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Python+QT美颜工具源码
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前言
这篇博客针对《Python+QT美颜》编写代码，功能包括了亮度，磨皮，风格化，铅笔化等多种特征修饰，代码整洁，规则，易读。 学习与应用推荐首选。
文章目录
一、所需工具软件
二、使用步骤
1. 引入库
2. 显示图片
3. 美颜特征函数定义
4. 运行结果
三、在线协助
一、所需工具软件
1. Python3.6以上
2. Pycharm代码编辑器
3. PyQt, OpenCV库
二、使用步骤
1.引入库
代码如下（示例）：
-
from PyQt5 import Qt
-
from PyQt5 import QtCore,QtWidgets,QtGui
-
import sys
-
import PyQt5
-
from PyQt5.QtWidgets import QApplication, QWidget, QLabel, QFileDialog, QGraphicsRectItem, QGraphicsScene
-
from PyQt5.QtCore import QSize
-
import cv2
-
import numpy as np
-
from matplotlib import pyplot as plt
2.显示图片
代码如下（示例）：
-
# 显示图片
-
def
show_image
(self):
-
img_cv = cv2.cvtColor(self.current_img, cv2.COLOR_RGB2BGR)
-
img_width, img_height, a = img_cv.shape
-
ratio_img = img_width/img_height
-
ratio_scene = self.ui.graphicsView.width()/self.ui.graphicsView.height()
-
if ratio_img > ratio_scene:
-
width =
int(self.ui.graphicsView.width())
-
height =
int(self.ui.graphicsView.width() / ratio_img)
-
else:
-
width =
int(self.ui.graphicsView.height() * ratio_img)
-
height =
int(self.ui.graphicsView.height())
-
img_resize = cv2.resize(img_cv, (height
-5, width
-5), interpolation=cv2.INTER_AREA)
-
h, w, c = img_resize.shape
-
bytesPerLine = w *
3
-
qimg = QImage(img_resize.data, w, h, bytesPerLine, QImage.Format_RGB888)
-
self.scene = QGraphicsScene()
-
pix = QPixmap(qimg)
-
self.scene.addPixmap(pix)
-
self.ui.graphicsView.setScene(self.scene)
3.美颜特征函数定义：
代码如下（示例）：
-
# 饱和度
-
def
change_saturation(
self):
-
if self.raw_image
is
None:
-
return
0
-
-
value = self.ui.horizontalSlider.value()
-
img_hsv = cv2.cvtColor(self.current_img, cv2.COLOR_BGR2HLS)
-
if value >
2:
-
img_hsv[:, :,
2] = np.log(img_hsv[:, :,
2] /
255* (value -
1)+
1) / np.log(value +
1) *
255
-
if value <
0:
-
img_hsv[:, :,
2] = np.uint8(img_hsv[:, :,
2] / np.log(- value + np.e))
-
self.current_img = cv2.cvtColor(img_hsv, cv2.COLOR_HLS2BGR)
-
-
# 明度调节
-
def
change_darker(
self):
-
if self.raw_image
is
None:
-
return
0
-
value = self.ui.horizontalSlider_4.value()
-
img_hsv = cv2.cvtColor(self.current_img, cv2.COLOR_BGR2HLS)
-
if value >
3:
-
img_hsv[:, :,
1] = np.log(img_hsv[:, :,
1] /
255* (value -
1)+
1) / np.log(value +
1) *
255
-
if value <
0:
-
img_hsv[:, :,
1] = np.uint8(img_hsv[:, :,
1] / np.log(- value + np.e))
-
self.last_image = self.current_img
-
self.current_img = cv2.cvtColor(img_hsv, cv2.COLOR_HLS2BGR)
-
-
# 人脸识别
-
def
detect_face(
self):
-
img = self.raw_image
-
face_cascade = cv2.CascadeClassifier(
'./haarcascade_frontalface_default.xml')
-
-
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
faces = face_cascade.detectMultiScale(gray,
1.3,
5)
-
return faces
-
-
# 皮肤识别
-
def
detect_skin(
self):
-
img = self.raw_image
-
rows, cols, channals = img.shape
-
for r
in
range(rows):
-
for c
in
range(cols):
-
B = img.item(r, c,
0)
-
G = img.item(r, c,
1)
-
