python的花卉识别系统源码+数据（课程设计）.zip

import tensorflow as tf from PyQt5.QtGui import * from PyQt5.QtCore import * from PyQt5.QtWidgets import * import sys import cv2 from PIL import Image import numpy as np class MainWindow(QTabWidget): def __init__(self): super().__init__() self.setWindowIcon(QIcon('images/1.png')) self.setWindowTitle('花卉识别系统') # 使用cnn模型 self.model = tf.keras.models.load_model("models/cnn_flower.h5") self.to_predict_name = "images/init.jpg" # 花的种类 self.class_names = ['风铃草','毛茛','款冬','牛至','藏红花','水仙花','雏菊','蒲公英','贝母','鸢尾花','百合','三色堇','雪花莲','向日葵','虎尾兰','郁金香','风信子'] self.class_enames =['Bluebell','Buttercup', "Colls' Foot", 'Cowslip','Crocus', 'Daffodil','Daisy', 'Dandelion', 'Fritillary ', 'Iris', 'Lily Valley', 'Pansy', 'Snowdrop', 'Sunflower', 'Tigerlily', 'Tulip', 'Windflower'] self.resize(1000, 900) self.initUI() def initUI(self): main_widget = QWidget() main_layout = QHBoxLayout() font = QFont('宋体', 15) left_widget = QWidget() left_layout = QVBoxLayout() self.img_title = QLabel("测试样本") self.img_title.setFont(QFont('宋体', 16)) # img_title.setAlignment(Qt.AlignCenter) self.img_label = QLabel() # img_init = cv2.imread(self.to_predict_name) img_init = cv2.imdecode(np.fromfile(self.to_predict_name, dtype=np.uint8), -1) img_init = cv2.resize(img_init, (224, 224)) cv2.imwrite('images/target.png', img_init) self.img_label.setPixmap(QPixmap('images/target.png')) #left_layout.addWidget(self.img_label) left_layout.addStretch() left_layout.addWidget(self.img_label, 0, Qt.AlignCenter) left_layout.addWidget(self.img_title, 0, Qt.AlignCenter) left_layout.addStretch() # left_layout.setAlignment(Qt.AlignCenter) # left_widget.setLayout(left_layout) right_widget = QWidget() right_layout = QVBoxLayout() btn_change = QPushButton(" 选择图片 ") btn_change.clicked.connect(self.change_img) btn_change.setFont(font) btn_predict = QPushButton(" 识 别 ") btn_predict.setFont(font) btn_predict.clicked.connect(self.predict_img) label_result = QLabel(' 识 别 结 果 ') self.result = QLabel("待识别") label_result.setFont(QFont('宋体', 16)) self.result.setFont(QFont('宋体', 24)) self.eresult = QLabel("") self.eresult.setFont(QFont('宋体', 24)) right_layout.addStretch() right_layout.addWidget(label_result, 0, Qt.AlignCenter) right_layout.addStretch() right_layout.addWidget(self.eresult, 0, Qt.AlignCenter) #right_layout.addStretch() right_layout.addWidget(self.result, 0, Qt.AlignCenter) right_layout.addStretch() # 加载测试图按钮，识别按钮： right_layout.addWidget(btn_change) right_layout.addWidget(btn_predict) right_layout.addStretch() # right_layout.addSpacing(5) # right_widget.setLayout(right_layout) right_widget.setLayout(left_layout) left_widget.setLayout(right_layout) # 关于页面 about_widget = QWidget() about_layout = QVBoxLayout() about_title = QLabel('花卉模型训练结果') about_title.setFont(QFont('宋体', 18)) about_title.setAlignment(Qt.AlignCenter) about_img = QLabel() # 热力图显示 about_img.setPixmap(QPixmap('./results/heatmap_cnn.png')) about_img.setAlignment(Qt.AlignCenter) label_super = QLabel() label_super.setFont(QFont('宋体', 12)) label_super.setOpenExternalLinks(True) label_super.setAlignment(Qt.AlignRight) # git_img = QMovie('images/') about_layout.addWidget(about_title) about_layout.addStretch() about_layout.addWidget(about_img) about_layout.addStretch() about_layout.addWidget(label_super) # 跳转到训练结果 about_widget.setLayout(about_layout) main_layout.addWidget(left_widget) main_layout.addWidget(right_widget) # 跳转到主界面，默认 main_widget.setLayout(main_layout) self.addTab(main_widget, '主页面') self.addTab(about_widget, '训练结果') self.setTabIcon(0, QIcon('images/主页面.png')) self.setTabIcon(1, QIcon('images/关于.png')) # 选择图片相应事件 def change_img(self): try: openfile_name = QFileDialog.getOpenFileName(self, '选择文件', '', 'Image files(*.jpg , *.png)') # print(openfile_name) img_name = openfile_name[0] if img_name == '': pass else: self.to_predict_name = img_name # img_init = cv2.imread(self.to_predict_name) img_init = cv2.imdecode(np.fromfile(self.to_predict_name, dtype=np.uint8), -1) # 图片像素更改为前面模型统一的224*224，because模型接收的参数为224*224 img_init = cv2.resize(img_init, (224, 224)) cv2.imwrite('images/target.png', img_init) self.img_label.setPixmap(QPixmap('images/target.png')) self.result.setText("待识别") self.eresult.setText("") except Exception as e: print("选择测试样本异常:" + repr(e)) # 识别相应事件 def predict_img(self): try: img = Image.open('images/target.png') img = np.asarray(img) # gray_img = img.convert('L') # img_torch = self.transform(gray_img) outputs = self.model.predict(img.reshape(1, 224, 224, 3)) # 224*224的图片数组转换为np数组，得到索引 result_index = np.argmax(outputs) # print(result_index) eresult = self.class_enames[result_index] result = self.class_names[result_index] self.eresult.setText(eresult) self.result.setText(result) print("识别结果:"+str(eresult)+'=>'+str(result)) except Exception as e: print("花卉识别异常:" + repr(e)) def closeEvent(self, event): """ 关闭确认框提示 :param event: """ quit_flag = QMessageBox.question(self, '退出确认', '确认退出应用程序?', QMessageBox.Yes | QMessageBox.No) event.ignore() if quit_flag == QMessageBox.Yes: event.accept() print("已经退出") super().closeEvent(event) sys.exit() if __name__ == "__main__": try: app = QApplication(sys.argv) x = MainWindow() x.show() sys.exit(app.exec_()) except Exception as e: print("异常信息:" + repr(e)) sys.exit(app.exec_())