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Crop features detected in OpenCV

I am trying to crop out features from a photo using opencv and haven't quite been able to find anything that helps to do so. I have photos in which I am trying to crop out rivets from metal panels to create a dataset of rivets that focus in on just the rivets. I have been able to use feature detection and feature matching using Orb to match features but I am unsure of how to then crop out those features. Ideally each photo should provide me with multiple cropped out photos of the rivets. Does anyone have any experience with anything such as this?

For template matching with OpenCV, you can use template matching (which is nicely described here)
If your template is skewed, rotated, etc. in the photo, you can use feature homography
For cropping the part of the image, you can look at this previously answered question.

How to change the attributes of cv2.StereoBM_create for depth map in OpenCV Python

I was implementing the depth map construction, code of which (in Python) is available here OpenCv Docs - depthMap I was successful in getting the depth map as they showed in the doc for their given images-pair (left and right stereo images) tsukuba_l.png and tsukuba_2.png. I considered testing my own image pairs, so I took from my mobile a pair of images, as shown below:
When I run the code, I'm getting the depth map something like this
I tried playing with numDisparities and blocksize, but it didn't help in getting the best map.
I thought of checking the script of cv2.StereoBM_create in its master folder in Github, but couldn't get that online. Can you help me with a way to implement depth maps for custom images taken by me? is there a way that we can play with the parameters or at least get me the link to GitHub master module that has all Stereo related modules. Thank you.

I guess you did not rectify the images which is fundamental for stereo matching.
You should first calibrate your stereo system (if you took them with mobile phone every image pair you take will have a different transform, the two cameras need to have always the same transformation between each other) and then rectify the images, in that way they are projected onto the same plane, then the stereo match algorithm looks for correspondences in the other image on the same rows.
Check in the docs for stereoRectify(), you will see some images as example of the rectification process.
By the way there is another python example based on SemiGlboal Block Matching algorithm in opencv/samples/python/stereo_match.py.

How to align multiple camera images using opencv

Imagine someone taking a burst shot from camera, he will be having multiple images, but since no tripod or stand was used, images taken will be slightly different.
How can I align them such that they overlay neatly and crop out the edges
I have searched a lot, but most of the solutions were either making a 3D reconstruction or using matlab.
e.g. https://github.com/royshil/SfM-Toy-Library
Since I'm very new to openCV, I will prefer a easy to implement solution
I have generated many datasets by manually rotating and cropping images in MSPaint but any link containing corresponding datasets(slightly rotated and translated images) will also be helpful.
EDIT:I found a solution here
http://www.codeproject.com/Articles/24809/Image-Alignment-Algorithms
which gives close approximations to rotation and translation vectors.
How can I do better than this?

It depends on what you mean by "better" (accuracy, speed, low memory requirements, etc). One classic approach is to align each frame #i (with i>2) with the first frame, as follows:
Local feature detection, for instance via SIFT or SURF (link)
Descriptor extraction (link)
Descriptor matching (link)
Alignment estimation via perspective transformation (link)
Transform image #i to match image 1 using the estimated transformation (link)

Horizontal and vertical edge profiles using python-opencv

I am trying to detect a vehicle in an image (actually a sequence of frames in a video). I am new to opencv and python and work under windows 7.
Is there a way to get horizontal edges and vertical edges of an image and then sum up the resultant images into respective vectors?
Is there a python code or function available for this.
I looked at this and this but would not get a clue how to do it.
You may use the following image for illustration.
EDIT
I was inspired by the idea presented in the following paper (sorry if you do not have access).
Betke, M.; Haritaoglu, E. & Davis, L. S. Real-time multiple vehicle detection and tracking from a moving vehicle Machine Vision and Applications, Springer-Verlag, 2000, 12, 69-83

I would take a look at the squares example for opencv, posted here. It uses canny and then does a contour find to return the sides of each square. You should be able to modify this code to get the horizontal and vertical lines you are looking for. Here is a link to the documentation for the python call of canny. It is rather helpful for all around edge detection. In about an hour I can get home and give you a working example of what you are wanting.

Do some reading on Sobel filters.
http://en.wikipedia.org/wiki/Sobel_operator
You can basically get vertical and horizontal gradients at each pixel.
Here is the OpenCV function for it.
http://docs.opencv.org/modules/imgproc/doc/filtering.html?highlight=sobel#sobel
Once you get this filtered images then you can collect statistics column/row wise and decide if its an edge and get that location.

Typically geometrical approaches to object detection are not hugely successful as the appearance model you assume can quite easily be violated by occlusion, noise or orientation changes.
Machine learning approaches typically work much better in my opinion and would probably provide a more robust solution to your problem. Since you appear to be working with OpenCV you could take a look at Casacade Classifiers for which OpenCV provides a Haar wavelet and a local binary pattern feature based classifiers.
The link I have provided is to a tutorial with very complete steps explaining how to create a classifier with several prewritten utilities. Basically you will create a directory with 'positive' images of cars and a directory with 'negative' images of typical backgrounds. A utiltiy opencv_createsamples can be used to create training images warped to simulate different orientations and average intensities from a small set of images. You then use the utility opencv_traincascade setting a few command line parameters to select different training options outputting a trained classifier for you.
Detection can be performed using either the C++ or the Python interface with this trained classifier.
For instance, using Python you can load the classifier and perform detection on an image getting back a selection of bounding rectangles using:
image = cv2.imread('path/to/image')
cc = cv2.CascadeClassifier('path/to/classifierfile')
objs = cc.detectMultiScale(image)

Separating similar object in an image - opencv python

I am trying to detect the objects in an image which look similar to the reference image. Here is how i'm trying to accomplish it:
Here is the sample Image:
and here is the image with SURF keypoints:
The rectangle is drawn based on Clustering method like "Hierarchial Clustering".
The main problem is, in this case it doesnt detect the objects individually, it detects everything as one object.
Is there a way to seperate these keypoints, so as to detect each vehicle seperately?
Is this a good way to detect objects or if there is a better way please suggest.

SURF keypoints are useful in detecting similar images, or images taken of the same place from different perspectives. Although you can use Haar classifiers for the purpose of object detection. It is also a part of OpenCV library.
Here is another great tutorial regarding object detection using OpenCV.