tarlen5 · November 6, 2024 19:45 · dmitrysarov · Sep 11, 2018 · frankdeepl · Sep 12, 2018
diff --git a/calculate_mean_ap.py b/calculate_mean_ap.py
 """
 author: Timothy C. Arlen
 date: 28 Feb 2018

 Calculate Mean Average Precision (mAP) for a set of bounding boxes corresponding to specific
 image Ids. Usage:

 > python calculate_mean_ap.py

 Will display a plot of precision vs recall curves at 10 distinct IoU thresholds as well as output
 summary information regarding the average precision and mAP scores.

 NOTE: Requires the files `ground_truth_boxes.json` and `predicted_boxes.json` which can be
 downloaded fromt this gist.
 """

 from __future__ import absolute_import, division, print_function

 from copy import deepcopy
 import json
 import glob
 import os
 import time

 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import seaborn as sns

 sns.set_style('white')
 sns.set_context('poster')

 COLORS = [
    '#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c',
    '#98df8a', '#d62728', '#ff9896', '#9467bd', '#c5b0d5',
    '#8c564b', '#c49c94', '#e377c2', '#f7b6d2', '#7f7f7f',
    '#c7c7c7', '#bcbd22', '#dbdb8d', '#17becf', '#9edae5']


 def calc_iou_individual(pred_box, gt_box):
    """Calculate IoU of single predicted and ground truth box

    Args:
        pred_box (list of floats): location of predicted object as
            [xmin, ymin, xmax, ymax]
        gt_box (list of floats): location of ground truth object as
            [xmin, ymin, xmax, ymax]

    Returns:
        float: value of the IoU for the two boxes.

    Raises:
        AssertionError: if the box is obviously malformed
    """
    x1_t, y1_t, x2_t, y2_t = gt_box
    x1_p, y1_p, x2_p, y2_p = pred_box

    if (x1_p > x2_p) or (y1_p > y2_p):
        raise AssertionError(
            "Prediction box is malformed? pred box: {}".format(pred_box))
    if (x1_t > x2_t) or (y1_t > y2_t):
        raise AssertionError(
            "Ground Truth box is malformed? true box: {}".format(gt_box))

    if (x2_t < x1_p or x2_p < x1_t or y2_t < y1_p or y2_p < y1_t):
        return 0.0

    far_x = np.min([x2_t, x2_p])
    near_x = np.max([x1_t, x1_p])
    far_y = np.min([y2_t, y2_p])
    near_y = np.max([y1_t, y1_p])

    inter_area = (far_x - near_x + 1) * (far_y - near_y + 1)
    true_box_area = (x2_t - x1_t + 1) * (y2_t - y1_t + 1)
    pred_box_area = (x2_p - x1_p + 1) * (y2_p - y1_p + 1)
    iou = inter_area / (true_box_area + pred_box_area - inter_area)
    return iou


 def get_single_image_results(gt_boxes, pred_boxes, iou_thr):
    """Calculates number of true_pos, false_pos, false_neg from single batch of boxes.

    Args:
        gt_boxes (list of list of floats): list of locations of ground truth
            objects as [xmin, ymin, xmax, ymax]
        pred_boxes (dict): dict of dicts of 'boxes' (formatted like `gt_boxes`)
            and 'scores'
        iou_thr (float): value of IoU to consider as threshold for a
            true prediction.

    Returns:
        dict: true positives (int), false positives (int), false negatives (int)
    """

    all_pred_indices = range(len(pred_boxes))
    all_gt_indices = range(len(gt_boxes))
    if len(all_pred_indices) == 0:
        tp = 0
        fp = 0
        fn = len(gt_boxes)
        return {'true_pos': tp, 'false_pos': fp, 'false_neg': fn}
    if len(all_gt_indices) == 0:
        tp = 0
        fp = len(pred_boxes)
        fn = 0
        return {'true_pos': tp, 'false_pos': fp, 'false_neg': fn}

    gt_idx_thr = []
    pred_idx_thr = []
    ious = []
    for ipb, pred_box in enumerate(pred_boxes):
        for igb, gt_box in enumerate(gt_boxes):
            iou = calc_iou_individual(pred_box, gt_box)
            if iou > iou_thr:
                gt_idx_thr.append(igb)
                pred_idx_thr.append(ipb)
                ious.append(iou)

    args_desc = np.argsort(ious)[::-1]
    if len(args_desc) == 0:
        # No matches
        tp = 0
        fp = len(pred_boxes)
        fn = len(gt_boxes)
    else:
        gt_match_idx = []
        pred_match_idx = []
        for idx in args_desc:
            gt_idx = gt_idx_thr[idx]
            pr_idx = pred_idx_thr[idx]
            # If the boxes are unmatched, add them to matches
            if (gt_idx not in gt_match_idx) and (pr_idx not in pred_match_idx):
                gt_match_idx.append(gt_idx)
                pred_match_idx.append(pr_idx)
        tp = len(gt_match_idx)
        fp = len(pred_boxes) - len(pred_match_idx)
        fn = len(gt_boxes) - len(gt_match_idx)

    return {'true_pos': tp, 'false_pos': fp, 'false_neg': fn}


 def calc_precision_recall(img_results):
    """Calculates precision and recall from the set of images

