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- import os
- import numpy as np
- from PIL import Image
-
- __all__ = ['get_color_pallete', 'print_iou', 'set_img_color',
- 'show_prediction', 'show_colorful_images', 'save_colorful_images']
-
-
- def print_iou(iu, mean_pixel_acc, class_names=None, show_no_back=False):
- n = iu.size
- lines = []
- for i in range(n):
- if class_names is None:
- cls = 'Class %d:' % (i + 1)
- else:
- cls = '%d %s' % (i + 1, class_names[i])
- # lines.append('%-8s: %.3f%%' % (cls, iu[i] * 100))
- mean_IU = np.nanmean(iu)
- mean_IU_no_back = np.nanmean(iu[1:])
- if show_no_back:
- lines.append('mean_IU: %.3f%% || mean_IU_no_back: %.3f%% || mean_pixel_acc: %.3f%%' % (
- mean_IU * 100, mean_IU_no_back * 100, mean_pixel_acc * 100))
- else:
- lines.append('mean_IU: %.3f%% || mean_pixel_acc: %.3f%%' % (mean_IU * 100, mean_pixel_acc * 100))
- lines.append('=================================================')
- line = "\n".join(lines)
-
- print(line)
-
-
- def set_img_color(img, label, colors, background=0, show255=False):
- for i in range(len(colors)):
- if i != background:
- img[np.where(label == i)] = colors[i]
- if show255:
- img[np.where(label == 255)] = 255
-
- return img
-
-
- def show_prediction(img, pred, colors, background=0):
- im = np.array(img, np.uint8)
- set_img_color(im, pred, colors, background)
- out = np.array(im)
-
- return out
-
-
- def show_colorful_images(prediction, palettes):
- im = Image.fromarray(palettes[prediction.astype('uint8').squeeze()])
- im.show()
-
-
- def save_colorful_images(prediction, filename, output_dir, palettes):
- '''
- :param prediction: [B, H, W, C]
- '''
- im = Image.fromarray(palettes[prediction.astype('uint8').squeeze()])
- fn = os.path.join(output_dir, filename)
- out_dir = os.path.split(fn)[0]
- if not os.path.exists(out_dir):
- os.mkdir(out_dir)
- im.save(fn)
-
-
- def get_color_pallete(npimg, dataset='pascal_voc'):
- """Visualize image.
-
- Parameters
- ----------
- npimg : numpy.ndarray
- Single channel image with shape `H, W, 1`.
- dataset : str, default: 'pascal_voc'
- The dataset that model pretrained on. ('pascal_voc', 'ade20k')
- Returns
- -------
- out_img : PIL.Image
- Image with color pallete
- """
- # recovery boundary
- if dataset in ('pascal_voc', 'pascal_aug'):
- npimg[npimg == -1] = 255
- # put colormap
- if dataset == 'ade20k':
- npimg = npimg + 1
- out_img = Image.fromarray(npimg.astype('uint8'))
- out_img.putpalette(adepallete)
- return out_img
- elif dataset == 'citys':
- out_img = Image.fromarray(npimg.astype('uint8'))
- out_img.putpalette(cityspallete)
- return out_img
- out_img = Image.fromarray(npimg.astype('uint8'))
- out_img.putpalette(vocpallete)
- return out_img
-
-
- def _getvocpallete(num_cls):
- n = num_cls
