Table of Contents Hide
- Steps to implement face swapping with OpenCV and Python
- #1 Face detection utilizing histogram of oriented gradients (HOG)
- #2 Facial landmark detection aka face alignment
- #three Discovering face border utilizing convex hull
- #Four Approximating nonlinear operations with linear operations
- #5 Discovering triangles in a picture utilizing Delaunay triangulation
- #6 Mixing one face into one other
- #7 Stabilization utilizing optical circulation with the Lucas-Kanade technique
- Learn Subsequent
No dialogue on picture processing might be full with out speaking about OpenCV. Its 2500+ algorithms, in depth documentation and pattern code are thought of world-class for exploring real-time laptop imaginative and prescient. OpenCV helps all kinds of programming languages resembling C++, Python, Java, and so forth., and can also be obtainable on completely different platforms together with Home windows, Linux, OS X, Android, and iOS.
OpenCV-Python, the Python API for OpenCV is without doubt one of the hottest libraries used to unravel laptop imaginative and prescient issues. It combines the perfect qualities of OpenCV, C++ API, and the Python language. The OpenCV-Python library makes use of Numpy, which is a extremely optimized library for numerical operations with a MATLAB-style syntax. This makes it simpler to combine the Python API with different libraries that use Numpy resembling SciPy and Matplotlib. That is the explanation why it’s utilized by many builders to execute completely different laptop imaginative and prescient experiments.
Need to know extra about OpenCV with Python?
On the PyData Warsaw 2018 convention, Sylwek Brzęczkowski walked by how you can implement a face swap utilizing OpenCV and Python. Face swaps are utilized by apps like Snapchat to dispense numerous face filters. Brzęczkowski is a Python developer at TrustStamp.
Steps to implement face swapping with OpenCV and Python
#1 Face detection utilizing histogram of oriented gradients (HOG)
Histogram of oriented gradients (HOG) is a characteristic descriptor that’s used to detect objects in laptop imaginative and prescient and picture processing. Brzęczkowski demonstrated the working of a HOG utilizing sq. patches which when hovered over an array of photographs produces a histogram of oriented gradients characteristic vectors. These characteristic vectors are then handed to the classifier to generate a consequence having the very best matching samples.
In an effort to implement face detection utilizing HOG in Python, the picture must be imported utilizing import OpenCV. Subsequent a frontal face detector object is created for the loaded picture detector=dlib.get_frontal_face_detector(). The detector then produces the vector with the detected face.
#2 Facial landmark detection aka face alignment
Face landmark detection is the method of discovering factors of curiosity in a picture of a human face. When dlib is used for facial landmark detection, it returns 68 distinctive trend landmarks for the entire face. After the primary iteration of the algorithm, the worth of T equals 0. This worth will increase linearly such that on the finish of the iteration, T will get the worth 10. The picture developed at this stage produces the ‘floor reality’, which implies that the iteration can cease now. As a result of this working, this stage of the method can also be referred to as as face alignment.
To implement this stage, Brzęczkowski confirmed how you can add a predictor within the Python program with the values shape_predictor_68_face_landmarks.dat such that it produces a mannequin of round 100 megabytes. This course of usually takes up a very long time as we have a tendency to choose the largest clearer picture for detection.
#three Discovering face border utilizing convex hull
The convex hull is a set of factors outlined because the smallest convex polygon, which encloses the entire factors within the set. Because of this for a given set of factors, the convex hull is the subset of those factors such that every one the given factors are contained in the subset. To search out the face border in a picture, we have to change the construction a bit. The construction is first handed to the convex hull operate with return factors to false, which means that we get an output of indexes. Brzęczkowski then exhibited the face border within the picture in blue coloration utilizing the find_convex_hull.py operate.
#Four Approximating nonlinear operations with linear operations
In a linear filtering of a picture, the worth of an output pixel is a linear mixture of the values of the pixels. Brzęczkowski put forth the instance of Affine transformation which is a kind of linear mapping technique and is used to protect factors, straight traces, and planes. However, a non-linear filtering produces an output which isn’t a linear operate of its enter. He then goes on to unveil each the transitions utilizing his personal picture. Brzęczkowski then suggested customers to verify the web site learnOpenCV.com to discover ways to create a nonlinear operation with a linear one.
