10000 Improved non-latin `Annotator()` plotting (#7488) · brinkqiang2python/yolov5@c9042dc · GitHub
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Improved non-latin Annotator() plotting (ultralytics#7488)
* Improved non-latin labels Annotator plotting May resolve ultralytics#7460 * Update train.py * Update train.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add progress arg Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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-8
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‎train.py

Lines changed: 5 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -48,13 +48,13 @@
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from utils.downloads import attempt_download< 10000 /span>
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from utils.general import (LOGGER, check_dataset, check_file, check_git_status, check_img_size, check_requirements,
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check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds,
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intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, one_cycle,
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print_args, print_mutation, strip_optimizer)
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intersect_dicts, is_ascii, labels_to_class_weights, labels_to_image_weights, methods,
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one_cycle, print_args, print_mutation, strip_optimizer)
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from utils.loggers import Loggers
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from utils.loggers.wandb.wandb_utils import check_wandb_resume
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from utils.loss import ComputeLoss
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from utils.metrics import fitness
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from utils.plots import plot_evolve, plot_labels
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from utils.plots import check_font, plot_evolve, plot_labels
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from utils.torch_utils import EarlyStopping, ModelEMA, de_parallel, select_device, torch_distributed_zero_first
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LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
@@ -105,6 +105,8 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
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init_seeds(1 + RANK)
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with torch_distributed_zero_first(LOCAL_RANK):
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data_dict = data_dict or check_dataset(data) # check if None
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if not is_ascii(data_dict['names']): # non-latin labels, i.e. asian, arabic, cyrillic
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check_font('Arial.Unicode.ttf', progress=True)
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train_path, val_path = data_dict['train'], data_dict['val']
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nc = 1 if single_cls else int(data_dict['nc']) # number of classes
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names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names

‎utils/general.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -424,13 +424,13 @@ def check_file(file, suffix=''):
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return files[0] # return file
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def check_font(font=FONT):
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def check_font(font=FONT, progress=False):
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# Download font to CONFIG_DIR if necessary
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font = Path(font)
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if not font.exists() and not (CONFIG_DIR / font.name).exists():
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url = "https://ultralytics.com/assets/" + font.name
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LOGGER.info(f'Downloading {url} to {CONFIG_DIR / font.name}...')
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torch.hub.download_url_to_file(url, str(font), progress=False)
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torch.hub.download_url_to_file(url, str(font), progress=progress)
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def check_dataset(data, autodownload=True):

‎utils/plots.py

Lines changed: 4 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -19,7 +19,7 @@
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from PIL import Image, ImageDraw, ImageFont
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from utils.general import (CONFIG_DIR, FONT, LOGGER, Timeout, check_font, check_requirements, clip_coords,
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increment_path, is_ascii, is_chinese, try_except, xywh2xyxy, xyxy2xywh)
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increment_path, is_ascii, try_except, xywh2xyxy, xyxy2xywh)
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from utils.metrics import fitness
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# Settings
@@ -72,11 +72,12 @@ class Annotator:
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# YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
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def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
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assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
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self.pil = pil or not is_ascii(example) or is_chinese(example)
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non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic
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self.pil = pil or non_ascii
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if self.pil: # use PIL
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self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
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self.draw = ImageDraw.Draw(self.im)
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self.font = check_pil_font(font='Arial.Unicode.ttf' if is_chinese(example) else font,
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self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font,
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size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
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else: # use cv2
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self.im = im

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