170 lines
6.4 KiB
Python
170 lines
6.4 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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'''
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Created on 2018年1月29日
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@author: Irony."[讽刺]
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@site: https://pyqt5.com , https://github.com/892768447
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@email: 892768447@qq.com
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@file: FacePoints
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@description: 人脸特征点
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'''
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from bz2 import BZ2Decompressor
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import cgitb
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import os
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import sys
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from PyQt5.QtCore import QTimer, QUrl, QFile, QIODevice
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from PyQt5.QtGui import QImage, QPixmap
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from PyQt5.QtNetwork import QNetworkAccessManager, QNetworkRequest
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from PyQt5.QtWidgets import QLabel, QMessageBox, QApplication
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import cv2 # @UnresolvedImport
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import dlib
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import numpy
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__Author__ = "By: Irony.\"[讽刺]\nQQ: 892768447\nEmail: 892768447@qq.com"
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__Copyright__ = "Copyright (c) 2018 Irony.\"[讽刺]"
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__Version__ = "Version 1.0"
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DOWNSCALE = 4
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URL = 'http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2'
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class OpencvWidget(QLabel):
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def __init__(self, *args, **kwargs):
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super(OpencvWidget, self).__init__(*args, **kwargs)
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self.httpRequestAborted = False
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self.fps = 24
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self.resize(800, 600)
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if not os.path.exists("Data/shape_predictor_68_face_landmarks.dat"):
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self.setText("正在下载数据文件。。。")
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self.outFile = QFile(
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"Data/shape_predictor_68_face_landmarks.dat.bz2")
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if not self.outFile.open(QIODevice.WriteOnly):
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QMessageBox.critical(self, '错误', '无法写入文件')
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return
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self.qnam = QNetworkAccessManager(self)
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self._reply = self.qnam.get(QNetworkRequest(QUrl(URL)))
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self._reply.finished.connect(self.httpFinished)
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self._reply.readyRead.connect(self.httpReadyRead)
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self._reply.downloadProgress.connect(self.updateDataReadProgress)
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else:
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self.startCapture()
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def httpFinished(self):
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self.outFile.close()
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if self.httpRequestAborted or self._reply.error():
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self.outFile.remove()
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self._reply.deleteLater()
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del self._reply
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# 下载完成解压文件并加载摄像头
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self.setText("正在解压数据。。。")
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try:
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bz = BZ2Decompressor()
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data = bz.decompress(
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open('Data/shape_predictor_68_face_landmarks.dat.bz2', 'rb').read())
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open('Data/shape_predictor_68_face_landmarks.dat', 'wb').write(data)
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except Exception as e:
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self.setText('解压失败：' + str(e))
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return
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self.setText('正在开启摄像头。。。')
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self.startCapture()
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def httpReadyRead(self):
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self.outFile.write(self._reply.readAll())
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self.outFile.flush()
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def updateDataReadProgress(self, bytesRead, totalBytes):
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self.setText('已下载：{} %'.format(round(bytesRead / 64040097 * 100, 2)))
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def startCapture(self):
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self.setText("请稍候，正在初始化数据和摄像头。。。")
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try:
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# 检测相关
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self.detector = dlib.get_frontal_face_detector()
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self.predictor = dlib.shape_predictor(
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"Data/shape_predictor_68_face_landmarks.dat")
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cascade_fn = "Data/lbpcascades/lbpcascade_frontalface.xml"
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self.cascade = cv2.CascadeClassifier(cascade_fn)
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if not self.cascade:
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return QMessageBox.critical(self, "错误", cascade_fn + " 无法找到")
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self.cap = cv2.VideoCapture(0)
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if not self.cap or not self.cap.isOpened():
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return QMessageBox.critical(self, "错误", "打开摄像头失败")
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# 开启定时器定时捕获
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self.timer = QTimer(self, timeout=self.onCapture)
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self.timer.start(1000 / self.fps)
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except Exception as e:
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QMessageBox.critical(self, "错误", str(e))
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def closeEvent(self, event):
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if hasattr(self, "_reply") and self._reply:
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self.httpRequestAborted = True
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self._reply.abort()
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try:
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os.unlink("Data/shape_predictor_68_face_landmarks.dat.bz2")
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except:
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pass
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try:
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os.unlink("Data/shape_predictor_68_face_landmarks.dat")
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except:
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pass
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if hasattr(self, "timer"):
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self.timer.stop()
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self.timer.deleteLater()
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self.cap.release()
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del self.predictor, self.detector, self.cascade, self.cap
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super(OpencvWidget, self).closeEvent(event)
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self.deleteLater()
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def onCapture(self):
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_, frame = self.cap.read()
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minisize = (
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int(frame.shape[1] / DOWNSCALE), int(frame.shape[0] / DOWNSCALE))
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tmpframe = cv2.resize(frame, minisize)
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tmpframe = cv2.cvtColor(tmpframe, cv2.COLOR_BGR2GRAY) # 做灰度处理
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tmpframe = cv2.equalizeHist(tmpframe)
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# minNeighbors表示每一个目标至少要被检测到5次
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faces = self.cascade.detectMultiScale(tmpframe, minNeighbors=5)
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del tmpframe
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if len(faces) < 1: # 没有检测到脸
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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img = QImage(
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frame.data, frame.shape[1], frame.shape[0], frame.shape[1] * 3, QImage.Format_RGB888)
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del frame
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return self.setPixmap(QPixmap.fromImage(img))
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# 特征点检测描绘
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for x, y, w, h in faces:
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x, y, w, h = x * DOWNSCALE, y * DOWNSCALE, w * DOWNSCALE, h * DOWNSCALE
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# 画脸矩形
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cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0))
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# 截取的人脸部分
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tmpframe = frame[y:y + h, x:x + w]
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# 进行特征点描绘
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rects = self.detector(tmpframe, 1)
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if len(rects) > 0:
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landmarks = numpy.matrix(
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[[p.x, p.y] for p in self.predictor(tmpframe, rects[0]).parts()])
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for _, point in enumerate(landmarks):
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pos = (point[0, 0] + x, point[0, 1] + y)
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# 在原来画面上画点
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cv2.circle(frame, pos, 3, color=(0, 255, 0))
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# 转成Qt能显示的
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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img = QImage(
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frame.data, frame.shape[1], frame.shape[0], frame.shape[1] * 3, QImage.Format_RGB888)
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del frame
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self.setPixmap(QPixmap.fromImage(img))
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if __name__ == "__main__":
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cgitb.enable(1, None, 5, '')
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app = QApplication(sys.argv)
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w = OpencvWidget()
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w.show()
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sys.exit(app.exec_())
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