I am new to object detection using USB webcam.
I have a USB webcam which is capable of recording at 30fps FHD. I've connected this camera to a linux machine to capture video. The USB camera is connected to USB 3.0 port.
ffmpeg command line is used to capture a minute long, 15fps, 640x720, bitrate 5M video.
A simple opencv based python program reads this video file, frame by frame using cap.read(). However, I've noticed that when there is an moving object (e.g. human) in the frame, it becomes very blurry. (Here is a link of an example) I am wondering if this is normal or some adjustments are missing.
I am asking this question because I would like to run an object detection algorithm (SSD + MobileNet v2) on this video that I am capturing. But for many of the frames, if the object is moving, object detection fails to spot the object. (Yes, of course there isn't a perfect detection algorithm for all video analytics and there are various reasons for it to fail object detection)
Could you give pointers to remove the blurriness of this video frames?
1) Is it due to the video recording resolution is too low?
2) Is it because the python program is reading at different frame rate? (approximately 13~14 fps)
I am using a SOM that is running a cortex A5 #500MHz and am trying to maximise the frame rate received from a USB camera. My camera supports video capture with YUYV and MJPEG.
Other posts suggested forcing opencv to read MJPEG frames from the camera, however this slowed down the frame rate.
I can currently get about 18 fps reading YUYV format and about 10 fps reading MJPEG's at 640x480. Currently I am just grabbing frames and am not doing any other processing. I am getting the CAP_PROP_FOURCC format each loop to ensure that opencv is setting the capture format correctly.
I am currently running opencv 4 and python3.5
Any ideas why this may be happening?
EDIT: Capture code:
# Repeatedly capture current image
while True:
ret, image = cap.read()
if image is None:
time.sleep(0.5)
continue
codec = cap.get(cv2.CAP_PROP_FOURCC)
print(codec)
# Print the framerate.
text = '{:.2f}, {:.2f}, {:.2f} fps'.format(*fps.tick())
print(text)
Please provide the exact SOM and the camera that you are using.
There are many factors, for example the format of the images captured by the camera, how they are transferred and how they are received and managed by the SOM.
Transferring them shouldn't be the problem in terms of bandwidth.
I am assuming that the settings in opencv only apply to the SOM and won't change the format of the camera capture and therefore the SOM has more processing to do, hence the frame rate drops.
[EDIT]
I cannot comment yet so I hope you read this ... your camera link is dead :/
I have a computer connected to two external cameras and am using the Python interface to openCV to do real-time video analysis for instrument control. I love it.
But there is an odd quirk: the numbers assigned to the two cameras seem to be switching. That is, my program will be working with the correct camera. Then, if I run it again, there is a decent probability that it will grab the other camera. To temporarily correct this I can toggle between the cameras by changing the integer in the command to initialize the camera:
cap = cv2.VideoCapture(0)
vs.
cap = cv2.VideoCapture(1)
or back again. But, no matter which number I choose, there is still a chance that I'll get the wrong camera. Do any of you have any ideas for how to choose the correct camera every time?
I'm using Opencv 2.4.5 with python 2.7 to track people in video surveillance. At the beginning I used .avi and .mpeg videos to test my code, now I want to use a hcv-m100c camera. I am using a simple difference between frames (an initial frame compared with each frame) to identify the objects in movement, It works very well with the .avi and .mpeg videos I have, but when I use the camera the results are so bad because a lot of noise and stains appear in my video. I thought that the problem was my camera, but I made an .avi video with the same camera and I tested that video with my code and it works fine.
Now, I'm using the cv2.BackgroundSubtractorMOG but the problem is still there.
So, I think I need to do a pre-processing when I use the camera
Just for completeness:
Solution concept:
Possibly you could stream the video camera with something like ffmpeg which can transcode as well and then use OpenCV to read the network stream. It might be easier to use VLC to stream instead.
Solution detail:
VLC Stream code (Shell):
vlc "http://192.168.180.60:82/videostream.cgi?user=admin&pwd=" --sout "#transcode{vcodec=mp2v,vb=800,scale=1,acodec=mpga,ab=128,channels=2,samplerate=??44100}:duplicate{dst=rtp{sdp=rtsp://:8554/output.mpeg},dst=display}" --sout-keep
OpenCV Code (Python):
cap=cv2.VideoCapture("rtsp://:8554/output.mpeg")
I am using OpenCV (2.4) and Python (2.7.3) with a USB camera from Thorlabs (DC1545M).
I am doing some image analysis on a video stream and I would like to be able to change some of the camera parameters from my video stream. The confusing thing is that I am able to change some of the camera properties but not all of them, and I am unsure of what I am doing wrong.
