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video2keyframe.py
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video2keyframe.py
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# 读视频进行均匀采样，生成帧数据集
import os
import cv2
import numpy as np
import math
from scipy import signal
from sklearn import preprocessing
import matplotlib.pyplot as plt
from OF import OpticalFlowCalculator
train_all = 'F:/dataset/CSL2018/gloss-zip/1/video/train/'
val_all = 'F:/dataset/CSL2018/gloss-zip/1/video/validate/'
test_all = 'F:/dataset/CSL2018/gloss-zip/1/video/test/'
train_key = 'F:/dataset/CSL2018/gloss-zip/1/key/train/'
val_key = 'F:/dataset/CSL2018/gloss-zip/1/key/validate/'
test_key = 'F:/dataset/CSL2018/gloss-zip/1/key/test/'
train_files = os.listdir(train_all)
val_files = os.listdir(val_all)
test_files = os.listdir(test_all)
frames = 16
flow = OpticalFlowCalculator()
import warnings
warnings.filterwarnings("ignore")
def read_images(folder_path, key_path, frames):
k_path, _ = key_path.split('.', 1)
if not os.path.exists(k_path):
os.mkdir(k_path)
videoFile = cv2.VideoCapture(folder_path)
ret, frame = videoFile.read()
height = 0.2
data = []
count = 0
imgs = []
while ret:
xvel, yvel = flow.processFrame(frame[120:720, 340:940])
img = cv2.resize(frame[120:720, 340:940], (224, 224))
imgs.append(img)
if count >= 2:
data.append(math.log(xvel * xvel + yvel * yvel + 1))
ret, frame = videoFile.read()
count += 1
min_max_scaler = preprocessing.MinMaxScaler()
data1 = np.squeeze(min_max_scaler.fit_transform(np.expand_dims(data, axis=1)), axis=1)
peaks = signal.find_peaks(data1, height=height)
begin = peaks[0][0]+2
end = peaks[0][-1]+2
body = end - begin + 1
while body < frames:
height = height/2
peaks = signal.find_peaks(data1, height=height)
begin = peaks[0][0]+2
end = peaks[0][-1]+2
body = end - begin + 1
assert body >= frames, "Too few images in your data folder: " + str(folder_path) + str(body)
images = []
K = []
sta = begin - 1
refer = imgs[sta]
for j in range(0, frames):
f = {}
h = {}
a = []
start = int(sta + j * body / frames + 0.5)
l = int(sta + (j + 1) * body / frames + 0.5)
if l - start == 1:
refer = imgs[l]
K.append(l)
img = imgs[l]
cv2.imwrite(k_path + '/' + "%d.jpg" % (l + 1), img)
else:
for m in range(start, l):
curr_frame = imgs[m]
diff = np.sqrt(np.sum(np.square(curr_frame - refer)))
f[m] = diff
f_order = sorted(f.items(), key=lambda x: x[1], reverse=False)
C = {}
for n in range(len(f_order) - 1):
sum1 = []
sum2 = []
if n == 0:
sum1 = f_order[n][1]
m1 = f_order[n][1]
for q in range(1, len(f_order)):
single2 = f_order[q][1]
sum2.append(single2)
m2 = np.mean(sum2)
sigma1 = np.std(sum1, ddof=0)
sigma2 = np.std(sum2, ddof=0)
else:
for p in range(n + 1):
single1 = f_order[p][1]
sum1.append(single1)
m1 = np.mean(sum1)
for q in range(n + 1, len(f_order)):
single2 = f_order[q][1]
sum2.append(single2)
m2 = np.mean(sum2)
sigma1 = np.std(sum1, ddof=0)
sigma2 = np.std(sum2, ddof=0)
c = np.square(m1 - m2) / (sigma1 ** 2 + sigma2 ** 2)
C[f_order[n][0]] = c
KK = max(C, key=lambda r: C[r]) # 25
# 对后（n-KK）帧计算每一帧的模糊程度，选择模糊程度最低的一帧作为当前段的关键帧，也作为下一段的参考帧
for y in range(len(f_order)):
Y = f_order[y][0]
a.append(Y)
for b in a:
if b == KK:
index_KK = a.index(b)
KK_latter = a[index_KK + 1:]
# 利用拉普拉斯算子计算图像的模糊程度
for e in KK_latter:
ima = imgs[e]
ima = cv2.Laplacian(ima, cv2.CV_64F).var()
h[e] = ima
key = max(h, key=lambda r: h[r])
img = imgs[key]
cv2.imwrite(k_path + '/' + "%d.jpg" % (key+1), img)
K.append(key)
refer = imgs[key]
return images
for videos_file in train_files:
if not os.path.exists(train_key + videos_file):
os.mkdir(train_key + videos_file)
videos_path = os.path.join(train_all, videos_file)
train_videos = os.listdir(videos_path)
for each_train_video in train_videos:
path = train_all + videos_file + '/' + each_train_video
key_path = train_key + videos_file + '/' + each_train_video
indexes = read_images(path, key_path, frames)
for videos_file in val_files:
if not os.path.exists(val_key + videos_file):
os.mkdir(val_key + videos_file)
videos_path = os.path.join(val_all, videos_file)
val_videos = os.listdir(videos_path)
for each_val_video in val_videos:
path = val_all + videos_file + '/' + each_val_video
key_path = val_key + videos_file + '/' + each_val_video
indexes = read_images(path, key_path, frames)
for videos_file in test_files:
if not os.path.exists(test_key + videos_file):
os.mkdir(test_key + videos_file)
videos_path = os.path.join(test_all, videos_file)
test_videos = os.listdir(videos_path)
for each_test_video in test_videos:
path = test_all + videos_file + '/' + each_test_video
key_path = test_key + videos_file + '/' + each_test_video
indexes = read_images(path, key_path, frames)