PaddleHub人脸关键点检测：一键生成蜡笔小新的远房表弟

!pip install --upgrade pip
!pip install opencv-python==4.5.4.60!pip install paddlehub==2.1.1

import cv2import paddlehub as hubimport matplotlib.pyplot as plt 
import matplotlib.image as mpimgimport numpy as npimport mathfrom PIL import Image
src_img = cv2.imread('example.jpg')# 加载模型并进行预测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]):	# 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(若未显示关键点可视化结果请再次运行此cell)plt.figure(figsize=(10,10))
plt.imshow(img) 
plt.axis('off') 
plt.show()

def thick_eyebrows(image, face_landmark, width):
	for i in range(18-1, 22-1):
		cv2.line(image, face_landmark[i], face_landmark[i+1], (0, 0, 0), width)	for i in range(23-1, 27-1):
		cv2.line(image, face_landmark[i], face_landmark[i+1], (0, 0, 0), width)	return image# 提取出人脸关键点坐标face_landmark = np.array(result[0]['data'][0], dtype='int')# 生成蜡笔小新版眉毛width = 8src_img = thick_eyebrows(src_img, face_landmark, width)
cv2.imwrite('thick_eyebrows.jpg', src_img)


img = mpimg.imread('thick_eyebrows.jpg') 
# 展示蜡笔眉plt.figure(figsize=(10,10))
plt.imshow(img) 
plt.axis('off') 
plt.show()

# 进行胖脸操作def fat_face(image, face_landmark):
    end_point = face_landmark[30]    # 胖左脸，3号点到5号点的距离作为一次胖脸距离
    dist_left = np.linalg.norm(face_landmark[3] - face_landmark[5])
    image = local_traslation_warp(image, face_landmark[3], end_point, dist_left)    # 胖右脸，13号点到15号点的距离作为一次胖脸距离
    dist_right = np.linalg.norm(face_landmark[13] - face_landmark[15])
    image = local_traslation_warp(image, face_landmark[13], end_point, dist_right)    return image

# 局部平移算法def local_traslation_warp(image, start_point, end_point, radius):
	radius_square = math.pow(radius, 2)
	image_cp = image.copy()

	dist_se = math.pow(np.linalg.norm(end_point - start_point), 2)
	height, width, channel = image.shape	for i in range(width):		for j in range(height):			# 计算该点是否在形变圆的范围之内
			# 优化，第一步，直接判断是会在（start_point[0], start_point[1])的矩阵框中
			if math.fabs(i - start_point[0]) > radius and math.fabs(j - start_point[1]) > radius:				continue

			distance = (i - start_point[0]) * (i - start_point[0]) + (j - start_point[1]) * (j - start_point[1])			if distance < radius_square:				# 计算出（i,j）坐标的原坐标
				# 计算公式中右边平方号里的部分
				ratio = (radius_square - distance) / (radius_square - distance + dist_se)
				ratio = ratio * ratio				# 映射原位置
				new_x = i + ratio * (end_point[0] - start_point[0])
				new_y = j + ratio * (end_point[1] - start_point[1])

				new_x = new_x if new_x >= 0 else 0
				new_x = new_x if new_x < height - 1 else height - 2
				new_y = new_y if new_y >= 0 else 0
				new_y = new_y if new_y < width - 1 else width - 2

				# 根据双线性插值法得到new_x, new_y的值
				image_cp[j, i] = bilinear_insert(image, new_x, new_y)	return image_cp# 双线性插值法def bilinear_insert(image, new_x, new_y):
	w, h, c = image.shape	if c == 3:
		x1 = int(new_x)
		x2 = x1 + 1
		y1 = int(new_y)
		y2 = y1 + 1

		part1 = image[y1, x1].astype(np.float) * (float(x2) - new_x) * (float(y2) - new_y)
		part2 = image[y1, x2].astype(np.float) * (new_x - float(x1)) * (float(y2) - new_y)
		part3 = image[y2, x1].astype(np.float) * (float(x2) - new_x) * (new_y - float(y1))
		part4 = image[y2, x2].astype(np.float) * (new_x - float(x1)) * (new_y - float(y1))

		insertvalue = part1 + part2 + part3 + part4		return insertvalue.astype(np.int8)

# 进行胖脸操作fat_nums = 3for i in range(1, fat_nums):
	src_img = fat_face(src_img, face_landmark)

cv2.imwrite('res.jpg', src_img)
img = mpimg.imread('res.jpg') 
# 展示蜡笔眉+嘟嘟嘴plt.figure(figsize=(10,10))
plt.imshow(img) 
plt.axis('off') 
plt.show()

img_path 输入图片路径
width 眉毛宽度
res_img_path 输出图片路径
fat_nums 嘟嘟脸系数

!python run.py --img_path example.jpg --width 8 --res_img_path res.jpg --fat_nums 3