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_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 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_ex(self, output, batch):
inp = img_utils.bgr_to_rgb(
img_utils.unnormalize_img(batch['inp'][0], mean,
std).permute(1, 2, 0))
ex = output['py']
ex = ex[-1] if isinstance(ex, list) else ex
ex = ex.detach().cpu().numpy() * snake_config.down_ratio
fig, ax = plt.subplots(1, figsize=(20, 10))
fig.tight_layout()
ax.axis('off')
ax.imshow(inp)
colors = np.array([[31, 119, 180], [255, 127, 14], [46, 160, 44],
[214, 40, 39], [148, 103, 189], [140, 86, 75],
[227, 119, 194], [126, 126, 126], [188, 189, 32],
[26, 190, 207]]) / 255.
np.random.shuffle(colors)
colors = cycle(colors)
for i in range(len(ex)):
color = next(colors).tolist()
poly = ex[i]
poly = np.append(poly, [poly[0]], axis=0)
ax.plot(poly[:, 0], poly[:, 1], color=color)
plt.show()
def visualize_training_box(self, output, batch, img_name):
inp = img_utils.bgr_to_rgb(img_utils.unnormalize_img(batch['inp'][0], mean, std).permute(1, 2, 0))
box = output['detection'][:, :4].detach().cpu().numpy() * snake_config.down_ratio
ex = output['py']
ex = ex[-1] if isinstance(ex, list) else ex
ex = ex.detach().cpu().numpy() * snake_config.down_ratio
#这里是查看显示结果的相关参数
tmp_file = open('/home/tianhao.lu/code/Deep_snake/snake/Result/Contour/result.log', 'w', encoding='utf8')
tmp_file.writelines("visualize training box -> inp：" + str(type(inp)) + "\n")
tmp_file.writelines("visualize training box -> box：" + str(len(box)) + "\n")
tmp_file.writelines("visualize training box -> ex:" + str(ex) + "\n")
tmp_file.writelines("visualize training box -> detection:" + str(output['detection']) + "\n")
# for tmp_data in train_loader:
# tmp_file.writelines("one train_loader data type:" + str(type(tmp_data)) + "\n")
# for key in tmp_data:
# tmp_file.writelines("one train_loader data key:" + str(key) + "\n")
# tmp_file.writelines("one train_loader data len:" + str(len(tmp_data[key])) + "\n")
# # tmp_file.writelines("one train_loader data:" + str(tmp_data) + "\n")
# break
tmp_file.writelines(str("*************************************************************** \n"))
tmp_file.close()
fig, ax = plt.subplots(1, figsize=(20, 10))
fig.tight_layout()
ax.axis('off')
ax.imshow(inp)
colors = np.array([
[31, 119, 180],
[255, 127, 14],
[46, 160, 44],
[214, 40, 39],
[148, 103, 189],
[140, 86, 75],
[227, 119, 194],
[126, 126, 126],
[188, 189, 32],
[26, 190, 207]
]) / 255.
np.random.shuffle(colors)
colors = cycle(colors)
for i in range(len(ex)):
color = next(colors).tolist()
poly = ex[i]
poly = np.append(poly, [poly[0]], axis=0)
ax.plot(poly[:, 0], poly[:, 1], color=color, linewidth=5)
x_min, y_min, x_max, y_max = box[i]
ax.plot([x_min, x_min, x_max, x_max, x_min], [y_min, y_max, y_max, y_min, y_min], color='w', linewidth=0.5)
# filename = random.randint(0,100000) #这个是名称取十万以内的随机数
filename = img_name
# fig = plt.gcf()
img_width = 400
img_height = 300
fig.set_size_inches(img_width / 96.0, img_height / 96.0) # 输出width*height像素
plt.subplots_adjust(top=1, bottom=0, left=0, right=1, hspace=0, wspace=0) # 输出图像#边框设置
plt.margins(0, 0)
plt.savefig("/home/tianhao.lu/code/Deep_snake/snake/Result/coco_test_result/%s.png"%filename,dpi=96.0,pad_inches=0.0)
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(self, output, batch):
inp = img_utils.bgr_to_rgb(img_utils.unnormalize_img(batch['inp'][0], mean, std).permute(1, 2, 0))
box = output['detection'][0, :, :4].detach().cpu().numpy() * tless_config.down_ratio
score = output['detection'][0, :, 4].detach().cpu().numpy()
plt.imshow(inp)
for i in range(len(box)):
x_min, y_min, x_max, y_max = box[i]
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_training_box(self, output, batch):
import cv2
inp = img_utils.bgr_to_rgb(img_utils.unnormalize_img(batch['inp'][0], mean, std).permute(1, 2, 0))
h, w, _ = inp.shape
box = output['detection'][:, :4].detach().cpu().numpy() * snake_config.down_ratio
#box = output['detection'][:, :4].detach().cpu().numpy()
nms_ct_hm = output['nms_ct_hm'].detach().cpu().numpy()
nms_ct_hm = nms_ct_hm[0,0,...]
