Python人脸表情识别QT窗体

.Python人脸表情识别QT窗体

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运行结下：

主要代码：

# coding:utf-8
import sys
#从转换的.py文件内调用类
import cv2
import numpy as np
import sys
import tensorflow as tffrom untitled import Ui_Dialog
from PyQt5 import QtWidgetsfrom PyQt5 import QtWidgets, QtCore, QtGui
from PyQt5.QtCore import *class myWin(QtWidgets.QWidget, Ui_Dialog):def __init__(self):super(myWin, self).__init__()self.setupUi(self)def openFileButton(self):imgName, imgType  = QFileDialog.getOpenFileName(self,"打开文件","./","files(*.*)")img = cv2.imread(imgName)cv2.imwrite("temp/original.jpg", img)height, width, pixels = img.shapeprint("width,height",width,height)print("self.label.width()",self.label.width())print("self.label.height()",self.label.height())frame = cv2.resize(img, (int(rwidth), int(rheight)))print("rwidth-elif,rheight-elfi", rwidth, rheight)img2 = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # opencv读取的bgr格式图片转换成rgb格式_image = QtGui.QImage(img2[:], img2.shape[1], img2.shape[0], img2.shape[1] * 3, QtGui.QImage.Format_RGB888)jpg_out = QtGui.QPixmap(_image).scaled(rwidth, rheight) #设置图片大小self.label.setPixmap(jpg_out) #设置图片显示def saveFileButton(self):img = cv2.imread("temp/original.jpg")file_path = QFileDialog.getSaveFileName(self, "save file", "./save/test","jpg files (*.jpg);;all files(*.*)")print(file_path[0])cv2.imwrite(file_path[0], img)cv2.waitKey(0)cv2.destroyAllWindows()def recogPerson(self):import osimport cv2img = cv2.imread("temp/original.jpg")cv2.imwrite("save/recognPerson2.jpg", img)face_detect = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')eye_detect = cv2.CascadeClassifier('haarcascade_eye.xml')# 灰度处理gray = cv2.cvtColor(img, code=cv2.COLOR_BGR2GRAY)# 检查人脸 按照1.1倍放到 周围最小像素为5face_zone = face_detect.detectMultiScale(gray,1.3,5)# print ('识别人脸的信息：\n',face_zone)l = len(face_zone)ints = 0# 绘制矩形和圆形检测人脸for x, y, w, h in face_zone:ints += 1# 绘制矩形人脸区域if w < 1000:cv2.rectangle(img, pt1=(x, y), pt2=(x + w, y + h), color=[0, 0, 255], thickness=2)# 绘制圆形人脸区域 radius表示半径cv2.circle(img, center=(x + w // 2, y + h // 2), radius=w // 2, color=[0, 255, 0], thickness=2)roi_face = gray[y:y + h, x:x + w]  # 灰度图roi_color = img[y:y + h, x:x + w]  # 彩色图eyes = eye_detect.detectMultiScale(roi_face)for (ex, ey, ew, eh) in eyes:cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)cv2.imwrite("save/recognPerson.jpg", img)#cv2.waitKey(0)#显示人数到窗体self.textEdit.setPlainText(str(ints))#self.textEdit.setPlainText('Hello PyQt5!\n单击按钮')frame = cv2.resize(img, (int(rwidth), int(rheight)))img2 = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # opencv读取的bgr格式图片转换成rgb格式_image = QtGui.QImage(img2[:], img2.shape[1], img2.shape[0], img2.shape[1] * 3, QtGui.QImage.Format_RGB888)jpg_out = QtGui.QPixmap(_image).scaled(rwidth, rheight) #设置图片大小self.label_2.setPixmap(QPixmap(""))self.label_2.setPixmap(jpg_out) #设置图片显示#emotion recogCASC_PATH = './data/haarcascade_files/haarcascade_frontalface_default.xml'cascade_classifier = cv2.CascadeClassifier(CASC_PATH)EMOTIONS = ['Angry', 'Disgusted', 'Fearful', 'Happy', 'Sad', 'Surprised', 'Neutral']def format_image(image):if len(image.shape) > 2 and image.shape[2] == 3:image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)faces = cascade_classifier.detectMultiScale(image,scaleFactor=1.3,minNeighbors=5)# None is no face found in imageif not len(faces) > 0:return None, Nonemax_are_face = faces[0]for face in faces:if face[2] * face[3] > max_are_face[2] * max_are_face[3]:max_are_face = face# face to imageface_coor = max_are_faceimage = image[face_coor[1]:(face_coor[1] + face_coor[2]), face_coor[0]:(face_coor[0] + face_coor[3])]# Resize image to network sizetry:image = cv2.resize(image, (48, 48), interpolation=cv2.INTER_CUBIC)except Exception:print("[+} Problem during resize")return None, Nonereturn image, face_coordef face_dect(image):"""Detecting faces in image:param image::return:  the coordinate of max face"""if len(image.shape) > 2 and image.shape[2] == 