自学教程：教你怎么用python删除相似度高的图片

#encoding:utf-8import osimport sysimport cv2video_path = '/home/pythonfile/video/'  # 绝对路径，video下有两段视频out_frame_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'frame_output')  #frame_output是视频切帧后的保存路径if not os.path.exists(out_frame_path):    os.makedirs(out_frame_path)print('out_frame_path', out_frame_path)files = []list1 = os.listdir(video_path)print('list', list1)for i in range(len(list1)):    item = os.path.join(video_path, list1[i])    files.append(item)print('files',files)for k,file in enumerate(files):    frame_dir = os.path.join(out_frame_path, '%d'%(k+1))    if not os.path.exists(frame_dir):        os.makedirs(frame_dir)    cap = cv2.VideoCapture(file)    j = 0    print('start prossing NO.%d video' % (k + 1))    while True:        ret, frame = cap.read()        j += 1        if ret:        #每三帧保存一张            if j % 3 == 0:                cv2.imwrite(os.path.join(frame_dir, '%d.jpg'%j), frame)        else:            cap.release()            break    print('prossed NO.%d video'%(k+1))

# coding: utf-8import osimport cv2# from skimage.measure import compare_ssim# from skimage.metrics import _structural_similarityfrom skimage.metrics import structural_similarity as ssimdef delete(filename1):    os.remove(filename1)def list_all_files(root):    files = []    list = os.listdir(root)    # os.listdir()方法：返回指定文件夹包含的文件或子文件夹名字的列表。该列表顺序以字母排序    for i in range(len(list)):        element = os.path.join(root, list[i])        # 需要先使用python路径拼接os.path.join()函数，将os.listdir()返回的名称拼接成文件或目录的绝对路径再传入os.path.isdir()和os.path.isfile().        if os.path.isdir(element):  # os.path.isdir()用于判断某一对象(需提供绝对路径)是否为目录            # temp_dir = os.path.split(element)[-1]            # os.path.split分割文件名与路径,分割为data_dir和此路径下的文件名，[-1]表示只取data_dir下的文件名            files.append(list_all_files(element))        elif os.path.isfile(element):            files.append(element)    # print('2',files)    return filesdef ssim_compare(img_files):    count = 0    for currIndex, filename in enumerate(img_files):        if not os.path.exists(img_files[currIndex]):            print('not exist', img_files[currIndex])            break        img = cv2.imread(img_files[currIndex])        img1 = cv2.imread(img_files[currIndex + 1])        #进行结构性相似度判断        # ssim_value = _structural_similarity.structural_similarity(img,img1,multichannel=True)        ssim_value = ssim(img,img1,multichannel=True)        if ssim_value > 0.9:            #基数            count += 1            imgs_n.append(img_files[currIndex + 1])            print('big_ssim:',img_files[currIndex], img_files[currIndex + 1], ssim_value)        # 避免数组越界        if currIndex+1 >= len(img_files)-1:            break    return countif __name__ == '__main__':    path = '/home/dj/pythonfile/frame_output/'    img_path = path    imgs_n = []       all_files = list_all_files(path) #返回包含完整路径的所有图片名的列表    print('1',len(all_files))       for files in all_files:        # 根据文件名排序，x.rfind('/')是从右边寻找第一个‘/'出现的位置，也就是最后出现的位置        # 注意sort和sorted的区别，sort作用于原列表，sorted生成新的列表，且sorted可以作用于所有可迭代对象        files.sort(key = lambda x: int(x[x.rfind('/')+1:-4]))#路径中包含“/”        # print(files)        img_files = []        for img in files:            if img.endswith('.jpg'):                # 将所有图片名都放入列表中                img_files.append(img)        count = ssim_compare(img_files)        print(img[:img.rfind('/')],"路径下删除的图片数量为：",count)    for image in imgs_n:        delete(image)

