Fix `save_one_box()` (#5545)
* Fix `save_one_box()` * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
60c8a4f696
commit
3f64ad1760
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@ -26,9 +26,9 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from models.experimental import attempt_load
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from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
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from utils.general import (LOGGER, apply_classifier, check_file, check_img_size, check_imshow, check_requirements,
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check_suffix, colorstr, increment_path, non_max_suppression, print_args, save_one_box,
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scale_coords, strip_optimizer, xyxy2xywh)
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from utils.plots import Annotator, colors
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check_suffix, colorstr, increment_path, non_max_suppression, print_args, scale_coords,
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strip_optimizer, xyxy2xywh)
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from utils.plots import Annotator, colors, save_one_box
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from utils.torch_utils import load_classifier, select_device, time_sync
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@ -18,9 +18,8 @@ from PIL import Image
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from torch.cuda import amp
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from utils.datasets import exif_transpose, letterbox
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from utils.general import (colorstr, increment_path, make_divisible, non_max_suppression, save_one_box, scale_coords,
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xyxy2xywh)
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from utils.plots import Annotator, colors
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from utils.general import colorstr, increment_path, make_divisible, non_max_suppression, scale_coords, xyxy2xywh
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from utils.plots import Annotator, colors, save_one_box
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from utils.torch_utils import time_sync
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LOGGER = logging.getLogger(__name__)
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@ -819,21 +819,6 @@ def apply_classifier(x, model, img, im0):
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return x
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def save_one_box(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=False, save=True):
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# Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
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xyxy = torch.tensor(xyxy).view(-1, 4)
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b = xyxy2xywh(xyxy) # boxes
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if square:
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b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square
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b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad
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xyxy = xywh2xyxy(b).long()
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clip_coords(xyxy, im.shape)
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crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
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if save:
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cv2.imwrite(str(increment_path(file, mkdir=True).with_suffix('.jpg')), crop)
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return crop
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def increment_path(path, exist_ok=False, sep='', mkdir=False):
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# Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.
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path = Path(path) # os-agnostic
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126
utils/plots.py
126
utils/plots.py
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@ -17,7 +17,7 @@ import seaborn as sn
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import torch
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from PIL import Image, ImageDraw, ImageFont
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from utils.general import is_ascii, is_chinese, user_config_dir, xywh2xyxy, xyxy2xywh
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from utils.general import clip_coords, increment_path, is_ascii, is_chinese, user_config_dir, xywh2xyxy, xyxy2xywh
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from utils.metrics import fitness
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# Settings
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@ -117,6 +117,33 @@ class Annotator:
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return np.asarray(self.im)
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def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
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"""
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x: Features to be visualized
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module_type: Module type
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stage: Module stage within model
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n: Maximum number of feature maps to plot
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save_dir: Directory to save results
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"""
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if 'Detect' not in module_type:
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batch, channels, height, width = x.shape # batch, channels, height, width
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if height > 1 and width > 1:
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f = f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename
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blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels
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n = min(n, channels) # number of plots
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fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols
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ax = ax.ravel()
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plt.subplots_adjust(wspace=0.05, hspace=0.05)
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for i in range(n):
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ax[i].imshow(blocks[i].squeeze()) # cmap='gray'
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ax[i].axis('off')
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print(f'Saving {save_dir / f}... ({n}/{channels})')
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plt.savefig(save_dir / f, dpi=300, bbox_inches='tight')
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plt.close()
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def hist2d(x, y, n=100):
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# 2d histogram used in labels.png and evolve.png
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xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
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@ -337,37 +364,6 @@ def plot_labels(labels, names=(), save_dir=Path('')):
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plt.close()
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def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
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# Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()
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ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()
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s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']
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files = list(Path(save_dir).glob('frames*.txt'))
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for fi, f in enumerate(files):
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try:
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results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows
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n = results.shape[1] # number of rows
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x = np.arange(start, min(stop, n) if stop else n)
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results = results[:, x]
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t = (results[0] - results[0].min()) # set t0=0s
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results[0] = x
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for i, a in enumerate(ax):
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if i < len(results):
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label = labels[fi] if len(labels) else f.stem.replace('frames_', '')
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a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5)
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a.set_title(s[i])
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a.set_xlabel('time (s)')
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# if fi == len(files) - 1:
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# a.set_ylim(bottom=0)
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for side in ['top', 'right']:
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a.spines[side].set_visible(False)
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else:
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a.remove()
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except Exception as e:
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print(f'Warning: Plotting error for {f}; {e}')
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ax[1].legend()
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plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
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def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *; plot_evolve()
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# Plot evolve.csv hyp evolution results
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evolve_csv = Path(evolve_csv)
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@ -420,28 +416,48 @@ def plot_results(file='path/to/results.csv', dir=''):
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plt.close()
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def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
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"""
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x: Features to be visualized
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module_type: Module type
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stage: Module stage within model
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n: Maximum number of feature maps to plot
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save_dir: Directory to save results
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"""
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if 'Detect' not in module_type:
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batch, channels, height, width = x.shape # batch, channels, height, width
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if height > 1 and width > 1:
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f = f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename
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def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
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# Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()
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ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()
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s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']
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files = list(Path(save_dir).glob('frames*.txt'))
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for fi, f in enumerate(files):
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try:
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results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows
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n = results.shape[1] # number of rows
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x = np.arange(start, min(stop, n) if stop else n)
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results = results[:, x]
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t = (results[0] - results[0].min()) # set t0=0s
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results[0] = x
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for i, a in enumerate(ax):
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if i < len(results):
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label = labels[fi] if len(labels) else f.stem.replace('frames_', '')
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a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5)
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a.set_title(s[i])
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a.set_xlabel('time (s)')
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# if fi == len(files) - 1:
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# a.set_ylim(bottom=0)
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for side in ['top', 'right']:
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a.spines[side].set_visible(False)
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else:
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a.remove()
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except Exception as e:
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print(f'Warning: Plotting error for {f}; {e}')
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ax[1].legend()
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plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
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blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels
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n = min(n, channels) # number of plots
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fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols
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ax = ax.ravel()
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plt.subplots_adjust(wspace=0.05, hspace=0.05)
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for i in range(n):
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ax[i].imshow(blocks[i].squeeze()) # cmap='gray'
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ax[i].axis('off')
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print(f'Saving {save_dir / f}... ({n}/{channels})')
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plt.savefig(save_dir / f, dpi=300, bbox_inches='tight')
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plt.close()
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def save_one_box(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=False, save=True):
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# Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
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xyxy = torch.tensor(xyxy).view(-1, 4)
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b = xyxy2xywh(xyxy) # boxes
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if square:
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b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square
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b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad
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xyxy = xywh2xyxy(b).long()
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clip_coords(xyxy, im.shape)
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crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
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if save:
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file.parent.mkdir(parents=True, exist_ok=True) # make directory
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cv2.imwrite(str(increment_path(file).with_suffix('.jpg')), crop)
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return crop
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