Notes

import cv2
import dlib
import numpy as np
import matplotlib.pyplot as plt
import os

# Load Dlib's face detector and shape predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

def get_landmarks(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 1)

if len(rects) == 0:
return None

shape = predictor(gray, rects[0])
coords = np.zeros((68, 2), dtype="int")

for i in range(0, 68):
coords[i] = (shape.part(i).x, shape.part(i).y)

# Add extra points
h, w = image.shape[:2]
coords = np.vstack([coords,
[(0, 0), (w//2, 0), (w-1, 0),
(0, h//2), (w-1, h//2),
(0, h-1), (w//2, h-1), (w-1, h-1)]])

return coords

def draw_landmarks(image, landmarks):
for (x, y) in landmarks:
cv2.circle(image, (x, y), 2, (0, 255, 0), -1)
return image

def apply_delaunay(image, landmarks):
rect = (0, 0, image.shape[1], image.shape[0])
subdiv = cv2.Subdiv2D(rect)

for point in landmarks:
subdiv.insert(tuple(point))

triangles = subdiv.getTriangleList()
return np.array(triangles, dtype=np.int32)

def draw_delaunay(image, landmarks, triangles):
for t in triangles:
pt1 = (t[0], t[1])
pt2 = (t[2], t[3])
pt3 = (t[4], t[5])
cv2.line(image, pt1, pt2, (0, 0, 255), 1)
cv2.line(image, pt2, pt3, (0, 0, 255), 1)
cv2.line(image, pt3, pt1, (0, 0, 255), 1)
return image

def warp_triangle(src, dst, src_tri, dst_tri):
M = cv2.getAffineTransform(np.float32(src_tri), np.float32(dst_tri))
warped = cv2.warpAffine(src, M, (dst.shape[1], dst.shape[0]), None, flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
mask = np.zeros((dst.shape[0], dst.shape[1], 3), dtype = np.float32)
cv2.fillConvexPoly(mask, np.int32(dst_tri), (1.0, 1.0, 1.0), 16, 0)
warped = warped * mask
return warped

def create_morph_sequence(img1, img2, landmarks1, landmarks2, triangles, num_frames):
frames = []
for i in range(num_frames):
t = i / (num_frames - 1)
landmarks = (1 - t) * landmarks1 + t * landmarks2
landmarks = landmarks.astype(np.int32)

morphed = np.zeros(img1.shape, dtype=np.float32)

for triangle in triangles:
x, y, z = triangle[::2], triangle[1::2], triangle[2::2]
t1 = np.array([landmarks1[x][0], landmarks1[y][0], landmarks1[z][0]])
t2 = np.array([landmarks2[x][0], landmarks2[y][0], landmarks2[z][0]])
t = np.array([landmarks[x][0], landmarks[y][0], landmarks[z][0]])

warped1 = warp_triangle(img1, morphed, t1, t)
warped2 = warp_triangle(img2, morphed, t2, t)

morphed += (1 - t) * warped1 + t * warped2

frames.append(np.uint8(morphed))

return frames

# Load images
img1 = cv2.imread("image1.jpg")
img2 = cv2.imread("image2.jpg")

# Detect landmarks
landmarks1 = get_landmarks(img1)
landmarks2 = get_landmarks(img2)

if landmarks1 is None or landmarks2 is None:
print("No face detected in one or both images.")
exit()

# Apply Delaunay triangulation
triangles = apply_delaunay(img1, landmarks1)

# Create morph sequence
num_frames = 20
frames = create_morph_sequence(img1, img2, landmarks1, landmarks2, triangles, num_frames)

# Save and display results
if not os.path.exists("output"):
os.makedirs("output")

plt.figure(figsize=(20, 10))

for i, frame in enumerate(frames):
# Draw landmarks and Delaunay triangulation
landmarks = (1 - i/(num_frames-1)) * landmarks1 + (i/(num_frames-1)) * landmarks2
landmarks = landmarks.astype(np.int32)
frame_with_landmarks = draw_landmarks(frame.copy(), landmarks)
frame_with_delaunay = draw_delaunay(frame_with_landmarks.copy(), landmarks, triangles)

# Save frame
cv2.imwrite(f"output/frame_{i:03d}.jpg", frame)
cv2.imwrite(f"output/frame_{i:03d}_landmarks.jpg", frame_with_landmarks)
cv2.imwrite(f"output/frame_{i:03d}_delaunay.jpg", frame_with_delaunay)

# Display frame
plt.subplot(4, 5, i+1)
plt.imshow(cv2.cvtColor(frame_with_delaunay, cv2.COLOR_BGR2RGB))
plt.axis('off')
plt.title(f"Frame {i}")

plt.tight_layout()
plt.savefig("output/morph_sequence.png")
plt.show()

print("Morphing sequence created and saved in the 'output' folder.")