我的目标是将视频转换为2D矩阵X，其中列向量表示帧。因此矩阵的维数为：X.shape---->(#帧的特征，#帧的总数(
我需要这个表单，因为我想在X上应用不同的ML算法。要获得X，我按如下步骤进行：
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使用OpenCV库在python中上传视频并保存所有帧。
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循环｛
a) Frame (=3D array with dimensions height, width, depth=3 rbg) is converted into a 1D vector x
b) Append vector x to Matrix X
}
对于步骤2b(，我使用
video_matrix = np.column_stack((video_matrix, frame_vector))
对于640x320帧，此操作大约需要0.5秒。对于一个3分钟(8000帧(的小视频，计算X几乎需要150分钟。有没有办法让它更快？
第一部分代码：
video = cv2.VideoCapture('path/video.mp4')
if not os.path.exists('data'):
os.makedirs('data')
counter = 0
while(True):
# reading from frame
ret,frame = video.read()
if ret:
# if video is still left continue creating images
name = './data/frame' + str(counter) + '.jpg'
#print ('Creating...' + name)
# writing the extracted images
cv2.imwrite(name, frame)
# increasing counter so that it will
# show how many frames are created
counter += 1
else:
break
# Release all space and windows once done
video.release()
cv2.destroyAllWindows()
第二部分是减缓
video_matrix = np.zeros(width * height * 3) # initialize 1D array which will become the 2D array; first column will be deleted at the end
for i in range(counter): # loops over the total amount of frames
current_frame = np.asarray(Image.open('./data/frame'+str(i)+'.jpg')) # 3D-array = current frame
frame_vector = image_to_vector(current_frame) #convert frame into a 1D array
video_matrix = np.column_stack((video_matrix, frame_vector)) # append frame x to a matrix X that will represent the video
video_matrix = np.delete(video_matrix, 0, 1) # delete the initialized zero column
不要重复将单个帧附加到累积的数据中。这将花费你O(n^2(，也就是说，程序的运行速度会越来越慢。numpy无法在适当的位置放大数组。它每次都必须创建一个副本。复制工作量随着每增加一帧而增加。
将每个帧附加到python列表中。看完视频后，将整个列表转换为numpy数组一次。
以下是生成"视频数据"的Python(Keras(代码&通过基于DL的分类模型之前的预处理：
import numpy as np
# preparing dataset
X_train = []
Y_train = []
labels = enumerate([‘left’, ‘right’, ‘up’, ‘down’]) #4 classes
num_vids = 30
num_imgs = 30
img_size = 20
min_object_size = 1
max_object_size = 5
# video frames with left moving object
for i_vid in range(num_vids):
imgs = np.zeros((num_imgs, img_size, img_size)) # set background to 0
#vid_name = ‘vid’ + str(i_vid) + ‘.mp4’
w, h = np.random.randint(min_object_size, max_object_size, size=2)
x = np.random.randint(0, img_size — w)
y = np.random.randint(0, img_size — h)
i_img = 0
while x>0:
imgs[i_img, y:y+h, x:x+w] = 255 # set rectangle as foreground
x = x-1
i_img = i_img+1
X_train.append(imgs)
for i in range(0,num_imgs):
Y_train.append(0)
# video frames with right moving object
for i_vid in range(num_vids):
imgs = np.zeros((num_imgs, img_size, img_size)) # set background to 0
#vid_name = ‘vid’ + str(i_vid) + ‘.mp4’
w, h = np.random.randint(min_object_size, max_object_size, size=2)
x = np.random.randint(0, img_size — w)
y = np.random.randint(0, img_size — h)
i_img = 0
while x<img_size:
imgs[i_img, y:y+h, x:x+w] = 255 # set rectangle as foreground
x = x+1
i_img = i_img+1
X_train.append(imgs)
for i in range(0,num_imgs):
Y_train.append(1)
# video frames with up moving object
for i_vid in range(num_vids):
imgs = np.zeros((num_imgs, img_size, img_size)) # set background to 0
#vid_name = ‘vid’ + str(i_vid) + ‘.mp4’
w, h = np.random.randint(min_object_size, max_object_size, size=2)
x = np.random.randint(0, img_size — w)
y = np.random.randint(0, img_size — h)
i_img = 0
while y>0:
imgs[i_img, y:y+h, x:x+w] = 255 # set rectangle as foreground
y = y-1
i_img = i_img+1
X_train.append(imgs)
for i in range(0,num_imgs):
Y_train.append(2)
# video frames with down moving object
for i_vid in range(num_vids):
imgs = np.zeros((num_imgs, img_size, img_size)) # set background to 0
#vid_name = ‘vid’ + str(i_vid) + ‘.mp4’
w, h = np.random.randint(min_object_size, max_object_size, size=2)
x = np.random.randint(0, img_size — w)
y = np.random.randint(0, img_size — h)
i_img = 0
while y<img_size:
imgs[i_img, y:y+h, x:x+w] = 255 # set rectangle as foreground
y = y+1
i_img = i_img+1
X_train.append(imgs)
for i in range(0,num_imgs):
Y_train.append(3)
# data pre-processing
from keras.utils import np_utils
X_train=np.array(X_train, dtype=np.float32) /255
X_train=X_train.reshape(X_train.shape[0], num_imgs, img_size, img_size, 1)
print(X_train.shape)
Y_train=np.array(Y_train, dtype=np.uint8)
Y_train = Y_train.reshape(X_train.shape[0], 1)
print(Y_train.shape)
Y_train = np_utils.to_categorical(Y_train, 4)
它应该对你有帮助。