Python+opencv实时的人眼识别+眨眼检测+闭眼检测源代码及详细教程

# imutils A series of convenience functions to make basic image processing functions such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV and ***both*** Python 2.7 and Python 3. For more information, along with a detailed code review check out the following posts on the [PyImageSearch.com](http://www.pyimagesearch.com) blog: - [http://www.pyimagesearch.com/2015/02/02/just-open-sourced-personal-imutils-package-series-opencv-convenience-functions/](http://www.pyimagesearch.com/2015/02/02/just-open-sourced-personal-imutils-package-series-opencv-convenience-functions/) - [http://www.pyimagesearch.com/2015/03/02/convert-url-to-image-with-python-and-opencv/](http://www.pyimagesearch.com/2015/03/02/convert-url-to-image-with-python-and-opencv/) - [http://www.pyimagesearch.com/2015/04/06/zero-parameter-automatic-canny-edge-detection-with-python-and-opencv/](http://www.pyimagesearch.com/2015/04/06/zero-parameter-automatic-canny-edge-detection-with-python-and-opencv/) - [http://www.pyimagesearch.com/2014/09/01/build-kick-ass-mobile-document-scanner-just-5-minutes/](http://www.pyimagesearch.com/2014/09/01/build-kick-ass-mobile-document-scanner-just-5-minutes/) - [http://www.pyimagesearch.com/2015/08/10/checking-your-opencv-version-using-python/](http://www.pyimagesearch.com/2015/08/10/checking-your-opencv-version-using-python/) ## Installation Provided you already have NumPy, SciPy, Matplotlib, and OpenCV already installed, the `imutils` package is completely `pip`-installable: <pre>$ pip install imutils</pre> ## Finding function OpenCV functions by name OpenCV can be a big, hard to navigate library, especially if you are just getting started learning computer vision and image processing. The `find_function` method allows you to quickly search function names across modules (and optionally sub-modules) to find the function you are looking for. #### Example: Let's find all function names that contain the text `contour`: <pre>import imutils imutils.find_function("contour")</pre> #### Output: <pre>1. contourArea 2. drawContours 3. findContours 4. isContourConvex</pre> The `contourArea` function could therefore be accessed via: `cv2.contourArea` ## Translation Translation is the shifting of an image in either the *x* or *y* direction. To translate an image in OpenCV you would need to supply the *(x, y)*-shift, denoted as *(t_x, t_y)* to construct the translation matrix *M*:  And from there, you would need to apply the `cv2.warpAffine` function. Instead of manually constructing the translation matrix *M* and calling `cv2.warpAffine`, you can simply make a call to the `translate` function of `imutils`. #### Example: <pre># translate the image x=25 pixels to the right and y=75 pixels up translated = imutils.translate(workspace, 25, -75)</pre> #### Output: <img src="docs/images/translation.png?raw=true" alt="Translation example" style="max-width: 500px;"> ## Rotation Rotating an image in OpenCV is accomplished by making a call to `cv2.getRotationMatrix2D` and `cv2.warpAffine`. Further care has to be taken to supply the *(x, y)*-coordinate of the point the image is to be rotated about. These calculation calls can quickly add up and make your code bulky and less readable. The `rotate` function in `imutils` helps resolve this problem. #### Example: <pre># loop over the angles to rotate the image for angle in xrange(0, 360, 90): # rotate the image and display it rotated = imutils.rotate(bridge, angle=angle) cv2.imshow("Angle=%d" % (angle), rotated)</pre> #### Output: <img src="docs/images/rotation.png?raw=true" alt="Rotation example" style="max-width: 500px;"> ## Resizing Resizing an image in OpenCV is accomplished by calling the `cv2.resize` function. However, special care needs to be taken to ensure that the aspect ratio is maintained. This `resize` function of `imutils` maintains the aspect ratio and provides the keyword arguments `width` and `height` so the image can be resized to the intended width/height while (1) maintaining aspect ratio and (2) ensuring the dimensions of the image do not have to be explicitly computed by the developer. Another optional keyword argument, `inter`, can be used to specify interpolation method as well. #### Example: <pre># loop over varying widths to resize the image to for width in (400, 300, 200, 100): # resize the image and display it resized = imutils.resize(workspace, width=width) cv2.imshow("Width=%dpx" % (width), resized)</pre> #### Output: <img src="docs/images/resizing.png?raw=true" alt="Resizing example" style="max-width: 500px;"> ## Skeletonization Skeletonization is the process of constructing the "topological skeleton" of an object in an image, where the object is presumed to be white on a black background. OpenCV does not provide a function to explicitly construct the skeleton, but does provide the morphological and binary functions to do so. For convenience, the `skeletonize` function of `imutils` can be used to construct the topological skeleton of the image. The first argument, `size` is the size of the structuring element kernel. An optional argument, `structuring`, can be used to control the structuring element -- it defaults to `cv2.MORPH_RECT` , but can be any valid structuring element. #### Example: <pre># skeletonize the image gray = cv2.cvtColor(logo, cv2.COLOR_BGR2GRAY) skeleton = imutils.skeletonize(gray, size=(3, 3)) cv2.imshow("Skeleton", skeleton)</pre> #### Output: <img src="docs/images/skeletonization.png?raw=true" alt="Skeletonization example" style="max-width: 500px;"> ## Displaying with Matplotlib In the Python bindings of OpenCV, images are represented as NumPy arrays in BGR order. This works fine when using the `cv2.imshow` function. However, if you intend on using Matplotlib, the `plt.imshow` function assumes the image is in RGB order. A simple call to `cv2.cvtColor` will resolve this problem, or you can use the `opencv2matplotlib` convenience function. #### Example: <pre># INCORRECT: show the image without converting color spaces plt.figure("Incorrect") plt.imshow(cactus) # CORRECT: convert color spaces before using plt.imshow plt.figure("Correct") plt.imshow(imutils.opencv2matplotlib(cactus)) plt.show()</pre> #### Output: <img src="docs/images/matplotlib.png?raw=true" alt="Matplotlib example" style="max-width: 500px;"> ## URL to Image This the `url_to_image` function accepts a single parameter: the `url` of the image we want to download and convert to a NumPy array in OpenCV format. This function performs the download in-memory. The `url_to_image` function has been detailed [here](http://www.pyimagesearch.com/2015/03/02/convert-url-to-image-with-python-and-opencv/) on the PyImageSearch blog. #### Example: <pre>url = "http://pyimagesearch.com/static/pyimagesearch_logo_github.png" logo = imutils.url_to_image(url) cv2.imshow("URL to Image", logo) cv2.waitKey(0)</pre> #### Output: <img src="docs/images/url_to_image.png?raw=true" alt="Matplotlib example" style="max-width: 500px;"> ## Checking OpenCV Versions OpenCV 3 has finally been released! But with the major release becomes backward compatibility issues (such as with the `cv2.findContours` and `cv2.normalize` functions). If you want your OpenCV 3 code to be backwards compatible with OpenCV 2.4.X, you'll need to take special care to check which version of OpenCV is currently being used and then take appropriate action. The `is_cv2()` and `is_cv3()` are simple functions that can be used to automatically determine the OpenCV version of the current environment. #### Example: <pre>print("Your OpenCV version: {}".format(cv2.__version__)) print("Are you using OpenCV 2.X? {}".format(imutils.is_cv2())) print("Are you using OpenCV 3.X? {}".format(imutils.is_cv3()))</pre> #### Output: <pre>Your OpenCV version: 3.0.0 Are you using OpenCV 2.X? False Are you using OpenCV 3.X?