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https://github.com/THU-MIG/yolov10.git
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ed73c0fedc
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
105 lines
3.8 KiB
Python
105 lines
3.8 KiB
Python
from typing import List
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import cv2
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import numpy as np
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from ultralytics.data.augment import LetterBox
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from ultralytics.utils.ops import xyxy2xywh
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from ultralytics.utils.plotting import plot_images
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def get_table_schema(vector_size):
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from lancedb.pydantic import LanceModel, Vector
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class Schema(LanceModel):
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im_file: str
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labels: List[str]
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cls: List[int]
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bboxes: List[List[float]]
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masks: List[List[List[int]]]
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keypoints: List[List[List[float]]]
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vector: Vector(vector_size)
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return Schema
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def get_sim_index_schema():
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from lancedb.pydantic import LanceModel
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class Schema(LanceModel):
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idx: int
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im_file: str
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count: int
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sim_im_files: List[str]
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return Schema
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def sanitize_batch(batch, dataset_info):
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batch['cls'] = batch['cls'].flatten().int().tolist()
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box_cls_pair = sorted(zip(batch['bboxes'].tolist(), batch['cls']), key=lambda x: x[1])
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batch['bboxes'] = [box for box, _ in box_cls_pair]
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batch['cls'] = [cls for _, cls in box_cls_pair]
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batch['labels'] = [dataset_info['names'][i] for i in batch['cls']]
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batch['masks'] = batch['masks'].tolist() if 'masks' in batch else [[[]]]
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batch['keypoints'] = batch['keypoints'].tolist() if 'keypoints' in batch else [[[]]]
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return batch
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def plot_similar_images(similar_set, plot_labels=True):
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"""
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Plot images from the similar set.
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Args:
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similar_set (list): Pyarrow table containing the similar data points
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plot_labels (bool): Whether to plot labels or not
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"""
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similar_set = similar_set.to_pydict()
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empty_masks = [[[]]]
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empty_boxes = [[]]
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images = similar_set.get('im_file', [])
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bboxes = similar_set.get('bboxes', []) if similar_set.get('bboxes') is not empty_boxes else []
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masks = similar_set.get('masks') if similar_set.get('masks')[0] != empty_masks else []
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kpts = similar_set.get('keypoints') if similar_set.get('keypoints')[0] != empty_masks else []
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cls = similar_set.get('cls', [])
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plot_size = 640
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imgs, batch_idx, plot_boxes, plot_masks, plot_kpts = [], [], [], [], []
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for i, imf in enumerate(images):
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im = cv2.imread(imf)
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im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
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h, w = im.shape[:2]
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r = min(plot_size / h, plot_size / w)
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imgs.append(LetterBox(plot_size, center=False)(image=im).transpose(2, 0, 1))
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if plot_labels:
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if len(bboxes) > i and len(bboxes[i]) > 0:
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box = np.array(bboxes[i], dtype=np.float32)
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box[:, [0, 2]] *= r
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box[:, [1, 3]] *= r
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plot_boxes.append(box)
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if len(masks) > i and len(masks[i]) > 0:
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mask = np.array(masks[i], dtype=np.uint8)[0]
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plot_masks.append(LetterBox(plot_size, center=False)(image=mask))
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if len(kpts) > i and kpts[i] is not None:
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kpt = np.array(kpts[i], dtype=np.float32)
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kpt[:, :, :2] *= r
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plot_kpts.append(kpt)
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batch_idx.append(np.ones(len(np.array(bboxes[i], dtype=np.float32))) * i)
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imgs = np.stack(imgs, axis=0)
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masks = np.stack(plot_masks, axis=0) if len(plot_masks) > 0 else np.zeros(0, dtype=np.uint8)
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kpts = np.concatenate(plot_kpts, axis=0) if len(plot_kpts) > 0 else np.zeros((0, 51), dtype=np.float32)
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boxes = xyxy2xywh(np.concatenate(plot_boxes, axis=0)) if len(plot_boxes) > 0 else np.zeros(0, dtype=np.float32)
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batch_idx = np.concatenate(batch_idx, axis=0)
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cls = np.concatenate([np.array(c, dtype=np.int32) for c in cls], axis=0)
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return plot_images(imgs,
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batch_idx,
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cls,
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bboxes=boxes,
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masks=masks,
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kpts=kpts,
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max_subplots=len(images),
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save=False,
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threaded=False)
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