Setting a frame rate doesn't always work like you expect. It depends on two things:
- What your camera is capable of outputting.
- Whether the current capture backend you're using supports changing frame rates.
So point (1). Your camera will have a list of formats which it is capable of delivering to a capture device (e.g. your computer). This might be 1920x1080 @ 30 fps or 1920x1080 @ 60 fps and it also specifies a pixel format. The vast majority of consumer cameras do not let you change their frame rates with any more granularity than that. And most capture libraries will refuse to change to a capture format that the camera isn't advertising.
Even machine vision cameras, which allow you much more control, typically only offer a selection of frame rates (e.g. 1, 2, 5, 10, 15, 25, 30, etc). If you want a non-supported frame rate at a hardware level, usually the only way to do it is to use hardware triggering.
And point (2). When you use cv.VideoCapture you're really calling a platform-specific library like DirectShow or V4L2. We call this a backend. You can specify exactly which backend is in use by using something like:
cv2.VideoCapture(0 + cv2.CAP_DSHOW)
There are lots of CAP_X's defined, but only some will apply to your platform (e.g CAP_V4L2 is for Linux only). On Windows, forcing the system to use DirectShow is a pretty good bet. However as above, if your camera only reports it can output 30fps and 60fps, requesting 10fps will be meaningless. Worse, a lot of settings simply report True in OpenCV when they're not actually implemented. You've seen that most of the time reading parameters will give you sensible results though, however if the parameter isn't implemented (e.g exposure is a common one that isn't) then you might get nonsense.
You're better off waiting for a period of time and then reading the last image.
Be careful with this strategy. Don't do this:
while capturing:
res, image = cap.read()
time.sleep(1)
you need to make sure you're continually purging the camera's frame buffer otherwise you will start to see lag in your videos. Something like the following should work:
frame_rate = 10
prev = 0
while capturing:
time_elapsed = time.time() - prev
res, image = cap.read()
if time_elapsed > 1./frame_rate:
prev = time.time()
# Do something with your image here.
process_image()
For an application like a hand detector, what works well is to have a thread capturing images and the detector running in another thread (which also controls the GUI). Your detector pulls the last image captured, runs and display the results (you may need to lock access to the image buffer while you're reading/writing it). That way your bottleneck is the detector, not the performance of the camera.
Answer from Josh on Stack OverflowSetting a frame rate doesn't always work like you expect. It depends on two things:
- What your camera is capable of outputting.
- Whether the current capture backend you're using supports changing frame rates.
So point (1). Your camera will have a list of formats which it is capable of delivering to a capture device (e.g. your computer). This might be 1920x1080 @ 30 fps or 1920x1080 @ 60 fps and it also specifies a pixel format. The vast majority of consumer cameras do not let you change their frame rates with any more granularity than that. And most capture libraries will refuse to change to a capture format that the camera isn't advertising.
Even machine vision cameras, which allow you much more control, typically only offer a selection of frame rates (e.g. 1, 2, 5, 10, 15, 25, 30, etc). If you want a non-supported frame rate at a hardware level, usually the only way to do it is to use hardware triggering.
And point (2). When you use cv.VideoCapture you're really calling a platform-specific library like DirectShow or V4L2. We call this a backend. You can specify exactly which backend is in use by using something like:
cv2.VideoCapture(0 + cv2.CAP_DSHOW)
There are lots of CAP_X's defined, but only some will apply to your platform (e.g CAP_V4L2 is for Linux only). On Windows, forcing the system to use DirectShow is a pretty good bet. However as above, if your camera only reports it can output 30fps and 60fps, requesting 10fps will be meaningless. Worse, a lot of settings simply report True in OpenCV when they're not actually implemented. You've seen that most of the time reading parameters will give you sensible results though, however if the parameter isn't implemented (e.g exposure is a common one that isn't) then you might get nonsense.
You're better off waiting for a period of time and then reading the last image.
Be careful with this strategy. Don't do this:
while capturing:
res, image = cap.read()
time.sleep(1)
you need to make sure you're continually purging the camera's frame buffer otherwise you will start to see lag in your videos. Something like the following should work:
frame_rate = 10
prev = 0
while capturing:
time_elapsed = time.time() - prev
res, image = cap.read()
if time_elapsed > 1./frame_rate:
prev = time.time()
# Do something with your image here.
process_image()
For an application like a hand detector, what works well is to have a thread capturing images and the detector running in another thread (which also controls the GUI). Your detector pulls the last image captured, runs and display the results (you may need to lock access to the image buffer while you're reading/writing it). That way your bottleneck is the detector, not the performance of the camera.
I could not set the FPS for my camera so I manage to limit the FPS based on time so that only 1 frame per second made it into the rest of my code. It is not exact, but I do not need exact, just a limiter instead of 30fps. HTH
import time
fpsLimit = 1 # throttle limit
startTime = time.time()
cv = cv2.VideoCapture(0)
While True:
frame = cv.read
nowTime = time.time()
if (int(nowTime - startTime)) > fpsLimit:
# do other cv2 stuff....
startTime = time.time() # reset time
python - Changing FPS in OpenCV - Stack Overflow
Capture speed / framerate of cv2.VideoCapture - Python - OpenCV
How to modify the FPS of a video when using cv::VideoCapture to read frames - C++ - OpenCV
Increase Camera FPS OpenCV python
You set the FPS for the output via the VideoWriter,
but you didn't attempt to set the FPS for the input via the VideoCapture.
In order to do that you can try to call cv2.VideoCapture, with the cv2.CAP_PROP_FPS property after you create cap.