R = img.item(r, c,
2)
-
if (
abs(R - G) >
15)
and (R > G)
and (R > B):
-
if (R >
95)
and (G >
40)
and (B >
20)
and (
max(R, G, B) -
min(R, G, B) >
15):
-
self.imgskin[r, c] = (
1,
1,
1)
-
elif (R >
220)
and (G >
210)
and (B >
170):
-
self.imgskin[r, c] = (
1,
1,
1)
-
-
# 皮肤磨皮(value1精细度，value2程度)
-
def
dermabrasion(
self, value1=3, value2=2):
-
value1 = self.ui.horizontalSlider_14.value()
-
value2 =
11 - self.ui.horizontalSlider_11.value()
-
if value1 ==
0
and value2 ==
0:
-
return
0
-
if value2 ==
0:
-
value2 =
2
-
if value1 ==
0:
-
value1 =
3
-
img = self.current_img
-
dx = value1 *
5
-
fc = value1 *
12.5
-
p =
50
-
temp1 = cv2.bilateralFilter(img, dx, fc, fc)
-
temp2 = (temp1 - img +
128)
-
temp3 = cv2.GaussianBlur(temp2, (
2 * value2 -
1,
2 * value2 -
1),
0,
0)
-
temp4 = img +
2 * temp3 -
255
-
dst = np.uint8(img * ((
100 - p) /
100) + temp4 * (p /
100))
-
-
-
imgskin_c = np.uint8(-(self.imgskin -
1))
-
-
dst = np.uint8(dst * self.imgskin + img * imgskin_c)
-
self.current_img = dst
-
-
# 美白算法(皮肤识别)
-
def
whitening_skin(
self, value=30):
-
# value = 30
-
value = self.ui.horizontalSlider_13.value()
-
img = self.current_img
-
imgw = np.zeros(img.shape, dtype=
'uint8')
-
imgw = img.copy()
-
midtones_add = np.zeros(
256)
-
-
for i
in
range(
256):
-
midtones_add[i] =
0.667 * (
1 - ((i -
127.0) /
127) * ((i -
127.0) /
127))
-
-
lookup = np.zeros(
256, dtype=
"uint8")
-
-
for i
in
range(
256):
-
red = i
-
red += np.uint8(value * midtones_add[red])
-
red =
max(
0,
min(
0xff, red))
-
lookup[i] = np.uint8(red)
-
-
-
-
rows, cols, channals = img.shape
-
for r
in
range(rows):
-
for c
in
range(cols):
-
-
if self.imgskin[r, c,
0] ==
1:
-
imgw[r, c,
0] = lookup[imgw[r, c,
0]]
-
imgw[r, c,
1] = lookup[imgw[r, c,
1]]
-
imgw[r, c,
2] = lookup[imgw[r, c,
2]]
-
self.current_img = imgw
-
-
# 美白算法(人脸识别)
-
def
whitening_face(
self, value=30):
-
# value = 30
-
value = self.ui.horizontalSlider_8.value()
-
img = self.current_img
-
imgw = np.zeros(img.shape, dtype=
'uint8')
-
imgw = img.copy()
-
midtones_add = np.zeros(
256)
-
-
for i
in
range(
256):
-
midtones_add[i] =
0.667 * (
1 - ((i -
127.0) /
127) * ((i -
127.0) /
127))
-
-
lookup = np.zeros(
256, dtype=
"uint8")
-
-
for i
in
range(
256):
-
red = i
-
red += np.uint8(value * midtones_add[red])
-
red =
max(
0,
min(
0xff, red))
-
lookup[i] = np.uint8(red)
-
-
# faces可全局变量
-
faces = self.faces
-
-
if faces == ():
-
rows, cols, channals = img.shape
-
for r
in
range(rows):
-
for c
in
range(cols):
-
imgw[r, c,
0] = lookup[imgw[r, c,
0]]
-
imgw[r, c,
1] = lookup[imgw[r, c,
1]]
-
imgw[r, c,
2] = lookup[imgw[r, c,
2]]
-
-
else:
-
x, y, w, h = faces[
0]
-
rows, cols, channals = img.shape
-
x =
max(x - (w * np.sqrt(
2) - w) /
2,
0)
-
y =
max(y - (h * np.sqrt(
2) - h) /
2,
0)
-
w = w * np.sqrt(
2)
-
h = h * np.sqrt(
2)
-
rows =
min(rows, y + h)
-
cols =
min(cols, x + w)
-
for r
in
range(
int(y),
int(rows)):
-
for c
in
range(
int(x),
int(cols)):
-
