基于ISIC2018数据集的皮肤癌分类项目(多种模型python源码)+详细可咨询

import argparse import os import torch from PIL import Image from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize from efficientnet_pytorch import EfficientNet from tqdm import tqdm # 初始化解析器 parser = argparse.ArgumentParser(description='Perform inference on a single image or a directory of images') parser.add_argument('input', type=str, help='Path to an image or directory of images to perform inference on') args = parser.parse_args() # 设置路径 model_path = "F:/skin_cancer/code/ISIC_HAM10000/zcyarchive/model.pth" # 设备配置 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 定义图像预处理，调整为EfficientNet的标准 transform = Compose([ Resize(256), # 调整为稍大的尺寸 CenterCrop(224), # 中心裁剪到224x224 ToTensor(), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # 标准化 ]) # 载入EfficientNet模型 model = EfficientNet.from_pretrained('efficientnet-b0', num_classes=7) model.load_state_dict(torch.load(model_path, map_location=device)) model.to(device) model.eval() # 推理单个图像 def infer_image(image_path): image = Image.open(image_path).convert("RGB") image = transform(image).unsqueeze(0).to(device) with torch.no_grad(): logits = model(image) probabilities = torch.nn.functional.softmax(logits, dim=1) predicted_class = probabilities.argmax(dim=1).item() confidence = probabilities.max(dim=1).values.item() return predicted_class, confidence # 推理目录中的图像 def process_directory(directory_path): images = [img for img in os.listdir(directory_path) if img.lower().endswith(('.png', '.jpg', '.jpeg'))] predictions = [] for img_name in tqdm(images, desc="Inferencing"): img_path = os.path.join(directory_path, img_name) predicted_class, confidence = infer_image(img_path) predictions.append((img_name, predicted_class, confidence)) print(f'Image: {img_name}, Class: {predicted_class}, Confidence: {confidence:.4f}') return predictions # 判断是单个文件还是目录 if __name__ == '__main__': if os.path.isdir(args.input): process_directory(args.input) elif os.path.isfile(args.input): predicted_class, confidence = infer_image(args.input) print(f'Image: {args.input}, Class: {predicted_class}, Confidence: {confidence:.4f}') else: print("The input path provided does not exist.")