PaddleHub人脸关键点实现贴纸之眼镜
busyboxs 发布于2020-04 浏览:2508 回复:3
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最后编辑于2022-04

AI Studio 项目地址 : https://aistudio.baidu.com/aistudio/projectdetail/402824

效果

代码

以下代码是在 notebook 中的代码,如果是在本地执行,推荐使用 opencv 进行图像展示

首先需要安装 paddlehub, -q 表示静默安装

!pip install -q paddlehub==1.6.1

然后简单测试一下 paddlehub 的关键点检测情况

import cv2
import paddlehub as hub
import matplotlib.pyplot as plt 
import matplotlib.image as mpimg
import numpy as np
import math
%matplotlib inline

src_img = cv2.imread('face/01.jpg')

module = hub.Module(name="face_landmark_localization")
result = module.keypoint_detection(images=[src_img])

tmp_img = src_img.copy()
for index, point in enumerate(result[0]['data'][0]):
	# print(point)
	# cv2.putText(img, str(index), (int(point[0]), int(point[1])), cv2.FONT_HERSHEY_COMPLEX, 3, (0,0,255), -1)
	cv2.circle(tmp_img, (int(point[0]), int(point[1])), 2, (0, 0, 255), -1)

res_img_path = 'face_landmark.jpg'
cv2.imwrite(res_img_path, tmp_img)

img = mpimg.imread(res_img_path) 
# 展示预测68个关键点结果
plt.figure(figsize=(10,10))
plt.imshow(img) 
plt.axis('off') 
plt.show()

下面的代码是融合两张图像的函数

def overlay_transparent(background_img, img_to_overlay_t, x, y, overlay_size=None):
    bg_img = background_img.copy()
    # convert 3 channels to 4 channels
    if bg_img.shape[2] == 3:
        bg_img = cv2.cvtColor(bg_img, cv2.COLOR_BGR2BGRA)

    if overlay_size is not None:
        img_to_overlay_t = cv2.resize(img_to_overlay_t.copy(), overlay_size)

    b, g, r, a = cv2.split(img_to_overlay_t)

    mask = cv2.medianBlur(a, 5)

    h, w, _ = img_to_overlay_t.shape
    roi = bg_img[int(y - h / 2):int(y + h / 2), int(x - w / 2):int(x + w / 2)]

    img1_bg = cv2.bitwise_and(roi.copy(), roi.copy(), mask=cv2.bitwise_not(mask))
    img2_fg = cv2.bitwise_and(img_to_overlay_t, img_to_overlay_t, mask=mask)

    bg_img[int(y - h / 2):int(y + h / 2), int(x - w / 2):int(x + w / 2)] = cv2.add(img1_bg, img2_fg)

    # convert 4 channels to 3 channels
    bg_img = cv2.cvtColor(bg_img, cv2.COLOR_BGRA2BGR)

    return bg_img

以下函数功能为计算两个点的角度,之后在旋转贴纸时会用到

from math import degrees, atan2

def angle_between(p1, p2):
    x_diff = p2[0] - p1[0]
    y_diff = p2[1] - p1[1]
    return degrees(atan2(y_diff, x_diff))

以下函数通过将贴纸旋转,然后粘贴到对应的位置

def wear_glasses(image, glasses, eye_left_center, eye_right_center):
    eye_left_center = np.array(eye_left_center)
    eye_right_center = np.array(eye_right_center)
    glasses_center = np.mean([eye_left_center, eye_right_center], axis=0)  # put glasses's center to this center
    glasses_size = np.linalg.norm(eye_left_center - eye_right_center) * 2  # the width of glasses mask
    angle = -angle_between(eye_left_center, eye_right_center)

    glasses_h, glasses_w = glasses.shape[:2]
    glasses_c = (glasses_w / 2, glasses_h / 2)
    M = cv2.getRotationMatrix2D(glasses_c, angle, 1)
    cos = np.abs(M[0, 0])
    sin = np.abs(M[0, 1])

    # compute the new bounding dimensions of the image
    nW = int((glasses_h * sin) + (glasses_w * cos))
    nH = int((glasses_h * cos) + (glasses_w * sin))

    # adjust the rotation matrix to take into account translation
    M[0, 2] += (nW / 2) - glasses_c[0]
    M[1, 2] += (nH / 2) - glasses_c[1]

    rotated_glasses = cv2.warpAffine(glasses, M, (nW, nH))

    try:
        image = overlay_transparent(image, rotated_glasses, glasses_center[0], glasses_center[1],
                                    overlay_size=(
                                        int(glasses_size),
                                        int(rotated_glasses.shape[0] * glasses_size / rotated_glasses.shape[1]))
                                    )
    except:
        print('failed overlay image')
    return image

以下函数用于计算眼睛的位置,用于计算出贴纸的尺寸和位置

def get_eye_center_point(landmarks, idx1, idx2):
    center_x = (landmarks[idx1][0] + landmarks[idx2][0]) // 2
    center_y = (landmarks[idx1][1] + landmarks[idx2][1]) // 2
    return (center_x, center_y)

最后是主函数,在 notebook 中展示动态效果图

import os
import matplotlib.animation as animation
from IPython.display import HTML

glasses_lists = []
fig = plt.figure()
module = hub.Module(name="face_landmark_localization")

for path in os.listdir('glasses'):
    image_file = 'face/01.jpg'
    glasses_file = './glasses/' + path

    image = cv2.imread(image_file)
    glasses = cv2.imread(glasses_file, cv2.IMREAD_UNCHANGED)
    result = module.keypoint_detection(images=[image])
    landmarks = result[0]['data'][0]
    eye_left_point = get_eye_center_point(landmarks, 36, 39)
    eye_right_point = get_eye_center_point(landmarks, 42, 45)
    image = wear_glasses(image, glasses, eye_left_point, eye_right_point)
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    im = plt.imshow(image, animated=True)
    plt.axis('off') 
    glasses_lists.append([im])

ani = animation.ArtistAnimation(fig, glasses_lists, interval=1000, blit=True, repeat_delay=1000)
HTML(ani.to_html5_video())

最终效果可以进入到 AI Studio 项目中进行查看

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共3条回复 最后由用户已被禁言回复于2022-04
#4自尊心3回复于2020-04
#3 busyboxs回复
不懂 GAN,没学过。用 GAN 直接生成想要的贴纸么

可以通过变量控制眼镜的样式,训练的时候学习对应眼镜的样式

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#3busyboxs回复于2020-04
#2 自尊心3回复
这个用gan做估计也可以

不懂 GAN,没学过。用 GAN 直接生成想要的贴纸么

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#2自尊心3回复于2020-04

这个用gan做估计也可以

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