#5 Discovering triangles in a picture utilizing Delaunay triangulation
A Delaunay triangulation subdivides a set of factors in a aircraft into triangles such that the factors change into vertices of the triangles. Because of this this technique subdivides the area or the floor into triangles in such a method that if you happen to take a look at any triangle on the picture, it is not going to have one other level contained in the triangle. Brzęczkowski then demonstrates how the picture developed within the earlier stage contained “face factors from which you’ll be able to determine my tooth after which create sub div to the item, insert all these factors that I created or all detected.” Subsequent, he deploys Delaunay triangulation to provide an inventory of two angles. This checklist is then used to acquire the triangles within the picture. Publish this step, he makes use of the delaunay_triangulation.py operate to generate these triangles on the photographs.
#6 Mixing one face into one other
To recap, we began from detecting a face utilizing HOG and discovering its border utilizing convex hull, adopted it by including mouth factors to point particular indexes. Subsequent, Delaunay triangulation was applied to acquire all of the triangles on the photographs.
Subsequent, Brzęczkowski begins the mixing of photographs utilizing seamless cloning. A seamless cloning combines the attributes of different cloning strategies to create a singular resolution to permit “sequence-independent and scarless insertion of a number of fragments of DNA right into a plasmid vector.” This cloning technique additionally supplies a wide range of pores and skin colours to select from.
Brzęczkowski then explains a characteristic referred to as ‘cross on edit picture’ within the Poisson picture enhancing which makes use of the worth of the gradients as a substitute of the identities or the values of the pixels of the picture.
To implement the identical technique in OpenCV, he additional demonstrates how info like supply, vacation spot, supply picture vacation spot, masks and heart (which is the situation the place the cloned half ought to be positioned) is required to mix the 2 faces. Brzęczkowski then depicts a string of illustrations to remodel his picture with the photographs of standard artists like Jamie Foxx, Clint Eastwood, and others.
#7 Stabilization utilizing optical circulation with the Lucas-Kanade technique
In laptop imaginative and prescient, the Lucas-Kanade technique is a broadly used differential technique for optical circulation estimation. It assumes that the circulation is basically fixed in an area neighborhood of the pixel into consideration, and solves the essential optical circulation equations for all of the pixels in that neighborhood, by the least-squares criterion. Thus by combining info from a number of close by pixels, the Lucas–Kanade technique resolves the inherent ambiguity of the optical circulation equation. This technique can also be much less delicate to noises in a picture.
Through the use of this technique to implement the stabilization of the face swapped picture, it’s assumed that the optical circulation is basically fixed in an area neighborhood of the pixel into consideration in human language. Because of this “if we’ve a purple level within the heart we assume that every one the factors round, let’s say on this instance is three on three pixels we assume that every one of them have the identical optical circulation and because of that assumption we’ve 9 equations and solely two unknowns.”
This makes the computation pretty straightforward to unravel. Through the use of this assumption the optical circulation works easily if we’ve the earlier grey place of the picture. Because of this for face swapping photographs utilizing OpenCV, a person must have particulars of the earlier factors of the picture together with the present factors of the picture. By combining all this info, the precise level turns into a mix of the detected landmark and the expected landmark.
Thus by implementing the Lucas-Kanade technique for stabilizing the picture, Brzęczkowski implements a non-shaky model of his face-swapped picture. Watch Brzęczkowski’s full video to see a step-by-step implementation of a face-swapping activity.
You may be taught superior purposes like facial recognition, goal monitoring, or augmented actuality from our e book, ‘Mastering OpenCV Four with Python’ written by Alberto Fernández Villán. This e book will even allow you to perceive the applying of synthetic intelligence and deep studying methods utilizing standard Python libraries like TensorFlow and Keras.
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