解释一下def default_loader(path): return Image.open(path).convert('RGB')

def default_loader(path)是一个函数，它的作用是加载一张图片到Python程序中。这个函数接受一个参数path，表示图片的路径。函数中使用Image.open方法打开指定路径的图片，并把这张图片转换为RGB格式。最后，函数返回这张图片对象，供程序后续处理使用。

相关问题

优化使得每检索50张照片，输出一次已检索文件数：import os from PIL import Image from multiprocessing import Pool def is_black_image(file_path): with Image.open(file_path) as img: pixels = list(img.getdata()) if all(sum(pixel) == 0 for pixel in pixels): return True else: return False def delete_black_images(file_path): if is_black_image(file_path): os.remove(file_path) print('{} has del'.format(file_path)) def batch_delete_black_images(folder_path): file_list = [] for f in os.listdir(folder_path): file_path = os.path.join(folder_path, f) if os.path.isfile(file_path): file_list.append(file_path) with Pool() as p: p.map(delete_black_images, file_list) if __name__ == '__main__': batch_delete_black_images(r'F:\H18\Dfinal640.tif\8-2')

为了每检索50张照片输出一次已检索文件数，您可以在 `batch_delete_black_images` 函数中添加一个计数器来实现。具体地，您可以在函数中添加一个变量 `count`，每删除一张黑色图片时，将计数器加1，并且当计数器能够被50整除时，打印当前已经检索的文件数。下面是修改后的代码：

import os
from PIL import Image
from multiprocessing import Pool

def is_black_image(file_path):
    with Image.open(file_path) as img:
        pixels = list(img.getdata())
        if all(sum(pixel) == 0 for pixel in pixels):
            return True
        else:
            return False

def delete_black_images(file_path):
    if is_black_image(file_path):
        os.remove(file_path)
        print('{} has del'.format(file_path))
        return True
    return False

def batch_delete_black_images(folder_path):
    file_list = []
    count = 0
    for f in os.listdir(folder_path):
        file_path = os.path.join(folder_path, f)
        if os.path.isfile(file_path):
            file_list.append(file_path)
    with Pool() as p:
        results = p.map(delete_black_images, file_list)
        for result in results:
            if result:
                count += 1
                if count % 50 == 0:
                    print('已检索 {} 个文件'.format(count))

if __name__ == '__main__':
    batch_delete_black_images(r'F:\H18\Dfinal640.tif\8-2')

注意，为了避免多个进程同时打印计数器的值，我们在每50次删除操作后只打印计数器的值，而不是实时打印。

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))