Here is the code, using the cv2 bindings in Python, and I can confirm that it runs:
import cv2
#capture from camera at location 0
cap = cv2.VideoCapture(0)
#set the width and height, and UNSUCCESSFULLY set the exposure time
cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 1024)
cap.set(cv2.cv.CV_CAP_PROP_EXPOSURE, 0.1)
while True:
ret, img = cap.read()
cv2.imshow("input", img)
#cv2.imshow("thresholded", imgray*thresh2)
key = cv2.waitKey(10)
if key == 27:
break
cv2.destroyAllWindows()
cv2.VideoCapture(0).release()
For reference, the first argument in the cap.set() command refers to the enumeration of the camera properties, listed below:
0. CV_CAP_PROP_POS_MSEC Current position of the video file in milliseconds.
1. CV_CAP_PROP_POS_FRAMES 0-based index of the frame to be decoded/captured next.
2. CV_CAP_PROP_POS_AVI_RATIO Relative position of the video file
3. CV_CAP_PROP_FRAME_WIDTH Width of the frames in the video stream.
4. CV_CAP_PROP_FRAME_HEIGHT Height of the frames in the video stream.
5. CV_CAP_PROP_FPS Frame rate.
6. CV_CAP_PROP_FOURCC 4-character code of codec.
7. CV_CAP_PROP_FRAME_COUNT Number of frames in the video file.
8. CV_CAP_PROP_FORMAT Format of the Mat objects returned by retrieve() .
9. CV_CAP_PROP_MODE Backend-specific value indicating the current capture mode.
10. CV_CAP_PROP_BRIGHTNESS Brightness of the image (only for cameras).
11. CV_CAP_PROP_CONTRAST Contrast of the image (only for cameras).
12. CV_CAP_PROP_SATURATION Saturation of the image (only for cameras).
13. CV_CAP_PROP_HUE Hue of the image (only for cameras).
14. CV_CAP_PROP_GAIN Gain of the image (only for cameras).
15. CV_CAP_PROP_EXPOSURE Exposure (only for cameras).
16. CV_CAP_PROP_CONVERT_RGB Boolean flags indicating whether images should be converted to RGB.
17. CV_CAP_PROP_WHITE_BALANCE Currently unsupported
18. CV_CAP_PROP_RECTIFICATION Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently)
(Please note, as commenter Markus Weber pointed out below, in OpenCV 4 you have to remove the "CV" prefix from the property name, eg
cv2.CV_CAP_PROP_FRAME_HEIGHT -> cv2.CAP_PROP_FRAME_HEIGHT)
My questions are:
Is it possible to set camera exposure time (or the other camera parameters) through python/opencv?
If not, how would I go about setting these parameters?
Note: There is C++ code provided by the camera manufacturer showing how to do this, but I'm not an expert (by a long shot) in C++ and would appreciate any python-based solution.
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
To avoid using integer values to identify the VideoCapture properties, one can use, e.g., cv2.cv.CV_CAP_PROP_FPS in OpenCV 2.4 and cv2.CAP_PROP_FPS in OpenCV 3.0. (See also Stefan's comment below.)
Here a utility function that works for both OpenCV 2.4 and 3.0:
# returns OpenCV VideoCapture property id given, e.g., "FPS"
def capPropId(prop):
return getattr(cv2 if OPCV3 else cv2.cv,
("" if OPCV3 else "CV_") + "CAP_PROP_" + prop)
OPCV3 is set earlier in my utilities code like this:
from pkg_resources import parse_version
OPCV3 = parse_version(cv2.__version__) >= parse_version('3')
I wasn't able to fix the problem OpenCV either, but a video4linux (V4L2) workaround does work with OpenCV when using Linux. At least, it does on my Raspberry Pi with Rasbian and my cheap webcam. This is not as solid, light and portable as you'd like it to be, but for some situations it might be very useful nevertheless.
Make sure you have the v4l2-ctl application installed, e.g. from the Debian v4l-utils package. Than run (before running the python application, or from within) the command:
v4l2-ctl -d /dev/video1 -c exposure_auto=1 -c exposure_auto_priority=0 -c exposure_absolute=10
It overwrites your camera shutter time to manual settings and changes the shutter time (in ms?) with the last parameter to (in this example) 10. The lower this value, the darker the image.
If anyone is still wondering what the value in CV_CAP_PROP_EXPOSURE might be:
Depends. For my cheap webcam I have to enter the desired value directly, e.g. 0.1 for 1/10s. For my expensive industrial camera I have to enter -5 to get an exposure time of 2^-5s = 1/32s.