    Args:
        img_results (dict): dictionary formatted like:
            {
                'img_id1': {'true_pos': int, 'false_pos': int, 'false_neg': int},
                'img_id2': ...
                ...
            }

    Returns:
        tuple: of floats of (precision, recall)
    """
    true_pos = 0; false_pos = 0; false_neg = 0
    for _, res in img_results.items():
        true_pos += res['true_pos']
        false_pos += res['false_pos']
        false_neg += res['false_neg']

    try:
        precision = true_pos/(true_pos + false_pos)
    except ZeroDivisionError:
        precision = 0.0
    try:
        recall = true_pos/(true_pos + false_neg)
    except ZeroDivisionError:
        recall = 0.0

    return (precision, recall)

 def get_model_scores_map(pred_boxes):
    """Creates a dictionary of from model_scores to image ids.

    Args:
        pred_boxes (dict): dict of dicts of 'boxes' and 'scores'

    Returns:
        dict: keys are model_scores and values are image ids (usually filenames)

    """
    model_scores_map = {}
    for img_id, val in pred_boxes.items():
        for score in val['scores']:
            if score not in model_scores_map.keys():
                model_scores_map[score] = [img_id]
            else:
                model_scores_map[score].append(img_id)
    return model_scores_map

 def get_avg_precision_at_iou(gt_boxes, pred_boxes, iou_thr=0.5):
    """Calculates average precision at given IoU threshold.

    Args:
        gt_boxes (list of list of floats): list of locations of ground truth
            objects as [xmin, ymin, xmax, ymax]
        pred_boxes (list of list of floats): list of locations of predicted
            objects as [xmin, ymin, xmax, ymax]
        iou_thr (float): value of IoU to consider as threshold for a
            true prediction.

    Returns:
        dict: avg precision as well as summary info about the PR curve

        Keys:
            'avg_prec' (float): average precision for this IoU threshold
            'precisions' (list of floats): precision value for the given
                model_threshold
            'recall' (list of floats): recall value for given
                model_threshold
            'models_thrs' (list of floats): model threshold value that
                precision and recall were computed for.
    """
    model_scores_map = get_model_scores_map(pred_boxes)
    sorted_model_scores = sorted(model_scores_map.keys())

    # Sort the predicted boxes in descending order (lowest scoring boxes first):
    for img_id in pred_boxes.keys():
        arg_sort = np.argsort(pred_boxes[img_id]['scores'])
        pred_boxes[img_id]['scores'] = np.array(pred_boxes[img_id]['scores'])[arg_sort].tolist()
        pred_boxes[img_id]['boxes'] = np.array(pred_boxes[img_id]['boxes'])[arg_sort].tolist()

    pred_boxes_pruned = deepcopy(pred_boxes)

    precisions = []
    recalls = []
    model_thrs = []
    img_results = {}
    # Loop over model score thresholds and calculate precision, recall
    for ithr, model_score_thr in enumerate(sorted_model_scores[:-1]):
        # On first iteration, define img_results for the first time:
        img_ids = gt_boxes.keys() if ithr == 0 else model_scores_map[model_score_thr]
        for img_id in img_ids:
            gt_boxes_img = gt_boxes[img_id]
            box_scores = pred_boxes_pruned[img_id]['scores']
            start_idx = 0
            for score in box_scores:
                if score <= model_score_thr:
                    pred_boxes_pruned[img_id]
                    start_idx += 1
                else:
                    break

            # Remove boxes, scores of lower than threshold scores:
            pred_boxes_pruned[img_id]['scores'] = pred_boxes_pruned[img_id]['scores'][start_idx:]
            pred_boxes_pruned[img_id]['boxes'] = pred_boxes_pruned[img_id]['boxes'][start_idx:]

            # Recalculate image results for this image
            img_results[img_id] = get_single_image_results(
                gt_boxes_img, pred_boxes_pruned[img_id]['boxes'], iou_thr)

        prec, rec = calc_precision_recall(img_results)
        precisions.append(prec)
        recalls.append(rec)
        model_thrs.append(model_score_thr)

    precisions = np.array(precisions)
    recalls = np.array(recalls)
    prec_at_rec = []
    for recall_level in np.linspace(0.0, 1.0, 11):
        try:
            args = np.argwhere(recalls >= recall_level).flatten()
            prec = max(precisions[args])
        except ValueError:
            prec = 0.0
        prec_at_rec.append(prec)
    avg_prec = np.mean(prec_at_rec)

    return {
        'avg_prec': avg_prec,
        'precisions': precisions,
        'recalls': recalls,
        'model_thrs': model_thrs}


 def plot_pr_curve(
    precisions, recalls, category='Person', label=None, color=None, ax=None):
    """Simple plotting helper function"""

    if ax is None:
        plt.figure(figsize=(10,8))
        ax = plt.gca()

    if color is None:
        color = COLORS[0]
    ax.scatter(recalls, precisions, label=label, s=20, color=color)
    ax.set_xlabel('recall')
    ax.set_ylabel('precision')
    ax.set_title('Precision-Recall curve for {}'.format(category))
    ax.set_xlim([0.0,1.3])
    ax.set_ylim([0.0,1.2])
    return ax


 if __name__ == "__main__":

    with open('ground_truth_boxes.json') as infile:
        gt_boxes = json.load(infile)

    with open('predicted_boxes.json') as infile:
        pred_boxes = json.load(infile)

