Integrated Circuits: Chapter I

Python Automation and Machine Learning for ICs: Chapter I
- Python Automation and Machine Learning for ICs -
- An Online Book: Python Automation and Machine Learning for ICs by Yougui Liao -

Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/

Table of Contents/Index

Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix

=================================================================================


Spark installation						Introduction
Data Ingestion in Apache Spark						Introduction
IoU (Intersection over Union)						Introduction
Apache Spark User Interface (UI)						Introduction
Comparison between Clouds (Amazon, IBM, Google ...)						Introduction
Skills Network Labs (SN Labs, IBM)						Introduction
Apache Spark on IBM Cloud						Introduction
Apache Data Ingestion Frameworks (ADIF) for CSV to DataFrame Conversion						Introduction
Apache Impala						Introduction
Feature Selection: Chi Square to select dependent and independent variables						Introduction
Image generation with ML						Introduction
Software/interface/API (Application Programming Interface) used in data science and machine learning						Introduction
Image classification with ML						Introduction
Identifying the business value of using ML						Introduction
Ingesting data in Hadoop						Introduction
Clean clipboard and/or check if clipboard is empty, text or image						Introduction
Add padding/black/colored edge to images						Introduction
Mirror/reflect image from left to right/from top to bottom						Introduction
Folder for saving images from screenshot obtained by Snipping Tool on windows						Introduction
Locate/find the center/coordinates of a bright (maximum/highest intensity) spot in an image & find nearest white pixel to a given/specifical pixel location on an binary image						Introduction
Line detection on image						Introduction
Retry a number of times/infinite retrying before exception or fail						Introduction
Examples of matplotlib (image/data) visualizations						Introduction
Save the image in clipboard to an image file						Introduction
Analysis of ROI (region of interest)/portion of an image						Introduction

issubclass						Introduction
.isin()						Introduction
Find duplicate items in a list						Introduction
Feature ingestion						Introduction
except ImportError						Introduction
input_fn						Introduction
tf.keras.layers.IntegerLookup						Introduction