imgw[r, c,
0] = lookup[imgw[r, c,
0]]
-
imgw[r, c,
1] = lookup[imgw[r, c,
1]]
-
imgw[r, c,
2] = lookup[imgw[r, c,
2]]
-
-
processWidth =
int(
max(
min(rows - y, cols -
1) /
8,
2))
-
for i
in
range(
1, processWidth):
-
alpha = (i -
1) / processWidth
-
for r
in
range(
int(y),
int(rows)):
-
imgw[r,
int(x) + i -
1] = np.uint8(
-
imgw[r,
int(x) + i -
1] * alpha + img[r,
int(x) + i -
1] * (
1 - alpha))
-
imgw[r,
int(cols) - i] = np.uint8(
-
imgw[r,
int(cols) - i] * alpha + img[r,
int(cols) - i] * (
1 - alpha))
-
for c
in
range(
int(x) + processWidth,
int(cols) - processWidth):
-
imgw[
int(y) + i -
1, c] = np.uint8(
-
imgw[
int(y) + i -
1, c] * alpha + img[
int(y) + i -
1, c] * (
1 - alpha))
-
imgw[
int(rows) - i, c] = np.uint8(
-
imgw[
int(rows) - i, c] * alpha + img[
int(rows) - i, c] * (
1 - alpha))
-
self.current_img = imgw
-
-
# Gamma矫正
-
def
gamma_trans(
self):
-
gamma = (self.ui.horizontalSlider_5.value() +
10) /
10
-
img = self.current_img
-
gamma_table = [np.power(x /
255.0, gamma) *
255.0
for x
in
range(
256)]
-
gamma_table = np.
round(np.array(gamma_table)).astype(np.uint8)
-
self.current_img = cv2.LUT(img, gamma_table)
-
self.show_image()
-
self.show_histogram()
-
-
# 响应滑动条的变化
-
def
slider_change(
self):
-
if self.raw_image
is
None:
-
return
0
-
-
self.current_img = self.raw_image
-
-
# 伽马变换
-
if self.ui.horizontalSlider_5.value() !=
0:
-
self.gamma_trans()
-
-
# 饱和度
-
if self.ui.horizontalSlider.value() !=
0:
-
self.change_saturation()
-
-
if self.ui.horizontalSlider_2.value() !=
0:
-
pass
-
-
# 边缘保持
-
if self.ui.horizontalSlider_3.value() !=
0:
-
self.edge_preserve()
-
-
# 亮度
-
if self.ui.horizontalSlider_4.value() !=
0:
-
self.change_darker()
-
-
# 美白（人脸识别）
-
if self.ui.horizontalSlider_8.value() !=
0:
-
self.whitening_face()
-
-
# 美白（皮肤识别）
-
if self.ui.horizontalSlider_13.value() !=
0:
-
self.whitening_skin()
-
-
-
# 风格化
-
if self.ui.horizontalSlider_2.value() !=
0:
-
self.stylize()
-
-
# 细节增强
-
if self.ui.horizontalSlider_6.value() !=
0:
-
self.detail_enhance()
-
-
# 铅笔画
-
if self.ui.horizontalSlider_12.value() !=
0:
-
self.pencil_color()
-
self.show_image()
-
-
-
# 计算人脸识别和皮肤识别的基本参数
-
def
calculate(
self):
-
if self.raw_image
is
None:
-
return
0
-
if self.calculated
is
False:
-
self.faces = self.detect_face()
-
if self.faces != ():
-
self.detect_skin()
-
self.calculated =
True
-
-
# 怀旧滤镜
-
def
reminiscene(
self):
-
if self.raw_image
is
None:
-
return
0
-
if self.ui.horizontalSlider_10.value() ==
0:
-
self.current_img = self.raw_image
-
self.show_image()
-
return
0
-
img = self.raw_image.copy()
-
rows, cols, channals = img.shape
-
for r
in
range(rows):
-
for c
in
range(cols):
-
B = img.item(r, c,
0)
-
G = img.item(r, c,
1)
-
R = img.item(r, c,
2)
-
img[r, c,
0] = np.uint8(
min(
max(
0.272 * R +
0.534 * G +
0.131 * B,
0),
255))
-
img[r, c,
1] = np.uint8(
min(
max(
0.349 * R +
0.686 * G +
0.168 * B,
0),
255))
-
img[r, c,
2] = np.uint8(
min(
max(
0.393 * R +