    # Runs it for one IoU threshold
    iou_thr = 0.7
    start_time = time.time()
    data = get_avg_precision_at_iou(gt_boxes, pred_boxes, iou_thr=iou_thr)
    end_time = time.time()
    print('Single IoU calculation took {:.4f} secs'.format(end_time - start_time))
    print('avg precision: {:.4f}'.format(data['avg_prec']))

    start_time = time.time()
    ax = None
    avg_precs = []
    iou_thrs = []
    for idx, iou_thr in enumerate(np.linspace(0.5, 0.95, 10)):
        data = get_avg_precision_at_iou(gt_boxes, pred_boxes, iou_thr=iou_thr)
        avg_precs.append(data['avg_prec'])
        iou_thrs.append(iou_thr)

        precisions = data['precisions']
        recalls = data['recalls']
        ax = plot_pr_curve(
            precisions, recalls, label='{:.2f}'.format(iou_thr), color=COLORS[idx*2], ax=ax)

    # prettify for printing:
    avg_precs = [float('{:.4f}'.format(ap)) for ap in avg_precs]
    iou_thrs = [float('{:.4f}'.format(thr)) for thr in iou_thrs]
    print('map: {:.2f}'.format(100*np.mean(avg_precs)))
    print('avg precs: ', avg_precs)
    print('iou_thrs:  ', iou_thrs)
    plt.legend(loc='upper right', title='IOU Thr', frameon=True)
    for xval in np.linspace(0.0, 1.0, 11):
        plt.vlines(xval, 0.0, 1.1, color='gray', alpha=0.3, linestyles='dashed')
    end_time = time.time()
    print('\nPlotting and calculating mAP takes {:.4f} secs'.format(end_time - start_time))
    plt.show()
diff --git a/ground_truth_boxes.json b/ground_truth_boxes.json
 {"img_00285.png": [[480, 457, 515, 529], [637, 435, 676, 536]], "img_00225.png": [[522, 540, 576, 660], [739, 460, 768, 545]], "img_00224.png": [[527, 539, 575, 660], [747, 459, 769, 546]], "img_00209.png": [[757, 472, 785, 567], [498, 562, 575, 687]], "img_00422.png": [[387, 588, 461, 725], [691, 500, 745, 601]], "img_00327.png": [[542, 433, 580, 508], [668, 454, 702, 537]], "img_00276.png": [[467, 465, 498, 541], [646, 411, 685, 519]], "img_00238.png": [[513, 520, 570, 627], [722, 452, 753, 534]], "img_00136.png": [[568, 700, 648, 853], [801, 722, 867, 936]], "img_00412.png": [[393, 579, 463, 711], [709, 482, 743, 577]], "img_00114.png": [[547, 818, 653, 1029], [827, 925, 912, 1164]], "img_00178.png": [[548, 591, 616, 720], [768, 533, 810, 646]], "img_00164.png": [[770, 569, 834, 718], [564, 613, 624, 754]], "img_00185.png": [[534, 587, 595, 707], [769, 513, 806, 634]], "img_00402.png": [[405, 564, 464, 697], [711, 472, 737, 558]], "img_00428.png": [[369, 525, 456, 704], [676, 503, 718, 603]], "img_00144.png": [[795, 684, 855, 858], [578, 673, 650, 841]], "img_00374.png": [[421, 488, 465, 570], [665, 455, 702, 524]], "img_00248.png": [[492, 507, 553, 598], [700, 455, 737, 525]], "img_00407.png": [[404, 580, 468, 711], [709, 479, 741, 562]], "img_00244.png": [[704, 453, 752, 535], [502, 512, 564, 614]], "img_00337.png": [[648, 451, 688, 537], [536, 436, 580, 510]], "img_00336.png": [[543, 435, 577, 508], [653, 453, 687, 535]], "img_00124.png": [[556, 766, 652, 937], [791, 846, 900, 1063]], "img_00371.png": [[431, 480, 475, 568], [658, 450, 699, 522]], "img_00162.png": [[564, 617, 631, 752], [773, 581, 844, 721]], "img_00163.png": [[565, 617, 626, 755], [772, 573, 839, 720]], "img_00357.png": [[494, 458, 534, 526], [643, 447, 673, 528]], "img_00405.png": [[404, 577, 473, 712], [708, 477, 738, 566]], "img_00189.png": [[764, 511, 805, 619], [527, 583, 585, 702]], "img_00305.png": [[673, 451, 707, 533], [515, 435, 554, 510]], "img_00381.png": [[404, 502, 455, 602], [683, 456, 714, 524]], "img_00175.png": [[768, 538, 814, 670], [557, 593, 618, 722]], "img_00151.png": [[784, 644, 841, 814], [578, 649, 650, 798]], "img_00372.png": [[428, 483, 471, 568], [662, 453, 699, 524]], "img_00358.png": [[490, 463, 522, 524], [644, 449, 675, 528]], "img_00382.png": [[402, 510, 453, 602], [688, 460, 721, 532]], "img_00204.png": [[499, 537, 574, 687], [758, 478, 794, 583]], "img_00315.png": [[527, 420, 563, 506], [676, 454, 715, 538]], "img_00314.png": [[673, 454, 713, 538], [527, 430, 563, 511]], "img_00230.png": [[508, 535, 572, 654], [736, 456, 765, 543]], "img_00280.png": [[466, 457, 504, 534], [643, 415, 675, 521]], "img_00257.png": [[686, 454, 720, 529], [460, 492, 526, 581]], "img_00344.png": [[651, 449, 678, 531], [524, 446, 578, 512]], "img_00239.png": [[514, 524, 572, 628], [723, 452, 750, 