#CV2 PALETTE SWAP CODE#

In our case, the polygon is a face, and the regions on the face are divided using triangles. ]ĭelaunay Triangulation provides triangles that can be made on the image which can be used for making a polygon. Triangles_id.append(triangle) #print list of indices used for drawing a triangle If id_pt1 is not None and id_pt2 is not None and id_pt3 is not None: The main reason for creati ng a list that stores the indexes of the points of the triangle is that obviously the same indexes of the 68-points are going to be used on the second image are used to draw the triangles on that face.

Please note that these are not the coordinates of the points these are the index of the points that we use to join to make a triangle. In the next part, we create a list for storing the pairs of points that are used for drawing the triangles on the face. Triangles = np.array(triangles, dtype=np.int32) Once the bounding box is created we can draw the triangles using the points that lie inside the triangle. To get those points we can draw a bounding box on the convex Hull that is created in the previous step.

We don’t pass all the points to getTriangleList() instead, the points that lie inside the rectangle of the bounding box on the face are used. But the parameters it accepts is a list of all the points that are used for drawing the triangles. OpenCV provides inbuilt functions for drawing such triangles known as getTriangleList(). To draw triangles on faces we need to join the 68 points that are on the face. Points_detector = dlib.shape_predictor('/content/drive/MyDrive/weights/shape_predictor_68_face_landmarks.dat') Img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) Img = cv2.imread('/content/drive/MyDrive/charles.jpg') face_image_1 = cv2.bitwise_and(img, img, mask=mask) import cv2 Now the final step for extracting the face is to apply cv2.bitwise_and() to the image using the mask of the same dimensions that contains the polygon with the same shape of the face. cv2.fillConvexPoly(mask, convexhull, 255) These boundary coordinates of the polygon are obtained from the Convex hull and we can draw the polygon on a mask we created in the previous step. fillConvexPoly() fills the enclosed polygon if the coordinates are provided. mask = np.zeros_like(img_gray)īy using all the points on the boundary of the face we can extract the pixels inside the face using cv2.fillConvexPoly(). We draw a polygon with the exact shape of the convex hull we create a blank image having the same dimensions. Convex Hull has several applications in the field of mathematics, statistics, economics, and geometric modeling. Convex Hull of a shape or a group of points is a tight-fitting convex boundary around the points or the shape. Once we detect all the face points the next we need to do is to obtain the boundary point which is known as the Convex hull. Since the points that we have detected are in the dlib encoded format, we need to normalize them into an array of x & y coordinates. Image = cv2.imread('/content/drive/MyDrive/Colab Notebooks/database/adam6.PNG') Points_detector = dlib.shape_predictor('/content/drive/MyDrive/weights/shape_predictor_68_face_landmarks.dat')

#CV2 PALETTE SWAP CODE#

Just as a reminder we are developing the code on google colab, and the dlib file for shape prediction can be downloaded from the link shape_predictor_68_face_landmarks.dat import dlibįrom import cv2_imshowįace_detector = dlib.get_frontal_face_detector() To extract face from the image we can use a pre-built library provided by OpenCV which is known cv2. To extract the Face from the image we first plot all the 68 points on the face and crop the image using the convex method.

Cv2 palette swap