3:image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)faces = cascade_classifier.detectMultiScale(image,scaleFactor=1.3,minNeighbors=5)if not len(faces) > 0:return Nonemax_face = faces[0]for face in faces:if face[2] * face[3] > max_face[2] * max_face[3]:max_face = faceface_image = image[max_face[1]:(max_face[1] + max_face[2]), max_face[0]:(max_face[0] + max_face[3])]try:image = cv2.resize(face_image, (48, 48), interpolation=cv2.INTER_CUBIC) / 255.except Exception:print("[+} Problem during resize")return Nonereturn face_imagedef resize_image(image, size):try:image = cv2.resize(image, size, interpolation=cv2.INTER_CUBIC) / 255.except Exception:print("+} Problem during resize")return Nonereturn imagedef draw_emotion():passimport osimport sysdef demo(modelPath, showBox=False):import cv2face_x = tf.placeholder(tf.float32, [None, 2304])y_conv = deepnn(face_x)probs = tf.nn.softmax(y_conv)print("test3")saver = tf.train.Saver()ckpt = tf.train.get_checkpoint_state(modelPath)sess = tf.Session()if ckpt and ckpt.model_checkpoint_path:saver.restore(sess, ckpt.model_checkpoint_path)print('Restore model sucsses!!\nNOTE: Press SPACE on keyboard to capture face.')feelings_faces = []for index, emotion in enumerate(EMOTIONS):feelings_faces.append(cv2.imread('./data/emojis/' + emotion + '.png', -1))#video_captor = cv2.VideoCapture(0)import cv2result = sess.run(probs, feed_dict={face_x: tensor})print("result: ", result)# name_list = ','.join(result)# print("name_list: ", name_list)print("test4")if os.path.exists("recv.txt"):os.remove("recv.txt")if os.path.exists("recv2.txt"):os.remove("recv2.txt")for index, emotion in enumerate(EMOTIONS):file = open('recv.txt', 'a')file.write(str(emotion)+": " + str(rate)+", ")file.write(";")file.close()file5 = open("recv.txt", 'rt')contents = file5.read()data = contents.replace(";", ";\n")fin = open('recv2.txt', "wt")fin.write(data)fin.close()with open("recv2.txt", "r") as file2:path = file2.read()print("path ", path)file2.close()self.textEdit_2.setPlainText(path)tf.reset_default_graph()# emoji_face = []# result = None## while True:#     #ret, frame = video_captor.read()#     import cv2#     frame = cv2.imread("save/recognPerson.jpg")#     detected_face, face_coor = format_image(frame)#     if showBox:#         if face_coor is not None:#             [x, y, w, h] = face_coor#             cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)##     if cv2.waitKey(1) & 0xFF == ord(' '):##         if detected_face is not None:#             # cv2.imwrite('a.jpg', detected_face)#             tensor = image_to_tensor(detected_face)#             result = sess.run(probs, feed_dict={face_x: tensor})#             print("result: ",result)#     if result is not None:#         for index, emotion in enumerate(EMOTIONS):#             print("emotion: ", emotion)#             print("index: ", index)#             cv2.putText(frame, emotion, (10, index * 20 + 20), cv2.FONT_HERSHEY_PLAIN, 1.2, (0, 255, 0), 1)#             cv2.rectangle(frame, (130, index * 20 + 10),#                           (130 + int(result[0][index] * 100), (index + 1) * 20 + 4),#                           (255, 0, 0), -1)#             emoji_face = feelings_faces[np.argmax(result[0])]#             print("int(result[0][index]: ", int(result[0][index] * 100))##         for c in range(0, 3):#             frame[200:320, 10:130, c] = emoji_face[:, :, c] * (emoji_face[:, :, 3] / 255.0) + frame[200:320,#                                                                                               10:130, c] * (#                                                     1.0 - emoji_face[:, :, 3] / 255.0)##     cv2.imshow('face', frame)#     if cv2.waitKey(1) & 0xFF == ord('q'):#         breakif __name__ == '__main__':modelPath = "ckpt"demo(modelPath)if __name__=="__main__":app=QtWidgets.QApplication(sys.argv)Widget=myWin()Widget.showMaximized();Widget.show()sys.exit(app.exec_())

运行结下：

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