For example:
cap.set(cv2.CAP_PROP_FPS, 3)
However - note that the actual behavior is dependant on the specific capture device you are using. Some support only certain FPSs. See also this post regarding it: change frame rate in opencv 3.4.2.
If it does work you will be able to simplify your code a lot - just capture frames, process them and save (without any manual fps management).
This method programmatically sets frames per second. A 6.1mb file was created when frame rate was set for 30fps, and a 0.9mb file when set for 3fps.
#!/usr/bin/env python3
import cv2
import numpy as np
import time
import datetime
import pathlib
import imutils
cap = cv2.VideoCapture(0)
if (cap.isOpened() == False):
print("Unable to read camera feed")
capture_duration = 15
frame_per_sec = 30
prev = 0
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))
out = cv2.VideoWriter('C:\\videoPy\\LZ\\'outpout.avi',cv2.VideoWriter_fourcc('m','j','p','g'),frame_per_sec, (frame_width,frame_height))
start_time = time.time()
while( int(time.time() - start_time) < capture_duration ):
#start fps
time_elapsed = time.time() - prev
while(time_elapsed > 1./frame_per_sec):
ret, frame = cap.read()
if not ret:
break
if time_elapsed > 1./frame_per_sec:
prev = time.time()
#end fps
if ret==True:
frame = imutils.resize(frame, width=frame_width)
out.write(frame)
cv2.imshow('frame',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cap.release()
out.release()
cv2.destroyAllWindows()
I'm working on an application in python using opencv for use in athletic coaching. I'd like it to run at 60fps, but I can't seem to get it to that speed. I've determined that the part that is slowing everything down is the capture process; frames aren't getting from the camera to the cpu fast enough. All testing I've done so far indicates that the processing and the rendering are not the bottleneck; it's specifically the video input that is slowing everything down.
Now, I know it is possible for the camera to run at 60fps, because when I open up the same camera in guvcview (at the same resolution), I can get a full 60fps.
I don't want to share my entire project, but here's the part that manages frame capture: https://pastebin.com/nnx32rQK. My main app calls the get_frame() function in its own thread. I'm running this on an intel NUC6CAYH (celeron CPU, 2.3 ghz), running linux.
Any help would be welcome!
EDIT: I went into more detail below, but it looks like having the cap.read() within the Feed class is a whole lot slower than just accessing cap.read() directly..... and I have no idea why
The following code can accomplish that:
import cv2
cap = cv2.VideoCapture(videopath)
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number-1)
res, frame = cap.read()
frame_number is an integer in the range 0 to the number of frames in the video.
Notice: you should set frame_number-1 to force reading frame frame_number. It's not documented well but that is how the VideoCapture module behaves.
One may obtain amount of frames by:
amount_of_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)
Thank you GPPK.
The video parameters should be given as integers. Each flag has its own value. See here for the code.
The correct solution is:
import numpy as np
import cv2
#Get video name from user
#Ginen video name must be in quotes, e.g. "pirkagia.avi" or "plaque.avi"
video_name = input("Please give the video name including its extension. E.g. \"pirkagia.avi\":\n")
#Open the video file
cap = cv2.VideoCapture(video_name)
#Set frame_no in range 0.0-1.0
#In this example we have a video of 30 seconds having 25 frames per seconds, thus we have 750 frames.
#The examined frame must get a value from 0 to 749.
#For more info about the video flags see here: https://stackoverflow.com/questions/11420748/setting-camera-parameters-in-opencv-python
#Here we select the last frame as frame sequence=749. In case you want to select other frame change value 749.
#BE CAREFUL! Each video has different time length and frame rate.
#So make sure that you have the right parameters for the right video!
time_length = 30.0
fps=25
frame_seq = 749
frame_no = (frame_seq /(time_length*fps))
#The first argument of cap.set(), number 2 defines that parameter for setting the frame selection.
#Number 2 defines flag CV_CAP_PROP_POS_FRAMES which is a 0-based index of the frame to be decoded/captured next.
#The second argument defines the frame number in range 0.0-1.0
cap.set(2,frame_no);
#Read the next frame from the video. If you set frame 749 above then the code will return the last frame.
ret, frame = cap.read()
#Set grayscale colorspace for the frame.
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#Cut the video extension to have the name of the video
my_video_name = video_name.split(".")[0]
#Display the resulting frame
cv2.imshow(my_video_name+' frame '+ str(frame_seq),gray)
#Set waitKey
cv2.waitKey()
#Store this frame to an image
cv2.imwrite(my_video_name+'_frame_'+str(frame_seq)+'.jpg',gray)
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
Not all parameters are supported by all cameras - actually, they are one of the most troublesome part of the OpenCV library. Each camera type - from android cameras to USB cameras to professional ones offer a different interface to modify its parameters. There are many branches in OpenCV code to support as many of them, but of course not all possibilities are covered.
What you can do is to investigate your camera driver, write a patch for OpenCV and send it to code.opencv.org. This way others will enjoy your work, the same way you enjoy others'.
There is also a possibility that your camera does not support your request - most USB cams are cheap and simple. Maybe that parameter is just not available for modifications.
If you are sure the camera supports a given param (you say the camera manufacturer provides some code) and do not want to mess with OpenCV, you can wrap that sample code in C++ with boost::python, to make it available in Python. Then, enjoy using it.
I had the same problem with openCV on Raspberry Pi... don't know if this can solve your problem, but what worked for me was
import time
import cv2
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1024)
time.sleep(2)
cap.set(cv2.CAP_PROP_EXPOSURE, -8.0)
the time you have to use can be different