I'm currently trying to take an image in the best quality during capturing video at a lower quality. The problem is, that i'm using the video stream to check if face are in front of the cam and this needs lot's of resources, so i'm using a lower quality video stream and if there are any faces detected I want to take a photo in high quality.
Best regards and thank's for your help!
You can not have multiple capture sessions so at some point you will need to swap to higher resolution. First thing you are saying that face detection takes too much resources when using high res snapshots.. Why not try to simply down-sample the image and keep using high resolution all the time (send the down sampled one to the face detection, display the high res):
I would start with most common apple's graphic context and try to down scale it. If that takes too much cpu you could try to do the same on the GPU (find some library that does that or just create a simple program) or you could even try to simply drop odd lines and columns of the image as the raw data. In any of those cases you should also note that you probably do not need the face detection on the same thread as displaying, also you most likely don't even need a high frame rate for the detection (you display camera a full FPS but update the face recognition at 10 FPS for instance).
Another thing you can do is simply have the whole thing in low res, then when you need to take the image stop the session, start high res session, take a screenshot and swap back to low res for face detection.
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I've written an app with an object detection model and process images when an object is detected. The problem I'm running into is when an object is detected with 99% confidence but the frame I'm processing is very blurry.
I've considered analyzing the frame and attempting to detect blurriness or detecting device movement and not analyzing frames when the device is moving a lot.
Do you have any other suggestions to only process un-blurry photos or solutions other than the ones I've proposed? Thanks
You might have issues detecting "movement" when for instance driving in car. In that case looking at something inside your car is not considered as movement while looking at something outside is (if it's not far away anyway). There can be many other cases for this.
I would start by checking if camera is in focus. It is not the same as checking if frame is blurry but it might be very close.
The other option I can think of is simply checking 2 or more sequential frames and see if they are relatively the same. To do something like that it is bast to define a grid for instance 16x16 on which you evaluate similar values. You would need to mipmap your photos which manually means resizing it by half till you get to 16x16 image (2000x1500 would become 1024x1024 -> 512x512 -> 256x256 ...). Then grab those 16x16 pixels and store them. Once you have enough frames (at least 2) you can start comparing these values. GPU is perfect for resizing but those 16x16 values are probably best evaluated on the CPU. What you need to do is basically find an average pixel difference in 2 sequential 16x16 buffers. Then use that to evaluate if detection should be enabled.
This procedure may still not be perfect but it should be relatively feasible from performance perspective. There may be some shortcuts as some tools maybe already do resizing so that you don't need to "halve" them manually. From theoretical perspective you are creating sectors and compute their average color. If all the sectors have almost same color between 2 or more frames there is a high chance the camera did not move in that time much and the image should not be blurry from movement. Still if camera is not in focus you can have multiple sequential frames that are exactly the same but in fact they are all blurry. Same happens if you detect phone movement.
My specific question is: What are the drawbacks to using a snipped frame from a video vs taking a photo?
Details:
I want to use frames from live video streams to replace taking pictures because it is faster. I have already researched and considered:
Videos need faster shutter speed, leading to higher possibility of blurring
Faster shutter speed also means less exposure to light, leading to potentially darker images
A snipped frame from a video will probably be lower resolution (although maybe we can possibly turn up the resolution to compensate for this?)
Video might take up more memory -- I am still exploring the details with another post (What is being stored and where when you use cv2.VideoCapture()?)
Anything else?
I will reword my question to make it (possibly) easier to answer: What changes must I make to a "snip frame from video" process to make the result equivalent to taking a photo? Are these changes worth it?
The maximum resolution in picamera is 2592x1944 for still photos and 1920x1080 for video recording. Other issues to take into account are that you cannot receive all formats from VideoCapture, so now conversion of the YUV frame to JPG will be your responsibility. OK, OpenCV can handle this, but it takes considerable CPU time and memory.
I have faced with such problem, when I capture photo from LCD display there are annoying rainbow strips.
Is there any way to clear image from them doing some computer vision stuff?
Which are the keywords should I google for? Or maybe some useful links/papers related for.
My goal is to OCRing after it.
In case of low threshold WolfJolion binarization finds a lot of connected components which cause slow and bad recognition.
Using higher threshold some characters are vanished from image.
Source photo:
First binarization:
Second binarization:
P.S. Photos are taken from MacBook Pro Retina Display with iPhone 6 camera.
What you see is called Moire effect. It is caused by subsampling the screen pixels with your camera pixels.
Simply slightly change angle and or distance to avoid these. Then you don't need any image processing.
Beside that these stripes should not bother any decent OCR.
If you insist on image processing then a global threshold should do the trick.
I am using opencv to capture from an IP camera and can capture the feed fine.
The feed is of a door entrance, and I am capturing people coming in the door.
However, when some people move too quickly, that person becomes slightly blurred due to the motion.
Does anyone know how to capture a frame differently or how to run an algorithm to fix the image?
Here is a sample image:
I have hidden the face of the image but you should get the idea.
As you can see the gate, which is stationary, is in focus.
Here is the key part of the frame capture code (obviously there is more)
this->_cvCap = cvCaptureFromCAM(-1);
IplImage * image = cvQueryFrame(this->_cvCap);
cvSaveImage(filenamename, image);
The blurring is likely due to low shutter speed (high integration time) which gives a long exposure time.
You can theoretically set this using OpenCV with the cvSetCaptureProperty function.
Be aware though, that this is not supported for a lot of cameras.
Here is a related question on SO: Setting Camera Parameters in OpenCV/Python
I have a video of soccer in which the players are relatively far away from the camera and thus represent small portions of the image. I'm using background subtraction to detect the players and the results are fine but I have been asked to try detecting using Hog.
I tried using the detect MultiScale using the default descriptors presented on opencv but i cant get any detection. I dont really understand how can I make it work on this case, because on other sequences where the people are closer to the camera, the detector works fine.
Here is a sample image link
Thanks.
The descriptor you use with HOG determines the minimum size of person you can detect: with the DefaultPeopleDetector the detection window is 128 pixels high x 64 wide, so you can detect people around 90px high. With the Daimler descriptor the size you can detect is a bit smaller.
Your pedestrians are still too small for this, so you may need to magnify the whole image, or just the parts which show up as foreground using background segmentation.
Have a look at the function definition for detectMultiscale http://docs.opencv.org/modules/objdetect/doc/cascade_classification.html#cascadeclassifier-detectmultiscale
It might be that you need to reduced the value of minsize so as to detect smaller people or the people might just be too far away.