我正在使用opencv进行某些神经样式转换技巧,但是我无法将视频保存到文件中。文件已创建,但只有6 kb。
from imutils.video import VideoStream
from imutils import paths
import itertools
import argparse
import imutils
import time
import cv2
import numpy as np
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--models", required=True,
help="path to directory containing neural style transfer models")
args = vars(ap.parse_args())
modelPaths = paths.list_files(args["models"], validExts=(".t7",))
modelPaths = sorted(list(modelPaths))
models = list(zip(range(0, len(modelPaths)), (modelPaths)))
modelIter = itertools.cycle(models)
(modelID, modelPath) = next(modelIter)
print("[INFO] loading style transfer model...")
net = cv2.dnn.readNetFromTorch(modelPath)
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)
print("[INFO] {}. {}".format(modelID + 1, modelPath))
out = cv2.VideoWriter('output2.avi', cv2.VideoWriter_fourcc(*'XVID'), 5, (451, 600))
while True:
frame = vs.read()
frame = imutils.resize(frame, width=600)
orig = frame.copy()
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, 1.0, (w, h),
(103.939, 116.779, 123.680), swapRB=False, crop=False)
net.setInput(blob)
output = net.forward()
output = output.reshape((3, output.shape[2], output.shape[3]))
output[0] += 103.939
output[1] += 116.779
output[2] += 123.680
output /= 255.0
output = output.transpose(1, 2, 0)
out.write(np.uint8(output))
cv2.imshow("Input", frame)
cv2.imshow("Output", output)
key = cv2.waitKey(1) & 0xFF
if key == ord("n"):
(modelID, modelPath) = next(modelIter)
print("[INFO] {}. {}".format(modelID + 1, modelPath))
net = cv2.dnn.readNetFromTorch(modelPath)
print(frame.shape)
elif key == ord("q"):
break
cv2.destroyAllWindows()
vs.stop()
主要内容发生在:
out = cv2.VideoWriter('output2.avi', cv2.VideoWriter_fourcc(*'XVID'), 5, (450, 600))
output = net.forward() # which computes the neural styled output image
并将输出/帧保存到文件中,我这样做了:
out.write(np.uint8(output))
关于我的代码为什么不起作用的任何想法?我尝试了许多编解码器和文件类型组合,但我不认为问题出在这里。你们认为这可能是维度吗? (我现在有450,600,因为我打印了output.shape,它返回了450,600,3,所以我认为450 x 600听起来不错)。
答案 0 :(得分:2)
@Dan Mašek的评论可能是正确的。您可以详细了解here。为了完整起见,您可以通过以下方式对其进行修复:
out.write(np.uint8(output))
更改为out.write(np.uint8(output * 255))
或
output /= 255.0
并将cv2.imshow("Output", output)
更改为cv2.imshow("Output", output / 255.)
您应该根据希望output
在(1)[0., 1.]
还是(2)[0, 255]
范围内来选择解决方案。