534]], "img_00356.png": [[496, 457, 539, 528], [646, 446, 672, 531]], "img_00113.png": [[545, 823, 657, 1031], [827, 921, 920, 1156]], "img_00331.png": [[545, 434, 580, 507], [665, 452, 687, 539]], "img_00245.png": [[501, 512, 559, 609], [709, 455, 747, 531]], "img_00200.png": [[760, 485, 795, 587], [500, 524, 576, 685]], "img_00269.png": [[658, 452, 691, 529], [463, 474, 499, 562]], "img_00192.png": [[525, 586, 586, 703], [765, 500, 802, 611]], "img_00172.png": [[562, 598, 618, 726], [769, 549, 815, 678]], "img_00306.png": [[517, 436, 560, 510], [679, 451, 708, 532]], "img_00430.png": [[668, 503, 709, 607], [368, 531, 454, 700]], "img_00316.png": [[679, 454, 715, 540], [529, 414, 564, 504]], "img_00229.png": [[736, 456, 765, 542], [511, 536, 572, 665]], "img_00326.png": [[670, 453, 702, 538], [541, 435, 578, 506]], "img_00134.png": [[802, 743, 875, 950], [567, 709, 653, 877]], "img_00429.png": [[671, 503, 712, 606], [370, 525, 451, 702]], "img_00243.png": [[706, 451, 749, 535], [506, 515, 565, 623]], "img_00342.png": [[648, 450, 683, 534], [529, 442, 578, 511]], "img_00362.png": [[644, 452, 683, 525], [471, 464, 519, 539]], "img_00266.png": [[663, 461, 692, 532], [457, 475, 503, 563]], "img_00416.png": [[386, 587, 458, 722], [705, 493, 744, 578]], "img_00262.png": [[450, 473, 512, 563], [671, 460, 701, 530]], "img_00369.png": [[659, 450, 700, 525], [438, 473, 479, 562]], "img_00168.png": [[564, 607, 618, 746], [771, 565, 820, 701]], "img_00180.png": [[544, 591, 613, 715], [769, 527, 808, 643]], "img_00205.png": [[501, 540, 576, 687], [756, 480, 790, 578]], "img_00246.png": [[496, 510, 550, 599], [705, 456, 744, 530]], "img_00287.png": [[645, 449, 677, 535], [487, 453, 517, 523]], "img_00434.png": [[658, 509, 693, 613], [368, 538, 457, 701]], "img_00425.png": [[682, 506, 738, 599], [380, 548, 463, 718]], "img_00235.png": [[728, 455, 756, 535], [517, 522, 572, 629]], "img_00328.png": [[670, 453, 698, 539], [545, 433, 581, 506]], "img_00251.png": [[482, 496, 551, 595], [698, 455, 725, 529]], "img_00227.png": [[742, 458, 765, 540], [517, 539, 573, 659]], "img_00406.png": [[708, 478, 744, 560], [403, 579, 463, 713]], "img_00122.png": [[558, 774, 655, 956], [801, 857, 895, 1092]], "img_00202.png": [[502, 533, 574, 684], [759, 482, 790, 584]], "img_00254.png": [[691, 455, 722, 531], [473, 496, 541, 585]], "img_00222.png": [[742, 461, 770, 551], [525, 542, 578, 664]], "img_00166.png": [[566, 610, 625, 752], [772, 568, 825, 713]], "img_00112.png": [[544, 828, 645, 1031], [822, 927, 925, 1150]], "img_00375.png": [[420, 490, 465, 569], [669, 455, 701, 523]], "img_00403.png": [[406, 565, 468, 701], [709, 472, 738, 562]], "img_00195.png": [[513, 554, 587, 700], [760, 493, 799, 606]], "img_00143.png": [[575, 678, 644, 849], [796, 691, 852, 862]], "img_00395.png": [[397, 537, 447, 656], [705, 468, 732, 545]], "img_00146.png": [[578, 670, 648, 829], [796, 671, 849, 850]], "img_00147.png": [[580, 663, 649, 818], [792, 659, 850, 846]], "img_00409.png": [[399, 580, 462, 713], [707, 479, 743, 565]], "img_00237.png": [[723, 451, 753, 534], [516, 521, 571, 629]], "img_00234.png": [[731, 457, 762, 536], [517, 525, 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diff --git a/predicted_boxes.json b/predicted_boxes.json
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458, 384, 483], [421, 432, 454, 486], [658, 484, 681, 532], [420, 432, 449, 461], [654, 454, 683, 535], [542, 435, 578, 506]], "scores": [0.0562, 0.0576, 0.0592, 0.0653, 0.1026, 0.1225, 0.1391, 0.1535, 0.191, 0.1952, 0.2666, 0.2772, 0.3412, 0.9768, 0.9971]}, "img_00207.png": {"boxes": [[356, 456, 388, 508], [417, 335, 497, 470], [656, 534, 735, 677], [360, 458, 384, 485], [530, 567, 568, 682], [502, 559, 570, 688], [757, 481, 785, 573]], "scores": [0.0743, 0.1086, 0.1264, 0.1558, 0.1824, 0.9832, 0.995]}, "img_00325.png": {"boxes": [[353, 457, 392, 522], [343, 456, 409, 550], [675, 475, 691, 532], [384, 418, 469, 501], [438, 321, 512, 457], [360, 458, 385, 483], [420, 432, 459, 496], [421, 433, 448, 461], [441, 339, 490, 435], [674, 455, 702, 534], [545, 433, 574, 502]], "scores": [0.0594, 0.0646, 0.0952, 0.1078, 0.113, 0.1189, 0.2751, 0.2892, 0.4707, 0.962, 0.9888]}, "img_00340.png": {"boxes": [[353, 456, 391, 530], [371, 387, 509, 478], [422, 431, 442, 451], [340, 461, 394, 497], [365, 381, 419, 