123 lines
3.7 KiB
Python
123 lines
3.7 KiB
Python
import os
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import subprocess
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import shutil
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import torch
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import open_clip
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import cv2
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from sentence_transformers import util
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from PIL import Image
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import sys
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from skimage import metrics
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16-plus-240', pretrained="laion400m_e32")
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model.to(device)
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def SSIM(imgPath0, imgPath1):
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image1 = cv2.imread(imgPath0)
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image2 = cv2.imread(imgPath1)
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image2 = cv2.resize(image2, (image1.shape[1], image1.shape[0]), interpolation=cv2.INTER_AREA)
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# Convert images to grayscale
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image1_gray = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
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image2_gray = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
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# Calculate SSIM
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ssim_score = metrics.structural_similarity(image1_gray, image2_gray, full=True)
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return round(ssim_score[0], 2) * 100
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def imageEncoder(img):
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img1 = Image.fromarray(img).convert('RGB')
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img1 = preprocess(img1).unsqueeze(0).to(device)
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img1 = model.encode_image(img1)
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return img1
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def generateScore(image1, image2):
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test_img = cv2.imread(image1, cv2.IMREAD_UNCHANGED)
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data_img = cv2.imread(image2, cv2.IMREAD_UNCHANGED)
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img1 = imageEncoder(test_img)
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img2 = imageEncoder(data_img)
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cos_scores = util.pytorch_cos_sim(img1, img2)
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score = round(float(cos_scores[0][0]) * 100, 2)
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return score
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def getCutList(imagePath, min_frame, max_frame):
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pngList = os.listdir(imagePath)
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cutList = []
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resList = []
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indexList = []
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i = 0
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num = 0
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while i < len(pngList) - 1:
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num += 1
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imgPath0 = os.path.join(imagePath, pngList[i])
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imgPath1 = os.path.join(imagePath, pngList[i + 1])
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res = generateScore(imgPath0, imgPath1)
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print("切割画面（" + str(i + 1) + "/" + str(len(pngList) - 1) + ") 相似度：" + str(res) + "%")
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resList.append(res)
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indexList.append(i)
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if num >= max_frame:
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num = 0
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cutList.append(pngList[i])
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i += min_frame
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elif res < 95:
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res2 = SSIM(imgPath0, imgPath1)
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if res2 < 60:
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res3 = (95 - res) ** 2 + (60 - res2) ** 2
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if (res3 > 150):
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num = 0
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cutList.append(pngList[i])
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i += min_frame
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else:
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i += 1
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else:
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i += 1
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else:
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i += 1
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return cutList
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def videoToPng(videopath, rate, save_name):
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current_file_path = __file__
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absolute_path = os.path.abspath(current_file_path)
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directory = os.path.dirname(absolute_path)
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all_cut_dir = os.path.join(directory, "VideoCutDir")
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if not os.path.exists(all_cut_dir):
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os.mkdir(all_cut_dir)
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root_dir = os.path.join(all_cut_dir, save_name)
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if os.path.exists(root_dir):
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shutil.rmtree(root_dir)
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os.mkdir(root_dir)
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ffmpegCMD = "ffmpeg -i " + videopath + " -r " + str(rate) + " " + root_dir + "/%05d.png"
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subprocess.run(ffmpegCMD)
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print("视频转图片完成")
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return root_dir
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def cutToDir(root_dir, cutList):
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dirIndex = 0
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cutIndex = 0
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pngList = os.listdir(root_dir)
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dirList = []
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dirPathString = ""
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for i in range(len(cutList) + 1):
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dirName = str(i).zfill(3)
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dirPath = os.path.join(root_dir, dirName)
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os.mkdir(dirPath)
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dirPathString += (dirPath + '\n')
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dirList.append(dirName)
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for png in pngList:
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src = os.path.join(root_dir, png)
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tgtDir = os.path.join(root_dir, dirList[dirIndex])
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shutil.move(src, tgtDir)
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if png == cutList[cutIndex]:
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dirIndex += 1
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if cutIndex < len(cutList) - 1:
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cutIndex += 1
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return dirPathString
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