From 244177ce0cd95f9f37f52fab72942a7ab47ec31f Mon Sep 17 00:00:00 2001 From: p80279463 <476480381@qq.com> Date: Fri, 30 Apr 2021 08:50:03 +0800 Subject: [PATCH] =?UTF-8?q?Delete=20'=E6=BA=90=E4=BB=A3=E7=A0=81=E6=A0=87?= =?UTF-8?q?=E6=B3=A8.txt'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 源代码标注.txt | 192 -------------------------------------------- 1 file changed, 192 deletions(-) delete mode 100644 源代码标注.txt diff --git a/源代码标注.txt b/源代码标注.txt deleted file mode 100644 index b340333..0000000 --- a/源代码标注.txt +++ /dev/null @@ -1,192 +0,0 @@ -#!/usr/bin/python3 -# -*- coding: utf-8 -*- - -import sys, os -import numpy as np -from dataset.mnist import load_mnist -from PIL import Image, ImageQt - - -from qt.layout import Ui_MainWindow -from qt.paintboard import PaintBoard - -from PyQt5.QtWidgets import QMainWindow, QDesktopWidget, QApplication -from PyQt5.QtWidgets import QLabel, QMessageBox, QPushButton, QFrame -from PyQt5.QtGui import QPainter, QPen, QPixmap, QColor, QImage -from PyQt5.QtCore import Qt, QPoint, QSize - -from simple_convnet import SimpleConvNet -from common.functions import softmax -from deep_convnet import DeepConvNet - - - -MODE_MNIST = 1 # MNIST随机抽取 -MODE_WRITE = 2 # 手写输入 - -Thresh = 0.5 # 识别结果置信度阈值 - - - -# 读取MNIST数据集 -(_, _), (x_test, _) = load_mnist(normalize=True, flatten=False, one_hot_label=False) - - -# 初始化网络 - -# 网络1：简单CNN -""" -conv - relu - pool - affine - relu - affine - softmax -""" -network = SimpleConvNet(input_dim=(1,28,28), - conv_param = {'filter_num': 30, 'filter_size': 5, 'pad': 0, 'stride': 1}, - hidden_size=100, output_size=10, weight_init_std=0.01) -network.load_params("params.pkl") - -# 网络2：深度CNN -# network = DeepConvNet() -# network.load_params("deep_convnet_params.pkl") - - -class MainWindow(QMainWindow,Ui_MainWindow): - def __init__(self): - super(MainWindow,self).__init__() - - # 初始化参数 - self.mode = MODE_MNIST - self.result = [0, 0] - - # 初始化UI - self.setupUi(self) - self.center() - - # 初始化画板 - self.paintBoard = PaintBoard(self, Size = QSize(224, 224), Fill = QColor(0,0,0,0)) - self.paintBoard.setPenColor(QColor(0,0,0,0)) - self.dArea_Layout.addWidget(self.paintBoard) - - self.clearDataArea() - - # 窗口居中 - def center(self): - # 获得窗口 - framePos = self.frameGeometry() - # 获得屏幕中心点 - scPos = QDesktopWidget().availableGeometry().center() - # 显示到屏幕中心 - framePos.moveCenter(scPos) - self.move(framePos.topLeft()) - - - # 窗口关闭事件 - def closeEvent(self, event): - reply = QMessageBox.question(self, 'Message', - "Are you sure to quit?", QMessageBox.Yes | - QMessageBox.No, QMessageBox.Yes) - - if reply == QMessageBox.Yes: - event.accept() - else: - event.ignore() - - # 清除数据待输入区 - def clearDataArea(self): - self.paintBoard.Clear() - self.lbDataArea.clear() - self.lbResult.clear() - self.lbCofidence.clear() - self.result = [0, 0] - - """ - 回调函数 - """ - # 模式下拉列表回调 - def cbBox_Mode_Callback(self, text): - if text == '1：MINIST随机抽取': - self.mode = MODE_MNIST - self.clearDataArea() - self.pbtGetMnist.setEnabled(True) - - self.paintBoard.setBoardFill(QColor(0,0,0,0)) - self.paintBoard.setPenColor(QColor(0,0,0,0)) - - elif text == '2：鼠标手写输入': - self.mode = MODE_WRITE - self.clearDataArea() - self.pbtGetMnist.setEnabled(False) - - # 更改背景 - self.paintBoard.setBoardFill(QColor(0,0,0,255)) - self.paintBoard.setPenColor(QColor(255,255,255,255)) - - - # 数据清除 - def pbtClear_Callback(self): - self.clearDataArea() - - - # 识别 - def pbtPredict_Callback(self): - __img, img_array =[],[] # 将图像统一从qimage->pil image -> np.array [1, 1, 28, 28] - - # 获取qimage格式图像 - if self.mode == MODE_MNIST: - __img = self.lbDataArea.pixmap() # label内若无图像返回None - if __img == None: # 无图像则用纯黑代替 - # __img = QImage(224, 224, QImage.Format_Grayscale8) - __img = ImageQt.ImageQt(Image.fromarray(np.uint8(np.zeros([224,224])))) - else: __img = __img.toImage() - elif self.mode == MODE_WRITE: - __img = self.paintBoard.getContentAsQImage() - - # 转换成pil image类型处理 - pil_img = ImageQt.fromqimage(__img) - pil_img = pil_img.resize((28, 28), Image.ANTIALIAS) - - # pil_img.save('test.png') - - img_array = np.array(pil_img.convert('L')).reshape(1,1,28, 28) / 255.0 - # img_array = np.where(img_array>0.5, 1, 0) - - # reshape成网络输入类型 - __result = network.predict(img_array) # shape:[1, 10] - - # print (__result) - - # 将预测结果使用softmax输出 - __result = softmax(__result) - - self.result[0] = np.argmax(__result) # 预测的数字 - self.result[1] = __result[0, self.result[0]] # 置信度 - - self.lbResult.setText("%d" % (self.result[0])) - self.lbCofidence.setText("%.8f" % (self.result[1])) - - - # 随机抽取 - def pbtGetMnist_Callback(self): - self.clearDataArea() - - # 随机抽取一张测试集图片，放大后显示 - img = x_test[np.random.randint(0, 9999)] # shape:[1,28,28] - img = img.reshape(28, 28) # shape:[28,28] - - img = img * 0xff # 恢复灰度值大小 - pil_img = Image.fromarray(np.uint8(img)) - pil_img = pil_img.resize((224, 224)) # 图像放大显示 - - # 将pil图像转换成qimage类型 - qimage = ImageQt.ImageQt(pil_img) - - # 将qimage类型图像显示在label - pix = QPixmap.fromImage(qimage) - self.lbDataArea.setPixmap(pix) - - - -if __name__ == "__main__": - app = QApplication(sys.argv) - Gui = MainWindow() - Gui.show() - - sys.exit(app.exec_()) \ No newline at end of file