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![Array Indexing & Slicing
• Indexing can be forward or backward.
• Forward indexing: from start to end.
• Backward indexing: from end to start.
• General form of indexing:
my_array[start:stop:step]
• In backward indexing, the index of the last element is -1.
Start End
0 2
End Start
-3 -1
Forward Backward](https://image.slidesharecdn.com/pythonforcomputervision-170924221538/75/Python-for-Computer-Vision-Revision-22-2048.jpg)
![Indexing & Slicing Examples – 1D Array
• Forward: my_array[start=0:stop=6:step=2]
• Backward: my_array[start=-1:stop=-6:step=-2]
• Get all elements starting from index 3](https://image.slidesharecdn.com/pythonforcomputervision-170924221538/75/Python-for-Computer-Vision-Revision-23-2048.jpg)
![Indexing & Slicing Examples – 2D Array
• For MD arrays, indexing can be applied for each individual dimension.
Intersection between the different dimensions will be returned.
• Forward: my_array[start=0:stop=3:step=2, start=1:stop=4:step=1]
• Forward: my_array[start=-1:stop=-3:step=-1, start=0:stop=3:step=1]](https://image.slidesharecdn.com/pythonforcomputervision-170924221538/75/Python-for-Computer-Vision-Revision-24-2048.jpg)
Uploaded byAhmed Gad
Python for Computer Vision - Revision
The document discusses the use of Python for computer vision, emphasizing the importance of efficient data storage for images using numpy arrays instead of Python lists. It outlines the advantages of numpy for image processing, including its ability to perform operations on multidimensional data efficiently. Additionally, it covers installation, importing modules, and basic array operations along with information about the scipy library for advanced image processing techniques.
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Python for Computer Vision - Revision
- 1. Python for ComputerVision Ahmed Fawzy Gad [email protected] MENOUFIA UNIVERSITY FACULTY OF COMPUTERS AND INFORMATION INFORMATION TECHNOLOGY COMPUTER VISION المنوفية جامعة والمعلومات الحاسبات كلية المعلومات تكنولوجيا بالحاسب الرؤية المنوفية جامعة Tuesday 26 September 2017
- 2. Index • Traditional DataStorage in Python • NumPy Arrays • Matplotlib • SciPy
- 3. Goals of ComputerVision • Computer vision aims to enable computer to see, identify objects, and analyze such images to understand them like or better than humans. • To do this, computer needs to store and process images to get useful information. CAT
- 4. Storing Data inPython List Tuple Data Structures Which one is suitable for storing images? Let`s See.
- 5. List Vs. Tuple •Image is MD. Which one supports MD storage? • Which one supports updating its elements? Both List Tuples are immutable.
- 6. Python List forImage Storage • List is mutable and thus we can edit the image pixels easily and apply operations. • So, lets start storing an image into a Python list. The following code reads an image in the img_list variable.
- 8. Very time consumingfor simple operations
- 9. Very time consumingfor simple operations. • Why not applying arithmetic operations rather than looping? img_list = img_list + 50
- 10. List is complex.What is the alternative? • List adds more complexity in making operations over the images. One drawback was seen previously is that list operations are time consuming because they require pixel by pixel processing. • The best way for storing images is using arrays. • Rather than being time efficient in processing images, arrays has many other advantages. Can you imagine what?
- 11. Python Arrays • Listsare already available in Python. To use Python arrays, additional libraries must be used. • The library supporting arrays in Python is called Numerical Python (NumPy). • NumPy can be installed using Python command-line. • It is also available in all-in-one packages like Anaconda.
- 12. Installing NumPy • Basedon your environment, you can install new modules. • For traditional Python distributions, use the PIP installer. pip install numpy • For Anaconda, use the conda installed conda install numpy • But it is by default available in Anaconda.
- 13. Importing a PythonModule • After installing NumPy, we can import it in our programs and scripts.
- 14. NumPy Array forMD Data
- 15.
- 16. Array Data Type •The problem is expecting uint8 data type but another data type was used. • To know what is the array data type, use the dtype array property. • Change array type to uint8. How to make the conversion to uint8?
- 17. Controlling Array dtype •When creating the array, set the dtype argument of numpy.array to the desired data type. • dtype argument can be set to multiple types.
- 18. Creating Array withdtype of uint8
- 19. Controlling Array dtype •After array being created, use the astype method of numpy.ndarray. • It make a new copy of the array after being casted to the specified type in the dtype argument.
- 20. Array Operations • ArithemeticOperations • Operations between arrays
- 21. More Array CreationMethods • Array Creation • arange • linspace arange vs. linspace
- 22. Array Indexing &Slicing • Indexing can be forward or backward. • Forward indexing: from start to end. • Backward indexing: from end to start. • General form of indexing: my_array[start:stop:step] • In backward indexing, the index of the last element is -1. Start End 0 2 End Start -3 -1 Forward Backward
- 23. Indexing & SlicingExamples – 1D Array • Forward: my_array[start=0:stop=6:step=2] • Backward: my_array[start=-1:stop=-6:step=-2] • Get all elements starting from index 3
- 24. Indexing & SlicingExamples – 2D Array • For MD arrays, indexing can be applied for each individual dimension. Intersection between the different dimensions will be returned. • Forward: my_array[start=0:stop=3:step=2, start=1:stop=4:step=1] • Forward: my_array[start=-1:stop=-3:step=-1, start=0:stop=3:step=1]
- 25.
- 26.
- 27. Scientific Python (SciPy) •The main use of NumPy is to support numerical arrays in Python. According to the official documentation, NumPy supports nothing but the array data type and most basic operations: indexing, sorting, reshaping, basic elementwise functions, etc. • SciPy supports everything in NumPy but also adds new features not existing in NumPy. We can imagine that NumPy is a subset of SciPy. • Let`s explore what is in SciPy. SciPy NumPy
- 28. SciPy • SciPy containsa collection of algorithms and functions based on NumPy. User can use high-level commands to perform complex operations. • SciPy is organized into a number of subpackages.
- 29. SciPy for ImageProcessing • SciPy provides modules for working with images from reading, processing, and saving an image. • This example applies the Sobel edge detector to an image using SciPy.
- 30. References • SciPy • https://docs.scipy.org/doc/scipy/reference •NumPy • https://docs.scipy.org/doc/numpy/reference • Matplotlib • https://matplotlib.org/contents.html