createimdb.txt

if __name__ == '__main__':
    dir_list = ["./ViolenceDataset/video/Violence/", "./ViolenceDataset/video/NonViolence/"]
    dir_list_out = ["./ViolenceDataset/image/Violence/", "./ViolenceDataset/image/NonViolence/"]
    for k, src in enumerate(dir_list):
        video_list = os.listdir(src)
        i = 1
        for video in video_list:
            video = dir_list[k] + video
            vidcap = cv.VideoCapture(video)

            #first read
            success, image = vidcap.read()
            count = 0
            base = os.path.basename(video)
            vname = os.path.splitext(base)[0]

            prev_gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
            savepath = ""

            while success:
                count = count+1

                success, image = vidcap.read()
                if (success == False):
                    break

                gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)

                (score, diff) = compare_ssim(prev_gray, gray, full=True)
                diff = (diff * 255).astype("uint8")

                if (score < 0.85):
                    savepath = dir_list_out[k] + vname + "frame%05d.jpg" % count
                    cv.imwrite(savepath, image)  # save frame as JPEG file               

                prev_gray = gray                
                count += 1
///////////////////////////////////////////////////////////////////////////////

def main():
    parser = argparse.ArgumentParser(description='image splite to train and validate for training ')
    parser.add_argument('--infolder', type=str, help='image folder path ')
    parser.add_argument('--classname', type=str, help='splite class name')
    parser.add_argument('--step', type=int, help='step for loop')
    parser.add_argument('--maxsample', type=int, help='max train data count')

    args = parser.parse_args()

    infolder = args.infolder
    classname = args.classname
    step = args.step
    maxval = args.maxsample


    traindir = "train/" + classname + "/"
    validationdir = "validation/" + classname + "/"

    try:
        os.makedirs(traindir)
    except OSError as e:
        if e.errno != errno.EEXIST:
            raise
    try:
        os.makedirs(validationdir)
    except OSError as e:
        if e.errno != errno.EEXIST:
            raise

    fileset = [file for file in glob.glob(infolder + "**/*.jpg", recursive=True)]

    count = len(fileset)
    i = 0
    k = 0
    for file in fileset:        
        if i % step == 0:
            print(file)
            fname = os.path.basename(file)
            if k % 10 == 0: #test
                destination_file = validationdir + fname
            else:#train
                destination_file = traindir + fname

            try:
                srcimg = cv2.imread(file)
                dstimg = cv2.resize(srcimg, (224, 224), interpolation=cv2.INTER_CUBIC)
                cv2.imwrite(destination_file, dstimg)
                #copyfile(file, destination_file)               

            except:
                pass
                
            k = k + 1
            
            if k >= maxval:
                break
                
        i = i + 1
        progress(i, count, status='processing')
        

    print("\nTask Completed")


if __name__== "__main__":
    main()