I am trying to change the resolution of PS5 camera in OpenCV, Python.
The problem is that PS5 Camera officially isn't supported on PC, and I have to use custom camera drivers from GitHub: https://github.com/Hackinside/PS5_camera_files
Default image resolution by this code is 640x376
self.capture = cv2.VideoCapture(name)
I found out that supported resolutions of this camera are 640x376 and 5148×1088, so I tried to do next:
res = self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 5148)
res = self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 1088)
But in both cases res is False, and resolution doesn't change. I can recieve only small resolution frame.
Camera 100% can work in 5148×1088, because if I launch Windows Camera application it shows me high quality images
Okay, the problem was, that I had a piece of code, where I read a frame from the capture using a loop:
while True:
self.capture.read()
It was in a parallel thread so changing the resolution was at the same time as reading images. It was a reason why the change resolution process always failed.
So the provided code in question should work if you do it before starting reading images.
In OpenCV with Python, when the fps of the webcam and a video file in the directory are same, why does the video file play in fast forward whereas the webcam continues to show the frames at a normal rate? What role does the cv2.waitKey() function play here
The fps of a video file means how it was encrypted, how many frames contain within a second as the name reveals. For example, if extracted 1 second of this video will produce only that number of frames (images).
The corresponding fps of the web camera means how many frames that camera can capture in a second. If saved to a video file that would mean how many frames are contained within each 1-second span.
There is a third (probably hidden to you) concept here though. How fast the opencv can read a video file. Normally, and for typical resolutions on a modern computer this fps is larger than the actual video. So, your computer seem to playback the video in fast forward mode because it reads (and displays) frames in a faster pace than the video file's fps.
Theoretically, you can calculate the delay you should import to the video playback to force it to displayed with normal pace. I am not sure how easily you can accomplish that (in a scientific way and not trial and error mode).
Hope this clarifies the issue.
I am trying to write a code for detecting the color green from a live video. I want to make a detector so that whenever the color green pops up in the screen, a counter starts counting how many times the color appears.
So for the video source, I am using the OBS Virtual Camera. But I have no idea how to input it as the source. I have seen codes inputting web cams as the source as shown below:
import numpy as np
import cv2
# Capturing video through webcam
webcam = cv2.VideoCapture(0)
Anyone have any idea how I can input the OBS virtual cam? Or does anyone know any alternative like switching to another language to do said task?
Windows will treat OBS Virtual Camera as a regular camera. The argument for cv2.VideoCapture is camera number. So up that number by 1 over and over again until the program uses the OBS Virtual Camera. And there you go.
Keep in mind that there is a bug currently reported that opencv is not parsing the stream from OBS virtual cam and just showing a black background.
https://github.com/obsproject/obs-studio/issues/3635
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 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.