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from typing import Dict, List
import torch
import colorsys
import random
import numpy as np
from skimage.draw import line_aa, circle_perimeter_aa
import cv2
from .util import select_data
def _gen_random_colors(N, bright=True):
brightness = 1.0 if bright else 0.7
hsv = [(i / N, 1, brightness) for i in range(N)]
colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))
random.shuffle(colors)
return colors
_static_label_colors = [
np.array((1.0, 1.0, 1.0), np.float32),
np.array((255, 250, 79), np.float32) / 255.0, # face
np.array([255, 125, 138], np.float32) / 255.0, # lb
np.array([213, 32, 29], np.float32) / 255.0, # rb
np.array([0, 144, 187], np.float32) / 255.0, # le
np.array([0, 196, 253], np.float32) / 255.0, # re
np.array([255, 129, 54], np.float32) / 255.0, # nose
np.array([88, 233, 135], np.float32) / 255.0, # ulip
np.array([0, 117, 27], np.float32) / 255.0, # llip
np.array([255, 76, 249], np.float32) / 255.0, # imouth
np.array((1.0, 0.0, 0.0), np.float32), # hair
np.array((255, 250, 100), np.float32) / 255.0, # lr
np.array((255, 250, 100), np.float32) / 255.0, # rr
np.array((250, 245, 50), np.float32) / 255.0, # neck
np.array((0.0, 1.0, 0.5), np.float32), # cloth
np.array((1.0, 0.0, 0.5), np.float32),
] + _gen_random_colors(256)
_names_in_static_label_colors = [
'background', 'face', 'lb', 'rb', 'le', 're', 'nose',
'ulip', 'llip', 'imouth', 'hair', 'lr', 'rr', 'neck',
'cloth', 'eyeg', 'hat', 'earr'
]
def _blend_labels(image, labels, label_names_dict=None,
default_alpha=0.6, color_offset=None):
assert labels.ndim == 2
bg_mask = labels == 0
if label_names_dict is None:
colors = _static_label_colors
else:
colors = [np.array((1.0, 1.0, 1.0), np.float32)]
for i in range(1, labels.max() + 1):
if isinstance(label_names_dict, dict) and i not in label_names_dict:
bg_mask = np.logical_or(bg_mask, labels == i)
colors.append(np.zeros((3)))
continue
label_name = label_names_dict[i]
if label_name in _names_in_static_label_colors:
color = _static_label_colors[
_names_in_static_label_colors.index(
label_name)]
else:
color = np.array((1.0, 1.0, 1.0), np.float32)
colors.append(color)
if color_offset is not None:
ncolors = []
for c in colors:
nc = np.array(c)
if (nc != np.zeros(3)).any():
nc += color_offset
ncolors.append(nc)
colors = ncolors
if image is None:
image = orig_image = np.zeros(
[labels.shape[0], labels.shape[1], 3], np.float32)
alpha = 1.0
else:
orig_image = image / np.max(image)
image = orig_image * (1.0 - default_alpha)
alpha = default_alpha
for i in range(1, np.max(labels) + 1):
image += alpha * \
np.tile(
np.expand_dims(
(labels == i).astype(np.float32), -1),
[1, 1, 3]) * colors[(i) % len(colors)]
image[np.where(image > 1.0)] = 1.0
image[np.where(image < 0)] = 0.0
image[np.where(bg_mask)] = orig_image[np.where(bg_mask)]
return image
def _draw_hwc(image: torch.Tensor, data: Dict[str, torch.Tensor]):
device = image.device
image = np.array(image.cpu().numpy(), copy=True)
dtype = image.dtype
h, w, _ = image.shape
draw_score_error = False
for tag, batch_content in data.items():
if tag == 'rects':
for cid, content in enumerate(batch_content):
x1, y1, x2, y2 = [int(v) for v in content]
y1, y2 = [max(min(v, h-1), 0) for v in [y1, y2]]
x1, x2 = [max(min(v, w-1), 0) for v in [x1, x2]]
for xx1, yy1, xx2, yy2 in [
[x1, y1, x2, y1],
[x1, y2, x2, y2],
[x1, y1, x1, y2],
[x2, y1, x2, y2]
]:
rr, cc, val = line_aa(yy1, xx1, yy2, xx2)
val = val[:, None][:, [0, 0, 0]]
image[rr, cc] = image[rr, cc] * (1.0-val) + val * 255
if 'scores' in data:
try:
import cv2
score = data['scores'][cid].item()
score_str = f'{score:0.3f}'
image_c = np.array(image).copy()
cv2.putText(image_c, score_str, org=(int(x1), int(y2)),
fontFace=cv2.FONT_HERSHEY_TRIPLEX,
fontScale=0.6, color=(255, 255, 255), thickness=1)
image[:, :, :] = image_c
except Exception as e:
if not draw_score_error:
print(f'Failed to draw scores on image.')
print(e)
draw_score_error = True
if tag == 'points':
for content in batch_content:
# content: npoints x 2
for x, y in content:
x = max(min(int(x), w-1), 0)
y = max(min(int(y), h-1), 0)
rr, cc, val = circle_perimeter_aa(y, x, 1)
valid = np.all([rr >= 0, rr < h, cc >= 0, cc < w], axis=0)
rr = rr[valid]
cc = cc[valid]
val = val[valid]
val = val[:, None][:, [0, 0, 0]]
image[rr, cc] = image[rr, cc] * (1.0-val) + val * 255
if tag == 'seg':
label_names = batch_content['label_names']
for seg_logits in batch_content['logits']:
# content: nclasses x h x w
seg_probs = seg_logits.softmax(dim=0)
seg_labels = seg_probs.argmax(dim=0).cpu().numpy()
image = (_blend_labels(image.astype(np.float32) /
255, seg_labels,
label_names_dict=label_names) * 255).astype(dtype)
return torch.from_numpy(image).to(device=device)
def draw_bchw(images: torch.Tensor, data: Dict[str, torch.Tensor]) -> torch.Tensor:
images2 = []
for image_id, image_chw in enumerate(images):
selected_data = select_data(image_id == data['image_ids'], data)
images2.append(
_draw_hwc(image_chw.permute(1, 2, 0), selected_data).permute(2, 0, 1))
return torch.stack(images2, dim=0)
def draw_landmarks(img, bbox=None, landmark=None, color=(0, 255, 0)):
"""
Input:
- img: gray or RGB
- bbox: type of BBox
- landmark: reproject landmark of (5L, 2L)
Output:
- img marked with landmark and bbox
"""
img = cv2.UMat(img).get()
if bbox is not None:
x1, y1, x2, y2 = np.array(bbox)[:4].astype(np.int32)
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
if landmark is not None:
for x, y in np.array(landmark).astype(np.int32):
cv2.circle(img, (int(x), int(y)), 2, color, -1)
return img |