import numpy as np from scipy.spatial.distance import cdist from fastdtw import fastdtw import json import cv2 # 读取视频文件 def read_video_frames(video_path): cap = cv2.VideoCapture(video_path) frames = [] while cap.isOpened(): ret, frame = cap.read() if not ret: break frames.append(frame) cap.release() return frames # 提取关键点 def extract_keypoints(sequence): keypoints_sequence = [] for frame in sequence: keypoints = frame['instances'][0]['keypoints'][5:13] + frame['instances'][0]['keypoints'][91:133] # 获取每一帧的关键点(仅考虑上半身及手部的点，共计50个) keypoints_sequence.append(np.array(keypoints)) return keypoints_sequence # 计算两帧之间的距离（这里使用欧氏距离） def calculate_distance_matrix(seq1, seq2): distances = [] for i in range(len(seq1)): frame_distances = [] for j in range(len(seq2)): distance = np.linalg.norm(seq1[i] - seq2[j], axis=1).mean() # 计算关键点的平均距离 frame_distances.append(distance) distances.append(frame_distances) return np.array(distances) # 计算两个手语序列的最佳对齐路径 def align_sequences(seq1, seq2): # 提取关键点 keypoints_seq1 = extract_keypoints(seq1) keypoints_seq2 = extract_keypoints(seq2) # 计算距离矩阵 distances = calculate_distance_matrix(keypoints_seq1, keypoints_seq2) # 使用 DTW 进行对齐 distance, path = fastdtw(keypoints_seq1, keypoints_seq2, dist=lambda x, y: np.linalg.norm(x - y)) return distance, path def filter_sequence_by_alignment(sequence, alignment_path, index): """ 根据alignment_path筛选序列 sequence: 输入序列 (sequence1 or sequence2) alignment_path: 对齐路径 index: 选择是sequence1还是sequence2 (0表示sequence1, 1表示sequence2) """ filtered_sequence = [] for path in alignment_path: frame_index = path[index] filtered_sequence.append(sequence[frame_index]) return filtered_sequence def scale_keypoints(standard, user, seq1_frames, seq2_frames): height1, width1, _ = seq1_frames[0].shape height2, width2, _ = seq2_frames[0].shape sequence1 = json.load(open(standard + ".json", 'r')) sequence2 = json.load(open(user + ".json", 'r')) unified_width = int(max(width1, width2)) unified_height = int(max(height1, height2)) # 计算标准视频和用户视频的缩放比例 float scale_x_standard = unified_width / width1 scale_y_standard = unified_height / height1 scale_x_user = unified_width / width2 scale_y_user = unified_height / height2 # 如果标准视频的宽、高需要缩放 if scale_x_standard != 1.0 or scale_y_standard != 1.0: for frame in range(len(sequence1)): keypoints_00 = sequence1[frame]["instances"][0]["keypoints"][5:13] + sequence1[frame]["instances"][0]["keypoints"][91:133] adjusted_keypoints_00 = [ [point[0] * scale_x_standard, point[1] * scale_y_standard] for point in keypoints_00 ] sequence1[frame]["instances"][0]["keypoints"][5:13] = adjusted_keypoints_00[:8] sequence1[frame]["instances"][0]["keypoints"][91:133] = adjusted_keypoints_00[8:] # 如果用户视频的宽、高需要缩放 if scale_x_user != 1.0 or scale_y_user != 1.0: for frame in range(len(sequence2)): keypoints_01 = sequence2[frame]["instances"][0]["keypoints"][5:13] + sequence2[frame]["instances"][0]["keypoints"][91:133] adjusted_keypoints_01 = [ [point[0] * scale_x_user, point[1] * scale_y_user] for point in keypoints_01 ] sequence2[frame]["instances"][0]["keypoints"][5:13] = adjusted_keypoints_01[:8] sequence2[frame]["instances"][0]["keypoints"][91:133] = adjusted_keypoints_01[8:] json.dump(sequence1, open(standard + ".json", 'w'), indent=4) json.dump(sequence2, open(user + ".json", 'w'), indent=4) # 根据对齐路径提取帧并创建新视频 def create_aligned_videos(seq1_frames, seq2_frames, alignment_path, output_combined_path, output_seq1_path, output_seq2_path, fps=30): height1, width1, _ = seq1_frames[0].shape height2, width2, _ = seq2_frames[0].shape # height = max(height1, height2) # width = width1 + width2 # 计算输出视频的统一宽度和高度 max_height = max(height1, height2) max_width = max(width1, width2) # 按比例计算缩放系数 scale1 = min(max_width / width1, max_height / height1) scale2 = min(max_width / width2, max_height / height2) # 缩放后统一的宽高 unified_width = int(max_width) unified_height = int(max_height) # 创建输出视频 fourcc = cv2.VideoWriter_fourcc(*'H264') # 创建并排视频 # combined_out = cv2.VideoWriter(output_combined_path, fourcc, fps, (unified_width * 2, unified_height)) # 创建单独的视频 seq1_out = cv2.VideoWriter(output_seq1_path, fourcc, fps, (unified_width, unified_height)) seq2_out = cv2.VideoWriter(output_seq2_path, fourcc, fps, (unified_width, unified_height)) for idx1, idx2 in alignment_path: # 获取对齐的帧 frame1 = seq1_frames[idx1] frame2 = seq2_frames[idx2] # # 如果两帧高度不一致，调整frame2的高度以匹配frame1 # if height1 != height2: # frame2 = cv2.resize(frame2, (width2, height1)) # 等比例缩放两个视频的帧到统一尺寸 frame1_resized = cv2.resize(frame1, (unified_width, unified_height), interpolation=cv2.INTER_AREA) frame2_resized = cv2.resize(frame2, (unified_width, unified_height), interpolation=cv2.INTER_AREA) # 拼接帧，左右并排 combined_frame = np.hstack((frame1_resized, frame2_resized)) # 写入到输出视频 # combined_out.write(combined_frame) seq1_out.write(frame1_resized) seq2_out.write(frame2_resized) # 释放资源 # combined_out.release() seq1_out.release() seq2_out.release() def align_filter(standard, user, tmpdir): # 示例数据 sequence1 = json.load(open(standard + ".json", 'r')) sequence2 = json.load(open(user + ".json", 'r')) # 对齐两个序列 distance, alignment_path = align_sequences(sequence1, sequence2) # 过滤sequence1和sequence2 filtered_sequence1 = filter_sequence_by_alignment(sequence1, alignment_path, index=0) filtered_sequence2 = filter_sequence_by_alignment(sequence2, alignment_path, index=1) print(f"DTW 最佳对齐路径: {alignment_path}") print(f"DTW 最小对齐距离: {distance}") # 存储对齐的json keypoint json.dump(filtered_sequence1, open(standard + ".json", 'w'), indent=4) json.dump(filtered_sequence2, open(user + ".json", 'w'), indent=4) # 读取视频帧 seq1_frames = read_video_frames(standard + '.mp4') # Sequence 1: 55 frames seq2_frames = read_video_frames(user + '.mp4') # Sequence 2: 34 frames # 输出视频路径 output_combined_path = tmpdir + '/aligned_combined_output.mp4' # 合并视频 output_seq1_path = standard + '.mp4' # 对齐后sequence1视频 output_seq2_path = user + '.mp4' # 对齐后sequence2视频 height1, width1, _ = seq1_frames[0].shape height2, width2, _ = seq2_frames[0].shape # 如果视频尺寸不相同则根据比例缩放关键点坐标 if height1 != height2 or width1 != width2: scale_keypoints(standard, user, seq1_frames, seq2_frames) # 创建对齐后的视频 create_aligned_videos(seq1_frames, seq2_frames, alignment_path, output_combined_path, output_seq1_path, output_seq2_path) # print(f"Combined video created at {output_combined_path}") print(f"Aligned Sequence 1 video created at {output_seq1_path}") print(f"Aligned Sequence 2 video created at {output_seq2_path}")