Match on images to find and to highlight unsimilar (threshold=0) to identical (threshold=1) regions of an image that match a template with a cross-correlation method	code
Match on images to find and to highlight unsimilar (threshold=0) to identical (threshold=1) regions of an image that match a template with a cross-correlation method	code
Coordinate at center of features, e.g. images	Introduction
Infinite loops (e.g. stop infinite cycling of opening the same images)	Introduction
Work (read, write, insert and delete rows and columns, and merge and unmerge cells) in Excel sheets	Introduction
Work with/insert images into excel sheets	Introduction
Work with/insert images into excel sheets	Introduction
(Single and multiple enter/input) box for pop-up window	Introduction
Thresholding with Match Template: Match on images to find and to highlight unsimilar (threshold=0) to identical (threshold=1) regions of an image that match a template with a cross-correlation method	Introduction
Term Frequency-Inverse Document Frequency (TF-IDF)	Introduction
Print colored text in Python IDLE's terminal	Introduction
Copy text into clipboard and then use it immediately (one time clipboard)	Introduction
API (Application Programming Interface), e.g. weather, temperature	Introduction
Select/input a folder/directory/path for later to be called to use	Introduction
Image matching with cross correlation and overlap of template edge. In this matching process, Normalized cross-correlation with those edge images is performed.	code.
Cross correlation between two images	code
Cross correlation between two images in any sizes. Multiscaling is used to avoid the issue caused by the different sizes of the template and original image, in order to find match in a original image, namely, the size of template is larger than the original image.	code
Image similarity search	code, introduction
Immediately invoked function expression	code
Convert CSV to images, row by row, with pixel values: each row is an image	code.
Convert a CSV file to an image with one column and another column as x-axis and y-axis, respectively.	code.
Get a specific output for every input	code.
Calibrate and put a scale bar, and draw a line segment on an image	Introduction
Matrix conversion to image	image.
Calculate/pass the arbitrary (any) number of variables or input arguments	Introduction
Access variable inside and outside a function	Introduction
Slicing and indexing in string	Introduction
Popup windows with inputs	Introduction
Draw an arrow segment pointing from the start point to the end point in an image.	code.
Draw a line in a image	code
Open webpages in internet browsers (Chrome, Microsoft edge, IE browser)	Introduction
Open an image to file from URL (webpage), then it can be saved in PC	code, code. open with color changed.
Open any files, e.g. text (.txt), image files, and so on. .txt file will be opened in Python program, e.g. IDLE shell.	code
Copy images to a different folder/Save an image in a new folder	code, code. code. code.
Set the output image to zero everywhere except my mask (color filter), and display red, green, and blue (RGB) channels of an image.	code, code.
Show/open images in any image viewer	code, code.
Creates an image from an colored images after remove the grey components (image conversion from color to gray involved)	code
Extract three blue, green, red images from a color image or grey image, or convert RGB (color) image into three blue, green, red images	code
Change/convert a colored image to a grey image(, and then show pixel values).	cv2, cv2, cv2/skimage. cv2/skimage. PIL. matplotlib.
Compute the difference between two images by using Structural Similarity Index with "pip install --upgrade imutils"	code. code
Subtract (minus) two images after resizing them	code, code.
Sum two images after resizing them	code
Create images with global, adaptive mean, adaptive Gaussian, binary, trunc, Tozero, and tozero thresholds.	code
Find edges of images	code
Reference list items by position	code1, code2
Remove an item/element from a list	Introduction
Check if an item/element is in a list or not	Introduction
Comparison between iteration algorithm and recursive algorithm: a function repeat itself	Introduction
Randomly open an image/file in a specific folder	Introduction
Insert all the images into a ppt file (one image per slide)	(Introduction)
Get/list immediate subdirectories/subfolders	Introduction
Replace/substitute a item in a list	Introduction
Get/list immediate subdirectories/subfolders; get only the last part of a path/folder	Introduction
Get maximum and minimum value of column and its index	Introduction
Add/insert a column into an existing csv file	Introduction
Check if a directory is empty, find the position/index of a particular file/folder in a file directory/path	Introduction
Placement/position of Python import statements	Introduction
Watchdog ignore/skip and take pattern in directory/path	Introduction
Find common elements/items between two lists	Introduction
Invert the contrasts of black and white images	(Code)
Plot pixel intensity (histogram) along a line (row/column/x-axis/y-axis) of an image	Introduction
Global access to a variable inside a function from outside of the function	Introduction
if not hasattr/if hasattr (attribute)	Introduction
Search text on an image	Introduction
Search/extract/find text on an image	Introduction
Take a screenshot using a mouse click and drag method. For instance, take a screenshot, and then insert the image and/or a text into a ppt file.	Introduction
Resize and then sum/mix/overlap two images	Introduction
Measure length/distance on an image w/o calibrated bar	Introduction
Rotate (alignment) an image by line along the x- or y-axis	Introduction
Get pixel location/coordinates on an image using mouse click/events	Introduction
Crop/snip part of a image with definition by a pixel line	Introduction
Shift/translate image along x-axis/y-axis	Introduction
Modify a list (e.g. add/insert/remove an item between items, merge all items)	Introduction
Find the file names of the images in a pptx	Introduction
Applications of artificial intelligence/machine learning in industry	Introduction
Libraries used to convert incident documents into numerical vectors	Introduction
Ranking and votes of essential/most important skills for data analysts	Introduction
Ranking/most popular IT automation software tools	Introduction
Installation of UiPath and its packages and creation of new projects	Introduction
Extract a mask from an image with a threshold	Introduction
Save generated images	Introduction
Human inspection of defects in wafer map	Introduction
Global defects and local defects identified by defect denoising	Introduction
Image segmentation ("clustering") in color	Introduction
Non-zero pixel values from an image	Introduction
Mask an image with a threshold or with a color as a threshold	Introduction
Pixel values on specific pixel in an image	Introduction
Add/insert text to an image	Introduction
Methods of data and information visualization	Introduction
Convert images between Cartesian and Polar forms	Introduction
Draw circles/lines on images	Introduction
Plot a list of x, y coordinates to an image	Introduction
Store images in pandas dataframe column	Introduction
Smooth images (make image blurry)	Introduction

self and __init__ method in Class

Introduction

Image convolution

Introduction

Global import

Introduction

Guess and check algorithm with a combination of a for loop and an if statement

Introduction

Independence (independent events) versus dependence (dependent events) in ML

Introduction

Sampling Methods for Approximate Inference

Introduction

Approximate inference

Introduction

Inference and Inference Rules

Introduction

Informed search algorithms/heuristic search algorithms

Introduction

Intrinsic motivation in ML

Introduction

Policy iteration versus value iteration

Introduction

Policy iteration

Introduction

Value iteration

Introduction

Nonlinear extensions of Independent Component Analysis (ICA)

