average_precision_evaluator.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
 An evaluator to compute the Pascal VOC-style mean average precision on a given dataset.
"""

from __future__ import division

import csv
import os
import sys

import numpy as np
from tqdm import trange

from object_detection.utils.bbox_utils import iou


class Evaluator:
    """
    Computes the mean average precision on the given dataset.

    Optionally also returns the average precisions, precisions, and recalls.
    """

    def __init__(self,
                 n_classes=1,
                 pred_format={'class_id': 0, 'conf': 1, 'xmin': 2, 'ymin': 3, 'xmax': 4, 'ymax': 5},
                 gt_format={'class_id': 0, 'xmin': 1, 'ymin': 2, 'xmax': 3, 'ymax': 4}):
        """
        Arguments:

            n_classes (int): The number of positive classes
            pred_format (dict, optional): A dictionary that defines which index in the last axis of the model's decoded predictions
                contains which bounding box coordinate. The dictionary must map the keywords 'class_id', 'conf' (for the confidence),
                'xmin', 'ymin', 'xmax', and 'ymax' to their respective indices within last axis.
            gt_format (list, optional): A dictionary that defines which index of a ground truth bounding box contains which of the five
                items class ID, xmin, ymin, xmax, ymax. The expected strings are 'xmin', 'ymin', 'xmax', 'ymax', 'class_id'.
        """

        self.n_classes = n_classes
        self.pred_format = pred_format
        self.gt_format = gt_format

        # The following lists all contain per-class data, i.e. all list have the length `n_classes + 1`,
        # where one element is for the background class, i.e. that element is just a dummy entry.
        self.prediction_results = None
        self.num_gt_per_class = None
        self.true_positives = None
        self.false_positives = None
        self.cumulative_true_positives = None
        self.cumulative_false_positives = None
        self.cumulative_precisions = None  # "Cumulative" means that the i-th element in each list represents the precision for the first i highest condidence predictions for that class.
        self.cumulative_recalls = None  # "Cumulative" means that the i-th element in each list represents the recall for the first i highest condidence predictions for that class.
        self.average_precisions = None
        self.mean_average_precision = None

    def __call__(self,
                 round_confidences=False,
                 matching_iou_threshold=0.5,
                 border_pixels='include',
                 sorting_algorithm='quicksort',
                 average_precision_mode='sample',
                 num_recall_points=11,
                 return_precisions=False,
                 return_recalls=False,
                 return_average_precisions=False,
                 verbose=True):
        """
        Computes the mean average precision on the given dataset.

        Optionally also returns the averages precisions, precisions, and recalls.


        Arguments:

            round_confidences (int, optional): `False` or an integer that is the number of decimals that the prediction
                confidences will be rounded to. If `False`, the confidences will not be rounded.
            matching_iou_threshold (float, optional): A prediction will be considered a true positive if it has a Jaccard overlap
                of at least `matching_iou_threshold` with any ground truth bounding box of the same class.
            border_pixels (str, optional): How to treat the border pixels of the bounding boxes.
                Can be 'include', 'exclude', or 'half'. If 'include', the border pixels belong
                to the boxes. If 'exclude', the border pixels do not belong to the boxes.
                If 'half', then one of each of the two horizontal and vertical borders belong
                to the boxex, but not the other.
            sorting_algorithm (str, optional): Which sorting algorithm the matching algorithm should use. This argument accepts
                any valid sorting algorithm for Numpy's `argsort()` function. You will usually want to choose between 'quicksort'
                (fastest and most memory efficient, but not stable) and 'mergesort' (slight slower and less memory efficient, but stable).
                The official Matlab evaluation algorithm uses a stable sorting algorithm, so this algorithm is only guaranteed
                to behave identically if you choose 'mergesort' as the sorting algorithm, but it will almost always behave identically
                even if you choose 'quicksort' (but no guarantees).
            average_precision_mode (str, optional): Can be either 'sample' or 'integrate'. In the case of 'sample', the average precision
                will be computed according to the Pascal VOC formula that was used up until VOC 2009, where the precision will be sampled
                for `num_recall_points` recall values. In the case of 'integrate', the average precision will be computed according to the
                Pascal VOC formula that was used from VOC 2010 onward, where the average precision will be computed by numerically integrating
                over the whole preciscion-recall curve instead of sampling individual points from it. 'integrate' mode is basically just
                the limit case of 'sample' mode as the number of sample points increases.
            num_recall_points (int, optional): The number of points to sample from the precision-recall-curve to compute the average
                precisions. In other words, this is the number of equidistant recall values for which the resulting precision will be
                computed. 11 points is the value used in the official Pascal VOC 2007 detection evaluation algorithm.
            return_precisions (bool, optional): If `True`, returns a nested list containing the cumulative precisions for each class.
            return_recalls (bool, optional): If `True`, returns a nested list containing the cumulative recalls for each class.
            return_average_precisions (bool, optional): If `True`, returns a list containing the average precision for each class.
            verbose (bool, optional): If `True`, will print out the progress during runtime.

