File size: 6,295 Bytes
9951234 e4f0b1d 9951234 e4f0b1d 9951234 e4f0b1d 9951234 e4f0b1d 9951234 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 |
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import cv2
import torch
def fast_process(
annotations,
image,
device,
scale,
better_quality=False,
mask_random_color=True,
bbox=None,
use_retina=True,
withContours=True,
):
if isinstance(annotations[0], dict):
annotations = [annotation['segmentation'] for annotation in annotations]
original_h = image.height
original_w = image.width
if better_quality:
if isinstance(annotations[0], torch.Tensor):
annotations = np.array(annotations.cpu())
for i, mask in enumerate(annotations):
mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
annotations[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))
if device == 'cpu':
annotations = np.array(annotations)
inner_mask = fast_show_mask(
annotations,
plt.gca(),
random_color=mask_random_color,
bbox=bbox,
retinamask=use_retina,
target_height=original_h,
target_width=original_w,
)
else:
if isinstance(annotations[0], np.ndarray):
annotations = torch.from_numpy(annotations)
inner_mask = fast_show_mask_gpu(
annotations,
plt.gca(),
random_color=mask_random_color,
bbox=bbox,
retinamask=use_retina,
target_height=original_h,
target_width=original_w,
)
if isinstance(annotations, torch.Tensor):
annotations = annotations.cpu().numpy()
if withContours:
contour_all = []
temp = np.zeros((original_h, original_w, 1))
for i, mask in enumerate(annotations):
if type(mask) == dict:
mask = mask['segmentation']
annotation = mask.astype(np.uint8)
if use_retina == False:
annotation = cv2.resize(
annotation,
(original_w, original_h),
interpolation=cv2.INTER_NEAREST,
)
contours, _ = cv2.findContours(annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
contour_all.append(contour)
cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2 // scale)
color = np.array([0 / 255, 0 / 255, 255 / 255, 0.9])
contour_mask = temp / 255 * color.reshape(1, 1, -1)
image = image.convert('RGBA')
overlay_inner = Image.fromarray((inner_mask * 255).astype(np.uint8), 'RGBA')
image.paste(overlay_inner, (0, 0), overlay_inner)
if withContours:
overlay_contour = Image.fromarray((contour_mask * 255).astype(np.uint8), 'RGBA')
image.paste(overlay_contour, (0, 0), overlay_contour)
return image
# CPU post process
def fast_show_mask(
annotation,
ax,
random_color=False,
bbox=None,
retinamask=True,
target_height=960,
target_width=960,
):
mask_sum = annotation.shape[0]
height = annotation.shape[1]
weight = annotation.shape[2]
# 将annotation 按照面积 排序
areas = np.sum(annotation, axis=(1, 2))
sorted_indices = np.argsort(areas)[::1]
annotation = annotation[sorted_indices]
index = (annotation != 0).argmax(axis=0)
if random_color:
color = np.random.random((mask_sum, 1, 1, 3))
else:
color = np.ones((mask_sum, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 255 / 255])
transparency = np.ones((mask_sum, 1, 1, 1)) * 0.6
visual = np.concatenate([color, transparency], axis=-1)
mask_image = np.expand_dims(annotation, -1) * visual
mask = np.zeros((height, weight, 4))
h_indices, w_indices = np.meshgrid(np.arange(height), np.arange(weight), indexing='ij')
indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
mask[h_indices, w_indices, :] = mask_image[indices]
if bbox is not None:
x1, y1, x2, y2 = bbox
ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1))
if not retinamask:
mask = cv2.resize(mask, (target_width, target_height), interpolation=cv2.INTER_NEAREST)
return mask
def fast_show_mask_gpu(
annotation,
ax,
random_color=False,
bbox=None,
retinamask=True,
target_height=960,
target_width=960,
):
device = annotation.device
mask_sum = annotation.shape[0]
height = annotation.shape[1]
weight = annotation.shape[2]
areas = torch.sum(annotation, dim=(1, 2))
sorted_indices = torch.argsort(areas, descending=False)
annotation = annotation[sorted_indices]
# 找每个位置第一个非零值下标
index = (annotation != 0).to(torch.long).argmax(dim=0)
if random_color:
color = torch.rand((mask_sum, 1, 1, 3)).to(device)
else:
color = torch.ones((mask_sum, 1, 1, 3)).to(device) * torch.tensor(
[30 / 255, 144 / 255, 255 / 255]
).to(device)
transparency = torch.ones((mask_sum, 1, 1, 1)).to(device) * 0.6
visual = torch.cat([color, transparency], dim=-1)
mask_image = torch.unsqueeze(annotation, -1) * visual
# 按index取数，index指每个位置选哪个batch的数，把mask_image转成一个batch的形式
mask = torch.zeros((height, weight, 4)).to(device)
h_indices, w_indices = torch.meshgrid(torch.arange(height), torch.arange(weight))
indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
# 使用向量化索引更新show的值
mask[h_indices, w_indices, :] = mask_image[indices]
mask_cpu = mask.cpu().numpy()
if bbox is not None:
x1, y1, x2, y2 = bbox
ax.add_patch(
plt.Rectangle(
(x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor="b", linewidth=1
)
)
if not retinamask:
mask_cpu = cv2.resize(
mask_cpu, (target_width, target_height), interpolation=cv2.INTER_NEAREST
)
return mask_cpu
|