# Select and Warp triangles – Face swapping Opencv with Python (part 4)

We’re going to see in this video how to select the corresponding triangles and warp them so that the triangles of the first image match exactly the triangles in the second image in shape and size.

We keep the first part unchanged from the last tutorial.

import cv2 import numpy as np import dlib def extract_index_nparray(nparray): index = None for num in nparray[0]: index = num break return index img = cv2.imread("bradley_cooper.jpg") img2 = cv2.imread("jim_carrey.jpg") img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) mask = np.zeros_like(img_gray) # Loading Face landmarks detector detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

In this second part of the code where we found the first face we do some changes from the previous cose. We’re going to remove or comment all the lines where we were drawing the triangles and the convex hull, as we don’t need to display them anymore.

# Face 1 faces = detector(img_gray) for face in faces: landmarks = predictor(img_gray, face) landmarks_points = [] for n in range(0, 68): x = landmarks.part(n).x y = landmarks.part(n).y landmarks_points.append((x, y)) #cv2.circle(img, (x, y), 3, (0, 0, 255), -1) points = np.array(landmarks_points, np.int32) convexhull = cv2.convexHull(points) #cv2.polylines(img, [convexhull], True, (255, 0, 0), 3) cv2.fillConvexPoly(mask, convexhull, 255) face_image_1 = cv2.bitwise_and(img, img, mask=mask) # Delaunay triangulation rect = cv2.boundingRect(convexhull) subdiv = cv2.Subdiv2D(rect) subdiv.insert(landmarks_points) triangles = subdiv.getTriangleList() triangles = np.array(triangles, dtype=np.int32) indexes_triangles = [] for t in triangles: pt1 = (t[0], t[1]) pt2 = (t[2], t[3]) pt3 = (t[4], t[5]) index_pt1 = np.where((points == pt1).all(axis=1)) index_pt1 = extract_index_nparray(index_pt1) index_pt2 = np.where((points == pt2).all(axis=1)) index_pt2 = extract_index_nparray(index_pt2) index_pt3 = np.where((points == pt3).all(axis=1)) index_pt3 = extract_index_nparray(index_pt3) if index_pt1 is not None and index_pt2 is not None and index_pt3 is not None: triangle = [index_pt1, index_pt2, index_pt3] indexes_triangles.append(triangle)

Now in the part where we’re finding the landmarks points of the second face we just do some small changes on the names of the variables to distinguish them from the one of the first face.

We also comment the line cv2.circle that was drawing the landmarks points as we don’t need to see them.

# Face 2 faces2 = detector(img2_gray) for face in faces2: landmarks = predictor(img2_gray, face) landmarks_points2 = [] for n in range(0, 68): x = landmarks.part(n).x y = landmarks.part(n).y landmarks_points2.append((x, y)) #cv2.circle(img2, (x, y), 3, (0, 255, 0), -1)

Now it’s the core part of this specific tutorial, we’re going to work with the triangles.

We loop trough the triangles indexes and we detect triangle of both faces.

We crop the area of each triangle to work with it specifically.

# Triangulation of both faces for triangle_index in indexes_triangles: # Triangulation of the first face tr1_pt1 = landmarks_points[triangle_index[0]] tr1_pt2 = landmarks_points[triangle_index[1]] tr1_pt3 = landmarks_points[triangle_index[2]] triangle1 = np.array([tr1_pt1, tr1_pt2, tr1_pt3], np.int32) rect1 = cv2.boundingRect(triangle1) (x, y, w, h) = rect1 cropped_triangle = img[y: y + h, x: x + w] cropped_tr1_mask = np.zeros((h, w), np.uint8) points = np.array([[tr1_pt1[0] - x, tr1_pt1[1] - y], [tr1_pt2[0] - x, tr1_pt2[1] - y], [tr1_pt3[0] - x, tr1_pt3[1] - y]], np.int32) cv2.fillConvexPoly(cropped_tr1_mask, points, 255) cropped_triangle = cv2.bitwise_and(cropped_triangle, cropped_triangle, mask=cropped_tr1_mask) cv2.line(img, tr1_pt1, tr1_pt2, (0, 0, 255), 2) cv2.line(img, tr1_pt3, tr1_pt2, (0, 0, 255), 2) cv2.line(img, tr1_pt1, tr1_pt3, (0, 0, 255), 2)

Same operation for the second face.

# Triangulation of second face tr2_pt1 = landmarks_points2[triangle_index[0]] tr2_pt2 = landmarks_points2[triangle_index[1]] tr2_pt3 = landmarks_points2[triangle_index[2]] triangle2 = np.array([tr2_pt1, tr2_pt2, tr2_pt3], np.int32) rect2 = cv2.boundingRect(triangle2) (x, y, w, h) = rect2 cropped_triangle2 = img2[y: y + h, x: x + w] cropped_tr2_mask = np.zeros((h, w), np.uint8) points2 = np.array([[tr2_pt1[0] - x, tr2_pt1[1] - y], [tr2_pt2[0] - x, tr2_pt2[1] - y], [tr2_pt3[0] - x, tr2_pt3[1] - y]], np.int32) cv2.fillConvexPoly(cropped_tr2_mask, points2, 255) cropped_triangle2 = cv2.bitwise_and(cropped_triangle2, cropped_triangle2, mask=cropped_tr2_mask) cv2.line(img2, tr2_pt1, tr2_pt2, (0, 0, 255), 2) cv2.line(img2, tr2_pt3, tr2_pt2, (0, 0, 255), 2) cv2.line(img2, tr2_pt1, tr2_pt3, (0, 0, 255), 2)

Finally once we have the triangles extracted we can warp them.

# Warp triangles points = np.float32(points) points2 = np.float32(points2) M = cv2.getAffineTransform(points, points2) warped_triangle = cv2.warpAffine(cropped_triangle, M, (w, h)) break

We then can show everything on the screen.

cv2.imshow("Image 1", img) cv2.imshow("image2", img2) cv2.imshow("cropped triangle 1", cropped_triangle) cv2.imshow("cropped triangle 2", cropped_triangle2) cv2.imshow("Warped triangle", warped_triangle) cv2.waitKey(0) cv2.destroyAllWindows()

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