教你怎么用python删除相似度高的图片

因为输入是视频，切完帧之后都是连续图片，所以我的目录结构如下：

其中frame_output是视频切帧后的保存路径，1和2文件夹分别对应两个是视频切帧后的图片。

#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 += 1if 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))3. 删除相似度高的图片

# 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]) breakimg = 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)4. 导入skimage.measure import compare_ssim出错的解决方法：

将

from skimage.measure import compare_ssim

改为

from skimage.metrics import _structural_similarity5. structural_similarity.py的源码

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 : ndarrayImages. Any dimensionality with same shape. win_size : int or None, optionalThe side-length of the sliding window used in comparison. Must be anodd value. If `gaussian_weights` is True, this is ignored and thewindow size will depend on `sigma`. gradient : bool, optionalIf True, also return the gradient with respect to im2. data_range : float, optionalThe data range of the input image (distance between minimum andmaximum possible values). By default, this is estimated from the imagedata-type. multichannel : bool, optionalIf True, treat the last dimension of the array as channels. Similaritycalculations are done independently for each channel then averaged. gaussian_weights : bool, optionalIf True, each patch has its mean and variance spatially weighted by anormalized Gaussian kernel of width sigma=1.5. full : bool, optionalIf True, also return the full structural similarity image. Other Parameters ---------------- use_sample_covariance : boolIf True, normalize covariances by N-1 rather than, N where N is thenumber of pixels within the sliding window. K1 : floatAlgorithm parameter, K1 (small constant, see [1]_). K2 : floatAlgorithm parameter, K2 (small constant, see [1]_). sigma : floatStandard deviation for the Gaussian when `gaussian_weights` is True. Returns ------- mssim : floatThe mean structural similarity index over the image. grad : ndarrayThe gradient of the structural similarity between im1 and im2 [2]_.This is only returned if `gradient` is set to True. S : ndarrayThe 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:: 0.16This 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 channelsargs = 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_resultmssim = mssim.mean()if gradient and full: return mssim, G, Selif gradient: return mssim, Gelif full: return mssim, Selse: 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 + 1else: 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_filterfilter_args = {’sigma’: sigma, ’truncate’: truncate} else:filter_func = uniform_filterfilter_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) * im1grad += filter_func(-S / B2, **filter_args) * im2grad += filter_func((ux * (A2 - A1) - uy * (B2 - B1) * S) / D, **filter_args)grad *= (2 / im1.size)if full: return mssim, grad, Selse: return mssim, grad else:if full: return mssim, Selse: return mssim

到此这篇关于教你怎么用python删除相似度高的图片的文章就介绍到这了,更多相关python删除相似度高的图片内容请搜索好吧啦网以前的文章或继续浏览下面的相关文章希望大家以后多多支持好吧啦网！