File size: 3,793 Bytes
e168a4d | 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 | import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from lib.utils.img_utils import colors
from lib.utils import img_utils
from lib.utils.snake import snake_cityscapes_utils, snake_config
R = 8
GREEN = (18, 127, 15)
WHITE = (255, 255, 255)
def visualize_snake_detection(img, data):
def blend_hm_img(hm, img):
hm = np.max(hm, axis=0)
h, w = hm.shape[:2]
img = cv2.resize(img, dsize=(w, h), interpolation=cv2.INTER_LINEAR)
hm = np.array([255, 255, 255]) - (hm.reshape(h, w, 1) * colors[0]).astype(np.uint8)
ratio = 0.5
blend = (img * ratio + hm * (1 - ratio)).astype(np.uint8)
return blend
img = img_utils.bgr_to_rgb(img)
blend = blend_hm_img(data['ct_hm'], img)
plt.imshow(blend)
ct_ind = np.array(data['ct_ind'])
w = img.shape[1] // snake_config.down_ratio
xs = ct_ind % w
ys = ct_ind // w
for i in range(len(data['wh'])):
w, h = data['wh'][i]
x_min, y_min = xs[i] - w / 2, ys[i] - h / 2
x_max, y_max = xs[i] + w / 2, ys[i] + h / 2
plt.plot([x_min, x_min, x_max, x_max, x_min], [y_min, y_max, y_max, y_min, y_min])
plt.show()
def visualize_cp_detection(img, data):
act_ind = data['act_ind']
awh = data['awh']
act_hm_w = data['act_hm'].shape[2]
cp_h, cp_w = data['cp_hm'][0].shape[1], data['cp_hm'][0].shape[2]
img = img_utils.bgr_to_rgb(img)
plt.imshow(img)
for i in range(len(act_ind)):
act_ind_ = act_ind[i]
ct = act_ind_ % act_hm_w, act_ind_ // act_hm_w
w, h = awh[i]
abox = np.array([ct[0] - w/2, ct[1] - h/2, ct[0] + w/2, ct[1] + h/2])
cp_ind_ = data['cp_ind'][i]
cp_wh_ = data['cp_wh'][i]
for j in range(len(cp_ind_)):
ct = cp_ind_[j] % cp_w, cp_ind_[j] // cp_w
x = ct[0] / cp_w * w
y = ct[1] / cp_h * h
x_min = (x - cp_wh_[j][0] / 2 + abox[0]) * snake_config.down_ratio
y_min = (y - cp_wh_[j][1] / 2 + abox[1]) * snake_config.down_ratio
x_max = (x + cp_wh_[j][0] / 2 + abox[0]) * snake_config.down_ratio
y_max = (y + cp_wh_[j][1] / 2 + abox[1]) * snake_config.down_ratio
plt.plot([x_min, x_min, x_max, x_max, x_min], [y_min, y_max, y_max, y_min, y_min])
plt.show()
def visualize_snake_evolution(img, data):
img = img_utils.bgr_to_rgb(img)
plt.imshow(img)
for poly in data['i_gt_py']:
poly = poly * 4
poly = np.append(poly, [poly[0]], axis=0)
plt.plot(poly[:, 0], poly[:, 1])
plt.scatter(poly[0, 0], poly[0, 1], edgecolors='w')
plt.show()
def visualize_snake_octagon(img, extreme_points):
img = img_utils.bgr_to_rgb(img)
octagons = []
bboxes = []
ex_points = []
for i in range(len(extreme_points)):
for j in range(len(extreme_points[i])):
bbox = get_bbox(extreme_points[i][j]*4)
octagon = snake_cityscapes_utils.get_octagon(extreme_points[i][j]*4)
bboxes.append(bbox)
octagons.append(octagon)
ex_points.append(extreme_points[i][j])
_, ax = plt.subplots(1)
ax.imshow(img)
n = len(octagons)
for i in range(n):
x, y, x_max, y_max = bboxes[i]
ax.add_patch(patches.Polygon(xy=[[x, y], [x, y_max], [x_max, y_max], [x_max, y]], fill=False, linewidth=1,
edgecolor='r'))
octagon = np.append(octagons[i], octagons[i][0]).reshape(-1, 2)
ax.plot(octagon[:, 0], octagon[:, 1])
ax.scatter(ex_points[i][:, 0] * 4, ex_points[i][:, 1] * 4, edgecolors='w')
plt.show()
def get_bbox(ex):
x = ex[:, 0]
y = ex[:, 1]
bbox = [np.min(x), np.min(y), np.max(x), np.max(y)]
return bbox
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