inference.py

infer.py

@@ -0,0 +1,64 @@
+import argparse
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from datasets import get_dataset,get_lettuce_dataset
+from losses import get_losses
+from extend_sam import get_model, get_optimizer, get_scheduler, get_opt_pamams, get_runner
+import random
+import numpy as np
+import torch
+import os
+import glob
+from torchvision import transforms
+from PIL import Image
+import cv2
+supported_tasks = ['detection', 'semantic_seg', 'instance_seg']
+parser = argparse.ArgumentParser()
+parser.add_argument('--task_name', default='semantic_seg', type=str)
+parser.add_argument('--cfg', default=None, type=str)
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    task_name = args.task_name
+    if args.cfg is not None:
+        config = OmegaConf.load(args.cfg)
+    else:
+        assert task_name in supported_tasks, "Please input the supported task name."
+        config = OmegaConf.load("./config/{task_name}.yaml".format(task_name=args.task_name))
+    test_cfg = config.test
+    train_cfg = config.train
+    model = get_model(model_name=train_cfg.model.sam_name, **train_cfg.model.params)
+    #我以./weed_data下的图片为例
+    test_data_path = '../test_data'
+    output_dir = './result'
+    model.load_state_dict(torch.load('experiment/model/semantic_sam/lam_vit_b_01ec64.pth'))
+    model.eval()
+    model.cuda()
+    preprocess = transforms.Compose([
+                        transforms.Resize((1024, 1024)),
+                        transforms.ToTensor(),
+                    ])
+    for img_path in glob.glob(os.path.join(test_data_path,'*.jpg')):
+        print(img_path)
+        # 读取图片并转换为 RGB
+        #img = Image.open(img_path).convert("RGB")
+        img = cv2.imread(img_path, cv2.IMREAD_COLOR)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        # 对图片进行预处理，并增加 batch 维度
+        #input_tensor = preprocess(img).unsqueeze(0).cuda()
+        img = cv2.resize(img, (1024, 1024))
+        input_tensor = torch.from_numpy(img.transpose(2, 0, 1)).float().cuda().unsqueeze(0)
+        with torch.no_grad():
+            output,_ = model(input_tensor)
+        pred = torch.argmax(output, dim=1)
+        pred = pred.squeeze(0).cpu().numpy()
+        img_name = os.path.splitext(os.path.basename(img_path))[0]
+        sub_output_dir = os.path.join(output_dir, img_name)
+        os.makedirs(sub_output_dir, exist_ok=True)
+        unique_labels = np.unique(pred)
+        for label in unique_labels:
+            # 生成二值 mask：像素属于该类别则为 255，否则为 0
+            binary_mask = (pred == label).astype(np.uint8) * 255
+            mask_filename = os.path.join(sub_output_dir, f'class_{label}.png')
+            cv2.imwrite(mask_filename, binary_mask)
+        print(f"Processed {img_path}, saved masks to {sub_output_dir}")