Python refactorings and simplifications (#7549)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Hassaan Farooq <103611273+hassaanfarooq01@users.noreply.github.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
0da13831cf
commit
f6309b8e70
40 changed files with 127 additions and 189 deletions
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@ -41,7 +41,7 @@ def auto_annotate(data, det_model="yolov8x.pt", sam_model="sam_b.pt", device="",
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sam_results = sam_model(result.orig_img, bboxes=boxes, verbose=False, save=False, device=device)
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segments = sam_results[0].masks.xyn # noqa
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with open(f"{str(Path(output_dir) / Path(result.path).stem)}.txt", "w") as f:
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with open(f"{Path(output_dir) / Path(result.path).stem}.txt", "w") as f:
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for i in range(len(segments)):
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s = segments[i]
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if len(s) == 0:
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@ -15,7 +15,6 @@ from ultralytics.utils.instance import Instances
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from ultralytics.utils.metrics import bbox_ioa
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from ultralytics.utils.ops import segment2box, xyxyxyxy2xywhr
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from ultralytics.utils.torch_utils import TORCHVISION_0_10, TORCHVISION_0_11, TORCHVISION_0_13
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from .utils import polygons2masks, polygons2masks_overlap
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DEFAULT_MEAN = (0.0, 0.0, 0.0)
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@ -1028,7 +1027,7 @@ def classify_transforms(
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if isinstance(size, (tuple, list)):
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assert len(size) == 2
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scale_size = tuple([math.floor(x / crop_fraction) for x in size])
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scale_size = tuple(math.floor(x / crop_fraction) for x in size)
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else:
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scale_size = math.floor(size / crop_fraction)
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scale_size = (scale_size, scale_size)
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@ -15,7 +15,6 @@ import psutil
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from torch.utils.data import Dataset
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from ultralytics.utils import DEFAULT_CFG, LOCAL_RANK, LOGGER, NUM_THREADS, TQDM
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from .utils import HELP_URL, IMG_FORMATS
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@ -22,7 +22,6 @@ from ultralytics.data.loaders import (
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from ultralytics.data.utils import IMG_FORMATS, VID_FORMATS
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from ultralytics.utils import RANK, colorstr
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from ultralytics.utils.checks import check_file
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from .dataset import YOLODataset
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from .utils import PIN_MEMORY
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@ -12,7 +12,6 @@ from PIL import Image
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from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM, colorstr, is_dir_writeable
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from ultralytics.utils.ops import resample_segments
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from .augment import Compose, Format, Instances, LetterBox, classify_augmentations, classify_transforms, v8_transforms
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from .base import BaseDataset
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from .utils import HELP_URL, LOGGER, get_hash, img2label_paths, verify_image, verify_image_label
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@ -7,9 +7,9 @@ from typing import Any, List, Tuple, Union
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import cv2
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import numpy as np
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import torch
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from PIL import Image
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from matplotlib import pyplot as plt
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from pandas import DataFrame
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from PIL import Image
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from tqdm import tqdm
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from ultralytics.data.augment import Format
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@ -17,7 +17,6 @@ from ultralytics.data.dataset import YOLODataset
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from ultralytics.data.utils import check_det_dataset
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from ultralytics.models.yolo.model import YOLO
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from ultralytics.utils import LOGGER, IterableSimpleNamespace, checks
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from .utils import get_sim_index_schema, get_table_schema, plot_query_result, prompt_sql_query, sanitize_batch
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@ -188,10 +187,10 @@ class Explorer:
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result = exp.sql_query(query)
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```
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"""
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assert return_type in [
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assert return_type in {
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"pandas",
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"arrow",
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], f"Return type should be either `pandas` or `arrow`, but got {return_type}"
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}, f"Return type should be either `pandas` or `arrow`, but got {return_type}"
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import duckdb
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if self.table is None:
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@ -208,10 +207,10 @@ class Explorer:
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LOGGER.info(f"Running query: {query}")
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rs = duckdb.sql(query)
