專案簡介：
電子科技大學中山學院2019級人工智能課程設計
語言：Python
開發工具：Pycharm、Python3.9
庫：PyQt、OpenCv、dlib
功能：
通過人工智能監督用戶使用電腦時的姿態與距離，
原始碼：
Qt界面：
"""
計算機視覺課程設計
ComputerVisual curriculum design
開發時間：2021年12月8日 下午 19：14
Development time: 2021.12.8 pm 19:14
著作權所有?：電子科技大學中山學院-陶凌杰
Property in copyright: University of Electronic Science and Technology of China_ZhongShan college-TonnyJack
僅供參考，禁止商用
For reference only, no commercial use
"""
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'CourseDesign.ui'
#
# Created by: PyQt5 UI code generator 5.15.6
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this file unless you know what you are doing.
import sys
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
from PyQt5 import QtCore, QtGui, QtWidgets
import demo
class Ui_Dialog(object):
def setupUi(self, Dialog):
Dialog.setObjectName("Dialog")
Dialog.resize(662, 565)
self.pushButton = QtWidgets.QPushButton(Dialog)
self.pushButton.setGeometry(QtCore.QRect(151, 150, 171, 51))
self.pushButton.setObjectName("pushButton")
self.pushButton_2 = QtWidgets.QPushButton(Dialog)
self.pushButton_2.setGeometry(QtCore.QRect(341, 150, 171, 51))
self.pushButton_2.setObjectName("pushButton_2")
self.pushButton_3 = QtWidgets.QPushButton(Dialog)
self.pushButton_3.setGeometry(QtCore.QRect(151, 210, 171, 51))
self.pushButton_3.setObjectName("pushButton_3")
self.pushButton_4 = QtWidgets.QPushButton(Dialog)
self.pushButton_4.setGeometry(QtCore.QRect(341, 210, 171, 51))
self.pushButton_4.setObjectName("pushButton_4")
self.pushButton_5 = QtWidgets.QPushButton(Dialog)
self.pushButton_5.setGeometry(QtCore.QRect(151, 270, 171, 51))
self.pushButton_5.setObjectName("pushButton_5")
self.pushButton_6 = QtWidgets.QPushButton(Dialog)
self.pushButton_6.setGeometry(QtCore.QRect(341, 270, 171, 51))
self.pushButton_6.setObjectName("pushButton_6")
self.label = QtWidgets.QLabel(Dialog)
self.label.setGeometry(QtCore.QRect(230, 40, 241, 61))
self.label.setLayoutDirection(QtCore.Qt.LeftToRight)
self.label.setObjectName("label")
self.textBrowser = QtWidgets.QTextBrowser(Dialog)
self.textBrowser.setGeometry(QtCore.QRect(90, 350, 501, 191))
self.textBrowser.setObjectName("textBrowser")
self.retranslateUi(Dialog)
self.pushButton.clicked.connect(self.push_buttom1)
self.pushButton_2.clicked.connect(self.push_buttom2)
self.pushButton_3.clicked.connect(self.push_buttom3)
self.pushButton_4.clicked.connect(self.push_buttom4)
self.pushButton_5.clicked.connect(self.push_buttom5)
self.pushButton_6.clicked.connect(self.push_buttom6)
QtCore.QMetaObject.connectSlotsByName(Dialog)
Dialog.show()
def retranslateUi(self, Dialog):
_translate = QtCore.QCoreApplication.translate
Dialog.setWindowTitle(_translate("Dialog", "AiProgramByZSC_TonnyJack"))
self.pushButton.setText(_translate("Dialog", "資料采集"))
self.pushButton_2.setText(_translate("Dialog", "訓練模型"))
self.pushButton_3.setText(_translate("Dialog", "人臉識別"))
self.pushButton_4.setText(_translate("Dialog", "面部標點"))
self.pushButton_5.setText(_translate("Dialog", "梯形方向"))
self.pushButton_6.setText(_translate("Dialog", "三維坐標"))
self.label.setText(_translate("Dialog",
"<html><head/><body><p><span style=\" "
"font-size:14pt;\">歡迎使用TonnyJack的程式</span></p></body></html>"))
self.textBrowser.setHtml(_translate("Dialog",
"<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0//EN\" "
"\"http://www.w3.org/TR/REC-html40/strict.dtd\">\n "
"<html><head><meta name=\"qrichtext\" content=\"1\" /><style "
"type=\"text/css\">\n "
"p, li { white-space: pre-wrap; }\n"
"</style></head><body style=\" font-family:\'SimSun\'; font-size:9pt; "
"font-weight:400; font-style:normal;\">\n "
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; "
"margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" "
"font-size:11pt;\">這是一個由Opencv和Python開發的人工智能程式</span></p>\n "