474], [411, 431, 468, 505], [379, 386, 460, 515], [384, 386, 413, 421], [357, 458, 385, 484], [421, 432, 455, 490], [420, 433, 448, 461], [657, 487, 680, 530], [654, 454, 681, 532], [538, 441, 576, 508]], "scores": [0.0516, 0.0589, 0.0744, 0.0796, 0.0999, 0.1231, 0.147, 0.1531, 0.1814, 0.2362, 0.2398, 0.4384, 0.9934, 0.9936]}, "img_00289.png": {"boxes": [[337, 463, 363, 496], [338, 462, 406, 546], [652, 479, 671, 541], [350, 437, 544, 507], [355, 457, 388, 507], [523, 505, 536, 515], [357, 458, 383, 484], [466, 433, 523, 511], [492, 448, 520, 515], [645, 456, 674, 534]], "scores": [0.055, 0.0577, 0.0628, 0.0774, 0.0916, 0.0945, 0.162, 0.2159, 0.8767, 0.9852]}, "img_00202.png": {"boxes": [[649, 536, 736, 658], [763, 494, 782, 582], [446, 387, 488, 442], [421, 433, 460, 499], [422, 380, 501, 500], [423, 433, 449, 459], [379, 393, 484, 498], [360, 458, 383, 483], [1418, 589, 1580, 892], [444, 388, 501, 467], [501, 545, 569, 677], [759, 481, 792, 583]], "scores": [0.0527, 0.0577, 0.058, 0.0643, 0.075, 0.0794, 0.0872, 0.0913, 0.1343, 0.2167, 0.9869, 0.9928]}, "img_00264.png": {"boxes": [[310, 471, 426, 527], [669, 464, 689, 504], [344, 458, 414, 544], [420, 433, 450, 500], [363, 459, 383, 477], [374, 421, 456, 520], [328, 463, 379, 506], [670, 489, 690, 528], [346, 459, 390, 493], [336, 463, 363, 496], [654, 479, 667, 491], [357, 458, 383, 485], [437, 453, 500, 562], [662, 465, 691, 527], [460, 473, 495, 560]], "scores": [0.0541, 0.0598, 0.0685, 0.0759, 0.0781, 0.0815, 0.093, 0.1036, 0.165, 0.1682, 0.2476, 0.3276, 0.4714, 0.9597, 0.9889]}, "img_00115.png": {"boxes": [[361, 458, 384, 483], [1414, 588, 1580, 894], [335, 461, 406, 547], [827, 918, 907, 1163], [552, 821, 643, 1017]], "scores": [0.057, 0.065, 0.0757, 0.9585, 0.9943]}, "img_00253.png": {"boxes": [[330, 464, 387, 505], [1099, 349, 1122, 370], [420, 432, 456, 503], [694, 457, 711, 508], [682, 470, 710, 514], [336, 464, 362, 496], [327, 465, 391, 538], [683, 486, 693, 499], [356, 457, 387, 503], [357, 459, 382, 485], [677, 478, 721, 527], [698, 493, 721, 528], [689, 457, 717, 523], [479, 497, 539, 588]], "scores": [0.0521, 0.0562, 0.0627, 0.0669, 0.0686, 0.0704, 0.0837, 0.0883, 0.1358, 0.1571, 0.1659, 0.1865, 0.6183, 0.9944]}, "img_00215.png": {"boxes": [[361, 458, 384, 483], [660, 537, 731, 656], [752, 468, 775, 560], [525, 558, 574, 685]], "scores": [0.0536, 0.0714, 0.9961, 0.9982]}, "img_00258.png": {"boxes": [[325, 465, 386, 540], [490, 498, 511, 562], [387, 474, 415, 534], [340, 461, 392, 497], [328, 464, 378, 506], [355, 457, 387, 507], [688, 459, 703, 498], [501, 502, 514, 546], [673, 457, 705, 512], [421, 433, 459, 502], [335, 464, 362, 500], [676, 478, 686, 490], [357, 458, 382, 485], [598, 450, 706, 625], [440, 479, 462, 507], [672, 472, 706, 536], [682, 459, 713, 527], [462, 489, 516, 577]], "scores": [0.0552, 0.0579, 0.0602, 0.0705, 0.0709, 0.0813, 0.0893, 0.1104, 0.1253, 0.1429, 0.1431, 0.1705, 0.1759, 0.1964, 0.2364, 0.2968, 0.9415, 0.9927]}, "img_00387.png": {"boxes": [[333, 462, 387, 498], [422, 432, 458, 489], [356, 456, 389, 514], [420, 432, 449, 463], [358, 458, 384, 486], [699, 462, 727, 536], [403, 537, 443, 637]], "scores": [0.0663, 0.0796, 0.0849, 0.0918, 0.1526, 0.9894, 0.9966]}, "img_00142.png": {"boxes": [[356, 456, 391, 520], [357, 458, 384, 485], [622, 694, 646, 798], [575, 683, 637, 845], [795, 700, 849, 861]], "scores": [0.0555, 0.0904, 0.101, 0.9971, 0.9985]}, "img_00268.png": {"boxes": [[355, 490, 367, 510], [447, 435, 500, 552], [386, 470, 417, 531], [421, 434, 455, 498], [332, 464, 401, 531], [331, 463, 386, 505], [336, 463, 362, 496], [669, 460, 684, 512], [348, 459, 390, 492], [357, 458, 383, 485], [654, 450, 686, 526], [454, 476, 491, 559]], "scores": [0.0503, 0.0595, 0.073, 0.0746, 0.0902, 0.0926, 0.1125, 0.1406, 0.1694, 0.2618, 0.9531, 0.9947]}, "img_00182.png": {"boxes": [[927, 531, 950, 582], [338, 459, 409, 546], [652, 531, 732, 674], [354, 457, 390, 508], [361, 459, 385, 485], [767, 520, 804, 634], [538, 591, 604, 703]], "scores": [0.057, 0.0618, 0.0706, 0.0782, 0.1293, 0.9973, 0.998]}, "img_00183.png": {"boxes": [[337, 461, 410, 546], [660, 534, 732, 667], [354, 457, 392, 525], [360, 458, 385, 485], [768, 516, 804, 632], [538, 591, 601, 703]], "scores": [0.0569, 0.0635, 0.0742, 0.108, 0.9977, 0.9978]}}
	"""
	author: Timothy C. Arlen
	date: 28 Feb 2018

