Why Can't I Import cv2 in Python?
Troubleshooting "Cannot Find Module cv2" Error When Using OpenCV
When importing OpenCV's cv2 module in a Python program, you may encounter an "ImportError: No module named cv2" issue. Here's an analysis of the cause and a solution to rectify the problem:
As mentioned in the query, you have installed OpenCV version 2.4.5 on a Raspberry Pi using a script. Upon attempting to import cv2, the error message indicates that the module cannot be located.
The first point to consider is if OpenCV is correctly installed. To verify this, ensure that the cv2.so file is present in the "/usr/local/lib/python2.7/site-packages/..." directory.
The presence of folders for Python 3.2 and 2.6 in "/usr/local/lib" suggests a potential path configuration issue.
To resolve this, you should run the following commands in Terminal/CMD:
conda update anaconda-navigator conda update navigator-updater
After executing these commands, the instruction "pip install opencv-python" should resolve the issue for Windows users with Anaconda installed.
For Linux systems, you can use:
pip install opencv-python
Alternatively, you can try:
conda install opencv
Refer to the provided links (Link1, Link2) for further details.
Update for Python 3.5 :
If you are using Python versions 3.5 or higher, please refer to these resources: Link3, Link4.
Additional Solution:
For users with Anaconda, you can also utilize the following command (eliminating the need to add the menpo channel):
conda install -c conda-forge opencv
The above is the detailed content of Why Can't I Import cv2 in Python?. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics











Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Pythonlistsarepartofthestandardlibrary,whilearraysarenot.Listsarebuilt-in,versatile,andusedforstoringcollections,whereasarraysareprovidedbythearraymoduleandlesscommonlyusedduetolimitedfunctionality.

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.