"<p style=\"-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; "
"margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; "
"font-size:11pt;\"><br /></p>\n "
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; "
"margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" "
"font-size:11pt;\">部分代碼來自：抖音-恩培</span></p>\n "
"<p style=\"-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; "
"margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; "
"font-size:11pt;\"><br /></p>\n "
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; "
"margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" "
"font-size:11pt;\">代碼已開源并發布在個人博客：</span></p>\n "
"<p style=\"-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; "
"margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; "
"font-size:11pt;\"><br /></p>\n "
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; "
"margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" "
"font-size:11pt;\">本程式僅供學習與參考，禁止商用！</span></p>\n "
"<p style=\"-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; "
"margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; "
"font-size:11pt;\"><br /></p>\n "
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; "
"margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" "
"font-size:11pt;\">著作權所有?：電子科技大學中山學院-陶凌杰</span></p>\n "
"<p style=\"-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; "
"margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; "
"font-size:11pt;\"><br /></p></body></html>"))
def push_buttom1(self):
self.m = demo.MonitorBabay()
self.m.collectFacesFromCamera(1, 3, 3)
def push_buttom2(self):
self.m = demo.MonitorBabay()
self.m.train()
def push_buttom3(self):
self.m = demo.MonitorBabay()
self.m.run(960, 720, 1)
def push_buttom4(self):
self.m = demo.MonitorBabay()
self.m.run(960, 720, 2)
def push_buttom5(self):
self.m = demo.MonitorBabay()
self.m.run(960, 720, 3)
def push_buttom6(self):
self.m = demo.MonitorBabay()
self.m.run(960, 720, 5)
if __name__ == "__main__":
app = QApplication(sys.argv)
form = QWidget()
ui = Ui_Dialog()
ui.setupUi(form)
form.show()
sys.exit(app.exec_())
主程式：
"""
主要功能：檢測孩子是否在看電視，看了多久，距離多遠
使用技術點：人臉檢測、人臉識別（采集照片、訓練、識別）、姿態估計
"""
import os
from argparse import ArgumentParser
import cv2
import dlib
import time
from pose_estimator import PoseEstimator
from utils import Utils
class MonitorBabay:
def __init__(self):
# 人臉檢測
self.face_detector = dlib.get_frontal_face_detector()
# 人臉識別模型：pip uninstall opencv-python，pip install opencv-contrib-python
self.face_model = cv2.face.LBPHFaceRecognizer_create()
# 人臉68個關鍵點
self.landmark_predictor = dlib.shape_predictor("./assets/shape_predictor_68_face_landmarks.dat")
# 站在1.5M遠處，左眼最左邊距離右眼最右邊的像素距離(請使用getEyePixelDist方法校準，然后修改這里的值)
self.eyeBaseDistance = 65
# pose_estimator.show_3d_model()
self.utils = Utils()
# 采集照片用于訓練
# 引數
# label_index: label的索引
# save_interval：隔幾秒存盤照片
# save_num：存盤總量
def collectFacesFromCamera(self, label_index, save_interval, save_num):
cap = cv2.VideoCapture(0)
width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
fpsTime = time.time()
last_save_time = fpsTime
saved_num = 0
while True:
_, frame = cap.read()
frame = cv2.flip(frame, 1)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = self.face_detector(gray)
for face in faces:
if saved_num < save_num:
if (time.time() - last_save_time) > save_interval:
self.utils.save_face(face, frame, label_index)
saved_num += 1
last_save_time = time.time()
print('label_index:{index}，成功采集第{num}張照片'.format(index=label_index, num=saved_num))
else:
print('照片采集完畢！')
exit()
self.utils.draw_face_box(face, frame, '', '', '')
cTime = time.time()
fps_text = 1 / (cTime - fpsTime)
fpsTime = cTime
frame = self.utils.cv2AddChineseText(frame, "幀率: " + str(int(fps_text)), (10, 30), textColor=(0, 255, 0),
textSize=50)