from skimage.measure import compare_ssim

from skimage.metrics import _structural_similarity

from warnings import warnimport numpy as npfrom scipy.ndimage import uniform_filter, gaussian_filterfrom ..util.dtype import dtype_rangefrom ..util.arraycrop import cropfrom .._shared.utils import warn, check_shape_equality__all__ = ['structural_similarity']def structural_similarity(im1, im2,                          *,                          win_size=None, gradient=False, data_range=None,                          multichannel=False, gaussian_weights=False,                          full=False, **kwargs):    """    Compute the mean structural similarity index between two images.    Parameters    ----------    im1, im2 : ndarray        Images. Any dimensionality with same shape.    win_size : int or None, optional        The side-length of the sliding window used in comparison. Must be an        odd value. If `gaussian_weights` is True, this is ignored and the        window size will depend on `sigma`.    gradient : bool, optional        If True, also return the gradient with respect to im2.    data_range : float, optional        The data range of the input image (distance between minimum and        maximum possible values). By default, this is estimated from the image        data-type.    multichannel : bool, optional        If True, treat the last dimension of the array as channels. Similarity        calculations are done independently for each channel then averaged.    gaussian_weights : bool, optional        If True, each patch has its mean and variance spatially weighted by a        normalized Gaussian kernel of width sigma=1.5.    full : bool, optional        If True, also return the full structural similarity image.    Other Parameters    ----------------    use_sample_covariance : bool        If True, normalize covariances by N-1 rather than, N where N is the        number of pixels within the sliding window.    K1 : float        Algorithm parameter, K1 (small constant, see [1]_).    K2 : float        Algorithm parameter, K2 (small constant, see [1]_).    sigma : float        Standard deviation for the Gaussian when `gaussian_weights` is True.    Returns    -------    mssim : float        The mean structural similarity index over the image.    grad : ndarray        The gradient of the structural similarity between im1 and im2 [2]_.        This is only returned if `gradient` is set to True.    S : ndarray        The full SSIM image.  This is only returned if `full` is set to True.    Notes    -----    To match the implementation of Wang et. al. [1]_, set `gaussian_weights`    to True, `sigma` to 1.5, and `use_sample_covariance` to False.    .. versionchanged:: 0.16        This function was renamed from ``skimage.measure.compare_ssim`` to        ``skimage.metrics.structural_similarity``.    References    ----------    .. [1] Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P.       (2004). Image quality assessment: From error visibility to       structural similarity. IEEE Transactions on Image Processing,       13, 600-612.       https://ece.uwaterloo.ca/~z70wang/publications/ssim.pdf,       :DOI:`10.1109/TIP.2003.819861`    .. [2] Avanaki, A. N. (2009). Exact global histogram specification       optimized for structural similarity. Optical Review, 16, 613-621.       :arxiv:`0901.0065`       :DOI:`10.1007/s10043-009-0119-z`    """    check_shape_equality(im1, im2)    if multichannel:        # loop over channels        args = dict(win_size=win_size,                    gradient=gradient,                    data_range=data_range,                    multichannel=False,                    gaussian_weights=gaussian_weights,                    full=full)        args.update(kwargs)        nch = im1.shape[-1]        mssim = np.empty(nch)        if gradient:            G = np.empty(im1.shape)        if full:            S = np.empty(im1.shape)        for ch in range(nch):            ch_result = structural_similarity(im1[..., ch],                                              im2[..., ch], **args)            if gradient and full:                mssim[..., ch], G[..., ch], S[..., ch] = ch_result            elif gradient:                mssim[..., ch], G[..., ch] = ch_result            elif full:                mssim[..., ch], S[..., ch] = ch_result            else:                mssim[..., ch] = ch_result        mssim = mssim.mean()        if gradient and full:            return mssim, G, S        elif gradient:            return mssim, G        elif full:            return mssim, S        else:            return mssim    K1 = kwargs.pop('K1', 0.01)    K2 = kwargs.pop('K2', 0.03)    sigma = kwargs.pop('sigma', 1.5)    if K1 < 0:        raise ValueError("K1 must be positive")    if K2 < 0:        raise ValueError("K2 must be positive")    if sigma < 0:        raise ValueError("sigma must be positive")    use_sample_covariance = kwargs.pop('use_sample_covariance', True)    if gaussian_weights:        # Set to give an 11-tap filter with the default sigma of 1.5 to match        # Wang et. al. 2004.        truncate = 3.5    if win_size is None:        if gaussian_weights:            # set win_size used by crop to match the filter size            r = int(truncate * sigma + 0.5)  # radius as in ndimage            win_size = 2 * r + 1        else:            win_size = 7   # backwards compatibility    if np.any((np.asarray(im1.shape) - win_size) < 0):        raise ValueError(            "win_size exceeds image extent.  If the input is a multichannel "            "(color) image, set multichannel=True.")    if not (win_size % 2 == 1):        raise ValueError('Window size must be odd.')    if data_range is None:        if im1.dtype != im2.dtype:            warn("Inputs have mismatched dtype.  Setting data_range based on "                 "im1.dtype.", stacklevel=2)        dmin, dmax = dtype_range[im1.dtype.type]        data_range = dmax - dmin    ndim = im1.ndim    if gaussian_weights:        filter_func = gaussian_filter        filter_args = {'sigma': sigma, 'truncate': truncate}    else:        filter_func = uniform_filter        filter_args = {'size': win_size}    # ndimage filters need floating point data    im1 = im1.astype(np.float64)    im2 = im2.astype(np.float64)    NP = win_size ** ndim    # filter has already normalized by NP    if use_sample_covariance:        cov_norm = NP / (NP - 1)  # sample covariance    else:        cov_norm = 1.0  # population covariance to match Wang et. al. 2004    # compute (weighted) means    ux = filter_func(im1, **filter_args)    uy = filter_func(im2, **filter_args)    # compute (weighted) variances and covariances    uxx = filter_func(im1 * im1, **filter_args)    uyy = filter_func(im2 * im2, **filter_args)    uxy = filter_func(im1 * im2, **filter_args)    vx = cov_norm * (uxx - ux * ux)    vy = cov_norm * (uyy - uy * uy)    vxy = cov_norm * (uxy - ux * uy)    R = data_range    C1 = (K1 * R) ** 2    C2 = (K2 * R) ** 2    A1, A2, B1, B2 = ((2 * ux * uy + C1,                       2 * vxy + C2,                       ux ** 2 + uy ** 2 + C1,                       vx + vy + C2))    D = B1 * B2    S = (A1 * A2) / D    # to avoid edge effects will ignore filter radius strip around edges    pad = (win_size - 1) // 2    # compute (weighted) mean of ssim    mssim = crop(S, pad).mean()    if gradient:        # The following is Eqs. 7-8 of Avanaki 2009.        grad = filter_func(A1 / D, **filter_args) * im1        grad += filter_func(-S / B2, **filter_args) * im2        grad += filter_func((ux * (A2 - A1) - uy * (B2 - B1) * S) / D,                            **filter_args)        grad *= (2 / im1.size)        if full:            return mssim, grad, S        else:            return mssim, grad    else:        if full:            return mssim, S        else:            return mssim