changed yolo

2026-05-21 11:14:41 +02:00
parent db4547dc05
commit e412738d3e
6 changed files with 174 additions and 241 deletions
@@ -57,69 +57,4 @@ class CocoDetectionDataset(Dataset):
        if self.transforms:
            image = self.transforms(image)
 
-        return image, target
-    
-
-class CocoDetectionDatasetResized(Dataset):
-    def __init__(self, image_dir, annotation_path, img_size=64, transforms=None):
-        self.image_dir = image_dir
-        self.coco = COCO(annotation_path)
-        self.image_ids = list(self.coco.imgs.keys())
-        self.transforms = transforms
-        self.img_size = img_size
-
-    def __len__(self):
-        return len(self.image_ids)
-
-    def __getitem__(self, idx):
-        image_id = self.image_ids[idx]
-        image_info = self.coco.loadImgs(image_id)[0]
-        image_path = os.path.join(self.image_dir, image_info['file_name'])
-
-        image = Image.open(image_path).convert("RGB")
-
-        orig_w, orig_h = image.size
-
-        scale_w = self.img_size / orig_w
-        scale_h = self.img_size / orig_h
-
-        # Load annotations
-        annotation_ids = self.coco.getAnnIds(imgIds=image_id)
-        annotations = self.coco.loadAnns(annotation_ids)
-
-        boxes = []
-        labels = []
-
-        for obj in annotations:
-            xmin, ymin, width, height = obj['bbox']
-            xmin, ymin, width, height = float(xmin), float(ymin), float(width), float(height)
-
-            xmin = xmin * scale_w
-            ymin = ymin * scale_h
-            xmax = (xmin + width * scale_w)
-            ymax = (ymin + height * scale_h)
-
-            boxes.append([xmin, ymin, xmax, ymax])
-            labels.append(obj['category_id'])
-
-        boxes = torch.tensor(boxes, dtype=torch.float32)
-        labels = torch.tensor(labels, dtype=torch.long)
-
-        area = torch.tensor([obj['area'] * scale_w * scale_h for obj in annotations], dtype=torch.float32)
-        iscrowd = torch.tensor([obj.get('iscrowd', 0) for obj in annotations], dtype=torch.long)
-
-        target = {
-            "boxes": boxes,
-            "labels": labels,
-            "image_id": torch.tensor([image_id]),
-            "area": area,
-            "iscrowd": iscrowd
-        }
-
-        # resize image
-        image = image.resize((self.img_size, self.img_size))
-
-        if self.transforms:
-            image = self.transforms(image)
-
        return image, target