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frozen frames detection openCV python

I'm trying to detect camera is capturing frozen frames or black frame. Suppose a camera is capturing video frames and suddenly same frame is capturing again and again. I spend long time to get any idea about this problem statement but i failed. So how we detect it or any idea/steps/procedure for this problem.

This was my approach to solve this issue.
Frozen frames: calculate absolute difference over HSV/RGB per every pixel in two consecutive frames np.arrays and determine max allowed diff that is valid for detecting frozen frames.
Black frames have naturally very low (or zero) V-value sum over the frame. Determine max V-sum of whole frame to determine, below which the frame is "black".

You can use this simple opencv method to detect black frame
if (cv::countNonZero(frame) == 0)
{
//do something if frame is black
}

Given a image of white lines forming a grid, how to get masks of the interior shapes using opencv?

I am trying to determine if there are pills in different cells of a pillbox. I have a raspberry pi camera that takes pictures of a pillbox at regular intervals. I would like to use computer vision using opencv and python to determine which of the 14 cells have pills in them.
I have gotten as far as isolating the borders of the cells. How do I create opencv masks for the interiors of the cells?
I tried running hough transform on the processed image, but it does not accurately find lines corresponding to each wall
Original image:
After Processing with cell walls isolated:

You can try Hough Line detection one the image to detect line segments, and use the information to either close the gaps in your current result or simply detect the cells using only the line segments.
The advantage of using line detections is that once you have the parameters of the line, you can extend it below what actually appears in the image. So, even if a cell has a gap, you should be able to close it.

Detect framed video content using python

I would like to detect framed video content (frequently used in TV advertising and referred to as single split, program split etc.)
Example 1:
Example 2 (3 screen captures, 2 seconds offset):
I have the video sequence as well as 3 screen captures available to analyze (middle, middle between middle and end, end).
To get started, I already tried a few methods like bounding box detection, and autocrop algorithms on the screen captures using opencv, imagemagick and PIL. This works to some extent, but not reliably.
Every TV station uses their own artwork for the surrounding frame
They sometimes animate the surrounding frame in the first few seconds
The background of the surrounding frame can be static but also animated, changing colors, etc
What would be an effective method to get a rather precise true/false reading on the media examples above? I would appreciate some ideas to build a suitable algorithm.
Thanks

You are essentially looking for two static, horizontal lines and two static, vertical lines that represent the edges of the inset video clip. What is inside the frame and what is outside the frame may/will be changing - only the edges of the inset frame are constant.
I would be applying a strong directional filter (Sobel) oriented at 0 and 90 degrees to find the horizontal and vertical lines. Then work through some number of frames accumulating all the edges that the two filters find. The brightest lines in the image at the end should be the best defined ones that have stayed still the longest.

Combine two overlapping videos frame by frame to form a single frame

I am getting video input from 2 separate cameras with some area of overlap between the output videos. I have tried out a code which combines the video output horizontally. Here is the link for that code:
https://github.com/rajatsaxena/NeuroscienceLab/blob/master/positiontracking/combinevid.py
To explain the problem visually:
The red part shows the overlap region between two image frame. I need the output to look like the second image, with first frame in blue and second frame in green (as shown in third illustration)
A solutions I can think of but unable to implement is, Using SIFT/SURF find out the maximum distance keypoints from both frames and then take the first video frame completely and just pick the non overlapping region from second video frame and horizontally combine them to get the stitched output.
Let me know of any other solutions possible as well. Thanks!

I read this post one hour ago. I tried some really easy approach. Not perfect but in some cases should work well. For example, if you have both cameras on one frame placed side by side.
I took 2 images from the phone like on a picture (color images). Program select Rectangles region from both source images and resize end extract this roi rectangles. The idea is to find the "best" overlapping Rect regions by normalized correlation.
M1 and M2 is mat roi to compare,
matchTemplate(M1, M2, res, TM_CCOEFF_NORMED);
After, I find this overlapping Rect use this to crop source images and combine by hconcat() function together.
My code is in C++ but is really simple to replicate this in python. It is not the best solution but one of the most simple solution. If your cameras are fixed in stable position between themselves. This is a good solution I think.
I hold my phone in hand :)
You can also use this simple approach on video. The speed depends only on the number of rectangle candidate you compare.
You can improve this by smart region to compare selection.
Also, I am thinking about another idea to use optical flow by putting your images from a camera at the same time to sequence behind each other. From the possible overlapping regions in one image extract good features to track and find them in the region of second images.
Surf and sift are great for this but this is the most simple idea on my mind.
Code is Here Code

openCV track object in video and obtain a better image from multiple frames

I'm working on detecting license plates with openCV, python and a raspberry pi. Most of it is already covered. What I want is to detect the ROI (Region of interest) of the plate, track it on a few frames and add those together to get a more clear and crisp image of the plate.
I want to get a better image of the plate by taking the information from several frames. I detect the plate, and have a collection of plates from several frames, as many as I wish and as many as the car is moving by the camera. How can I take all those and get a better version?

You need to ensure that your frame rate is fast enough to get a decent still of the moving car. When filming, each frame will most likely be blurry, and our brain pieces together the number plate on playback. Of course a blurry frame is no good for letter recognition, so is something you'll need to deal with on the hardware side, rather than software side.
Remember the old saying: Garbage in; Garbage out.