	Calculate Mean Average Precision (mAP) for a set of bounding boxes corresponding to specific
	image Ids. Usage:

	> python calculate_mean_ap.py

	Will display a plot of precision vs recall curves at 10 distinct IoU thresholds as well as output
	summary information regarding the average precision and mAP scores.

	NOTE: Requires the files `ground_truth_boxes.json` and `predicted_boxes.json` which can be
	downloaded fromt this gist.
	"""

	from __future__ import absolute_import, division, print_function

	from copy import deepcopy
	import json
	import glob
	import os
	import time

	import matplotlib.pyplot as plt
	import numpy as np
	import pandas as pd
	import seaborn as sns

	sns.set_style('white')
	sns.set_context('poster')

	COLORS = [
	'#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c',
	'#98df8a', '#d62728', '#ff9896', '#9467bd', '#c5b0d5',
	'#8c564b', '#c49c94', '#e377c2', '#f7b6d2', '#7f7f7f',
	'#c7c7c7', '#bcbd22', '#dbdb8d', '#17becf', '#9edae5']


	def calc_iou_individual(pred_box, gt_box):
	"""Calculate IoU of single predicted and ground truth box

	Args:
	pred_box (list of floats): location of predicted object as
	[xmin, ymin, xmax, ymax]
	gt_box (list of floats): location of ground truth object as
	[xmin, ymin, xmax, ymax]

	Returns:
	float: value of the IoU for the two boxes.

