Tutorial Playlist
200 Lessons1. Introduction to Python
2. Features of Python
3. How to install python in windows
4. How to Install Python on macOS
5. Install Python on Linux
6. Hello World Program in Python
7. Python Variables
8. Global Variable in Python
9. Python Keywords and Identifiers
10. Assert Keyword in Python
11. Comments in Python
12. Escape Sequence in Python
13. Print In Python
14. Python-if-else-statement
15. Python for Loop
16. Nested for loop in Python
17. While Loop in Python
18. Python’s do-while Loop
19. Break in Python
20. Break Pass and Continue Statement in Python
21. Python Try Except
22. Data Types in Python
23. Float in Python
24. String Methods Python
25. List in Python
26. List Methods in Python
27. Tuples in Python
28. Dictionary in Python
29. Set in Python
30. Operators in Python
31. Boolean Operators in Python
32. Arithmetic Operators in Python
33. Assignment Operator in Python
34. Bitwise operators in Python
35. Identity Operator in Python
36. Operator Precedence in Python
37. Functions in Python
38. Lambda and Anonymous Function in Python
39. Range Function in Python
40. len() Function in Python
41. How to Use Lambda Functions in Python?
42. Random Function in Python
43. Python __init__() Function
44. String Split function in Python
45. Round function in Python
46. Find Function in Python
47. How to Call a Function in Python?
48. Python Functions Scope
49. Method Overloading in Python
50. Method Overriding in Python
51. Static Method in Python
52. Python List Index Method
53. Python Modules
54. Math Module in Python
55. Module and Package in Python
56. OS module in Python
57. Python Packages
58. OOPs Concepts in Python
59. Class in Python
60. Abstract Class in Python
61. Object in Python
62. Constructor in Python
63. Inheritance in Python
64. Multiple Inheritance in Python
65. Encapsulation in Python
66. Data Abstraction in Python
67. Opening and closing files in Python
68. How to open JSON file in Python
69. Read CSV Files in Python
70. How to Read a File in Python
71. How to Open a File in Python?
72. Python Write to File
73. JSON Python
74. Python JSON – How to Convert a String to JSON
75. Python JSON Encoding and Decoding
76. Exception Handling in Python
77. Recursion in Python
78. Python Decorators
79. Python Threading
80. Multithreading in Python
81. Multiprocеssing in Python
82. Python Regular Expressions
83. Enumerate() in Python
84. Map in Python
85. Filter in Python
86. Eval in Python
87. Difference Between List, Tuple, Set, and Dictionary in Python
88. List to String in Python
89. Linked List in Python
90. Length of list in Python
91. Reverse a List in Python
92. Python List remove() Method
93. How to Add Elements in a List in Python
94. How to Reverse a List in Python?
95. Difference Between List and Tuple in Python
96. List Slicing in Python
97. Sort in Python
98. Merge Sort in Python
99. Selection Sort in Python
100. Sort Array in Python
101. Sort Dictionary by Value in Python
102. Datetime Python
103. Random Number in Python
104. 2D Array in Python
105. Abs in Python
106. Advantages of Python
107. Anagram Program in Python
108. Append in Python
109. Applications of Python
110. Armstrong Number in Python
111. Assert in Python
112. Binary Search in Python
113. Binary to Decimal in Python
114. Bool in Python
115. Calculator Program in Python
116. chr in Python
117. Control Flow Statements in Python
118. Convert String to Datetime Python
119. Count in python
120. Counter in Python
121. Data Visualization in Python
122. Datetime in Python
123. Extend in Python
124. F-string in Python
125. Fibonacci Series in Python
126. Format in Python
127. GCD of Two Numbers in Python
128. How to Become a Python Developer
129. How to Run Python Program
130. In Which Year Was the Python Language Developed?
131. Indentation in Python
132. Index in Python
133. Interface in Python
134. Is Python Case Sensitive?
135. Isalpha in Python
136. Isinstance() in Python
137. Iterator in Python
138. Join in Python
139. Leap Year Program in Python
140. Lexicographical Order in Python
141. Literals in Python
142. Matplotlib
143. Matrix Multiplication in Python
144. Memory Management in Python
145. Modulus in Python
146. Mutable and Immutable in Python
147. Namespace and Scope in Python
148. OpenCV Python
Now Reading
149. Operator Overloading in Python
150. ord in Python
151. Palindrome in Python