Introduction

Jensen's inequality

Introduction

Isolation forest algorithm

Introduction

Weight initialization

Introduction

Variance of input data for ML

Introduction

Normalizing input in neural network

Introduction

Impact of machine learning on ICs (integrated circuits)

Introduction

Edge detection of images in neural network

Introduction

Dependently Identically Distributed/Correlated Identically Distributed

Introduction

Gini impurity

Introduction

Bayes error/Bayes risk/Bayes rate/irreducible error

Introduction

CIFAR (Canadian Institute for Advanced Research) (CIFAR-10 and CIFAR-100)

Introduction

Mathematical equations, formulas and inequalities used in ML

Introduction

Custom AI/ML chips/ICs

Introduction

Indicator function/Kronecker delta function

Introduction

Independent and identically distributed (i.i.d./IID)

Introduction

Iterative algorithms

Introduction

Input data (sample and feature) in ML

Introduction

Independent Component Analysis (ICA)

Introduction

Bayesian Probability, Bayesian Statistics (Distribution Over a Distribution), versus Bayesian Inference

Introduction

Infinite Hypothesis Class

Introduction

Union Bound/Boole's Inequality

Introduction

Finite Hypothesis Class versus Infinite Hypothesis Class

Introduction

Complementary inequality

Introduction

Concentration inequality

Introduction

Hoeffding Inequality

Introduction

IR (Information Retrieval)

Introduction

send_keys() and its uploading images to webpage

Introduction

Fréchet Inception Distance (FID) coefficient

Introduction

Feature analysis/feature importance analysis/weight of feature

Introduction

Feature importance for Multinomial Naive Bayes algorithm

Introduction

Percentages of information received through different senses (eye, nose, ear and hand feeling)

Introduction

Variance Inflation Factors (VIFs)

Introduction

"Input space" in machine learning

Introduction

Check if a variable is a number, string or integer

Introduction

except ValueError (exception if it is not an integer)

Introduction

Find common/different elements/items between two lists/sets

Introduction

Last n days/weeks/months (.to_datetime(x), .set_index(y), .last(z), .reset_index(), and .max() in pandas)

Introduction

Check all the imported/current modules/libraries

Introduction

Combine multiple images into a single multi-page image or vice versa

Introduction

Count the number of the pages in a single multi-page/frame image

Introduction

Plot graph/figure/image from CSV file

Introduction

Plot graph/figure/image from CSV file/DataFrame by removing/hiding blank/empty cells with axis range (plt.xlim())

Introduction

Plot data into the same graph/figure/image from different csv files

Introduction

Plot images with certain image size and in color

Introduction

Plot multiple images on the same figure by hiding x- and y-labels

Introduction

Inside/outside edges/margins of plotted images

Introduction

Multimodal text and image similarity

Introduction

Calculating the area fraction of each circle overlapped (filled with color) by a square grid and build wafer map with integration

Introduction

Check if two circles intersect or overlap

Introduction

Convert strings to number (integers/float)

Introduction

Select/skip columns by index in DataFrame without changing the DataFrame itself, and change the order of of the selected columns

Introduction

Check if two lists are same/identical

Introduction

Different behavior of automation execution (e.g. pyautogui) locally or remotely through internet

Introduction

Module import and execution are skipped during script execution

Introduction

Detection of tables from an image

Introduction

Natural language inference

Introduction

Multimodal text and image search

Introduction

Add an item to a dictionary

Introduction

IDLE (integrated development and learning environment) and integrated development environment (IDE)

