Do not install multiple different packages in the same environment. There is no plugin architecture: all the packages use the same namespace cv2. If you installed multiple different packages in the same environment, uninstall them all with pip uninstall and reinstall only one package. Packages for server headless environments such as Docker, cloud environments etc. This means that the packages avoid a heavy dependency chain to X11 libraries and you will have for example smaller Docker images as a result.
You should always use these packages if you do not use cv2. All packages contain Haar cascade files. For example:. CascadeClassifier cv2. Read OpenCV documentation. Before opening a new issue, read the FAQ below and have a look at the other issues which are already open. A: No, the packages are special wheel binary packages and they already contain statically built OpenCV binaries. Since opencv-python version 4.
If your pip is too old, it will try to use the new source distribution introduced in 4. However, source build will also fail because of too old pip because it does not understand build dependencies in pyproject.
Please upgrade pip with pip install --upgrade pip. If you are using older Windows version than Windows 10 and latest system updates are not installed, Universal C Runtime might be also required. Beware, some posts advise to install "Windows Server Essentials Media Pack", but this one requires the "Windows Server Essentials Experience" role, and this role will deeply affect your Windows Server configuration by enforcing active directory integration etc. If the above does not help, check if you are using Anaconda.
Old Anaconda versions have a bug which causes the error, see this issue for a manual fix. If you still encounter the error after you have checked all the previous solutions, download Dependencies and open the cv2. Q: Function foo or method bar returns wrong result, throws exception or crashes interpreter.
What should I do? A: It's easier for users to understand opencv-python than cv2 and it makes it easier to find the package with search engines. This is kept as the import name to be consistent with different kind of tutorials around the internet. Changing the import name or behaviour would be also confusing to experienced users who are accustomed to the import cv2. The aim of this repository is to provide means to package each new OpenCV release for the most used Python versions and platforms.
The project is structured like a normal Python package with a standard setup. The build process for a single entry in the build matrices is as follows see for example. The build can be customized with environment variables. In addition to any variables that OpenCV's build accepts, we recognize:. If some dependency is not enabled in the pre-built wheels, you can also run the build locally to create a custom wheel.
In order to build opencv-python in an unoptimized debug build, you need to side-step the normal process a bit. If you would like the build produce all compiler commands, then the following combination of flags and environment variables has been tested to work on Linux:. Since OpenCV version 4. This means that if your system is not compatible with any of the wheels in PyPI, pip will attempt to build OpenCV from sources. If you need a OpenCV version which is not available in PyPI as a source distribution, please follow the manual build guidance above instead of this one.
You can also force pip to build the wheels from the source distribution. Some examples:. If you need contrib modules or headless version, just change the package name step 4 in the previous section is not needed. However, any additional CMake flags can be provided via environment variables as described in step 3 of the manual build section.
If none are provided, OpenCV's CMake scripts will attempt to find and enable any suitable dependencies. I simply did not have the time to moderate and respond to them all, and the sheer volume of requests was taking a toll on me. If you need help learning computer vision and deep learning, I suggest you refer to my full catalog of books and courses — they have helped tens of thousands of developers, students, and researchers just like yourself learn Computer Vision, Deep Learning, and OpenCV.
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We have an empty form left , a partially completed form that is out of alignment middle , and our processed form that has been aligned by our algorithm right. I know — you thought you were done with the W-4 forms until next tax season. Figure 2: We use a feature-based image alignment technique that detects keypoints, which are just the location of regions of interest.
The keypoints found are highlighted in yellow. Figure 3: Here we see the keypoints matched between our partially completed form that is out of alignment with our original form that is properly aligned. You can see there are colored lines drawn using OpenCV between each corresponding keypoint pair. Figure 4: The homography matrix represents the rotation, translation, and scale to convert warp from the plane of our input image to the plane of our template image. Figure 5: We have two similar W-4 forms in different orientations.
We have our original template form in the desired orientation left and the partially completed form that needs to be aligned right. Our goal is to use OpenCV to align the right image to the left template image using keypoint matching and a homography matrix so that we can apply OCR next week to the form fields. The form itself, from an arbitrary viewpoint, should be identical to the template image but with form data present.
By default, keypoints are not displayed; however I recommend setting this value to True for debugging purposes. Figure 6: We can see that the keypoint matches from our OpenCV algorithm are working well for our sample images. Allocating memory to store the keypoints ptsA and ptsB for our top matches Initiating a loop over our top matches , and inside indicating that A and B keypoints map to one another. Aligning our image can be boiled down to our final two steps:.
Align our image by means of applying a warp perspective cv2. This aligned image result is returned to the caller via Line Figure 7: We have our partially completed form, which has been taken with our iPhone from the perspective of looking down at the image on the table. The resulting image is at an angle and rotated.
Figure 8: We have a side-by-side comparison. Our partially completed form has been aligned and registered left to be more similar to our template form right. Our OpenCV keypoint matching algorithm did a pretty nice job! Our overlay method really makes the differences pop. And, here we will use image segmentation technique called contours to extract the parts of an image.
So we have found a total of three contours. Now, in the above code we had also printed the contour file using [print contours ], this file tells how these contours looks like, as printed in above console output. In the above console output we have a matrix which looks like coordinates of x, y points. OpenCV stores contours in a lists of lists.
We can simply show the above console output as follows:. Now, as we use the length function on contour file, we get the length equal to 3, it means there were three lists of lists in that file, i.
Now, imagine CONTOUR 1 is the first element in that array and that list contains list of all the coordinates and these coordinates are the points along the contours that we just saw, as the green rectangular boxes. There are different methods to store these coordinates and these are called approximation methods, basically approximation methods are of two types.
In the above code cv2. Retrieval mode defines the hierarchy in contours like sub contours, or external contour or all the contours. Hierarchy is stored in the following format [Next, Previous, First child, parent].
Printing the contour file to know what contours comprises of. So through the demonstration of above codes we could clearly see the difference between the cv2. While in cv2. In approximating contours, a contour shape is approximated over another contour shape, which may be not that much similar to the first contour shape. Iterate through each contour and compute their bounding rectangle.
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