152. Pass in Python
153. Pattern Program in Python
154. Perfect Number in Python
155. Permutation and Combination in Python
156. Prime Number Program in Python
157. Python Arrays
158. Python Automation Projects Ideas
159. Python Frameworks
160. Python Graphical User Interface GUI
161. Python IDE
162. Python input and output
163. Python Installation on Windows
164. Python Object-Oriented Programming
165. Python PIP
166. Python Seaborn
167. Python Slicing
168. type() function in Python
169. Queue in Python
170. Replace in Python
171. Reverse a Number in Python
172. Reverse a string in Python
173. Reverse String in Python
174. Stack in Python
175. scikit-learn
176. Selenium with Python
177. Self in Python
178. Sleep in Python
179. Speech Recognition in Python
180. Split in Python
181. Square Root in Python
182. String Comparison in Python
183. String Formatting in Python
184. String Slicing in Python
185. Strip in Python
186. Subprocess in Python
187. Substring in Python
188. Sum of Digits of a Number in Python
189. Sum of n Natural Numbers in Python
190. Sum of Prime Numbers in Python
191. Switch Case in Python
192. Python Program to Transpose a Matrix
193. Type Casting in Python
194. What are Lists in Python?
195. Ways to Define a Block of Code
196. What is Pygame
197. Why Python is Interpreted Language?
198. XOR in Python
199. Yield in Python
200. Zip in Python
The fusion of computer science and image processing in today's world has given birth to the captivating realm of Computer Vision. This field empowers computers to revolutionize various industries, including healthcare, automotive, agriculture, and more, by comprehending and interpreting visual information from our surroundings. At the core of this transformation lies OpenCV (Open Source Computer Vision Library) for Python, a pivotal tool. This comprehensive blog aims to dive deep into the realm of OpenCV Python, shedding light on its origins, inner mechanisms, and its indispensable role within the domain of Computer Vision.
OpenCV Python, an influential open-source library, furnishes a suite of tools and algorithms for image and video manipulation and analysis. With its widespread adoption in academia and industry, it has evolved into an indispensable resource for computer vision endeavors. Its Python bindings streamline integration into a variety of Python applications, making it the favored option for developers engaged in a broad spectrum of projects.
OpenCV (Open Source Computer Vision), an open-source multi-platform computer vision framework for real-time image analysis, was first created by Intel. The OpenCV program is now the de facto industry standard for anything computer vision-related. OpenCV continues to be very well-liked in 2023, receiving over 29 000 openCV downloads weekly. C and C are used to create OpenCV. It is compatible with the most widely used operating systems, including GNU/Linux, OS X, Windows, Android, and iOS. The Apache 2 license makes it freely accessible. Interfaces for Python, Matlab, and other languages are actively being developed. For real-time computer vision, the OpenCV library has over 2500 algorithms, substantial cv2 documentation, and example code.
OpenCV has been utilized in various applications, and research projects since its initial release in 2000 under the BSD agreement and then under the Apache 2 license. Some of these uses include putting together camera images for satellite or web maps, noise mitigation in medical images, security, monitoring, and detection of intrusion systems, mechanical monitoring, as well as security networks, production AI inspection, and military uses, and unsupervised aerial, ground, and submerged vehicles.
The OpenCV library offers a rich set of features that empower developers to undertake a wide array of image and video processing tasks. With OpenCV, you can:
OpenCV's journey dates back to the late 1990s when Intel initiated its development. Over the years, it evolved into a comprehensive library with a rich set of features for computer vision and machine learning tasks. Willow Garage provided further support and resources for its development, and later, Itseez took over the project. In 2016, Intel reabsorbed Itseez, reaffirming its commitment to OpenCV's growth. Today, OpenCV is a community-driven project with a vibrant ecosystem of contributors and users.
