Identification:

Begin.

Trying to get closer to what it takes to identify a person, object.

>Realised the concept of how the computer can use histograms to sort of identify things.
>Plotted various histograms of the R,G and Blue channels of an image to see how different colors are distributed.
> Tried plotting histograms for real time Video, turns out its a bad idea :D
>
 image example - clearly green component is more

> Next to explore more of mouse events, I want to be able to get a histogram of a selected custom area from a real time stream.
>Using multiple flags (top_left, bottom_right - extracting the x and y ranges of the bounding box - slicing)
>

Pretty decent output. In the end, the result was:
a. Draw a bounding box over the live Video stream - (draw box using mouseCallBackEvents by locating position of the pointers)
b. Crop out the image inside the bounding box.
c. The cropped imaged is passed to a function that 'splits'  the image into the 3 channels.
d. flatten the channel and plot the histogram with 255 bins (0-255) for each of the color.
As seen in the above pic, the histogram says the cropped image has a distribution of the green color .


TakeAways:
> A call back mouse event can be registered to track co-ords of the pointer and also various events.
> Use the above technique to draw a bounding box. Using this for selection in the image.
> Use plt.hist() function to plot the color distributions of the image.

End.