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

import cv2
import paddlehub as hub
import matplotlib.pyplot as plt 
import matplotlib.image as mpimg
import numpy as np
import math
from 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 = 8
src_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 = 3
for 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()

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