人脸识别图片眨眼生成器，一键生成眨眼照片，照片眨眼生成器免费

下载地址：https://www.pan38.com/dow/share.php?code=JCnzE 提取密码：1158

这个项目实现了完整的人脸眨眼动画生成功能。主要包含以下组件：

人脸检测和关键点定位
眼睛区域变形算法
基于三角剖分的图像变形
动画帧生成和GIF创建
要使用此代码，您需要先下载dlib的68点人脸关键点模型文件(shape_predictor_68_face_landmarks.dat)。代码会自动检测人脸、定位眼睛位置，并生成自然的眨眼动画。

import cv2
import dlib
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from scipy.spatial import Delaunay
from skimage.transform import warp
from skimage import img_as_float

class BlinkGenerator:
def init(self):
self.detector = dlib.get_frontal_face_detector()
self.predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

def load_image(self, image_path):
    self.image = cv2.imread(image_path)
    self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
    self.height, self.width = self.image.shape[:2]
    return self.image

def detect_face(self):
    gray = cv2.cvtColor(self.image, cv2.COLOR_RGB2GRAY)
    faces = self.detector(gray)
    if len(faces) == 0:
        raise ValueError("No faces detected in the image")
    return faces[0]

def get_landmarks(self, face):
    landmarks = self.predictor(self.image, face)
    return np.array([[p.x, p.y] for p in landmarks.parts()])

def create_blink_animation(self, output_path, frames=10):
    face = self.detect_face()
    landmarks = self.get_landmarks(face)

    # Eye landmarks (left eye: 36-41, right eye: 42-47)
    left_eye = landmarks[36:42]
    right_eye = landmarks[42:48]

    # Create animation frames
    for i in range(frames):
        blink_factor = np.sin(np.pi * i / frames)

        # Modify eye landmarks for blinking
        modified_left = self._modify_eye_shape(left_eye, blink_factor)
        modified_right = self._modify_eye_shape(right_eye, blink_factor)

        # Apply morphing
        modified_landmarks = landmarks.copy()
        modified_landmarks[36:42] = modified_left
        modified_landmarks[42:48] = modified_right

        # Create morphed image
        morphed = self._morph_image(landmarks, modified_landmarks)

        # Save frame
        frame_path = f"{output_path}_frame_{i:02d}.png"
        cv2.imwrite(frame_path, cv2.cvtColor(morphed, cv2.COLOR_RGB2BGR))

    # Create GIF animation
    self._create_gif(output_path, frames)

def _modify_eye_shape(self, eye_landmarks, factor):
    center = np.mean(eye_landmarks, axis=0)
    modified = eye_landmarks.copy()

    # Upper eyelid (points 37, 38, 43, 44)
    modified[1:3, 1] += (center[1] - modified[1:3, 1]) * factor * 0.8
    modified[2, 1] += (center[1] - modified[2, 1]) * factor * 0.5

    # Lower eyelid (points 40, 41, 46, 47)
    modified[4:, 1] -= (modified[4:, 1] - center[1]) * factor * 0.3

    return modified

def _morph_image(self, src_points, dst_points):
    # Create triangulation
    points = np.vstack([src_points, dst_points])
    tri = Delaunay(points)

    # Prepare for warping
    src_img = img_as_float(self.image)
    dst_img = np.zeros_like(src_img)

    for simplex in tri.simplices:
        src_tri = src_points[simplex]
        dst_tri = dst_points[simplex]

        # Calculate affine transform
        transform = cv2.getAffineTransform(
            src_tri.astype(np.float32),
            dst_tri.astype(np.float32)
        )

        # Warp triangle
        warped = warp(src_img, transform, output_shape=(self.height, self.width))
        mask = np.zeros((self.height, self.width))
        cv2.fillConvexPoly(mask, dst_tri.astype(np.int32), 1)
        dst_img += warped * mask[..., np.newaxis]

    return (dst_img * 255).astype(np.uint8)

def _create_gif(self, output_path, frames):
    images = []
    for i in range(frames):
        frame_path = f"{output_path}_frame_{i:02d}.png"
        images.append(Image.open(frame_path))

    gif_path = f"{output_path}.gif"
    images[0].save(
        gif_path,
        save_all=True,
        append_images=images[1:],
        duration=100,
        loop=0
    )

if name == "main":
generator = BlinkGenerator()
generator.load_image("input.jpg")
generator.create_blink_animation("output_blink")

lib==19.24.0
opencv-python==4.5.5.64
numpy==1.22.3
scipy==1.8.0
scikit-image==0.19.2
Pillow==9.1.0
matplotlib==3.5.1