Opencv=3.2.0 -> python=3.6 -> pip -> cachecontrol -> msgpack-python To be incompatible with the existing python installation in your environment: UnsatisfiableError: The following specifications were found Retrying with flexible solve.Ĭollecting package metadata (repodata.json): doneįound conflicts! Looking for incompatible packages. Solving environment: failed with initial frozen solve. (base) C:\Users\Kumar> conda install -c conda-forge opencv=3.2.0Ĭollecting package metadata (current_repodata.json): done it appears as the follows.Ĭould you please kindly help that how to solve this problem? So, now we have access to the latest versions of OpenCV and Dlib in almost any of the environments we want. These contain the packages for Windows, Linux, and Mac OS, for Python 2.7, 3.5, and 3.6, for 32-Bit and 64-Bit.Ĭonda install -c conda-forge opencv=3.2.0 If you check my posts on Installing Dlib on Anaconda Python on Windows, and Installing OpenCV 3 on Anaconda Python 3.5 on Windows, you know how easy it is to use conda to install them on Python 3.5 64-Bit on Windows.īut there was a catch: The Anaconda registry only had OpenCV 3.1 and Dlib 19.0 - not the latest versions.Ĭhecking the change-logs for OpenCV 3.2, and Dlib 19.4, we were missing quite a lot of features and optimizations.Ĭonda-Forge has now added the conda packages for OpenCV 3.2 and Dlib 19.4. OpenCV and Dlib working perfectly together, thanks to Conda If you tried installing Dlib or OpenCV 3 on Windows 64-Bit, then you know the effort it takes to get them setup, if it wasn't for Anaconda. In some scenarios, to get some Python packages to work in certain environments, getting them from Anaconda was the only way that worked. Not only does it have the ability to create fully isolated Python environments, its pre-built packages for many operating systems and architectures helps you to spend less time setting up, and more time doing actual machine learning stuff. Anaconda is an asset for us Machine Learning enthusiasts.
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