Import Annotator class from `ultralytics` package (#11930)
* Import Annotator class from `ultralytics` package * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Import Annotator class from `ultralytics` package * Update requirements.txt Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>pull/11931/head
Glenn Jocher
GitHub
parent
05e4c0543b
commit
2270f0d1e7
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@ -43,12 +43,13 @@ if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from ultralytics.utils.plotting import Annotator
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from models.common import DetectMultiBackend
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from utils.augmentations import classify_transforms
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from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
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from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
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increment_path, print_args, strip_optimizer)
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from utils.plots import Annotator
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from utils.torch_utils import select_device, smart_inference_mode
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@ -144,7 +145,7 @@ def run(
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# Write results
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text = '\n'.join(f'{prob[j]:.2f} {names[j]}' for j in top5i)
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if save_img or view_img: # Add bbox to image
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annotator.text((32, 32), text, txt_color=(255, 255, 255))
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annotator.text([32, 32], text, txt_color=(255, 255, 255))
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if save_txt: # Write to file
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with open(f'{txt_path}.txt', 'a') as f:
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f.write(text + '\n')
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@ -42,11 +42,12 @@ if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from ultralytics.utils.plotting import Annotator, colors, save_one_box
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from models.common import DetectMultiBackend
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from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
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from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
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increment_path, non_max_suppression, print_args, scale_boxes, 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 select_device, smart_inference_mode
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@ -23,13 +23,13 @@ import torch
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import torch.nn as nn
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from PIL import Image
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from torch.cuda import amp
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from ultralytics.utils.plotting import Annotator, colors, save_one_box
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from utils import TryExcept
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from utils.dataloaders import exif_transpose, letterbox
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from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
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increment_path, is_jupyter, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
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xyxy2xywh, yaml_load)
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from utils.plots import Annotator, colors, save_one_box
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from utils.torch_utils import copy_attr, smart_inference_mode
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@ -15,7 +15,7 @@ thop>=0.1.1 # FLOPs computation
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torch>=1.7.0 # see https://pytorch.org/get-started/locally (recommended)
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torchvision>=0.8.1
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tqdm>=4.64.0
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ultralytics>=8.0.145
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ultralytics>=8.0.146
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# protobuf<=3.20.1 # https://github.com/ultralytics/yolov5/issues/8012
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# Logging ---------------------------------------------------------------------
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@ -42,12 +42,13 @@ if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from ultralytics.utils.plotting import Annotator, colors, save_one_box
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from models.common import DetectMultiBackend
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from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
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from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
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increment_path, non_max_suppression, print_args, scale_boxes, scale_segments,
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strip_optimizer)
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from utils.plots import Annotator, colors, save_one_box
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from utils.segment.general import masks2segments, process_mask, process_mask_native
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from utils.torch_utils import select_device, smart_inference_mode
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@ -5,8 +5,7 @@ from pathlib import Path
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import numpy as np
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import yaml
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from utils.plots import Annotator, colors
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from ultralytics.utils.plotting import Annotator, colors
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try:
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import clearml
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124
utils/plots.py
124
utils/plots.py
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@ -8,7 +8,6 @@ import math
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import os
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from copy import copy
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from pathlib import Path
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from urllib.error import URLError
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import cv2
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import matplotlib
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@ -17,14 +16,13 @@ import numpy as np
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import pandas as pd
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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 PIL import Image, ImageDraw
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from scipy.ndimage.filters import gaussian_filter1d
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from ultralytics.utils.plotting import Annotator
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from utils import TryExcept, threaded
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from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_boxes, increment_path,
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is_ascii, xywh2xyxy, xyxy2xywh)
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from utils.general import LOGGER, clip_boxes, increment_path, xywh2xyxy, xyxy2xywh
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from utils.metrics import fitness
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from utils.segment.general import scale_image
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# Settings
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RANK = int(os.getenv('RANK', -1))
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@ -53,120 +51,6 @@ class Colors:
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colors = Colors() # create instance for 'from utils.plots import colors'
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def check_pil_font(font=FONT, size=10):
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# Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
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font = Path(font)
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font = font if font.exists() else (CONFIG_DIR / font.name)
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try:
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return ImageFont.truetype(str(font) if font.exists() else font.name, size)
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except Exception: # download if missing
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try:
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check_font(font)
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return ImageFont.truetype(str(font), size)
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except TypeError:
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check_requirements('Pillow>=8.4.0') # known issue https://github.com/ultralytics/yolov5/issues/5374
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except URLError: # not online
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return ImageFont.load_default()
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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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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 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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self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
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def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
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# Add one xyxy box to image with label
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if self.pil or not is_ascii(label):
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self.draw.rectangle(box, width=self.lw, outline=color) # box
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if label:
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w, h = self.font.getsize(label) # text width, height (WARNING: deprecated) in 9.2.0
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# _, _, w, h = self.font.getbbox(label) # text width, height (New)
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outside = box[1] - h >= 0 # label fits outside box
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self.draw.rectangle(
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(box[0], box[1] - h if outside else box[1], box[0] + w + 1,
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box[1] + 1 if outside else box[1] + h + 1),
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fill=color,
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)
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# self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
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self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
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else: # cv2
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p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
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cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
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if label:
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tf = max(self.lw - 1, 1) # font thickness
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w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
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outside = p1[1] - h >= 3
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p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
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cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
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cv2.putText(self.im,
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label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
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0,
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self.lw / 3,
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txt_color,
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thickness=tf,
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lineType=cv2.LINE_AA)
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def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):
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"""Plot masks at once.
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Args:
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masks (tensor): predicted masks on cuda, shape: [n, h, w]
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colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n]
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im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1]
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alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque
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"""
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if self.pil:
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# convert to numpy first
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self.im = np.asarray(self.im).copy()
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if len(masks) == 0:
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self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
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colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0
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colors = colors[:, None, None] # shape(n,1,1,3)
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masks = masks.unsqueeze(3) # shape(n,h,w,1)
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masks_color = masks * (colors * alpha) # shape(n,h,w,3)
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inv_alph_masks = (1 - masks * alpha).cumprod(0) # shape(n,h,w,1)
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mcs = (masks_color * inv_alph_masks).sum(0) * 2 # mask color summand shape(n,h,w,3)
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im_gpu = im_gpu.flip(dims=[0]) # flip channel
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im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3)
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im_gpu = im_gpu * inv_alph_masks[-1] + mcs
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im_mask = (im_gpu * 255).byte().cpu().numpy()
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self.im[:] = im_mask if retina_masks else scale_image(im_gpu.shape, im_mask, self.im.shape)
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if self.pil:
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# convert im back to PIL and update draw
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self.fromarray(self.im)
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def rectangle(self, xy, fill=None, outline=None, width=1):
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# Add rectangle to image (PIL-only)
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self.draw.rectangle(xy, fill, outline, width)
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def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
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# Add text to image (PIL-only)
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if anchor == 'bottom': # start y from font bottom
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w, h = self.font.getsize(text) # text width, height
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xy[1] += 1 - h
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self.draw.text(xy, text, fill=txt_color, font=self.font)
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def fromarray(self, im):
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# Update self.im from a numpy array
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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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def result(self):
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# Return annotated image as array
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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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@ -266,7 +150,7 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None):
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x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
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annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
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if paths:
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annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
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annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
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if len(targets) > 0:
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ti = targets[targets[:, 0] == i] # image targets
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boxes = xywh2xyxy(ti[:, 2:6]).T
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@ -54,7 +54,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
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x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
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annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
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if paths:
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annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
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annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
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if len(targets) > 0:
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idx = targets[:, 0] == i
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ti = targets[idx] # image targets
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