frame = cv2.resize(frame, (int(width) // 2, int(height) // 2))
cv2.imshow('Collect data', frame)
if cv2.waitKey(5) & 0xFF == 27:
break
cap.release()
# 訓練人臉模型
def train(self):
print('訓練開始！')
label_list, img_list = self.utils.getFacesLabels()
self.face_model.train(img_list, label_list)
self.face_model.save("./face_model/model.yml")
print('訓練完畢！')
# 獲取兩個眼角像素距離
def getEyePixelDist(self):
cap = cv2.VideoCapture(0)
width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
# 姿態估計
self.pose_estimator = PoseEstimator(img_size=(height, width))
fpsTime = time.time()
while True:
_, frame = cap.read()
frame = cv2.flip(frame, 1)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = self.face_detector(gray)
pixel_dist = 0
for face in faces:
# 關鍵點
landmarks = self.landmark_predictor(gray, face)
image_points = self.pose_estimator.get_image_points(landmarks)
left_x = int(image_points[36][0])
left_y = int(image_points[36][1])
right_x = int(image_points[45][0])
right_y = int(image_points[45][1])
pixel_dist = abs(right_x - left_x)
cv2.circle(frame, (left_x, left_y), 8, (255, 0, 255), -1)
cv2.circle(frame, (right_x, right_y), 8, (255, 0, 255), -1)
# 人臉框
frame = self.utils.draw_face_box(face, frame, '', '', '')
cTime = time.time()
fps_text = 1 / (cTime - fpsTime)
fpsTime = cTime
frame = self.utils.cv2AddChineseText(frame, "幀率: " + str(int(fps_text)), (20, 30), textColor=(0, 255, 0),
textSize=50)
frame = self.utils.cv2AddChineseText(frame, "像素距離: " + str(int(pixel_dist)), (20, 100),
textColor=(0, 255, 0), textSize=50)
# frame = cv2.resize(frame, (int(width)//2, int(height)//2) )
cv2.imshow('Baby wathching TV', frame)
if cv2.waitKey(5) & 0xFF == 27:
break
cap.release()
# 運行主程式
def run(self, w, h, display):
model_path = "./face_model/model.yml"
if not os.path.exists(model_path):
print('人臉識別模型檔案不存在，請先采集訓練')
exit()
label_zh = self.utils.loadLablZh()
self.face_model.read(model_path)
cap = cv2.VideoCapture(0)
width = w
height = h
print(width, height)
# 姿態估計
self.pose_estimator = PoseEstimator(img_size=(height, width))
fpsTime = time.time()
zh_name = ''
x_label = ''
z_label = ''
is_watch = ''
angles = [0, 0, 0]
person_distance = 0
watch_start_time = fpsTime
watch_duration = 0
# fps = 12 videoWriter = cv2.VideoWriter('./record_video/out'+str(time.time())+'.mp4',
# cv2.VideoWriter_fourcc(*'H264'), fps, (width,height))
while True:
_, frame = cap.read()
frame = cv2.resize(frame, (width, height))
frame = cv2.flip(frame, 1)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = self.face_detector(gray)
for face in faces:
x1, y1, x2, y2 = self.utils.getFaceXY(face)
face_img = gray[y1:y2, x1:x2]
try:
# 人臉識別
idx, confidence = self.face_model.predict(face_img)
zh_name = label_zh[str(idx)]
except cv2.error:
print('cv2.error')
# 關鍵點
landmarks = self.landmark_predictor(gray, face)
# 計算旋轉矢量
rotation_vector, translation_vector = self.pose_estimator.solve_pose_by_68_points(landmarks)
# 計算距離
person_distance = round(self.pose_estimator.get_distance(self.eyeBaseDistance), 2)
# 計算角度
rmat, jac = cv2.Rodrigues(rotation_vector)
angles, mtxR, mtxQ, Qx, Qy, Qz = cv2.RQDecomp3x3(rmat)
if angles[1] < -15:
x_label = '左'
elif angles[1] > 15:
x_label = '右'
else:
x_label = '前'
if angles[0] < -15:
z_label = "下"
elif angles[0] > 15:
z_label = "上"
else:
z_label = "中"
is_watch = '是' if (x_label == '前' and z_label == '中') else '否'
if is_watch == '是':
now = time.time()
watch_duration += (now - watch_start_time)
watch_start_time = time.time()
if display == 1:
# 人臉框
frame = self.utils.draw_face_box(face, frame, zh_name, is_watch, person_distance)
if display == 2:
# 68個關鍵點
self.utils.draw_face_points(landmarks, frame)
if display == 3:
# 梯形方向
self.pose_estimator.draw_annotation_box(
frame, rotation_vector, translation_vector, is_watch)
if display == 4:
# 指標
self.pose_estimator.draw_pointer(frame, rotation_vector, translation_vector)