0.769 * G +
0.189 * B,
0),
255))
-
self.current_img = img
-
self.show_image()
-
-
# 木刻滤镜
-
def
woodcut(
self):
-
if self.raw_image
is
None:
-
return
0
-
if self.ui.horizontalSlider_9.value() ==
0:
-
# self.current_img = self.raw_image
-
self.show_image()
-
return
0
-
self.gray_image = cv2.cvtColor(self.raw_image, cv2.COLOR_BGR2GRAY)
-
gray = self.gray_image
-
value =
70 + self.ui.horizontalSlider_9.value()
-
rows, cols = gray.shape
-
for r
in
range(rows):
-
for c
in
range(cols):
-
if gray[r, c] > value:
-
gray[r, c] =
255
-
else:
-
gray[r, c] =
0
-
self.gray_image = gray
-
self.show_grayimage()
-
-
# 铅笔画(灰度)
-
def
pencil_gray(
self):
-
if self.raw_image
is
None:
-
return
0
-
if self.ui.horizontalSlider_7.value() ==
0:
-
# self.current_img = self.raw_image
-
self.show_image()
-
return
0
-
value = self.ui.horizontalSlider_7.value() *
0.05
-
dst1_gray, dst1_color = cv2.pencilSketch(self.current_img, sigma_s=
50, sigma_r=value, shade_factor=
0.04)
-
self.gray_image = dst1_gray
-
self.show_grayimage()
-
-
# 铅笔画(彩色)
-
def
pencil_color(
self):
-
if self.raw_image
is
None:
-
return
0
-
if self.ui.horizontalSlider_12.value() ==
0:
-
self.current_img = self.raw_image
-
self.show_image()
-
return
0
-
value = self.ui.horizontalSlider_12.value() *
0.05
-
dst1_gray, dst1_color = cv2.pencilSketch(self.current_img, sigma_s=
50, sigma_r=value, shade_factor=
0.04)
-
self.current_img = dst1_color
-
-
-
# 风格化
-
def
stylize(
self):
-
if self.raw_image
is
None:
-
return
0
-
if self.ui.horizontalSlider_2.value() ==
0:
-
self.current_img = self.raw_image
-
self.show_image()
-
return
0
-
value = self.ui.horizontalSlider_2.value() *
0.05
-
self.current_img = cv2.stylization(self.current_img, sigma_s=
50, sigma_r=value)
-
-
-
# 细节增强
-
def
detail_enhance(
self):
-
if self.raw_image
is
None:
-
return
0
-
if self.ui.horizontalSlider_6.value() ==
0:
-
self.current_img = self.raw_image
-
self.show_image()
-
return
0
-
value = self.ui.horizontalSlider_6.value() *
0.05
-
self.current_img = cv2.detailEnhance(self.current_img, sigma_s=
50, sigma_r=value)
-
-
# 边缘保持
-
def
edge_preserve(
self):
-
if self.raw_image
is
None:
-
return
0
-
if self.ui.horizontalSlider_3.value() ==
0:
-
self.current_img = self.raw_image
-
self.show_image()
-
return
0
-
value = self.ui.horizontalSlider_3.value() *
0.05
-
self.current_img = cv2.edgePreservingFilter(self.current_img, flags=
1, sigma_s=
50, sigma_r=value)
-
-
# 显示摄像照片
-
def
show_camera(
self):
-
flag, self.camera_image = self.cap.read()
-
show = cv2.resize(self.image, (
640,
480))
-
show = cv2.cvtColor(show, cv2.COLOR_BGR2RGB)
-
showImage = QtGui.QImage(show.data, show.shape[
1], show.shape[
0], QtGui.QImage.Format_RGB888)
-
self.label_show_camera.setPixmap(QtGui.QPixmap.fromImage(showImage))
4.运行结果如下：
三、在线协助：
如需安装运行环境或远程调试，见文章底部微信名片，由专业技术人员远程协助！
转载：https://blog.csdn.net/alicema1111/article/details/128346509
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