nms_ct_hm = Image.fromarray(nms_ct_hm)
nms_ct_hm = nms_ct_hm.resize((w, h))
ex = output['py']
ex = ex[-1] if isinstance(ex, list) else ex
ex = ex.detach().cpu().numpy() * snake_config.down_ratio
#ex = ex.detach().cpu().numpy()
fig, ax = plt.subplots(1, figsize=(10, 10))
fig.tight_layout()
ax.axis('off')
ax.imshow(inp)
ax.imshow(np.array(nms_ct_hm))
colors = np.array([
[31, 119, 180],
[255, 127, 14],
[46, 160, 44],
[214, 40, 39],
[148, 103, 189],
[140, 86, 75],
[227, 119, 194],
[126, 126, 126],
[188, 189, 32],
[26, 190, 207]
]) / 255.
np.random.shuffle(colors)
colors = cycle(colors)
for i in range(len(ex)):
color = next(colors).tolist()
poly = ex[i]
poly = np.append(poly, [poly[0]], axis=0)
ax.plot(poly[:, 0], poly[:, 1], color=color, linewidth=1)
x_min, y_min, x_max, y_max = box[i]
ax.plot([x_min, x_min, x_max, x_max, x_min], [y_min, y_max, y_max, y_min, y_min], color='w', linewidth=0.5)
print("box:", box[i])
plt.show()
def visualize_training_box(self, output, batch):
inp = img_utils.bgr_to_rgb(
img_utils.unnormalize_img(batch['inp'][0], mean,
std).permute(1, 2, 0))
box = output['detection'][:, :4].detach().cpu().numpy(
) * snake_config.down_ratio
# box = output['cp_box'][:, :4].detach().cpu().numpy() * snake_config.down_ratio
_, ax = plt.subplots(1)
ax.imshow(inp)
n = len(box)
for i in range(n):
x_min, y_min, x_max, y_max = box[i]
ax.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_gt(self, output, batch, index):
inp = img_utils.bgr_to_rgb(
img_utils.unnormalize_img(batch['inp'][0], mean,
std).permute(1, 2, 0))
box = output['detection'][:, :4].detach().cpu().numpy(
) * snake_config.down_ratio
ex = output['i_gt_4py']
ex = ex[-1] if isinstance(ex, list) else ex
ex = ex.detach().cpu().numpy() * snake_config.down_ratio
fig, ax = plt.subplots(1, figsize=(20, 10))
fig.tight_layout()
ax.axis('off')
ax.imshow(inp)
colors = np.array([[31, 119, 180], [255, 127, 14], [46, 160, 44],
[214, 40, 39], [148, 103, 189], [140, 86, 75],
[227, 119, 194], [126, 126, 126], [188, 189, 32],
[26, 190, 207]]) / 255.