if display == 5:
# 三維坐標系
self.pose_estimator.draw_axes(frame, rotation_vector, translation_vector)
# 僅測驗單人
break
cTime = time.time()
fps_text = 1 / (cTime - fpsTime)
fpsTime = cTime
frame = self.utils.cv2AddChineseText(frame, "幀率: " + str(int(fps_text)), (20, 30), textColor=(0, 255, 0),
textSize=50)
color = (255, 0, 255) if person_distance <= 1 else (0, 255, 0)
frame = self.utils.cv2AddChineseText(frame, "距離: " + str(person_distance) + "m", (20, 100), textColor=color,
textSize=50)
color = (255, 0, 255) if is_watch == '是' else (0, 255, 0)
frame = self.utils.cv2AddChineseText(frame, "觀看: " + str(is_watch), (20, 170), textColor=color, textSize=50)
#
duration_str = str(round((watch_duration / 60), 2)) + "min"
frame = self.utils.cv2AddChineseText(frame, "時長: " + duration_str, (20, 240), textColor=(0, 255, 0),
textSize=50)
color = (255, 0, 255) if x_label == '前' else (0, 255, 0)
frame = self.utils.cv2AddChineseText(frame, "X軸: {degree}° {x_label} ".format(x_label=str(x_label),
degree=str(int(angles[1]))),
(20, height - 220), textColor=color, textSize=40)
color = (255, 0, 255) if z_label == '中' else (0, 255, 0)
frame = self.utils.cv2AddChineseText(frame, "Z軸: {degree}° {z_label}".format(z_label=str(z_label),
degree=str(int(angles[0]))),
(20, height - 160), textColor=color, textSize=40)
frame = self.utils.cv2AddChineseText(frame, "Y軸: {degree}°".format(degree=str(int(angles[2]))),
(20, height - 100), textColor=(0, 255, 0), textSize=40)
# videoWriter.write(frame)
# frame = cv2.resize(frame, (int(width)//2, int(height)//2) )
cv2.imshow('Baby wathching TV', frame)
if cv2.waitKey(5) & 0xFF == 27:
break
cap.release()
if __name__ == "__main__":
m = MonitorBabay()
m.run(960, 720, 5)
# 引數
# parser = ArgumentParser()
# parser.add_argument("--mode", type=str, default='run',
# help="運行模式：collect,train,distance,run對應：采集、訓練、評估距離、主程式")
# parser.add_argument("--label_id", type=int, default=1,
# help="采集照片標簽id.")
# parser.add_argument("--img_count", type=int, default=3,
# help="采集照片數量")
# parser.add_argument("--img_interval", type=int, default=3,
# help="采集照片間隔時間")
#
# parser.add_argument("--display", type=int, default=1,
# help="顯示模式，取值1-5")
#
# parser.add_argument("--w", type=int, default=960,
# help="畫面寬度")
# parser.add_argument("--h", type=int, default=720,
# help="畫面高度")
# args = parser.parse_args()
#
# mode = args.mode
#
# if mode == 'collect':
# print("即將采集照片.")
# if args.label_id and args.img_count and args.img_interval:
# m.collectFacesFromCamera(args.label_id, args.img_interval, args.img_count)
#
# if mode == 'train':
# m.train()
#
# if mode == 'distance':
# m.getEyePixelDist()
#
# if mode == 'run':
# m.run(args.w, args.h, args.display)
封裝姿態判斷工具：
"""
封裝常用工具，降低Demo復雜度
"""
import cv2
import numpy as np
class PoseEstimator:
"""Estimate head pose according to the facial landmarks"""
def __init__(self, img_size=(480, 640)):
self.size = img_size
self.model_points_68 = self._get_full_model_points()
self.image_points = None
# Camera internals
self.focal_length = self.size[1]
self.camera_center = (self.size[1] / 2, self.size[0] / 2)
self.camera_matrix = np.array(
[[self.focal_length, 0, self.camera_center[0]],
[0, self.focal_length, self.camera_center[1]],
[0, 0, 1]], dtype="double")
# Assuming no lens distortion
self.dist_coeefs = np.zeros((4, 1))
# Rotation vector and translation vector
self.r_vec = np.array([[0.01891013], [0.08560084], [-3.14392813]])
self.t_vec = np.array(
[[-14.97821226], [-10.62040383], [-2053.03596872]])
# self.r_vec = None
# self.t_vec = None
def _get_full_model_points(self, filename='assets/model.txt'):
"""Get all 68 3D model points from file"""
raw_value = []
with open(filename) as file:
for line in file:
raw_value.append(line)
model_points = np.array(raw_value, dtype=np.float32)
