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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()