At its core, OpenCV provides a vast collection of tools and functions for image and video processing. These include image manipulation, feature detection, object recognition, machine learning, and more. It functions by leveraging algorithms and mathematical operations to analyze and manipulate pixel values in images and video frames.
OpenCV is capable of reading and writing pictures from scratch, drawing an image using code, capturing and saving films, processing images, performing feature detection, identifying particular objects in movies, and calculating an object's direction and motion.
The primary OpenCV library modules are listed below:
i) Essential Functioning
The OpenCV library's primary features include operations on fundamental data structures like Scalar, Point, Range, etc. It has the multidimensional array Mat for picture storage.
ii) Processing images
This subject covers a variety of image processing techniques, including histograms, color space conversion, geometric picture modifications, and image filtering.
iii) Video
Concepts for video analysis including object tracking, background removal, and motion estimation are covered in this session.
iv) I/O video
The video capture and video codecs utilizing the OpenCV library are explained in this module.
v) Calib3d
Fundamental multiple-view geometry methods, single- and stereo camera setup, object pose calculation, and 3D reconstruction components are all covered by the algorithms in this subject.
vi) Features2d
The ideas of identifying features and description are covered in this module.
vii) Objdetect
The detection of items and examples of preset classes, such as faces, eyes, automobiles, etc., is included in this module.
viii) Highgui
This interface has straightforward UI features and is simple to use.
Computer Vision, enabled by OpenCV, allows machines to recognize patterns and objects within images and videos. This recognition is achieved through a series of steps, which include:
OpenCV simplifies and accelerates each of these steps through its rich set of functions and pre-trained models.
OpenCV's widespread adoption for Computer Vision finds its roots in a constellation of compelling reasons:
OpenCV Python has emerged as a foundational tool in the domain of Computer Vision, empowering developers to create innovative solutions for a wide range of applications. Its rich history, open-source nature, cross-platform support, and integration with Python make it an indispensable asset for anyone working in the field of image and video processing. As Computer Vision continues to drive technological advancements, OpenCV Python remains at the forefront, enabling the next generation of intelligent applications.
1. Why use Python with OpenCV?
You may carry out image analysis and computer vision applications using the Python package OpenCV. It offers a variety of capabilities, including tracking, facial recognition, and object detection.
2. What is OpenCV's full name?
Open Source Computer Vision Library is how OpenCV is officially referred to. It is a collection of programming tools, mostly for in-the-moment computer vision. It was first created by Intel and afterwards sponsored by Willow Garage, Itseez, and Intel (which eventually purchased Itseez).
3. Is Python's OpenCV open source?
OpenCV is free software distributed under the terms of the Apache 2 License. For business usage, it is free.
4. What are the benefits of OpenCV?
OpenCV was created to execute computing-intensive vision tasks as effectively and quickly as possible. As a result, it places a lot of emphasis on real-time AI vision applications. The program is multithreaded and written in C that has been optimized for multicore CPUs.
5. What kind of system runs OpenCV?
Android, Maemo, and iOS are just a few of the mobile and desktop operating systems that support OpenCV.
6. Is MATLAB comparable to OpenCV?
Well, MATLAB is more user-friendly for producing and displaying data, while OpenCV executes considerably more quickly. When using OpenCV, the speed ratio can occasionally exceed 80. Due to a lack of information and error handling procedures, OpenCV is nonetheless more challenging to master.
7. How to import cv2 in python?
To import the OpenCV library (cv2) in Python, you can use the following one-liner:
python
import cv2
8. How to install opencv?
To install OpenCV using pip, you can use the following one-liner:
bash
pip install opencv-python-headless
PAVAN VADAPALLI
Director of Engineering
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working …Read More
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upGrad does not grant credit; credits are granted, accepted or transferred at the sole discretion of the relevant educational institution offering the diploma or degree. We advise you to enquire further regarding the suitability of this program for your academic, professional requirements and job prospects before enr...