model_points = np.reshape(model_points, (3, -1)).T
# Transform the model into a front view.
model_points[:, 2] *= -1
return model_points
def show_3d_model(self):
import matplotlib.pyplot as plt
x = self.model_points_68[:, 0]
y = self.model_points_68[:, 1]
z = self.model_points_68[:, 2]
fig = plt.figure()
ax = plt.axes(projection="3d")
ax.scatter3D(x, y, z)
plt.show()
# 利用相似三角形估算距離
def get_distance(self, eyeBaseDistance):
image_points = self.image_points
left_x = int(image_points[36][0])
right_x = int(image_points[45][0])
pixel_dist = abs(right_x - left_x)
return (eyeBaseDistance / pixel_dist) * 1.5
def get_image_points(self, landmarks):
landmarks_list = []
for n in range(0, 68):
x = landmarks.part(n).x
y = landmarks.part(n).y
landmarks_list.append([x, y])
image_points = np.array(landmarks_list, dtype="double")
self.image_points = image_points
return image_points
def solve_pose_by_68_points(self, landmarks):
image_points = self.get_image_points(landmarks)
"""
Solve pose from all the 68 image points
Return (rotation_vector, translation_vector) as pose.
"""
if self.r_vec is None:
(_, rotation_vector, translation_vector) = cv2.solvePnP(
self.model_points_68, image_points, self.camera_matrix, self.dist_coeefs)
self.r_vec = rotation_vector
self.t_vec = translation_vector
(_, rotation_vector, translation_vector) = cv2.solvePnP(
self.model_points_68,
image_points,
self.camera_matrix,
self.dist_coeefs,
rvec=self.r_vec,
tvec=self.t_vec,
useExtrinsicGuess=True)
return (rotation_vector, translation_vector)
def draw_annotation_box(self, image, rotation_vector, translation_vector, is_watch, line_width=2):
"""Draw a 3D box as annotation of pose"""
point_3d = []
rear_size = 75
rear_depth = 0
point_3d.append((-rear_size, -rear_size, rear_depth))
point_3d.append((-rear_size, rear_size, rear_depth))
point_3d.append((rear_size, rear_size, rear_depth))
point_3d.append((rear_size, -rear_size, rear_depth))
point_3d.append((-rear_size, -rear_size, rear_depth))
front_size = 100
front_depth = 100
point_3d.append((-front_size, -front_size, front_depth))
point_3d.append((-front_size, front_size, front_depth))
point_3d.append((front_size, front_size, front_depth))
point_3d.append((front_size, -front_size, front_depth))
point_3d.append((-front_size, -front_size, front_depth))
point_3d = np.array(point_3d, dtype=np.float).reshape(-1, 3)
# Map to 2d image points
(point_2d, _) = cv2.projectPoints(point_3d,
rotation_vector,
translation_vector,
self.camera_matrix,
self.dist_coeefs)
point_2d = np.int32(point_2d.reshape(-1, 2))
color = (255, 0, 255) if is_watch == '是' else (0, 255, 0)
# Draw all the lines
cv2.polylines(image, [point_2d], True, color, line_width, cv2.LINE_AA)
cv2.line(image, tuple(point_2d[1]), tuple(
point_2d[6]), color, line_width, cv2.LINE_AA)
cv2.line(image, tuple(point_2d[2]), tuple(
point_2d[7]), color, line_width, cv2.LINE_AA)
cv2.line(image, tuple(point_2d[3]), tuple(
point_2d[8]), color, line_width, cv2.LINE_AA)
# 坐標系
def draw_axes(self, img, R, t):
img = cv2.drawFrameAxes(img, self.camera_matrix, self.dist_coeefs, R, t, 200)
# 方向指標
def draw_pointer(self, img, R, t):
point1 = (int(self.image_points[30][0]), int(self.image_points[30][1]))
nose_end_point2D, jacobian = cv2.projectPoints(np.array([(0.0, 0.0, 300.0)]), R, t, self.camera_matrix,
