本文主要是介绍数据标注：批量转换json文件，出现AttributeError: module ‘labelme.utils‘ has no attribute ‘draw_label‘错误，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
labelme版本更换为3.11.2
"D:\Anaconda3\Lib\site-packages\labelme\utils\draw.py"缺失？：
import io
import os.path as ospimport numpy as np
import PIL.Image
import PIL.ImageDraw
import PIL.ImageFontdef label_colormap(N=256):def bitget(byteval, idx):return ((byteval & (1 << idx)) != 0)cmap = np.zeros((N, 3))for i in range(0, N):id = ir, g, b = 0, 0, 0for j in range(0, 8):r = np.bitwise_or(r, (bitget(id, 0) << 7 - j))g = np.bitwise_or(g, (bitget(id, 1) << 7 - j))b = np.bitwise_or(b, (bitget(id, 2) << 7 - j))id = (id >> 3)cmap[i, 0] = rcmap[i, 1] = gcmap[i, 2] = bcmap = cmap.astype(np.float32) / 255return cmapdef _validate_colormap(colormap, n_labels):if colormap is None:colormap = label_colormap(n_labels)else:assert colormap.shape == (colormap.shape[0], 3), \'colormap must be sequence of RGB values'assert 0 <= colormap.min() and colormap.max() <= 1, \'colormap must ranges 0 to 1'return colormap# similar function as skimage.color.label2rgb
def label2rgb(lbl, img=None, n_labels=None, alpha=0.5, thresh_suppress=0, colormap=None,
):if n_labels is None:n_labels = len(np.unique(lbl))colormap = _validate_colormap(colormap, n_labels)colormap = (colormap * 255).astype(np.uint8)lbl_viz = colormap[lbl]lbl_viz[lbl == -1] = (0, 0, 0) # unlabeledif img is not None:img_gray = PIL.Image.fromarray(img).convert('LA')img_gray = np.asarray(img_gray.convert('RGB'))# img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)# img_gray = cv2.cvtColor(img_gray, cv2.COLOR_GRAY2RGB)lbl_viz = alpha * lbl_viz + (1 - alpha) * img_graylbl_viz = lbl_viz.astype(np.uint8)return lbl_vizdef draw_label(label, img=None, label_names=None, colormap=None, **kwargs):"""Draw pixel-wise label with colorization and label names.label: ndarray, (H, W)Pixel-wise labels to colorize.img: ndarray, (H, W, 3), optionalImage on which the colorized label will be drawn.label_names: iterableList of label names."""import matplotlib.pyplot as pltbackend_org = plt.rcParams['backend']plt.switch_backend('agg')plt.subplots_adjust(left=0, right=1, top=1, bottom=0,wspace=0, hspace=0)plt.margins(0, 0)plt.gca().xaxis.set_major_locator(plt.NullLocator())plt.gca().yaxis.set_major_locator(plt.NullLocator())if label_names is None:label_names = [str(l) for l in range(label.max() + 1)]colormap = _validate_colormap(colormap, len(label_names))label_viz = label2rgb(label, img, n_labels=len(label_names), colormap=colormap, **kwargs)plt.imshow(label_viz)plt.axis('off')plt_handlers = []plt_titles = []for label_value, label_name in enumerate(label_names):if label_value not in label:continuefc = colormap[label_value]p = plt.Rectangle((0, 0), 1, 1, fc=fc)plt_handlers.append(p)plt_titles.append('{value}: {name}'.format(value=label_value, name=label_name))plt.legend(plt_handlers, plt_titles, loc='lower right', framealpha=.5)f = io.BytesIO()plt.savefig(f, bbox_inches='tight', pad_inches=0)plt.cla()plt.close()plt.switch_backend(backend_org)out_size = (label_viz.shape[1], label_viz.shape[0])out = PIL.Image.open(f).resize(out_size, PIL.Image.BILINEAR).convert('RGB')out = np.asarray(out)return outdef draw_instances(image=None,bboxes=None,labels=None,masks=None,captions=None,
):import matplotlib# TODO(wkentaro)assert image is not Noneassert bboxes is not Noneassert labels is not Noneassert masks is Noneassert captions is not Noneviz = PIL.Image.fromarray(image)draw = PIL.ImageDraw.ImageDraw(viz)font_path = osp.join(osp.dirname(matplotlib.__file__),'mpl-data/fonts/ttf/DejaVuSans.ttf')font = PIL.ImageFont.truetype(font_path)colormap = label_colormap(255)for bbox, label, caption in zip(bboxes, labels, captions):color = colormap[label]color = tuple((color * 255).astype(np.uint8).tolist())xmin, ymin, xmax, ymax = bboxdraw.rectangle((xmin, ymin, xmax, ymax), outline=color)draw.text((xmin, ymin), caption, font=font)return np.asarray(viz)
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