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Using the previously calculated Gaussians, compute the Differences of Gaussians:.

Your result should look like scaleSpace.png. You will find the OpenCV function GaussianBlur useful. A stub function for this exercise is scaleSpaceEdges, which will use your previously completed ComputeGradients and ComputeEdges functions to calculate the edges of the resulting images.

Create a scale-space representation of the input image with sigma values of 1, 1.6, and 2.56.

Your results should look like combinedGradients.png (for part a) and combinedEdges.png (for parts b and c). A stub function for this exercise is ComputeCanny. Experiment with the OpenCV Canny edge detection function with various threshold values.

Locating zero-crossing in a 2D image is non-trivial, so instead we will use the Canny edge detector.

For this you can use the OpenCV function threshold (note that the function expects the CV_32F type).

A stub function for this exercise is ComputeEdges. Extract the edges from the first-order partial derivatives and the gradient magnitude images using amplitude thresholding.A stub function for this exercise is ComputeGradients. Using convolution with appropriate kernels calculate the approximations of the first and second-order partial derivatives:.

Panorama stitcher for four images python open cv python 64 Bit#

Be careful about the input and output types of the library functions you will use as some of them expect floating point numbers at 32 or 64 bit precision whilst others take in 8 bit integers, be sure to read their documentation carefully. The input image is already smoothed and subsampled using pyrDown function, this is to supress some of the noise.

Panorama stitcher for four images python open cv python code#

It provides function stubs for the helper functions you will need for the exercises, together with the code for loading and storing required images.

Panorama stitcher for four images python open cv python download#

To get started, first download the script week2.py.

Finally extend your previous implementation to produce the same result as exercise 2 (centred result with clamp-to-edge border treatment).

A good starting point for your own implementation is the convolveDFT function in the documentation of OpenCV’s dft function.

Now apply the convolution theorem to speed up convolution.

This can be done by conceptually extending the input image by replicating the edge pixels of the image (‘clamp-to-edge’).

Improve your implementation from the previous exercise by first centering the filtered image inside the black border and then filling the black border.

Please also include a progress indicator that prints to the console iff verbose is true. You can assume that kernels are normalised, so the term /(mend*nend) should be left out. The function basic_convolution should return the result as an image with the same size and datatype as the input image.

Implement basic convolution by translating the C code on page 26 of the lecture notes to Python.

Useful functions for this supervision include np.zeros_like and cv2.copyMakeBorder. It provides function stubs for the four exercises below which it feeds with a test image and convolution kernel. To get started, first download the script week1.py.

The OpenCV and NumPy documentations will come in handy while solving the exercises.

Panorama stitcher for four images python open cv python install#

However, if you know what you are doing, you are welcome to install OpenCV (2.3 or higher) with Python bindings yourself. We encourage you to use the provided virtual machine for solving the exercises. You can also experiment with the interactive Python shell ipython, which can run scripts using the %run script.pycommand. On the virtual machine, you can use gedit (Texteditor) for editing your Python scripts with syntax highlighting, and python.

Panorama stitcher for four images python open cv python mac os x#

The virtual machine (1.1 GB zipped) can be opened with the free VirtualBox software on most operating systems (tested on Windows 7 and Mac OS X 10.7). To spare you the installation effort, we have prepared a fully-functional virtual machine based on Ubuntu. However, the new Python bindings (introduced in OpenCV 2.3) are more accessible and thus used in the following exercises. Unfortunately, it is not trivial to install OpenCV and the first few steps are always quite tricky – particularly using the C or C++ interfaces. This work was created by Christian Richardt and Tadas Baltrušaitis in spring 2012. OpenCV is perhaps the most popular library for computer vision tasks, as it provides optimised implementations of many common computer vision algorithms. The following exercises aim to give you hands-on experience with some common computer vision algorithms.