        Returns:
            A float, the mean average precision, plus any optional returns specified in the arguments.
        """

        #############################################################################################
        # Predict on the entire dataset.
        #############################################################################################

        self.predict_on_dataset(ret=False)

        #############################################################################################
        # Get the total number of ground truth boxes for each class.
        #############################################################################################

        self.get_num_gt_per_class(ret=False)

        #############################################################################################
        # Match predictions to ground truth boxes for all classes.
        #############################################################################################

        self.match_predictions(matching_iou_threshold=matching_iou_threshold,
                               border_pixels=border_pixels,
                               sorting_algorithm=sorting_algorithm,
                               verbose=verbose,
                               ret=False)

        #############################################################################################
        # Compute the cumulative precision and recall for all classes.
        #############################################################################################

        self.compute_precision_recall(verbose=verbose, ret=False)

        #############################################################################################
        # Compute the average precision for this class.
        #############################################################################################

        self.compute_average_precisions(mode=average_precision_mode,
                                        num_recall_points=num_recall_points,
                                        verbose=verbose,
                                        ret=False)

        #############################################################################################
        # Compute the mean average precision.
        #############################################################################################

        mean_average_precision = self.compute_mean_average_precision(ret=True)

        #############################################################################################

        # Compile the returns.
        if return_precisions or return_recalls or return_average_precisions:
            ret = [mean_average_precision]
            if return_average_precisions:
                ret.append(self.average_precisions)
            if return_precisions:
                ret.append(self.cumulative_precisions)
            if return_recalls:
                ret.append(self.cumulative_recalls)
            return ret
        else:
            return mean_average_precision

    def predict_on_dataset(self, ret=False):

        #############################################################################################
        # Store the predictions from csv file
        #############################################################################################

        # We have to generate a separate results list for each class.
        results = [list() for _ in range(self.n_classes + 1)]

        # Create a dictionary that maps image IDs to ground truth annotations.
        # We'll need it below.
        image_ids_to_labels = {}
        FILE_PATH = os.path.join(os.getcwd(), 'data/polyps/test_predictions/')
        prediction_csv = 'prediction.csv'
        count = 0
        with open(FILE_PATH + prediction_csv, mode='r') as bbox_file:
            bbox_reader = csv.reader(bbox_file, delimiter=',')
            # print(filecsv)
            for row in bbox_reader:
                if count == 0:
                    count += 1
                else:
                    image_id = row[0]
                    class_id = 1
                    confidence = 1
                    xmin = int(row[3])
                    ymin = int(row[4])
                    xmax = int(row[5])
                    ymax = int(row[6])
                    prediction = (image_id, confidence, xmin, ymin, xmax, ymax)
                    # Append the predicted box to the results list for its class.
                    results[class_id].append(prediction)
                    count += 1

        self.prediction_results = results

        if ret:
            return results

    def get_num_gt_per_class(self, ret=False):
        """
        Counts the number of ground truth boxes for each class across the dataset.

        Arguments:
            ret (bool, optional): If `True`, returns the list of counts.

