from functools import partial
import numpy as np
from violajones.HaarLikeFeature import HaarLikeFeature
from violajones.HaarLikeFeature import FeatureTypes
import progressbar
from multiprocessing import Pool

LOADING_BAR_LENGTH = 50


# TODO: select optimal threshold for each feature
# TODO: attentional cascading

def learn(positive_iis, negative_iis, num_classifiers=-1, min_feature_width=1, max_feature_width=-1, min_feature_height=1, max_feature_height=-1):
    """
    Selects a set of classifiers. Iteratively takes the best classifiers based
    on a weighted error.
    :param positive_iis: List of positive integral image examples
    :type positive_iis: list[numpy.ndarray]
    :param negative_iis: List of negative integral image examples
    :type negative_iis: list[numpy.ndarray]
    :param num_classifiers: Number of classifiers to select, -1 will use all
    classifiers
    :type num_classifiers: int

    :return: List of selected features
    :rtype: list[violajones.HaarLikeFeature.HaarLikeFeature]
    """
    num_pos = len(positive_iis)
    num_neg = len(negative_iis)
    num_imgs = num_pos + num_neg
    img_height, img_width = positive_iis[0].shape

    # Maximum feature width and height default to image width and height
    max_feature_height = img_height if max_feature_height == -1 else max_feature_height
    max_feature_width = img_width if max_feature_width == -1 else max_feature_width

    # Create initial weights and labels
    pos_weights = np.ones(num_pos) * 1. / (2 * num_pos)
    neg_weights = np.ones(num_neg) * 1. / (2 * num_neg)
    weights = np.hstack((pos_weights, neg_weights))
    labels = np.hstack((np.ones(num_pos), np.ones(num_neg) * -1))

    images = positive_iis + negative_iis

    # Create features for all sizes and locations
    features = _create_features(img_height, img_width, min_feature_width, max_feature_width, min_feature_height, max_feature_height)
    num_features = len(features)
    feature_indexes = list(range(num_features))

    num_classifiers = num_features if num_classifiers == -1 else num_classifiers

    print('Calculating scores for images..')

    votes = np.zeros((num_imgs, num_features))
    bar = progressbar.ProgressBar()
    # Use as many workers as there are CPUs
    pool = Pool(processes=None)
    for i in bar(range(num_imgs)):
        votes[i, :] = np.array(list(pool.map(partial(_get_feature_vote, image=images[i]), features)))

    # select classifiers

    classifiers = []

    print('Selecting classifiers..')
    bar = progressbar.ProgressBar()
    for _ in bar(range(num_classifiers)):

        classification_errors = np.zeros(len(feature_indexes))

        # normalize weights
        weights *= 1. / np.sum(weights)

        # select best classifier based on the weighted error
        for f in range(len(feature_indexes)):
            f_idx = feature_indexes[f]
            # classifier error is the sum of image weights where the classifier
            # is right
            error = sum(map(lambda img_idx: weights[img_idx] if labels[img_idx] != votes[img_idx, f_idx] else 0, range(num_imgs)))
            classification_errors[f] = error

        # get best feature, i.e. with smallest error
        min_error_idx = np.argmin(classification_errors)
        best_error = classification_errors[min_error_idx]
        best_feature_idx = feature_indexes[min_error_idx]

        # set feature weight
        best_feature = features[best_feature_idx]
        feature_weight = 0.5 * np.log((1 - best_error) / best_error)
        best_feature.weight = feature_weight

        classifiers.append(best_feature)

        # update image weights
        weights = np.array(list(map(lambda img_idx: weights[img_idx] * np.sqrt((1-best_error)/best_error) if labels[img_idx] != votes[img_idx, best_feature_idx] else weights[img_idx] * np.sqrt(best_error/(1-best_error)), range(num_imgs))))

        # remove feature (a feature can't be selected twice)
        feature_indexes.remove(best_feature_idx)

    return classifiers


def _get_feature_vote(feature, image):
    return feature.get_vote(image)


def _create_features(img_height, img_width, min_feature_width, max_feature_width, min_feature_height, max_feature_height):
    print('Creating haar-like features..')
    features = []
    for feature in FeatureTypes:
        # FeatureTypes are just tuples
        feature_start_width = max(min_feature_width, feature[0])
        for feature_width in range(feature_start_width, max_feature_width, feature[0]):
            feature_start_height = max(min_feature_height, feature[1])
            for feature_height in range(feature_start_height, max_feature_height, feature[1]):
                for x in range(img_width - feature_width):
                    for y in range(img_height - feature_height):
                        features.append(HaarLikeFeature(feature, (x, y), feature_width, feature_height, 0, 1))
                        features.append(HaarLikeFeature(feature, (x, y), feature_width, feature_height, 0, -1))
    print('..done. ' + str(len(features)) + ' features created.\n')
    return features

from functools import partial
import numpy as np
from violajones.HaarLikeFeature import HaarLikeFeature
from violajones.HaarLikeFeature import FeatureTypes
import progressbar
from multiprocessing import Pool

