image = QImage(canvas.buffer_rgba()) TypeError: QImage(): argument 1 has unexpected type 'memoryview'
时间: 2024-05-13 16:16:44 浏览: 7
这个错误通常是因为 `QImage()` 函数接受的参数类型与实际传入的类型不匹配。在这种情况下,`canvas.buffer_rgba()` 返回的是一个 `memoryview` 对象,而 `QImage()` 函数期望的是一个 `bytes` 类型的对象。
可以尝试将 `memoryview` 对象转换为 `bytes` 对象,例如:
```
image = QImage(bytes(canvas.buffer_rgba()))
```
如果这样还是无法解决问题,可以尝试使用 `numpy` 库将 `memoryview` 对象转换为 `numpy` 数组,然后再将其转换为 `bytes` 对象,例如:
```
import numpy as np
buffer = np.asarray(canvas.buffer_rgba())
image = QImage(bytes(buffer))
```
这样应该就可以成功创建一个 `QImage` 对象了。
相关问题
image = QImage(bytes(buffer)) TypeError: QImage(): argument 1 has unexpected type 'bytes'
这个错误可能是因为传递给 `QImage` 的参数类型不正确。`QImage` 的构造函数需要一个 `bytearray` 或者 `str` 类型的参数,而不是一个 `bytes` 类型的参数。
你可以尝试将 `bytes` 转换为 `bytearray` 或者 `str` 类型,然后再传递给 `QImage` 函数。例如:
```python
# 将 bytes 转换为 bytearray
bytearray_buffer = bytearray(buffer)
image = QImage(bytearray_buffer)
# 将 bytes 转换为 str
str_buffer = buffer.decode()
image = QImage(str_buffer)
```
你可以根据你的需求选择其中的一种方法来解决这个问题。
修改以下代码使其能够输出模型预测结果: def open_image(self): file_dialog = QFileDialog() file_paths, _ = file_dialog.getOpenFileNames(self, "选择图片", "", "Image Files (*.png *.jpg *.jpeg)") if file_paths: self.display_images(file_paths) def preprocess_images(self, image_paths): data_transform = transforms.Compose([ transforms.CenterCrop(150), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) self.current_image_paths = [] images = [] for image_path in image_paths: image = Image.open(image_path) image = data_transform(image) image = torch.unsqueeze(image, dim=0) images.append(image) self.current_image_paths.append(image_path) return images def predict_images(self): if not self.current_image_paths: return for i, image_path in enumerate(self.current_image_paths): image = self.preprocess_image(image_path) output = self.model(image) predicted_class = self.class_dict[output.argmax().item()] self.result_labels[i].setText(f"Predicted Class: {predicted_class}") self.progress_bar.setValue((i+1)*20) def display_images(self, image_paths): for i, image_path in enumerate(image_paths): image = QImage(image_path) image = image.scaled(300, 300, Qt.KeepAspectRatio) if i == 0: self.image_label_1.setPixmap(QPixmap.fromImage(image)) elif i == 1: self.image_label_2.setPixmap(QPixmap.fromImage(image)) elif i == 2: self.image_label_3.setPixmap(QPixmap.fromImage(image)) elif i == 3: self.image_label_4.setPixmap(QPixmap.fromImage(image)) elif i == 4: self.image_label_5.setPixmap(QPixmap.fromImage(image))
请注意,代码中的 preprocess_images 函数和 predict_images 函数中的 self.preprocess_image(image_path) 需要修改为 self.preprocess_images([image_path])。修改后的代码如下:
```
def open_image(self):
file_dialog = QFileDialog()
file_paths, _ = file_dialog.getOpenFileNames(self, "选择图片", "", "Image Files (*.png *.jpg *.jpeg)")
if file_paths:
self.display_images(file_paths)
def preprocess_images(self, image_paths):
data_transform = transforms.Compose([
transforms.CenterCrop(150),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
self.current_image_paths = []
images = []
for image_path in image_paths:
image = Image.open(image_path)
image = data_transform(image)
image = torch.unsqueeze(image, dim=0)
images.append(image)
self.current_image_paths.append(image_path)
return images
def predict_images(self):
if not self.current_image_paths:
return
for i, image_path in enumerate(self.current_image_paths):
image = self.preprocess_images([image_path])
output = self.model(image[0])
predicted_class = self.class_dict[output.argmax().item()]
self.result_labels[i].setText(f"Predicted Class: {predicted_class}")
self.progress_bar.setValue((i+1)*20)
def display_images(self, image_paths):
for i, image_path in enumerate(image_paths):
image = QImage(image_path)
image = image.scaled(300, 300, Qt.KeepAspectRatio)
if i == 0:
self.image_label_1.setPixmap(QPixmap.fromImage(image))
elif i == 1:
self.image_label_2.setPixmap(QPixmap.fromImage(image))
elif i == 2:
self.image_label_3.setPixmap(QPixmap.fromImage(image))
elif i == 3:
self.image_label_4.setPixmap(QPixmap.fromImage(image))
elif i == 4:
self.image_label_5.setPixmap(QPixmap.fromImage(image))
```