        Returns:
            None by default. Optionally, a list containing a count of the number of ground truth boxes for each class across the
            entire dataset.
        """

        FILE_PATH = os.path.join(os.getcwd(), 'data/polyps/test_predictions/')
        gt_csv = 'ground_truth.csv'
        count = 0
        with open(FILE_PATH + gt_csv, mode='r') as bbox_file:
            bbox_reader = csv.reader(bbox_file, delimiter=',')
            for every in bbox_reader:
                count += 1
        count -= 1
        num_gt_per_class = {1: count, 0: 0}
        self.num_gt_per_class = num_gt_per_class

        if ret:
            return num_gt_per_class

    def match_predictions(self,
                          matching_iou_threshold=0.5,
                          border_pixels='include',
                          sorting_algorithm='quicksort',
                          verbose=True,
                          ret=False):
        """
        Matches predictions to ground truth boxes.

        Note that `predict_on_dataset()` must be called before calling this method.

        Arguments:
            matching_iou_threshold (float, optional): A prediction will be considered a true positive if it has a Jaccard overlap
                of at least `matching_iou_threshold` with any ground truth bounding box of the same class.
            border_pixels (str, optional): How to treat the border pixels of the bounding boxes.
                Can be 'include', 'exclude', or 'half'. If 'include', the border pixels belong
                to the boxes. If 'exclude', the border pixels do not belong to the boxes.
                If 'half', then one of each of the two horizontal and vertical borders belong
                to the boxex, but not the other.
            sorting_algorithm (str, optional): Which sorting algorithm the matching algorithm should use. This argument accepts
                any valid sorting algorithm for Numpy's `argsort()` function. You will usually want to choose between 'quicksort'
                (fastest and most memory efficient, but not stable) and 'mergesort' (slight slower and less memory efficient, but stable).
                The official Matlab evaluation algorithm uses a stable sorting algorithm, so this algorithm is only guaranteed
                to behave identically if you choose 'mergesort' as the sorting algorithm, but it will almost always behave identically
                even if you choose 'quicksort' (but no guarantees).
            verbose (bool, optional): If `True`, will print out the progress during runtime.
            ret (bool, optional): If `True`, returns the true and false positives.

        Returns:
            None by default. Optionally, four nested lists containing the true positives, false positives, cumulative true positives,
            and cumulative false positives for each class.
        """

        xmin_gt = self.gt_format['xmin']
        ymin_gt = self.gt_format['ymin']
        xmax_gt = self.gt_format['xmax']
        ymax_gt = self.gt_format['ymax']

        # Convert the ground truth to a more efficient format for what we need
        # to do, which is access ground truth by image ID repeatedly.
        ground_truth = {}

        FILE_PATH = os.path.join(os.getcwd(), 'data/polyps/test_predictions/')
        gt_csv = 'ground_truth.csv'
        count = 0
        with open(FILE_PATH + gt_csv, mode='r') as bbox_file:
            bbox_reader = csv.reader(bbox_file, delimiter=',')
            for every in bbox_reader:
                if count == 0:
                    count += 1
                else:
                    image_id = every[0]
                    class_id = 1.0
                    xmin = every[3]
                    ymin = every[4]
                    xmax = every[5]
                    ymax = every[6]
                    ground_truth[image_id] = np.asarray(
                        [[class_id, float(xmin), float(ymin), float(xmax), float(ymax)]])

        true_positives = [[]]  # The true positives for each class, sorted by descending confidence.
        false_positives = [[]]  # The false positives for each class, sorted by descending confidence.
        cumulative_true_positives = [[]]
        cumulative_false_positives = [[]]

        # Iterate over all classes.
        for class_id in range(1, self.n_classes + 1):

            predictions = self.prediction_results[class_id]

            # Store the matching results in these lists:
            true_pos = np.zeros(len(predictions),
                                dtype=np.int)  # 1 for every prediction that is a true positive, 0 otherwise
            false_pos = np.zeros(len(predictions),
                                 dtype=np.int)  # 1 for every prediction that is a false positive, 0 otherwise

            # In case there are no predictions at all for this class, we're done here.
            if len(predictions) == 0:
                print("No predictions for class {}/{}".format(class_id, self.n_classes))
                true_positives.append(true_pos)
                false_positives.append(false_pos)
                continue

