前言
光流flow特征中包含了一个视频当中运动相关的信息，在视频动作定位当中光流特征使用的比较多，所以记录一下提取光流特征的方法。
使用的方法是TVL1方法，最终提取的光流图片还可以配合I3D模型进行特征的提取。光流的计算先需要将视频一帧一帧提取出来，然后再通过连续两帧之间的差异进行计算。
提取帧
提取视频的帧的算法如下：
其中video_list.txt中写的是视频的名字，也就是告诉程序需要将那些视频提取帧:
videos中存放视频，与video_list.txt中写的视频名字对应
import cv2
import numpy as np
import os
import multiprocessing
video_root = 'video_list.txt'
root = 'videos'
out_root = 'frames'
suffix = '.jpg'
def save_image(root, vid_name, num, image):
file_name = os.path.join(root, vid_name, str(num) suffix)
# print(file_name)
cv2.imwrite(file_name, image)
def process(vid_path, preffix):
videoCapture = cv2.VideoCapture(vid_path)
i = 0
while True:
success, frame = videoCapture.read()
if success:
i = i 1
save_image(out_root, preffix, i, frame)
# print('save image vid name: ', file_name, '; frame num: ', i)
else:
break
def main(root):
if not os.path.exists(out_root):
os.mkdir(out_root)
# path_list = os.listdir(root)
path_list = []
#### 读取txt中视频信息 ####
with open(video_root, 'r') as f:
for id, line in enumerate(f):
video_name = line.strip().split()
path_list.append(video_name[0])
pool = multiprocessing.Pool(processes=4)
for file_name in path_list:
path = os.path.join(root, file_name)
preffix = file_name.split('.')[0]
dir_name = os.path.join(out_root, preffix)
if not os.path.exists(dir_name):
os.mkdir(dir_name)
pool.apply_async(process, args=(path, preffix))
# process(path,preffix)
pool.close()
pool.join()
if __name__ == '__main__':
main(root)
print("finish!!!!!!!!!!!!!!!!!!")运行完这个程序就能将需要提取的视频帧放在frames对应的目录下。
提取flow光流
提取光流使用了opencv模块，主要通过上面提取的视频帧进行计算，光流计算使用cpu资源比较多，所以会计算很长时间。
光流提取的代码如下：
import cv2
import os
import numpy as np
import glob
import multiprocessing
###### 使用frames帧进行 flow光流计算
video_root = 'video_list.txt'
root = 'frames'
out_root = 'flow'
def cal_for_frames(video_path):
# print(video_path)
frames = glob.glob(os.path.join(video_path, '*.jpg'))
frames.sort()
flow = []
prev = cv2.imread(frames[0])
prev = cv2.cvtColor(prev, cv2.COLOR_BGR2GRAY)
for i, frame_curr in enumerate(frames[1:]):
curr = cv2.imread(frame_curr)
curr = cv2.cvtColor(curr, cv2.COLOR_BGR2GRAY)
tmp_flow = compute_TVL1(prev, curr)
flow.append(tmp_flow)
prev = curr
return flow
def compute_TVL1(prev, curr, bound=15):
TVL1 = cv2.optflow.DualTVL1OpticalFlow_create()
flow = TVL1.calc(prev, curr, None)
assert flow.dtype == np.float32
flow = (flow bound) * (255.0 / (2 * bound))
flow = np.round(flow).astype(int)
flow[flow >= 255] = 255
flow[flow <= 0] = 0
return flow
def save_flow(video_flows, flow_path):
if not os.path.exists(flow_path):
os.mkdir(os.path.join(flow_path))
for i, flow in enumerate(video_flows):
cv2.imwrite(os.path.join(flow_path, str(i) '_x.jpg'), flow[:, :, 0])
cv2.imwrite(os.path.join(flow_path, str(i) '_y.jpg'), flow[:, :, 1])
def process(video_path, flow_path):
flow = cal_for_frames(video_path)
save_flow(flow, flow_path)
def extract_flow(root, out_root):
if not os.path.exists(out_root):
os.mkdir(out_root)
# dir_list = os.listdir(root)
dir_list = []
### 读取txt中视频信息
with open(video_root, 'r') as f:
for id, line in enumerate(f):
video_name = line.strip().split()
preffix = video_name[0].split('.')[0]
dir_list.append(preffix)
pool = multiprocessing.Pool(processes=4)
for dir_name in dir_list:
video_path = os.path.join(root, dir_name)
flow_path = os.path.join(out_root, dir_name)
# flow = cal_for_frames(video_path)
# save_flow(flow,flow_path)
# print('save flow data: ',flow_path)
# process(video_path,flow_path)
pool.apply_async(process, args=(video_path, flow_path))
pool.close()
pool.join()
if __name__ == '__main__':
extract_flow(root, out_root)
print("finish!!!!!!!!!!!!!!!!!!")环境配置
提取光流时需要使用到cv2.optflow.DualTVL1OpticalFlow_create()，这玩意安装有时候会有版本问题，所以安装的opencv-python和pencv-contrib-python最好版本相同
pip install opencv-python==4.1.2.30 pip install opencv-contrib-python==4.1.2.30
结果
最终flow光流图和提取的帧之间如下图所示，可以看到一些梳头发的动作变化。
总结
记录一下光流特征提取的算法，方便自己之后进行使用。
代码仓库：https://github.com/zhou-ning/pytorch-i3d
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