LOADING_BAR_LENGTH = 50


# TODO: select optimal threshold for each feature
# TODO: attentional cascading

def learn(positive_iis, negative_iis, num_classifiers=-1, min_feature_width=1, max_feature_width=-1, min_feature_height=1, max_feature_height=-1):
    """
    Selects a set of classifiers. Iteratively takes the best classifiers based
    on a weighted error.
    :param positive_iis: List of positive integral image examples
    :type positive_iis: list[numpy.ndarray]
    :param negative_iis: List of negative integral image examples
    :type negative_iis: list[numpy.ndarray]
    :param num_classifiers: Number of classifiers to select, -1 will use all
    classifiers
    :type num_classifiers: int

    :return: List of selected features
    :rtype: list[violajones.HaarLikeFeature.HaarLikeFeature]
    """
    num_pos = len(positive_iis)
    num_neg = len(negative_iis)
    num_imgs = num_pos + num_neg
    img_height, img_width = positive_iis[0].shape

    # Maximum feature width and height default to image width and height
    max_feature_height = img_height if max_feature_height == -1 else max_feature_height
    max_feature_width = img_width if max_feature_width == -1 else max_feature_width

    # Create initial weights and labels
    pos_weights = np.ones(num_pos) * 1. / (2 * num_pos)
    neg_weights = np.ones(num_neg) * 1. / (2 * num_neg)
    weights = np.hstack((pos_weights, neg_weights))
    labels = np.hstack((np.ones(num_pos), np.ones(num_neg) * -1))

    images = positive_iis + negative_iis

    # Create features for all sizes and locations
    features = _create_features(img_height, img_width, min_feature_width, max_feature_width, min_feature_height, max_feature_height)
    num_features = len(features)
    feature_indexes = list(range(num_features))

    num_classifiers = num_features if num_classifiers == -1 else num_classifiers

    print('Calculating scores for images..')

    votes = np.zeros((num_imgs, num_features))
    bar = progressbar.ProgressBar()
    # Use as many workers as there are CPUs
    pool = Pool(processes=None)
    for i in bar(range(num_imgs)):
        votes[i, :] = np.array(list(pool.map(partial(_get_feature_vote, image=images[i]), features)))

    # select classifiers

    classifiers = []

    print('Selecting classifiers..')
    bar = progressbar.ProgressBar()
    for _ in bar(range(num_classifiers)):

        classification_errors = np.zeros(len(feature_indexes))

        # normalize weights
        weights *= 1. / np.sum(weights)

        # select best classifier based on the weighted error
        for f in range(len(feature_indexes)):
            f_idx = feature_indexes[f]
            # classifier error is the sum of image weights where the classifier
            # is right
            error = sum(map(lambda img_idx: weights[img_idx] if labels[img_idx] != votes[img_idx, f_idx] else 0, range(num_imgs)))
            classification_errors[f] = error

        # get best feature, i.e. with smallest error
        min_error_idx = np.argmin(classification_errors)
        best_error = classification_errors[min_error_idx]
        best_feature_idx = feature_indexes[min_error_idx]

        # set feature weight
        best_feature = features[best_feature_idx]
        feature_weight = 0.5 * np.log((1 - best_error) / best_error)
        best_feature.weight = feature_weight

        classifiers.append(best_feature)

        # update image weights
        weights = np.array(list(map(lambda img_idx: weights[img_idx] * np.sqrt((1-best_error)/best_error) if labels[img_idx] != votes[img_idx, best_feature_idx] else weights[img_idx] * np.sqrt(best_error/(1-best_error)), range(num_imgs))))

        # remove feature (a feature can't be selected twice)
        feature_indexes.remove(best_feature_idx)

    return classifiers


def _get_feature_vote(feature, image):
    return feature.get_vote(image)


def _create_features(img_height, img_width, min_feature_width, max_feature_width, min_feature_height, max_feature_height):
    print('Creating haar-like features..')
    features = []
    for feature in FeatureTypes:
        # FeatureTypes are just tuples
        feature_start_width = max(min_feature_width, feature[0])
        for feature_width in range(feature_start_width, max_feature_width, feature[0]):
            feature_start_height = max(min_feature_height, feature[1])
            for feature_height in range(feature_start_height, max_feature_height, feature[1]):
                for x in range(img_width - feature_width):
                    for y in range(img_height - feature_height):
                        features.append(HaarLikeFeature(feature, (x, y), feature_width, feature_height, 0, 1))
                        features.append(HaarLikeFeature(feature, (x, y), feature_width, feature_height, 0, -1))
    print('..done. ' + str(len(features)) + ' features created.\n')
    return features

# TODO: select optimal threshold for each feature # TODO: attentional cascading Selects a set of classifiers. Iteratively takes the best classifiers based     on a weighted error.     :param positive_iis: List of positive integral image examples     :type positive_iis: list[numpy.ndarray]     :param negative_iis: List of negative integral image examples     :type negative_iis: list[numpy.ndarray]     :param num_classifiers: Number of classifiers to select, -1 will use all     classifiers     :type num_classifiers: int      :return: List of selected features     :rtype: list[violajones.HaarLikeFeature.HaarLikeFeature] # Maximum feature width and height default to image width and height # Create initial weights and labels # Create features for all sizes and locations # Use as many workers as there are CPUs # select classifiers # normalize weights # select best classifier based on the weighted error # classifier error is the sum of image weights where the classifier # is right # get best feature, i.e. with smallest error # set feature weight # update image weights # remove feature (a feature can't be selected twice) # FeatureTypes are just tuples