	Raises:
	AssertionError: if the box is obviously malformed
	"""
	x1_t, y1_t, x2_t, y2_t = gt_box
	x1_p, y1_p, x2_p, y2_p = pred_box

	if (x1_p > x2_p) or (y1_p > y2_p):
	raise AssertionError(
	"Prediction box is malformed? pred box: {}".format(pred_box))
	if (x1_t > x2_t) or (y1_t > y2_t):
	raise AssertionError(
	"Ground Truth box is malformed? true box: {}".format(gt_box))

	if (x2_t < x1_p or x2_p < x1_t or y2_t < y1_p or y2_p < y1_t):
	return 0.0

	far_x = np.min([x2_t, x2_p])
	near_x = np.max([x1_t, x1_p])
	far_y = np.min([y2_t, y2_p])
	near_y = np.max([y1_t, y1_p])

	inter_area = (far_x - near_x + 1) * (far_y - near_y + 1)
	true_box_area = (x2_t - x1_t + 1) * (y2_t - y1_t + 1)
	pred_box_area = (x2_p - x1_p + 1) * (y2_p - y1_p + 1)
	iou = inter_area / (true_box_area + pred_box_area - inter_area)
	return iou


	def get_single_image_results(gt_boxes, pred_boxes, iou_thr):
	"""Calculates number of true_pos, false_pos, false_neg from single batch of boxes.

	Args:
	gt_boxes (list of list of floats): list of locations of ground truth
	objects as [xmin, ymin, xmax, ymax]
	pred_boxes (dict): dict of dicts of 'boxes' (formatted like `gt_boxes`)
	and 'scores'
	iou_thr (float): value of IoU to consider as threshold for a
	true prediction.

	Returns:
	dict: true positives (int), false positives (int), false negatives (int)
	"""

	all_pred_indices = range(len(pred_boxes))
	all_gt_indices = range(len(gt_boxes))
	if len(all_pred_indices) == 0:
	tp = 0
	fp = 0
	fn = len(gt_boxes)
	return {'true_pos': tp, 'false_pos': fp, 'false_neg': fn}
	if len(all_gt_indices) == 0:
	tp = 0
	fp = len(pred_boxes)
	fn = 0
	return {'true_pos': tp, 'false_pos': fp, 'false_neg': fn}

	gt_idx_thr = []
	pred_idx_thr = []
	ious = []
	for ipb, pred_box in enumerate(pred_boxes):
	for igb, gt_box in enumerate(gt_boxes):
	iou = calc_iou_individual(pred_box, gt_box)
	if iou > iou_thr:
	gt_idx_thr.append(igb)
	pred_idx_thr.append(ipb)
	ious.append(iou)

	args_desc = np.argsort(ious)[::-1]
	if len(args_desc) == 0:
	# No matches
	tp = 0
	fp = len(pred_boxes)
	fn = len(gt_boxes)
	else:
	gt_match_idx = []
	pred_match_idx = []
	for idx in args_desc:
	gt_idx = gt_idx_thr[idx]
	pr_idx = pred_idx_thr[idx]
	# If the boxes are unmatched, add them to matches
	if (gt_idx not in gt_match_idx) and (pr_idx not in pred_match_idx):
	gt_match_idx.append(gt_idx)
	pred_match_idx.append(pr_idx)
	tp = len(gt_match_idx)
	fp = len(pred_boxes) - len(pred_match_idx)
	fn = len(gt_boxes) - len(gt_match_idx)

	return {'true_pos': tp, 'false_pos': fp, 'false_neg': fn}


	def calc_precision_recall(img_results):
	"""Calculates precision and recall from the set of images

	Args:
	img_results (dict): dictionary formatted like:
	{
	'img_id1': {'true_pos': int, 'false_pos': int, 'false_neg': int},
	'img_id2': ...
	...
	}

	Returns:
	tuple: of floats of (precision, recall)
	"""
	true_pos = 0; false_pos = 0; false_neg = 0
	for _, res in img_results.items():
	true_pos += res['true_pos']
	false_pos += res['false_pos']
	false_neg += res['false_neg']

	try:
	precision = true_pos/(true_pos + false_pos)
	except ZeroDivisionError:
	precision = 0.0
	try:
	recall = true_pos/(true_pos + false_neg)
	except ZeroDivisionError:
	recall = 0.0

	return (precision, recall)

	def get_model_scores_map(pred_boxes):
	"""Creates a dictionary of from model_scores to image ids.

	Args:
	pred_boxes (dict): dict of dicts of 'boxes' and 'scores'

	Returns:
	dict: keys are model_scores and values are image ids (usually filenames)

	"""
	model_scores_map = {}
	for img_id, val in pred_boxes.items():
	for score in val['scores']:
	if score not in model_scores_map.keys():
	model_scores_map[score] = [img_id]
	else:
	model_scores_map[score].append(img_id)
	return model_scores_map

	def get_avg_precision_at_iou(gt_boxes, pred_boxes, iou_thr=0.5):
	"""Calculates average precision at given IoU threshold.

	Args:
	gt_boxes (list of list of floats): list of locations of ground truth
	objects as [xmin, ymin, xmax, ymax]
	pred_boxes (list of list of floats): list of locations of predicted
	objects as [xmin, ymin, xmax, ymax]
	iou_thr (float): value of IoU to consider as threshold for a
	true prediction.

