opencv_contrib/modules/bgsegm/samples/evaluation.py

import argparse
import cv2 as cv
import glob
import numpy as np
import os
import time


# This tool is intended for evaluation of different background subtraction algorithms presented in OpenCV.
# Several presets with different settings are available. You can see them below.
# This tool measures quality metrics as well as speed.


ALGORITHMS_TO_EVALUATE = [
    (cv.bgsegm.createBackgroundSubtractorMOG, 'MOG', {}),
    (cv.bgsegm.createBackgroundSubtractorGMG, 'GMG', {}),
    (cv.bgsegm.createBackgroundSubtractorCNT, 'CNT', {}),
    (cv.bgsegm.createBackgroundSubtractorLSBP, 'LSBP-vanilla', {'nSamples': 20, 'LSBPRadius': 4, 'Tlower': 2.0, 'Tupper': 200.0, 'Tinc': 1.0, 'Tdec': 0.05, 'Rscale': 5.0, 'Rincdec': 0.05, 'LSBPthreshold': 8}),
    (cv.bgsegm.createBackgroundSubtractorLSBP, 'LSBP-speed', {'nSamples': 10, 'LSBPRadius': 16, 'Tlower': 2.0, 'Tupper': 32.0, 'Tinc': 1.0, 'Tdec': 0.05, 'Rscale': 10.0, 'Rincdec': 0.005, 'LSBPthreshold': 8}),
    (cv.bgsegm.createBackgroundSubtractorLSBP, 'LSBP-quality', {'nSamples': 20, 'LSBPRadius': 16, 'Tlower': 2.0, 'Tupper': 32.0, 'Tinc': 1.0, 'Tdec': 0.05, 'Rscale': 10.0, 'Rincdec': 0.005, 'LSBPthreshold': 8}),
    (cv.bgsegm.createBackgroundSubtractorLSBP, 'LSBP-camera-motion-compensation', {'mc': 1}),
    (cv.bgsegm.createBackgroundSubtractorGSOC, 'GSOC', {}),
    (cv.bgsegm.createBackgroundSubtractorGSOC, 'GSOC-camera-motion-compensation', {'mc': 1})
]


def contains_relevant_files(root):
    return os.path.isdir(os.path.join(root, 'groundtruth')) and os.path.isdir(os.path.join(root, 'input'))


def find_relevant_dirs(root):
    relevant_dirs = []
    for d in sorted(os.listdir(root)):
        d = os.path.join(root, d)
        if os.path.isdir(d):
            if contains_relevant_files(d):
                relevant_dirs += [d]
            else:
                relevant_dirs += find_relevant_dirs(d)
    return relevant_dirs


def load_sequence(root):
    gt_dir, frames_dir = os.path.join(root, 'groundtruth'), os.path.join(root, 'input')
    gt = sorted(glob.glob(os.path.join(gt_dir, '*.png')))
    f = sorted(glob.glob(os.path.join(frames_dir, '*.jpg')))
    assert(len(gt) == len(f))
    return gt, f


def evaluate_algorithm(gt, frames, algo, algo_arguments):
    bgs = algo(**algo_arguments)
    mask = []
    t_start = time.time()

    for i in range(len(gt)):
        frame = np.uint8(cv.imread(frames[i], cv.IMREAD_COLOR))
        mask.append(bgs.apply(frame))

    average_duration = (time.time() - t_start) / len(gt)
    average_precision, average_recall, average_f1, average_accuracy = [], [], [], []

    for i in range(len(gt)):
        gt_mask = np.uint8(cv.imread(gt[i], cv.IMREAD_GRAYSCALE))
        roi = ((gt_mask == 255) | (gt_mask == 0))
        if roi.sum() > 0:
            gt_answer, answer = gt_mask[roi], mask[i][roi]

            tp = ((answer == 255) & (gt_answer == 255)).sum()
            tn = ((answer == 0) & (gt_answer == 0)).sum()
            fp = ((answer == 255) & (gt_answer == 0)).sum()
            fn = ((answer == 0) & (gt_answer == 255)).sum()

            if tp + fp > 0:
                average_precision.append(float(tp) / (tp + fp))
            if tp + fn > 0:
                average_recall.append(float(tp) / (tp + fn))
            if tp + fn + fp > 0:
                average_f1.append(2.0 * tp / (2.0 * tp + fn + fp))
            average_accuracy.append(float(tp + tn) / (tp + tn + fp + fn))

    return average_duration, np.mean(average_precision), np.mean(average_recall), np.mean(average_f1), np.mean(average_accuracy)


def evaluate_on_sequence(seq, summary):
    gt, frames = load_sequence(seq)
    category, video_name = os.path.basename(os.path.dirname(seq)), os.path.basename(seq)
    print('=== %s:%s ===' % (category, video_name))

    for algo, algo_name, algo_arguments in ALGORITHMS_TO_EVALUATE:
        print('Algorithm name: %s' % algo_name)
        sec_per_step, precision, recall, f1, accuracy = evaluate_algorithm(gt, frames, algo, algo_arguments)
        print('Average accuracy: %.3f' % accuracy)
        print('Average precision: %.3f' % precision)
        print('Average recall: %.3f' % recall)
        print('Average F1: %.3f' % f1)
        print('Average sec. per step: %.4f' % sec_per_step)
        print('')

        if category not in summary:
            summary[category] = {}
        if algo_name not in summary[category]:
            summary[category][algo_name] = []
        summary[category][algo_name].append((precision, recall, f1, accuracy))


def main():
    parser = argparse.ArgumentParser(description='Evaluate all background subtractors using Change Detection 2014 dataset')
    parser.add_argument('--dataset_path', help='Path to the directory with dataset. It may contain multiple inner directories. It will be scanned recursively.', required=True)
    parser.add_argument('--algorithm', help='Test particular algorithm instead of all.')

    args = parser.parse_args()
    dataset_dirs = find_relevant_dirs(args.dataset_path)
    assert len(dataset_dirs) > 0, ("Passed directory must contain at least one sequence from the Change Detection dataset. There is no relevant directories in %s. Check that this directory is correct." % (args.dataset_path))
    if args.algorithm is not None:
        global ALGORITHMS_TO_EVALUATE
        ALGORITHMS_TO_EVALUATE = filter(lambda a: a[1].lower() == args.algorithm.lower(), ALGORITHMS_TO_EVALUATE)
    summary = {}

    for seq in dataset_dirs:
        evaluate_on_sequence(seq, summary)

    for category in summary:
        for algo_name in summary[category]:
            summary[category][algo_name] = np.mean(summary[category][algo_name], axis=0)

    for category in summary:
        print('=== SUMMARY for %s (Precision, Recall, F1, Accuracy) ===' % category)
        for algo_name in summary[category]:
            print('%05s: %.3f %.3f %.3f %.3f' % ((algo_name,) + tuple(summary[category][algo_name])))


if __name__ == '__main__':
    main()