Introduction

Image embeddings

Introduction

(Text and image) contrastive learning

Introduction

Access and use SQL Database on SSMS (Microsoft SQL Server Management Studio Express) with pyodbc: localhost, insert rows, update, count updated, delete rows, comparision between extract data by Python and SQL itself

Introduction

Insert data/row into SQL Database on SSMS with pyodbc

Introduction

Remove/reload/unload an imported module/function/script

Introduction

Modify HTML webpage (e.g. with graph network by adding/inserting text/hyperlink in)

Introduction

Insert paragraphs of texts into Python script

Introduction

Python modules to interact with the operating system (os, platform, subprocess, shutils, glob and sys)

Introduction

Pyvis: An interactive geometric graph network/link/landscape

Introduction

Inside/outside edges/margins of plotted images

Introduction

Avoid two or multiple plots being wrongly/incorrectly/unnecessarily mixed/overlap

Introduction

Exception LookupError (string index)

Introduction

Iterate over rows in a DataFrame/read and print row by row (number of columns and rows, df.shape[0]/df.shape[1])

Introduction

from keyboard import is_pressed (Esc)

Introduction

Extract the index of a string element in a list

Introduction

igraph for clustering, network and tree

Introduction

Input a sentence and then output a sentence based on a dictionary obtained from csv

Introduction

Delete the column/row in a CSV file if they are empty or less than a number (or header/index only)

Introduction

__init__.py

Introduction

◆

__init__: Is short for initialize and is capable of creating objects from within a class. As a method, it’s just a function that’s defined inside of a class. But it must have the specific name __init__ — that’s two underscores followed by init followed by two more underscores. It is sometimes spoken as “dunder init.” The dunder part is short for double underline.

Introduction

◆

__init__(stat, listdir, polling_interval=1): Parameters: stat – stat function. See os.stat for details. listdir – listdir function. See os.listdir for details. polling_interval (float) – interval in seconds between polling the file system.

◆

self and __init__ method in Class

Introduction

__add__, __call__, __contains__, __delitem__, __delattr__, __eq__, __enter__, __ge__, __getattribute__, __getnewargs__, __getattr__, __getitem__, __gt__, __hash__, __reduce__, __iadd__, __imul__, __init_subclass__, __index__, __int__, __invert__, __new__, __neg__, __reduce_ex__, __reversed__, __rmul__, __radd__, __rand__, __rdivmod__, __rfloordiv__, __rlshift__, __rmod__, __ror__, __round__,__rpow__, __rrshift__, __rshift__, __rsub__, __rtruediv__, __rxor__, __dir__, __doc__, __divmod__, __iter__, __le__, __lt__, __len__, __ne__, __repr__, __setattr__, __setitem__, __sizeof__, __lshift__, __sub__, __subclasshook__, __str__

Introduction

Python IDLE restart during execution

Introduction

iter()