            # Convert the predictions list for this class into a structured array so that we can sort it by confidence.

            # Get the number of characters needed to store the image ID strings in the structured array.
            num_chars_per_image_id = len(str(
                predictions[0][0])) + 6  # Keep a few characters buffer in case some image IDs are longer than others.
            # Create the data type for the structured array.
            preds_data_type = np.dtype([('image_id', 'U{}'.format(num_chars_per_image_id)),
                                        ('confidence', 'f4'),
                                        ('xmin', 'f4'),
                                        ('ymin', 'f4'),
                                        ('xmax', 'f4'),
                                        ('ymax', 'f4')])
            # Create the structured array
            predictions = np.array(predictions, dtype=preds_data_type)

            # Sort the detections by decreasing confidence.
            descending_indices = np.argsort(-predictions['confidence'], kind=sorting_algorithm)
            predictions_sorted = predictions[descending_indices]

            if verbose:
                tr = trange(len(predictions), file=sys.stdout)
                tr.set_description(
                    "Matching predictions to ground truth, class {}/{}.".format(class_id, self.n_classes))
            else:
                tr = range(len(predictions.shape))

            # Keep track of which ground truth boxes were already matched to a detection.
            gt_matched = {}

            # Iterate over all predictions.
            for i in tr:

                prediction = predictions_sorted[i]
                image_id = prediction['image_id']
                pred_box = np.asarray(list(prediction[['xmin', 'ymin', 'xmax',
                                                       'ymax']]))  # Convert the structured array element to a regular array.

                # Get the relevant ground truth boxes for this prediction,
                # i.e. all ground truth boxes that match the prediction's
                # image ID and class ID.

                # The ground truth could either be a tuple with `(ground_truth_boxes)

                gt = ground_truth[image_id]
                gt = np.asarray(gt)

                if gt.size == 0:
                    # If the image doesn't contain any objects of this class,
                    # the prediction becomes a false positive.
                    false_pos[i] = 1
                    continue

                # Compute the IoU of this prediction with all ground truth boxes of the same class.
                overlaps = iou(boxes1=gt[:, [xmin_gt, ymin_gt, xmax_gt, ymax_gt]],
                               boxes2=pred_box,
                               coords='corners',
                               mode='element-wise',
                               border_pixels=border_pixels)

                # For each detection, match the ground truth box with the highest overlap.
                # It's possible that the same ground truth box will be matched to multiple
                # detections.
                gt_match_index = np.argmax(overlaps)
                gt_match_overlap = overlaps[gt_match_index]

                if gt_match_overlap < matching_iou_threshold:
                    # False positive, IoU threshold violated:
                    # Those predictions whose matched overlap is below the threshold become
                    # false positives.
                    false_pos[i] = 1
                else:
                    # if not (ignore_neutral_boxes and eval_neutral_available) or (eval_neutral[gt_match_index] == False):
                    # If this is not a ground truth that is supposed to be evaluation-neutral
                    # (i.e. should be skipped for the evaluation) or if we don't even have the
                    # concept of neutral boxes.
                    if not (image_id in gt_matched):
                        # True positive:
                        # If the matched ground truth box for this prediction hasn't been matched to a
                        # different prediction already, we have a true positive.
                        true_pos[i] = 1
                        gt_matched[image_id] = np.zeros(shape=(gt.shape[0]), dtype=np.bool)
                        gt_matched[image_id][gt_match_index] = True
                    elif not gt_matched[image_id][gt_match_index]:
                        # True positive:
                        # If the matched ground truth box for this prediction hasn't been matched to a
                        # different prediction already, we have a true positive.
                        true_pos[i] = 1
                        gt_matched[image_id][gt_match_index] = True
                    else:
                        # False positive, duplicate detection:
                        # If the matched ground truth box for this prediction has already been matched
                        # to a different prediction previously, it is a duplicate detection for an
                        # already detected object, which counts as a false positive.
                        false_pos[i] = 1

            true_positives.append(true_pos)
            false_positives.append(false_pos)

            cumulative_true_pos = np.cumsum(true_pos)  # Cumulative sums of the true positives
            cumulative_false_pos = np.cumsum(false_pos)  # Cumulative sums of the false positives

            cumulative_true_positives.append(cumulative_true_pos)
            cumulative_false_positives.append(cumulative_false_pos)

        self.true_positives = true_positives
        self.false_positives = false_positives
        self.cumulative_true_positives = cumulative_true_positives
        self.cumulative_false_positives = cumulative_false_positives