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if return_type == "pandas":
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return rs.df()
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elif return_type == "arrow":
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if return_type == "arrow":
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return rs.arrow()
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elif return_type == "pandas":
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return rs.df()
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def plot_sql_query(self, query: str, labels: bool = True) -> Image.Image:
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"""
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@ -264,17 +263,17 @@ class Explorer:
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similar = exp.get_similar(img='https://ultralytics.com/images/zidane.jpg')
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```
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"""
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assert return_type in [
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assert return_type in {
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"pandas",
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"arrow",
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], f"Return type should be either `pandas` or `arrow`, but got {return_type}"
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}, f"Return type should be either `pandas` or `arrow`, but got {return_type}"
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img = self._check_imgs_or_idxs(img, idx)
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similar = self.query(img, limit=limit)
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if return_type == "pandas":
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return similar.to_pandas()
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elif return_type == "arrow":
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if return_type == "arrow":
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return similar
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elif return_type == "pandas":
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return similar.to_pandas()
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def plot_similar(
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self,
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@ -98,9 +98,9 @@ def plot_query_result(similar_set, plot_labels=True):
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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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masks = np.stack(plot_masks, axis=0) if plot_masks else np.zeros(0, dtype=np.uint8)
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kpts = np.concatenate(plot_kpts, axis=0) if plot_kpts else np.zeros((0, 51), dtype=np.float32)
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boxes = xyxy2xywh(np.concatenate(plot_boxes, axis=0)) if plot_boxes 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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@ -139,10 +139,9 @@ def get_window_obj(anno, windows, iof_thr=0.7):
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label[:, 2::2] *= h
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iofs = bbox_iof(label[:, 1:], windows)
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# Unnormalized and misaligned coordinates
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window_anns = [(label[iofs[:, i] >= iof_thr]) for i in range(len(windows))]
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return [(label[iofs[:, i] >= iof_thr]) for i in range(len(windows))] # window_anns
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else:
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window_anns = [np.zeros((0, 9), dtype=np.float32) for _ in range(len(windows))]
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return window_anns
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return [np.zeros((0, 9), dtype=np.float32) for _ in range(len(windows))] # window_anns
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def crop_and_save(anno, windows, window_objs, im_dir, lb_dir):
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@ -170,7 +169,7 @@ def crop_and_save(anno, windows, window_objs, im_dir, lb_dir):
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name = Path(anno["filepath"]).stem
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for i, window in enumerate(windows):
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x_start, y_start, x_stop, y_stop = window.tolist()
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new_name = name + "__" + str(x_stop - x_start) + "__" + str(x_start) + "___" + str(y_start)
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new_name = f"{name}__{x_stop - x_start}__{x_start}___{y_start}"
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patch_im = im[y_start:y_stop, x_start:x_stop]
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ph, pw = patch_im.shape[:2]
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@ -271,7 +270,7 @@ def split_test(data_root, save_dir, crop_size=1024, gap=200, rates=[1.0]):
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save_dir.mkdir(parents=True, exist_ok=True)
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im_dir = Path(os.path.join(data_root, "images/test"))
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assert im_dir.exists(), f"Can't find {str(im_dir)}, please check your data root."
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assert im_dir.exists(), f"Can't find {im_dir}, please check your data root."
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im_files = glob(str(im_dir / "*"))
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for im_file in tqdm(im_files, total=len(im_files), desc="test"):
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w, h = exif_size(Image.open(im_file))
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@ -280,7 +279,7 @@ def split_test(data_root, save_dir, crop_size=1024, gap=200, rates=[1.0]):
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name = Path(im_file).stem
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for window in windows:
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x_start, y_start, x_stop, y_stop = window.tolist()
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new_name = name + "__" + str(x_stop - x_start) + "__" + str(x_start) + "___" + str(y_start)
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new_name = f"{name}__{x_stop - x_start}__{x_start}___{y_start}"
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patch_im = im[y_start:y_stop, x_start:x_stop]
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cv2.imwrite(os.path.join(str(save_dir), f"{new_name}.jpg"), patch_im)
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