Introduction

Reindex a DataFrame

Introduction

Save dynamic graph as a movie/video or split a movie to image frames

Introduction


inplace=True	CSV: Change the items permanently. (code).
index_col	CSV: column number, column name, or list of column numbers/names, to use as the index (row labels) of the resulting DataFrame. By default, it will number the rows without using any column, unless there is one more data column than there are headers, in which case the first column is taken as the index.
iterator	CSV: If True, return a TextFileReader to enable reading a file into memory piece by piece
index	CSV: whether to write row (index) names (default True)
index_label	CSV: Column label(s) for index column(s) if desired. If None (default), and header and index are True, then the index names are used. (A sequence should be given if the DataFrame uses MultiIndex).
line_terminator	CSV: Character sequence denoting line end (default ‘\n’)
import pandas as pd	CSV: code.
index_col	CSV: Skip column index. code. code. code.
.loc[] and .iloc[]	Introduction
inplace=True	(code)
info()	(code)
.items()	Return the dictionary's key-value pairs. General.
if	if <test condition>: <block if the test condition is true> An if can have any number of if's nested within. Introduction. code.
if-else	if <test condition>: <block if the test condition is true> else: <block if the test condition is not true> If there are multiple else statements, then the second else is taken along with the nearest if. code1, code2, code3, code4.
Difference between if and if-else	Group 1: if and if-else.
if else ladder	"else" covers all the cases other than "if". if <test condition>: < The task to be performed if the condition 1 is true> elif <test 2>: <The task to be performed if the condition 2 is true> elif <test 3>: <The task to be performed if the condition 3 is true> else: <The task to be performed if none of the above condition is true> It is used when there are multiple conditions and the outcomes decide the action. Here, a switch is used in the case where different conditions lead to different actions..
If ... elif ...	Introduction. "elif" is a short word of "else if" and can have as many as "elif" as you need. code.
if ... in ...	E.g. find a letter in a string: code.
if ... not (in) ...	Introduction. code.
if not	(code)
if not hasattr/if hasattr (attribute)	Introduction
if ... is None	code.
if()	Introduction
import <library name>	import the entire library.
import ... as ...
insert()	Insert a list/item in a list.
index	index of row (the first column). Introduction
index=False, index=True	Keep or remove index column. (code)
image	The Image module provides a class with the same name which is used to represent a PIL image. The module also provides a number of factory functions, including functions to load images from files, and to create new images. E.g. Open, rotate, and display an image, code.
import	The import statement allows you to make use of data and functionality from a module which isn’t the current one.
import sys	The sys module provides various functions and variables that are used to manipulate different parts of the Python runtime environment. It allows operating on the interpreter as it provides access to the variables and functions that interact strongly with the interpreter. sys can be used to exit from the script as well. code
import textwrap	code.
from PIL import ImageTk
.iconbitmap()	(code)
ipadx=	(code)
Instance	One unit of data plus code generated from a class as an instance of that class. Each instance of a class is also called an object
from pptx.util import Inches	Inches, Emu, Cm, Mm, Pt, and Px are base class for length classes, providing properties for converting length values to convenient units. (code)
from PIL import Image	(code)
.insert_picture()	(code)

ignore_patterns = None	(code)
ignore_directories=False	(code)
ignore_directories	(Read-only) True if directories should be ignored; False otherwise.
ignore_patterns	(Read-only) Patterns to ignore matching event paths.
watchdog.observers.api: Immutables
is_recursive	Determines whether subdirectories are watched for the path.