        FILE_PATH = os.path.join(os.getcwd(), 'data/polyps/test_predictions/')
        gt_csv = 'ground_truth.csv'
        count = 0
        with open(FILE_PATH + gt_csv, mode='r') as bbox_file:
            bbox_reader = csv.reader(bbox_file, delimiter=',')
            for every in bbox_reader:
                count += 1
        count -= 1

        tp_count = np.sum(self.true_positives[1])
        fp_count = np.sum(self.false_positives[1])

        print("Precision %.4f: " % (tp_count / (tp_count + fp_count)))
        print("Recall : %.4f" % (tp_count / (count)))

        if ret:
            return true_positives, false_positives, cumulative_true_positives, cumulative_false_positives

    def compute_precision_recall(self, verbose=True, ret=False):
        """
        Computes the precisions and recalls for all classes.

        Note that `match_predictions()` must be called before calling this method.

        Arguments:
            verbose (bool, optional): If `True`, will print out the progress during runtime.
            ret (bool, optional): If `True`, returns the precisions and recalls.

        Returns:
            None by default. Optionally, two nested lists containing the cumulative precisions and recalls for each class.
        """

        if (self.cumulative_true_positives is None) or (self.cumulative_false_positives is None):
            raise ValueError(
                "True and false positives not available. You must run `match_predictions()` before you call this method.")

        if (self.num_gt_per_class is None):
            raise ValueError(
                "Number of ground truth boxes per class not available. You must run `get_num_gt_per_class()` before you call this method.")

        cumulative_precisions = [[]]
        cumulative_recalls = [[]]

        # Iterate over all classes.
        for class_id in range(1, self.n_classes + 1):

            if verbose:
                print("Computing precisions and recalls, class {}/{}".format(class_id, self.n_classes))

            tp = self.cumulative_true_positives[class_id]
            fp = self.cumulative_false_positives[class_id]

            cumulative_precision = np.where(tp + fp > 0, tp / (tp + fp), 0)  # 1D array with shape `(num_predictions,)`
            cumulative_recall = tp / self.num_gt_per_class[class_id]  # 1D array with shape `(num_predictions,)`

            cumulative_precisions.append(cumulative_precision)
            cumulative_recalls.append(cumulative_recall)

        self.cumulative_precisions = cumulative_precisions
        self.cumulative_recalls = cumulative_recalls

        if ret:
            return cumulative_precisions, cumulative_recalls

    def compute_average_precisions(self, mode='sample', num_recall_points=11, verbose=True, ret=False):
        """
        Computes the average precision for each class.

        Can compute the Pascal-VOC-style average precision in both the pre-2010 (k-point sampling)
        and post-2010 (integration) algorithm versions.

        Note that `compute_precision_recall()` must be called before calling this method.

        Arguments:
            mode (str, optional): Can be either 'sample' or 'integrate'. In the case of 'sample', the average precision will be computed
                according to the Pascal VOC formula that was used up until VOC 2009, where the precision will be sampled for `num_recall_points`
                recall values. In the case of 'integrate', the average precision will be computed according to the Pascal VOC formula that
                was used from VOC 2010 onward, where the average precision will be computed by numerically integrating over the whole
                preciscion-recall curve instead of sampling individual points from it. 'integrate' mode is basically just the limit case
                of 'sample' mode as the number of sample points increases. For details, see the references below.
            num_recall_points (int, optional): Only relevant if mode is 'sample'. The number of points to sample from the precision-recall-curve
                to compute the average precisions. In other words, this is the number of equidistant recall values for which the resulting
                precision will be computed. 11 points is the value used in the official Pascal VOC pre-2010 detection evaluation algorithm.
            verbose (bool, optional): If `True`, will print out the progress during runtime.
            ret (bool, optional): If `True`, returns the average precisions.

