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 = Image.ImageQt(pil_img)
# 将qimage类型图像显示在label
pix = QPixmap.fromImage(qimage)
self.lbDataArea.setPixmap(pix)
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 = Image.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 = Image.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]))