ident	Thread identifier of this thread or None if it has not been started. This is a nonzero integer. See the thread.get_ident() function. Thread identifiers may be recycled when a thread exits and another thread is created. The identifier is available even after the thread has exited.
isAlive()	Return whether the thread is alive. This method returns True just before the run() method starts until just after the run() method terminates. The module function enumerate() returns a list of all alive threads.
is_alive()	Return whether the thread is alive. This method returns True just before the run() method starts until just after the run() method terminates. The module function enumerate() returns a list of all alive threads.
inode(path)	Returns an id for path.
isupper()	Returns the boolean True value if the all the characters of the string are in upper case. code.
islower()	Returns the boolean True value if the all the characters of the string are in lower case. code.
isspace()	Returns the boolean True value if there is a space in a string. code.
integrate.quad	Numerical integration. code.
'123'.isdigit()	Check if all the characters in the text are digits: "False" or "True". Superscript and subscripts (usually written using unicode) are also considered digit characters. The roman numerals, currency numerators and fractions (usually written using unicode) are considered numeric characters but not digits. Introduction. code.
'xyz'.isalpha()	Check if string is alphabet (letter, or one type of character)
import mss	(code)
import mss.tools	(code)
Insert/add text or line	Insert text or new text lines to a specific position, at the end of a line, in a file. Introduction
input()	The function is used to gather data from the user. Introduction. code1, code2, code3, file path . code5. code6.
math.isnan(x)	Returns True if x is not a number, otherwise returns False.
if __name__ == '__main__'	The processes starts reading the current file in order to execute the function specified. Without this clause, the import would first execute more process start calls, before getting to the function execution. code. code. code. code. Application example: run the page4853main3 program (as a module) through page4853main4 program. A similar example with defined functions is page4853main5.py. (code).
isinstance()	Returns True, if the first argument is an instance of that class. Multiple classes can be also be checked at once. E.g. isinstance(4, (int, float)). (code).
imag
Integer/fractions/round /decimal/digits/floating /ceil/floor	It does not have any fractional part. Introduction. int: Example code.
int()	Converts a float number or a string to an integer, cast the number. code1
Identity	The identity, in Python, refers to the address and does not change.
import datetime as ...	code
isnumeric()	Check if all the characters in the text are numeric or not: Introduction. Check if all the characters in the text are numeric. "-6" and "1.9" are NOT considered numeric values, because all the characters in the string must be numeric, and the - and the . are not. code.
isalnum()	Returns True if all characters in the string are alphanumeric (either alphabets or numbers). code.
.index()	It is an inbuilt function in Python, which searches for a given element from the start of the list and returns the lowest index where the element appears. The index of the first location is 0. Syntax: list_name.index(element, start, end). Introduction. code1, code2
.index(sub[start, [end]])	Same as ".find()", but it gives Error message if it does not find. code.
image[:,:,0]	The : is used to get all values. For example, image[:, :, 0] means get all rows, all columns, and the first (at index 0) color channel, which is the red channel. code.
image[:,:,1]	code.
image[:,:,2]	code.
scipy.integrate	Numerical integration
scipy.linalg.invhilbert	Create the inverse of a Hilbert matrix.
scipy.linalg.invpascal	Create the inverse of a Pascal matrix.
inner(a, b)	Inner product of two arrays.
linalg.inv(a)	Compute the (multiplicative) inverse of a matrix. code.
from scipy.linalg import eigh	Print "selected eigenvalues" and "complex ndarray": code.
io (I/O)	Core tools for working with streams and provides Python’s main facilities for dealing with various types of I/O, with three main types of I/O: text I/O, binary I/O and raw I/O. These are generic categories, and various backing stores can be used for each of them. A concrete object belonging to any of these categories is called a file object. Other common terms are stream and file-like object.
Text I/O	f = open("myfile.txt", "r", encoding="utf-8"), f = io.StringIO("some initial text data").
Binary I/O, io.BytesIO	f = open("myfile.jpg", "rb"), f = io.BytesIO(b"some initial binary data: \x00\x01"). Code.
Raw I/O	Also called unbuffered I/O. f = open("myfile.jpg", "rb", buffering=0).
from PIL import Image	code1, code2.