	Returns:
	dict: avg precision as well as summary info about the PR curve

	Keys:
	'avg_prec' (float): average precision for this IoU threshold
	'precisions' (list of floats): precision value for the given
	model_threshold
	'recall' (list of floats): recall value for given
	model_threshold
	'models_thrs' (list of floats): model threshold value that
	precision and recall were computed for.
	"""
	model_scores_map = get_model_scores_map(pred_boxes)
	sorted_model_scores = sorted(model_scores_map.keys())

	# Sort the predicted boxes in descending order (lowest scoring boxes first):
	for img_id in pred_boxes.keys():
	arg_sort = np.argsort(pred_boxes[img_id]['scores'])
	pred_boxes[img_id]['scores'] = np.array(pred_boxes[img_id]['scores'])[arg_sort].tolist()
	pred_boxes[img_id]['boxes'] = np.array(pred_boxes[img_id]['boxes'])[arg_sort].tolist()

	pred_boxes_pruned = deepcopy(pred_boxes)

	precisions = []
	recalls = []
	model_thrs = []
	img_results = {}
	# Loop over model score thresholds and calculate precision, recall
	for ithr, model_score_thr in enumerate(sorted_model_scores[:-1]):
	# On first iteration, define img_results for the first time:
	img_ids = gt_boxes.keys() if ithr == 0 else model_scores_map[model_score_thr]
	for img_id in img_ids:
	gt_boxes_img = gt_boxes[img_id]
	box_scores = pred_boxes_pruned[img_id]['scores']
	start_idx = 0
	for score in box_scores:
	if score <= model_score_thr:
	pred_boxes_pruned[img_id]
	start_idx += 1
	else:
	break

	# Remove boxes, scores of lower than threshold scores:
	pred_boxes_pruned[img_id]['scores'] = pred_boxes_pruned[img_id]['scores'][start_idx:]
	pred_boxes_pruned[img_id]['boxes'] = pred_boxes_pruned[img_id]['boxes'][start_idx:]

	# Recalculate image results for this image
	img_results[img_id] = get_single_image_results(
	gt_boxes_img, pred_boxes_pruned[img_id]['boxes'], iou_thr)

	prec, rec = calc_precision_recall(img_results)
	precisions.append(prec)
	recalls.append(rec)
	model_thrs.append(model_score_thr)

	precisions = np.array(precisions)
	recalls = np.array(recalls)
	prec_at_rec = []
	for recall_level in np.linspace(0.0, 1.0, 11):
	try:
	args = np.argwhere(recalls >= recall_level).flatten()
	prec = max(precisions[args])
	except ValueError:
	prec = 0.0
	prec_at_rec.append(prec)
	avg_prec = np.mean(prec_at_rec)

	return {
	'avg_prec': avg_prec,
	'precisions': precisions,
	'recalls': recalls,
	'model_thrs': model_thrs}


	def plot_pr_curve(
	precisions, recalls, category='Person', label=None, color=None, ax=None):
	"""Simple plotting helper function"""

	if ax is None:
	plt.figure(figsize=(10,8))
	ax = plt.gca()

	if color is None:
	color = COLORS[0]
	ax.scatter(recalls, precisions, label=label, s=20, color=color)
	ax.set_xlabel('recall')
	ax.set_ylabel('precision')
	ax.set_title('Precision-Recall curve for {}'.format(category))
	ax.set_xlim([0.0,1.3])
	ax.set_ylim([0.0,1.2])
	return ax


	if __name__ == "__main__":

	with open('ground_truth_boxes.json') as infile:
	gt_boxes = json.load(infile)

	with open('predicted_boxes.json') as infile:
	pred_boxes = json.load(infile)

	# Runs it for one IoU threshold
	iou_thr = 0.7
	start_time = time.time()
	data = get_avg_precision_at_iou(gt_boxes, pred_boxes, iou_thr=iou_thr)
	end_time = time.time()
	print('Single IoU calculation took {:.4f} secs'.format(end_time - start_time))
	print('avg precision: {:.4f}'.format(data['avg_prec']))

	start_time = time.time()
	ax = None
	avg_precs = []
	iou_thrs = []
	for idx, iou_thr in enumerate(np.linspace(0.5, 0.95, 10)):
	data = get_avg_precision_at_iou(gt_boxes, pred_boxes, iou_thr=iou_thr)
	avg_precs.append(data['avg_prec'])
	iou_thrs.append(iou_thr)

	precisions = data['precisions']
	recalls = data['recalls']
	ax = plot_pr_curve(
	precisions, recalls, label='{:.2f}'.format(iou_thr), color=COLORS[idx*2], ax=ax)

	# prettify for printing:
	avg_precs = [float('{:.4f}'.format(ap)) for ap in avg_precs]
	iou_thrs = [float('{:.4f}'.format(thr)) for thr in iou_thrs]
	print('map: {:.2f}'.format(100*np.mean(avg_precs)))
	print('avg precs: ', avg_precs)
	print('iou_thrs: ', iou_thrs)
	plt.legend(loc='upper right', title='IOU Thr', frameon=True)
	for xval in np.linspace(0.0, 1.0, 11):
	plt.vlines(xval, 0.0, 1.1, color='gray', alpha=0.3, linestyles='dashed')
	end_time = time.time()
	print('\nPlotting and calculating mAP takes {:.4f} secs'.format(end_time - start_time))
	plt.show()