np.random.shuffle(colors)
colors = cycle(colors)
for i in range(len(ex)):
color = next(colors).tolist()
poly = ex[i]
poly = np.append(poly, [poly[0]], axis=0)
ax.plot(poly[:, 0], poly[:, 1], color=color, linewidth=3)
x_min, y_min, x_max, y_max = box[i]
ax.plot([x_min, x_min, x_max, x_max, x_min],
[y_min, y_max, y_max, y_min, y_min],
color='w',
linewidth=0.5)
plt.show()
# os.mkdir()
os.makedirs("KINS_SNAKE_GT_VIS/", exist_ok=True)
out_png_path = "KINS_SNAKE_GT_VIS/" + str(index) + ".jpg"
# plt.savefig(out_png_path, format='jpg', transparent=True, dpi=100, pad_inches = 0)
plt.savefig(out_png_path,
format='jpg',
transparent=True,
dpi=100,
bbox_inches='tight')
def visualize_snake_evolution(img, data):
img = img_utils.bgr_to_rgb(img)
i_gt_py = data['i_gt_py']
i_it_py = data['i_it_py']
plt.imshow(img)
for k in range(len(i_gt_py)):
poly = i_gt_py[k] * 4
poly = np.append(poly, [poly[0]], axis=0)
it_poly = i_it_py[k] * 4
it_poly = np.append(it_poly, [it_poly[0]], axis=0)
plt.plot(poly[:, 0], poly[:, 1], 'g')
plt.plot(it_poly[:, 0], it_poly[:, 1], 'r')
#plt.scatter(poly[0, 0], poly[0, 1], edgecolors='w')
#plt.show()
plt.savefig('b.png')
plt.close()
def visualize_ex(self, output, batch):
inp = img_utils.bgr_to_rgb(
img_utils.unnormalize_img(batch['inp'][0], mean,
std).permute(1, 2, 0))
detection = output['detection']
score = detection[:, 4].detach().cpu().numpy()
label = detection[:, 5].detach().cpu().numpy().astype(int)
cp_ind = output['cp_ind'].detach().cpu().numpy().astype(int)
py = output['py'][-1].detach().cpu().numpy() * snake_config.down_ratio
if len(py) == 0:
return
ct_ind = np.unique(cp_ind)
score = score[ct_ind]
label = label[ct_ind]
ind_group = [
np.argwhere(ct_ind[i] == cp_ind).ravel()
for i in range(len(ct_ind))
]
py = [[py[ind] for ind in inds] for inds in ind_group]
fig, ax = plt.subplots(1, figsize=(20, 10))
fig.tight_layout()
ax.axis('off')
ax.imshow(inp)
colors = np.array([[31, 119, 180], [255, 127, 14], [46, 160, 44],
[214, 40, 39], [148, 103, 189], [140, 86, 75],
[227, 119, 194], [126, 126, 126], [188, 189, 32],
[26, 190, 207]]) / 255.
# colors = cycle(colors)
for i in range(len(py)):
color = colors[np.random.randint(len(colors))]
# color = next(colors).tolist()
for poly in py[i]:
poly = np.append(poly, [poly[0]], axis=0)
ax.plot(poly[:, 0], poly[:, 1], color=color, linewidth=3)
plt.show()
def visualize_heatmap_on_img(self, output, batch, vis_file=None):
import cv2
inp = img_utils.bgr_to_rgb(img_utils.unnormalize_img(batch['inp'][0], mean, std).permute(1, 2, 0))
h, w, _ = inp.shape
raw_ct_hm = output['ct_hm'].detach().cpu().numpy()
raw_ct_hm = raw_ct_hm[0,0,...]
raw_ct_hm = Image.fromarray(raw_ct_hm)
raw_ct_hm = raw_ct_hm.resize((w, h))
nms_ct_hm = output['nms_ct_hm'].detach().cpu().numpy()
nms_ct_hm = nms_ct_hm[0,0,...]
nms_ct_hm = Image.fromarray(nms_ct_hm)
nms_ct_hm = nms_ct_hm.resize((w, h))
raw_ct_hm = np.array(raw_ct_hm)
plt.imshow(inp)
plt.imshow(raw_ct_hm, alpha=0.8, cmap='rainbow')
plt.axis('off')
if vis_file is not None:
plt.savefig(vis_file,format='png',dpi=600)
plt.close()
else:
plt.show()