imageio.imread	Is a library that provides an easy interface to read and write a wide range of image data, including animated images, volumetric data, and scientific formats. It runs on Python 3.5+.
iPython	Has an architecture that facilitates parallel and distributed computing. You can develop, execute, debug, and monitor parallel applications.
Image.open	PIL.Image.open() Opens and identifies the given image file. code. image. code. code.
Image.save()	Saves this image under the given filename. If no format is specified, the format to use is determined from the filename extension, if possible. code1, code2.
v2.rectangle(image, start_point, end_point, color of border line, border thickness)	border. Compute the bounding box of the contour and then draw the bounding box on an image to represent where the ROI is. code. code. code.
cv2.imwrite()	Save images. Introduction. code. code. code. code.
cv2.imshow() and "xyz"..imshow()	A method is used to display an image in a window. The window automatically fits to the image size. First argument is a window name which is a string. Second argument is our image. Introduction. code. code. Code.
cv2.IMREAD_UNCHANGED	= -1. reads the image as is from the source. If the source image is an RGB, it loads the image into array with Red, Green and Blue channels. If the source image is ARGB, it loads the image with three color components along with the alpha or transparency channel.
cv2.IMREAD_GRAYSCALE	Reads the image as grey image. If the source image is color image, grey value of each pixel is calculated by taking the average of color channels. = 0, code.
cv2.IMREAD_COLOR	Reads the image with RGB colors but no transparency channel. = 1
cv.THRESH_TRUNC	If pixel intensity value is greater than threshold, it is truncated to the threshold. The pixel values are set to be the same as the threshold. All other values remain the same. code. code
cv.THRESH_TOZERO	Pixel intensity is set to 0, for all the pixels intensity, less than the threshold value. code. code
cv.THRESH_TOZERO_INV	Inverted or Opposite case of cv2.THRESH_TOZERO. code. code
cv::IMREAD_ANYDEPTH = 2
cv::IMREAD_ANYCOLOR = 4
cv::IMREAD_LOAD_GDAL = 8
cv::IMREAD_REDUCED_GRAYSCALE_2 = 16
cv::IMREAD_REDUCED_COLOR_2 = 17
cv::IMREAD_REDUCED_GRAYSCALE_4 = 32
cv::IMREAD_REDUCED_COLOR_4 = 33
cv::IMREAD_REDUCED_GRAYSCALE_8 = 64
cv::IMREAD_REDUCED_COLOR_8 = 65
cv::IMREAD_IGNORE_ORIENTATION = 128
cv::IMWRITE_EXR_TYPE_HALF = 1
cv::IMWRITE_EXR_TYPE_FLOAT = 2
cv::IMWRITE_JPEG_QUALITY = 1
cv::IMWRITE_JPEG_PROGRESSIVE = 2
cv::IMWRITE_JPEG_OPTIMIZE = 3
cv::IMWRITE_JPEG_RST_INTERVAL = 4
cv::IMWRITE_JPEG_LUMA_QUALITY = 5
cv::IMWRITE_JPEG_CHROMA_QUALITY = 6
cv::IMWRITE_PNG_COMPRESSION = 16
cv::IMWRITE_PNG_STRATEGY = 17
cv::IMWRITE_PNG_BILEVEL = 18
cv::IMWRITE_PXM_BINARY = 32
cv::IMWRITE_EXR_TYPE = (3 << 4) + 0
cv::IMWRITE_WEBP_QUALITY = 64
cv::IMWRITE_PAM_TUPLETYPE = 128
cv::IMWRITE_TIFF_RESUNIT = 256
cv::IMWRITE_TIFF_XDPI = 257
cv::IMWRITE_TIFF_YDPI = 258
cv::IMWRITE_TIFF_COMPRESSION = 259
cv::IMWRITE_PAM_FORMAT_NULL = 0
cv::IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1
cv::IMWRITE_PAM_FORMAT_GRAYSCALE = 2
cv::IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3
cv::IMWRITE_PAM_FORMAT_RGB = 4
cv::IMWRITE_PAM_FORMAT_RGB_ALPHA = 5
cv::IMWRITE_PNG_STRATEGY_DEFAULT = 0
cv::IMWRITE_PNG_STRATEGY_FILTERED = 1
cv::IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY = 2
cv::IMWRITE_PNG_STRATEGY_RLE = 3
cv::IMWRITE_PNG_STRATEGY_FIXED = 4
numpy.identity(n, dtype = None)	Return a identity matrix i.e. a square matrix with ones on the main diagonal. code.
hotkey('i')	Introduction
.insert_rows()	(code)
.insert_cols	For excel files: (code)
.isMaximized	(code)
.isMinimized	(code)
glob.iglob()	(code)
.idf_	(code)
.implicitly_wait()	Different from time.sleep(), implicitly_wait(time_to_wait) is to specify the amount of time the WebDriver instance i.e. the driver should wait when searching for an element if it is not immediately present in the HTML DOM in-terms of SECONDS when trying to find an element or elements if they are not immediately available. (code).
iframe in webpage	<Introduction>
iat[]	Introduction
os.path.isabs()	(code)
os.path.isfile()	(code)
os.path.isdir()	(code)
os.path.islink()	(code)
os.path.ismount()	(code)
ImageFilter.BLUR() ImageFilter.CONTOUR() ImageFilter.DETAIL() ImageFilter.EDGE_ENHANCE() ImageFilter.EDGE_ENHANCE_MORE() ImageFilter.EMBOSS() ImageFilter.FIND_EDGES() ImageFilter.SMOOTH() ImageFilter.SMOOTH_MORE() ImageFilter.SHARPEN() ImageFilter.MaxFilter() ImageFilter.MedianFilter() ImageFilter.MinFilter() ImageFilter.ModeFilter() ImageFilter.GaussianBlur() ImageFilter.GaussianBlur() ImageFilter.BoxBlur() ImageFilter.UnsharpMask() ImageFilter.UnsharpMask()
index("")	In list. (code)
.DataFrame()	Introduction. .drop(), index, columns, axes, dtypes, size, shape, ndim, empty, T, values

=================================================================================