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tarlen5/calculate_mean_ap.py

Last active Jul 26, 2021
Calculate mean Average Precision (mAP) for a set of ground truth and predicted bounding boxes for a set of images.
 """ 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()
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piojanu commented Apr 5, 2018

 Hi! First of all thanks for the code and blog post on Medium :) I think you have a bug in your code. In line 123, shouldn't you count all predicted boxes that have IoU below threshold as false positives? So line 123 would be `fp = len(pred_boxes)`.

Serge3006 commented Apr 8, 2018 • edited

 @piojanu I also think so, if the iou is below the threshold the predicted boxes must be labeled as false positives, so the line 123 would be what you mention. By the way, thanks for your code men, I had some doubts about some details of this topic, but now everything is clear.

tanndx17 commented Apr 13, 2018

 Can this code calculate AP for each class in multiple class of object detection? Looks like in the example json this is only one kind of object

paragon00 commented May 4, 2018 • edited

 thanks for this code!! its the clearest complete mAP calc I could find

tarlen5 commented May 5, 2018 • edited

 @piojanu Yes, you're absolutely correct-great catch! That is a bug-I'll fix it in the next version of the code. @tanndx17 Yes, a simple solution is to just group the gt_boxes and pred_boxes by class_id and loop over these class_ids. You'd have to extend the code a little to do this. @paragon00 you're welcome!

omzoughi commented May 13, 2018

 Thanks for this code Could you explain how did you get the confidence score. If I take An example, I want to detect faces, I will get a set of Bounding boxes (for a given value of threshod for iou), then do i have not to use a threshold for my confidence (e.g, confidence > 0.8) , I have to use all remaining to find the precision recall curve, that's right?

tarlen5 commented May 15, 2018

 hi @omzoughi yes, that's correct, you will need your detector to output all instances that it detects at any confidence score, so essentially just make sure your model has the lowest possible confidence score (threshold of 0.0 or 0.01). Then output the detection boxes and the confidence score, as I have shown in my data file examples here. That's what's needed to create the precision/recall curve.

skyler54 commented Aug 27, 2018

 Hi, Thanks for the Code. I'm assuming that in your example each image has two objects and its ground truth bounding box is there in the `ground_truth_boxes.json` file and in the `predicted_boxes.json` holds the predicted bounding boxes and their score and the IoU is a very less value 0.xx. Let me know if i have understood it right ?

skyler54 commented Aug 28, 2018

 I'm in need of HELP!!! The above source code breaks if any of the image in the `ground_truth_boxes.json` is missing in the `predicted_boxes.json`. In the line no : 227 `box_scores = pred_boxes_pruned[img_id]['scores']`

 @skyler54 you are right, this code assumes you have at least one detection per each image, while if there is no pred_box for an img_id, it will fail. I have added couple of if condition and also calculate the tp/fp/fn for those cases separately, so it won't fail. I can share those lines if needed.

freelist commented Sep 9, 2018

 I think @skyler54 suggestion is right, it may happens that the model doesn't predict any bbox for that object in that image.. I think that those lines are needed.

 Couple of people asked for the change in code, Here is the code with support for cases with no object detected by the model (for a given image with gt bboxes): https://gist.github.com/sadjadasghari/dc066e3fb2e70162fbb838d4acb93ffc @skyler54 and @freelist, let me know if that helps. Also, @tarlen5, it might be better to check this in to your code as well. Best.

dmitrysarov commented Sep 11, 2018 • edited

 Hello. Thanx for your code. Have one question. From line 231 you loop over model score thresholds, and picking images indices only under certain score (after first loop where ithr == 0 condition fire), rather then picking all images above that threshold, is it correct? `````` 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] ``````

frankdeepl commented Sep 12, 2018

 Hi guys, what is ground true boxes mean? how can I get it?

 @dmitrysarov Yes, you are right. After first loop, it picks up img_ids from model_scores_map which is a dictionary with scores as keys and values as IDs of images including bounding box(es) with that specific score. @dongsheng20170812 you need coordinates of the bounding boxes for your input images, labeled by human!

beric7 commented Oct 18, 2018

 Can this code calculate AP for each class in multiple class of object detection? Looks like in the example json this is only one kind of object It looks like only 1 object... :(

MarynaLongnickel commented Feb 2, 2019

 what's happening on line 240?

nivintw commented Feb 27, 2019

 I think there is a bug on line 239... The way it is currently implemented, you will exclude results that are equal to the threshold, which is not what is usually done. This is because you use `<=` instead of just `<`. If I understand correctly, you would only want to exclude scores below the threshold, right? @MarynaLongnickel as far as I can tell, nothing. It's a bug.

szali commented Jul 24, 2019

 Thank you! What is the license of the code?

Naveennavik commented Dec 11, 2019

 how to generate ground_truth_boxes.json and predicted_boxes.json ?

aaroncnb commented Feb 5, 2020

 Do you think this could be easily extended to N-sided polygons? I was thinking it would work as long as the IOU function was adapted for N-sided polygons, and then the rest of the code would work more or less the same.

shreejalt commented May 8, 2020

 Thank you for this code. I had one doubt regarding the calculation. While calculating the PR-Curve, we have to sort the confidence in descending order right? Can you help me find where it is done in the code? Because AP@0.5 is deviating a lot from the AP calculated in Darknet with the same weights and CFG file.

 ``````Traceback (most recent call last): File "mAP_calc_git.py", line 282, in data = get_avg_precision_at_iou(gt_boxes, pred_boxes, iou_thr=iou_thr) File "mAP_calc_git.py", line 187, in get_avg_precision_at_iou model_scores_map = get_model_scores_map(pred_boxes) File "mAP_calc_git.py", line 160, in get_model_scores_map for score in val['scores']: TypeError: list indices must be integers or slices, not str `````` I am getting this error while running. Can you pls help me. I have used the same files as in the above.