresholded_image


class _ContourWrapper:

    def __init__(self, contour):
        self.contour = contour
        self.bounding_rect = cv2.boundingRect(contour)
        self.x, self.y, self.width, self.height = self.bounding_rect
        self.area = cv2.contourArea(contour)

import cv2
import numpy as np
import operator
RESIZED_IMAGE_WIDTH = 20
RESIZED_IMAGE_HEIGHT = 30
MIN_CONTOUR_AREA = 100


class ImageClassifier:

    def __init__(self):
        self.knn = cv2.KNearest()

    def train(self, label_path, training_set_path):
        labels = np.loadtxt(label_path, np.float32)
        labels = labels.reshape((labels.size, 1))
        training_set = np.loadtxt(training_set_path, np.float32)
        self.knn.train(training_set, labels)

    def recognize(self, image):
        final_string = ''
        preprocessed_image = self._preprocess_image(image)
        valid_contours = self._find_valid_contours(preprocessed_image.copy())
        for contour in valid_contours:
            cv2.rectangle(image, (contour.x, contour.y), (contour.x +
                contour.width, contour.y + contour.height), (0, 255, 0), 2)
            crop = preprocessed_image[contour.y:contour.y + contour.height,
                contour.x:contour.x + contour.width]
            resized_crop = cv2.resize(crop, (RESIZED_IMAGE_WIDTH,
                RESIZED_IMAGE_HEIGHT))
            flattened_crop = resized_crop.reshape((1, RESIZED_IMAGE_WIDTH *
                RESIZED_IMAGE_HEIGHT))
            float_array = np.float32(flattened_crop)
            value, npa_results, neigh_resp, dists = self.knn.find_nearest(
                float_array, k=1)
            current_char = str(chr(int(npa_results[0][0])))
            final_string = final_string + current_char
        return final_string, image

    def _find_valid_contours(self, image):
        image_contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.
            CHAIN_APPROX_SIMPLE)
        contours = [_ContourWrapper(contour) for contour in image_contours]
        valid_contours = [contour for contour in contours if contour.area >
            MIN_CONTOUR_AREA]
        valid_contours.sort(key=operator.attrgetter('x'))
        return valid_contours

    def _preprocess_image(self, image):
        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
        dilated_image = cv2.dilate(image, kernel, iterations=1)
        eroded_image = cv2.erode(dilated_image, kernel, iterations=1)
        grayscale_image = cv2.cvtColor(eroded_image, cv2.COLOR_BGR2GRAY)
        blurred_image = cv2.GaussianBlur(grayscale_image, (5, 5), 0)
        thresholded_image = cv2.adaptiveThreshold(blurred_image, 255, cv2.
            ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
        return thresholded_image


class _ContourWrapper:

    def __init__(self, contour):
        self.contour = contour
        self.bounding_rect = cv2.boundingRect(contour)
        self.x, self.y, self.width, self.height = self.bounding_rect
        self.area = cv2.contourArea(contour)

import cv2
import numpy as np
import operator

RESIZED_IMAGE_WIDTH = 20
RESIZED_IMAGE_HEIGHT = 30
MIN_CONTOUR_AREA = 100


class ImageClassifier:

    def __init__(self):
        self.knn = cv2.KNearest()

    def train(self, label_path, training_set_path):

        labels = np.loadtxt(label_path, np.float32)  # Labels
        labels = labels.reshape((labels.size, 1))
        training_set = np.loadtxt(training_set_path, np.float32)   # Training set

        self.knn.train(training_set, labels)

    def recognize(self, image):

        final_string = ""
        preprocessed_image = self._preprocess_image(image)
        valid_contours = self._find_valid_contours(preprocessed_image.copy())

        for contour in valid_contours:
            cv2.rectangle(image,
                          (contour.x, contour.y),
                          (contour.x + contour.width, contour.y + contour.height),
                          (0, 255, 0), 2)

            crop = preprocessed_image[contour.y: contour.y + contour.height, contour.x: contour.x + contour.width]
            resized_crop = cv2.resize(crop, (RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT))
            flattened_crop = resized_crop.reshape((1, RESIZED_IMAGE_WIDTH * RESIZED_IMAGE_HEIGHT))
            float_array = np.float32(flattened_crop)
            value, npa_results, neigh_resp, dists = self.knn.find_nearest(float_array, k=1)
            current_char = str(chr(int(npa_results[0][0])))
            final_string = final_string + current_char

        return final_string, image

    def _find_valid_contours(self, image):

        image_contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        contours = [_ContourWrapper(contour) for contour in image_contours]
        valid_contours = [contour for contour in contours if contour.area > MIN_CONTOUR_AREA]

        valid_contours.sort(key=operator.attrgetter("x"))

        return valid_contours

    def _preprocess_image(self, image):

        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
        dilated_image = cv2.dilate(image, kernel, iterations=1)
        eroded_image = cv2.erode(dilated_image, kernel, iterations=1)

        grayscale_image = cv2.cvtColor(eroded_image, cv2.COLOR_BGR2GRAY)
        blurred_image = cv2.GaussianBlur(grayscale_image, (5, 5), 0)

        thresholded_image = cv2.adaptiveThreshold(blurred_image, 255,
                                                  cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)

        return thresholded_image


class _ContourWrapper:

    def __init__(self, contour):

        self.contour = contour
        self.bounding_rect = cv2.boundingRect(contour)
        self.x, self.y, self.width, self.height = self.bounding_rect
        self.area = cv2.contourArea(contour)
