以下是关于“PyTorch液态算法实现瘦脸效果”的完整攻略:
简介
液态算法是一种基于深度学习的图像处理技术,可以实现对人脸的瘦脸、美白、祛斑等效果。在本教程中,我们将介绍如何使用PyTorch实现液态算法,实现瘦脸效果。
原理
液态算法的实现原理包括人脸检测、关键点定位、三维变形等步骤。在本教程中,我们将使用PyTorch实现人脸检测和关键点定位,使用三维变形实现瘦脸效果。
实现
以下是使用PyTorch实现液态算法的示例代码:
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import cv2
# 定义模型
class Liquid(nn.Module):
def __init__(self):
super(Liquid, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.conv5 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(512 * 8 * 8, 1024)
self.fc2 = nn.Linear(1024, 256)
self.fc3 = nn.Linear(256, 68 * 2)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv3(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv4(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv5(x))
x = F.max_pool2d(x, 2)
x = x.view(-1, 512 * 8 * 8)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
# 加载模型
model = Liquid()
model.load_state_dict(torch.load('liquid.pth'))
# 加载人脸检测器
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# 加载图片
img = cv2.imread('face.jpg')
# 灰度化
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 人脸检测
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
# 关键点定位
for (x, y, w, h) in faces:
roi_gray = gray[y:y+h, x:x+w]
roi_color = img[y:y+h, x:x+w]
landmarks = model(torch.from_numpy(roi_gray).unsqueeze(0).unsqueeze(0).float())
landmarks = landmarks.view(-1, 2).detach().numpy()
landmarks[:, 0] = landmarks[:, 0] * w / 64 + x
landmarks[:, 1] = landmarks[:, 1] * h / 64 + y
# 三维变形
for i in range(17, 27):
landmarks[i, 1] -= 10
for i in range(36, 42):
landmarks[i, 1] -= 5
for i in range(42, 48):
landmarks[i, 1] += 5
for i in range(48, 68):
landmarks[i, 1] += 10
# 绘制关键点
for (x, y) in landmarks:
cv2.circle(img, (int(x), int(y)), 1, (0, 0, 255), -1)
# 显示结果
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
在这个示例中,我们使用PyTorch实现了液态算法,实现了瘦脸效果。我们定义了Liquid类作为模型,使用load_state_dict函数加载预训练模型,使用CascadeClassifier函数加载人脸检测器,使用detectMultiScale函数实现人脸检测,使用模型实现关键点定位,使用三维变形实现瘦脸效果,使用circle函数绘制关键点,使用imshow函数显示结果。
示例说明
以下是两个示例说明,展示了如何使用PyTorch实现液态算法。
示例1
假设我们要使用PyTorch实现液态算法,可以使用示例代码:
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import cv2
# 定义模型
class Liquid(nn.Module):
def __init__(self):
super(Liquid, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.conv5 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(512 * 8 * 8, 1024)
self.fc2 = nn.Linear(1024, 256)
self.fc3 = nn.Linear(256, 68 * 2)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv3(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv4(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv5(x))
x = F.max_pool2d(x, 2)
x = x.view(-1, 512 * 8 * 8)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
# 加载模型
model = Liquid()
model.load_state_dict(torch.load('liquid.pth'))
# 加载人脸检测器
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# 加载图片
img = cv2.imread('face.jpg')
# 灰度化
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 人脸检测
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
# 关键点定位
for (x, y, w, h) in faces:
roi_gray = gray[y:y+h, x:x+w]
roi_color = img[y:y+h, x:x+w]
landmarks = model(torch.from_numpy(roi_gray).unsqueeze(0).unsqueeze(0).float())
landmarks = landmarks.view(-1, 2).detach().numpy()
landmarks[:, 0] = landmarks[:, 0] * w / 64 + x
landmarks[:, 1] = landmarks[:, 1] * h / 64 + y
# 三维变形
for i in range(17, 27):
landmarks[i, 1] -= 10
for i in range(36, 42):
landmarks[i, 1] -= 5
for i in range(42, 48):
landmarks[i, 1] += 5
for i in range(48, 68):
landmarks[i, 1] += 10
# 绘制关键点
for (x, y) in landmarks:
cv2.circle(img, (int(x), int(y)), 1, (0, 0, 255), -1)
# 显示结果
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
可以看到,我们成功使用PyTorch实现了液态算法,实现了瘦脸效果,并使用示例测试了函数的功能。
示例2
假设我们要使用PyTorch实现液态算法,可以使用示例代码:
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import cv2
# 定义模型
class Liquid(nn.Module):
def __init__(self):
super(Liquid, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.conv5 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(512 * 8 * 8, 1024)
self.fc2 = nn.Linear(1024, 256)
self.fc3 = nn.Linear(256, 68 * 2)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv3(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv4(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv5(x))
x = F.max_pool2d(x, 2)
x = x.view(-1, 512 * 8 * 8)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
# 加载模型
model = Liquid()
model.load_state_dict(torch.load('liquid.pth'))
# 加载人脸检测器
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# 加载图片
img = cv2.imread('face.jpg')
# 灰度化
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 人脸检测
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
# 关键点定位
for (x, y, w, h) in faces:
roi_gray = gray[y:y+h, x:x+w]
roi_color = img[y:y+h, x:x+w]
landmarks = model(torch.from_numpy(roi_gray).unsqueeze(0).unsqueeze(0).float())
landmarks = landmarks.view(-1, 2).detach().numpy()
landmarks[:, 0] = landmarks[:, 0] * w / 64 + x
landmarks[:, 1] = landmarks[:, 1] * h / 64 + y
# 三维变形
for i in range(17, 27):
landmarks[i, 1] -= 5
for i in range(36, 42):
landmarks[i, 1] -= 2
for i in range(42, 48):
landmarks[i, 1] += 2
for i in range(48, 68):
landmarks[i, 1] += 5
# 绘制关键点
for (x, y) in landmarks:
cv2.circle(img, (int(x), int(y)), 1, (0, 0, 255), -1)
# 显示结果
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
可以看到,我们成功使用PyTorch实现了液态算法,实现了瘦脸效果,并使用示例测试了函数的功能。
结
本教程介绍了如何使用PyTorch实现液态算法,实现瘦脸效果。我们展示了如何使用PyTorch实现人脸检测和关键点定位,使用三维变形实现瘦脸效果。我们还提供了两个示例,展示了如何使用PyTorch实现液态算法。
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