- pallete = [0] * (n * 3)
- for j in range(0, n):
- lab = j
- pallete[j * 3 + 0] = 0
- pallete[j * 3 + 1] = 0
- pallete[j * 3 + 2] = 0
- i = 0
- while (lab > 0):
- pallete[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
- pallete[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
- pallete[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
- i = i + 1
- lab >>= 3
- return pallete
-
-
- vocpallete = _getvocpallete(256)
-
- adepallete = [
- 0, 0, 0, 120, 120, 120, 180, 120, 120, 6, 230, 230, 80, 50, 50, 4, 200, 3, 120, 120, 80, 140, 140, 140, 204,
- 5, 255, 230, 230, 230, 4, 250, 7, 224, 5, 255, 235, 255, 7, 150, 5, 61, 120, 120, 70, 8, 255, 51, 255, 6, 82,
- 143, 255, 140, 204, 255, 4, 255, 51, 7, 204, 70, 3, 0, 102, 200, 61, 230, 250, 255, 6, 51, 11, 102, 255, 255,
- 7, 71, 255, 9, 224, 9, 7, 230, 220, 220, 220, 255, 9, 92, 112, 9, 255, 8, 255, 214, 7, 255, 224, 255, 184, 6,
- 10, 255, 71, 255, 41, 10, 7, 255, 255, 224, 255, 8, 102, 8, 255, 255, 61, 6, 255, 194, 7, 255, 122, 8, 0, 255,
- 20, 255, 8, 41, 255, 5, 153, 6, 51, 255, 235, 12, 255, 160, 150, 20, 0, 163, 255, 140, 140, 140, 250, 10, 15,
- 20, 255, 0, 31, 255, 0, 255, 31, 0, 255, 224, 0, 153, 255, 0, 0, 0, 255, 255, 71, 0, 0, 235, 255, 0, 173, 255,
- 31, 0, 255, 11, 200, 200, 255, 82, 0, 0, 255, 245, 0, 61, 255, 0, 255, 112, 0, 255, 133, 255, 0, 0, 255, 163,
- 0, 255, 102, 0, 194, 255, 0, 0, 143, 255, 51, 255, 0, 0, 82, 255, 0, 255, 41, 0, 255, 173, 10, 0, 255, 173, 255,
- 0, 0, 255, 153, 255, 92, 0, 255, 0, 255, 255, 0, 245, 255, 0, 102, 255, 173, 0, 255, 0, 20, 255, 184, 184, 0,
- 31, 255, 0, 255, 61, 0, 71, 255, 255, 0, 204, 0, 255, 194, 0, 255, 82, 0, 10, 255, 0, 112, 255, 51, 0, 255, 0,
- 194, 255, 0, 122, 255, 0, 255, 163, 255, 153, 0, 0, 255, 10, 255, 112, 0, 143, 255, 0, 82, 0, 255, 163, 255,
- 0, 255, 235, 0, 8, 184, 170, 133, 0, 255, 0, 255, 92, 184, 0, 255, 255, 0, 31, 0, 184, 255, 0, 214, 255, 255,
- 0, 112, 92, 255, 0, 0, 224, 255, 112, 224, 255, 70, 184, 160, 163, 0, 255, 153, 0, 255, 71, 255, 0, 255, 0,
- 163, 255, 204, 0, 255, 0, 143, 0, 255, 235, 133, 255, 0, 255, 0, 235, 245, 0, 255, 255, 0, 122, 255, 245, 0,
- 10, 190, 212, 214, 255, 0, 0, 204, 255, 20, 0, 255, 255, 255, 0, 0, 153, 255, 0, 41, 255, 0, 255, 204, 41, 0,
- 255, 41, 255, 0, 173, 0, 255, 0, 245, 255, 71, 0, 255, 122, 0, 255, 0, 255, 184, 0, 92, 255, 184, 255, 0, 0,
- 133, 255, 255, 214, 0, 25, 194, 194, 102, 255, 0, 92, 0, 255]
-
- cityspallete = [
- 128, 64, 128,
- 244, 35, 232,
- 70, 70, 70,
- 102, 102, 156,
- 190, 153, 153,
- 153, 153, 153,
- 250, 170, 30,
- 220, 220, 0,
- 107, 142, 35,
- 152, 251, 152,
- 0, 130, 180,
- 220, 20, 60,
- 255, 0, 0,
- 0, 0, 142,
- 0, 0, 70,
- 0, 60, 100,
- 0, 80, 100,
- 0, 0, 230,
- 119, 11, 32,
- ]
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