        Returns:
            None by default. Optionally, a list containing average precision for each class.

        References:
            http://host.robots.ox.ac.uk/pascal/VOC/voc2012/htmldoc/devkit_doc.html#sec:ap
        """

        if (self.cumulative_precisions is None) or (self.cumulative_recalls is None):
            raise ValueError(
                "Precisions and recalls not available. You must run `compute_precision_recall()` before you call this method.")

        if not (mode in {'sample', 'integrate'}):
            raise ValueError("`mode` can be either 'sample' or 'integrate', but received '{}'".format(mode))

        average_precisions = [0.0]

        # Iterate over all classes.
        for class_id in range(1, self.n_classes + 1):

            if verbose:
                print("Computing average precision, class {}/{}".format(class_id, self.n_classes))

            cumulative_precision = self.cumulative_precisions[class_id]
            cumulative_recall = self.cumulative_recalls[class_id]
            average_precision = 0.0

            if mode == 'sample':

                for t in np.linspace(start=0, stop=1, num=num_recall_points, endpoint=True):

                    cum_prec_recall_greater_t = cumulative_precision[cumulative_recall >= t]

                    if cum_prec_recall_greater_t.size == 0:
                        precision = 0.0
                    else:
                        precision = np.amax(cum_prec_recall_greater_t)

                    average_precision += precision

                average_precision /= num_recall_points

            elif mode == 'integrate':

                # We will compute the precision at all unique recall values.
                unique_recalls, unique_recall_indices, unique_recall_counts = np.unique(cumulative_recall,
                                                                                        return_index=True,
                                                                                        return_counts=True)

                # Store the maximal precision for each recall value and the absolute difference
                # between any two unique recal values in the lists below. The products of these
                # two nummbers constitute the rectangular areas whose sum will be our numerical
                # integral.
                maximal_precisions = np.zeros_like(unique_recalls)
                recall_deltas = np.zeros_like(unique_recalls)

                # Iterate over all unique recall values in reverse order. This saves a lot of computation:
                # For each unique recall value `r`, we want to get the maximal precision value obtained
                # for any recall value `r* >= r`. Once we know the maximal precision for the last `k` recall
                # values after a given iteration, then in the next iteration, in order compute the maximal
                # precisions for the last `l > k` recall values, we only need to compute the maximal precision
                # for `l - k` recall values and then take the maximum between that and the previously computed
                # maximum instead of computing the maximum over all `l` values.
                # We skip the very last recall value, since the precision after between the last recall value
                # recall 1.0 is defined to be zero.
                for i in range(len(unique_recalls) - 2, -1, -1):
                    begin = unique_recall_indices[i]
                    end = unique_recall_indices[i + 1]
                    # When computing the maximal precisions, use the maximum of the previous iteration to
                    # avoid unnecessary repeated computation over the same precision values.
                    # The maximal precisions are the heights of the rectangle areas of our integral under
                    # the precision-recall curve.
                    maximal_precisions[i] = np.maximum(np.amax(cumulative_precision[begin:end]),
                                                       maximal_precisions[i + 1])
                    # The differences between two adjacent recall values are the widths of our rectangle areas.
                    recall_deltas[i] = unique_recalls[i + 1] - unique_recalls[i]

                average_precision = np.sum(maximal_precisions * recall_deltas)

            average_precisions.append(average_precision)

        self.average_precisions = average_precisions

        if ret:
            return average_precisions

    def compute_mean_average_precision(self, ret=True):
        """
        Computes the mean average precision over all classes.

        Note that `compute_average_precisions()` must be called before calling this method.

        Arguments:
            ret (bool, optional): If `True`, returns the mean average precision.

        Returns:
            A float, the mean average precision, by default. Optionally, None.
        """

        if self.average_precisions is None:
            raise ValueError(
                "Average precisions not available. You must run `compute_average_precisions()` before you call this method.")

        mean_average_precision = np.average(
            self.average_precisions[1:])  # The first element is for the background class, so skip it.
        self.mean_average_precision = mean_average_precision

        if ret:
            return mean_average_precision


if __name__ == '__main__':
    eval = Evaluator()
    results = eval()
    print(results)