Spaces:
Sleeping
Sleeping
File size: 5,815 Bytes
ad3ee60 19327c9 ad3ee60 45a5416 ad3ee60 45a5416 ad3ee60 45a5416 ad3ee60 45a5416 ad3ee60 45a5416 ad3ee60 45a5416 ad3ee60 45a5416 ad3ee60 45a5416 ad3ee60 45a5416 ad3ee60 45a5416 ad3ee60 45a5416 ad3ee60 45a5416 ad3ee60 45a5416 ad3ee60 45a5416 ad3ee60 45a5416 ad3ee60 45a5416 ad3ee60 45a5416 19327c9 45a5416 ad3ee60 45a5416 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
import os
import json
import random
import torch
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import shutil
from prismer.utils import create_ade20k_label_colormap
obj_label_map = torch.load('prismer/dataset/detection_features.pt')['labels']
coco_label_map = torch.load('prismer/dataset/coco_features.pt')['labels']
ade_color = create_ade20k_label_colormap()
def islight(rgb):
r, g, b = rgb
hsp = np.sqrt(0.299 * (r * r) + 0.587 * (g * g) + 0.114 * (b * b))
if hsp > 127.5:
return True
else:
return False
def depth_prettify(file_path):
pretty_path = file_path.replace('.png', '_p.png')
if not os.path.exists(pretty_path):
depth = plt.imread(file_path)
plt.imsave(pretty_path, depth, cmap='rainbow')
def obj_detection_prettify(rgb_path, path_name):
pretty_path = path_name.replace('.png', '_p.png')
if not os.path.exists(pretty_path):
rgb = plt.imread(rgb_path)
obj_labels = plt.imread(path_name)
obj_labels_dict = json.load(open(path_name.replace('.png', '.json')))
plt.imshow(rgb)
if len(np.unique(obj_labels)) == 1:
plt.axis('off')
plt.savefig(path_name, bbox_inches='tight', transparent=True, pad_inches=0)
plt.close()
else:
num_objs = np.unique(obj_labels)[:-1].max()
plt.imshow(obj_labels, cmap='terrain', vmax=num_objs + 1 / 255., alpha=0.8)
cmap = matplotlib.colormaps.get_cmap('terrain')
for i in np.unique(obj_labels)[:-1]:
obj_idx_all = np.where(obj_labels == i)
x, y = obj_idx_all[1].mean(), obj_idx_all[0].mean()
obj_name = obj_label_map[obj_labels_dict[str(int(i * 255))]]
obj_name = obj_name.split(',')[0]
if islight([c*255 for c in cmap(i / num_objs)[:3]]):
plt.text(x, y, obj_name, c='black', horizontalalignment='center', verticalalignment='center', clip_on=True)
else:
plt.text(x, y, obj_name, c='white', horizontalalignment='center', verticalalignment='center', clip_on=True)
plt.axis('off')
plt.savefig(pretty_path, bbox_inches='tight', transparent=True, pad_inches=0)
plt.close()
def seg_prettify(rgb_path, file_name):
pretty_path = file_name.replace('.png', '_p.png')
if not os.path.exists(pretty_path):
rgb = plt.imread(rgb_path)
seg_labels = plt.imread(file_name)
plt.imshow(rgb)
seg_map = np.zeros(list(seg_labels.shape) + [3], dtype=np.int16)
for i in np.unique(seg_labels):
seg_map[seg_labels == i] = ade_color[int(i * 255)]
plt.imshow(seg_map, alpha=0.8)
for i in np.unique(seg_labels):
obj_idx_all = np.where(seg_labels == i)
if len(obj_idx_all[0]) > 20: # only plot the label with its number of labelled pixel more than 20
obj_idx = random.randint(0, len(obj_idx_all[0]) - 1)
x, y = obj_idx_all[1][obj_idx], obj_idx_all[0][obj_idx]
obj_name = coco_label_map[int(i * 255)]
obj_name = obj_name.split(',')[0]
if islight(seg_map[int(y), int(x)]):
plt.text(x, y, obj_name, c='black', horizontalalignment='center', verticalalignment='center', clip_on=True)
else:
plt.text(x, y, obj_name, c='white', horizontalalignment='center', verticalalignment='center', clip_on=True)
plt.axis('off')
plt.savefig(pretty_path, bbox_inches='tight', transparent=True, pad_inches=0)
plt.close()
def ocr_detection_prettify(rgb_path, file_name):
pretty_path = file_name.replace('.png', '_p.png')
if not os.path.exists(pretty_path):
if os.path.exists(file_name):
rgb = plt.imread(rgb_path)
ocr_labels = plt.imread(file_name)
ocr_labels_dict = torch.load(file_name.replace('.png', '.pt'))
plt.imshow(rgb)
plt.imshow(ocr_labels, cmap='gray', alpha=0.8)
for i in np.unique(ocr_labels)[:-1]:
text_idx_all = np.where(ocr_labels == i)
x, y = text_idx_all[1].mean(), text_idx_all[0].mean()
text = ocr_labels_dict[int(i * 255)]['text']
plt.text(x, y, text, c='white', horizontalalignment='center', verticalalignment='center', clip_on=True)
plt.axis('off')
plt.savefig(pretty_path, bbox_inches='tight', transparent=True, pad_inches=0)
plt.close()
else:
rgb = plt.imread(rgb_path)
ocr_labels = np.ones_like(rgb, dtype=np.float32())
plt.imshow(rgb)
plt.imshow(ocr_labels, cmap='gray', alpha=0.8)
x, y = rgb.shape[1] / 2, rgb.shape[0] / 2
plt.text(x, y, 'No text detected', c='black', horizontalalignment='center', verticalalignment='center', clip_on=True)
plt.axis('off')
os.makedirs(os.path.dirname(file_name), exist_ok=True)
plt.savefig(pretty_path, bbox_inches='tight', transparent=True, pad_inches=0)
plt.close()
def label_prettify(rgb_path, expert_paths):
for expert_path in expert_paths:
if 'depth' in expert_path:
depth_prettify(expert_path)
elif 'seg' in expert_path:
seg_prettify(rgb_path, expert_path)
elif 'ocr' in expert_path:
ocr_detection_prettify(rgb_path, expert_path)
elif 'obj' in expert_path:
obj_detection_prettify(rgb_path, expert_path)
else:
pretty_path = expert_path.replace('.png', '_p.png')
if not os.path.exists(pretty_path):
shutil.copyfile(expert_path, pretty_path)
|