self.dist_coeefs)
point2 = (int(nose_end_point2D[0][0][0]), int(nose_end_point2D[0][0][1]))
cv2.line(img, point1, point2, (0, 255, 0), 4)
封裝常用工具：
"""
封裝常用工具，降低Demo復雜度
"""
# 匯入PIL
from PIL import Image, ImageDraw, ImageFont
# 匯入OpenCV
import cv2
from matplotlib.pyplot import xlabel
import numpy as np
import time
import os
import glob
from sys import platform as _platform
class Utils:
def __init__(self):
pass
# 添加中文
def cv2AddChineseText(self, img, text, position, textColor=(0, 255, 0), textSize=30):
if isinstance(img, np.ndarray): # 判斷是否OpenCV圖片型別
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
# 創建一個可以在給定影像上繪圖的物件
draw = ImageDraw.Draw(img)
# 字體的格式
fontStyle = ImageFont.truetype(
"./fonts/simsun.ttc", textSize, encoding="utf-8")
# 繪制文本
draw.text(position, text, textColor, font=fontStyle)
# 轉換回OpenCV格式
return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
def getFaceXY(self, face):
x1 = face.left()
y1 = face.top()
x2 = face.right()
y2 = face.bottom()
return x1, y1, x2, y2
# 人臉框框
def draw_face_box(self, face, frame, zh_name, is_watch, distance):
color = (255, 0, 255) if is_watch == '是' else (0, 255, 0)
x1, y1, x2, y2 = self.getFaceXY(face)
frame = cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
m_str = '' if distance == '' else 'm'
frame = self.cv2AddChineseText(frame,
"{name} {distance}{m_str}".format(name=zh_name, distance=distance, m_str=m_str),
(x1, y1 - 60), textColor=color, textSize=50)
return frame
# 保存人臉照片
def save_face(self, face, frame, face_label_index):
path = './face_imgs/' + str(face_label_index)
if not os.path.exists(path):
os.makedirs(path)
x1, y1, x2, y2 = self.getFaceXY(face)
face_img = frame[y1:y2, x1:x2]
filename = path + '/' + str(face_label_index) + '-' + str(time.time()) + '.jpg'
cv2.imwrite(filename, face_img)
# 人臉點
def draw_face_points(self, landmarks, frame):
for n in range(0, 68):
x = landmarks.part(n).x
y = landmarks.part(n).y
cv2.circle(frame, (x, y), 4, (255, 0, 255), -1)
# 獲取人臉和label資料
def getFacesLabels(self):
# 遍歷所有檔案
label_list = []
img_list = []
for file_path in glob.glob('./face_imgs/*/*'):
dir_str = '/'
if _platform == "linux" or _platform == "linux2":
# linux
pass
elif _platform == "darwin":
# MAC OS X
pass
elif _platform == "win32":
# Windows
dir_str = '\\'
elif _platform == "win64":
# Windows 64-bit
dir_str = '\\'
label_list.append(int(file_path.split(dir_str)[-1].split('-')[0]))
img = cv2.imread(file_path)
# img = cv2.resize(img, (224, 224))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_list.append(img)
return np.array(label_list), img_list
# 加載label 對應中文
def loadLablZh(self):
with open('./face_model/label.txt', encoding='utf-8') as f:
back_dict = {}
for line in f.readlines():
label_index = line.split(',')[0]
label_zh = line.split(',')[1].split('\n')[0]
back_dict[label_index] = label_zh
return back_dict
使用方法：
一：搜索Python，進入官網，下載Python3.9解釋器
二：搜索Pycharm，進入官網，下載Pycharm community并安裝
三：在Pycharm中配置python解釋器，打開終端，輸入以下三個命令
pip install OpenCV-python -i https://mirrors.aliyun.com/pypi/simple/
pip install mediapipe -i https://mirrors.aliyun.com/pypi/simple/
pip install PyQt5==5.10.1 -i https://mirrors.aliyun.com/pypi/simple/
四：將三個代碼檔案放在同一檔案目錄下，運行QT界面的代碼
轉載請註明出處，本文鏈接：https://www.uj5u.com/qita/401498.html
標籤：其他
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