本文整理汇总了Python中cv2.COLORMAP_RAINBOW属性的典型用法代码示例。如果您正苦于以下问题：Python cv2.COLORMAP_RAINBOW属性的具体用法？Python cv2.COLORMAP_RAINBOW怎么用？Python cv2.COLORMAP_RAINBOW使用的例子？那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类cv2的用法示例。
在下文中一共展示了cv2.COLORMAP_RAINBOW属性的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: draw_anchors_rect
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_RAINBOW [as 别名]
def draw_anchors_rect(img_arr, anchor_posi, sample = 1, ratio = 1):
ori_dtype = img_arr.dtype
joint_num = len(anchor_posi)
seed_arr = np.array([range(1,255,255/joint_num)]).astype(np.uint8)
color_list = cv2.applyColorMap(seed_arr, cv2.COLORMAP_RAINBOW)[0]
draw_arr = img_arr.astype(np.float)
for i in range(joint_num):
if (i%sample)!=0:
continue
draw_arr = draw_rect(draw_arr, anchor_posi[i],
size = 32,
color = color_list[i].tolist())
if ratio < 1:
draw_arr = draw_arr*ratio + img_arr.astype(np.float)*(1-ratio)
return draw_arr.astype(ori_dtype)
# write OBJ from vertex
# not tested yet 示例2: draw_joints_rect
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_RAINBOW [as 别名]
def draw_joints_rect(img_arr, joint_posi, ratio = 1):
ori_dtype = img_arr.dtype
joint_num = len(joint_posi)
seed_arr = np.array([range(1,255,255/joint_num)]).astype(np.uint8)
color_list = cv2.applyColorMap(seed_arr, cv2.COLORMAP_RAINBOW)[0]
draw_arr = img_arr.astype(np.float)
for i in range(joint_num):
draw_arr = draw_rect(draw_arr, joint_posi[i],
color = color_list[i].tolist())
if ratio < 1:
draw_arr = draw_arr*ratio + img_arr.astype(np.float)*(1-ratio)
return draw_arr.astype(ori_dtype)
# for visualizing predict window in images 示例3: tensor2array
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_RAINBOW [as 别名]
def tensor2array(tensor, max_value=None, colormap='rainbow'):
if max_value is None:
tensor=(tensor-tensor.min())/(tensor.max()-tensor.min()+1e-6)
max_value = tensor.max().item()
if tensor.ndimension() == 2 or tensor.size(0) == 1:
try:
import cv2
if cv2.__version__.startswith('3'):
color_cvt = cv2.COLOR_BGR2RGB
else: # 2.4
color_cvt = cv2.cv.CV_BGR2RGB
if colormap == 'rainbow':
colormap = cv2.COLORMAP_RAINBOW
elif colormap == 'bone':
colormap = cv2.COLORMAP_BONE
array = (tensor.squeeze().numpy()*255./max_value).clip(0, 255).astype(np.uint8)
colored_array = cv2.applyColorMap(array, colormap)
array = cv2.cvtColor(colored_array, color_cvt).astype(np.float32)/255
except ImportError:
if tensor.ndimension() == 2:
tensor.unsqueeze_(2)
array = (tensor.expand(tensor.size(0), tensor.size(1), 3).numpy()/max_value).clip(0,1)
elif tensor.ndimension() == 3:
assert(tensor.size(0) == 3)
array = 0.5 + tensor.numpy().transpose(1, 2, 0)*0.5
#for tensorboardx 1.4
#array=array.transpose(2,0,1)
return array 示例4: tensor2array
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_RAINBOW [as 别名]
def tensor2array(tensor, max_value=255, colormap='rainbow'):
if max_value is None:
max_value = tensor.max()
if tensor.ndimension() == 2 or tensor.size(0) == 1:
try:
import cv2
if cv2.__version__.startswith('2'): # 2.4
color_cvt = cv2.cv.CV_BGR2RGB
else:
color_cvt = cv2.COLOR_BGR2RGB
if colormap == 'rainbow':
colormap = cv2.COLORMAP_RAINBOW
elif colormap == 'bone':
colormap = cv2.COLORMAP_BONE
array = (255*tensor.squeeze().numpy()/max_value).clip(0, 255).astype(np.uint8)
colored_array = cv2.applyColorMap(array, colormap)
array = cv2.cvtColor(colored_array, color_cvt).astype(np.float32)/255
#array = array.transpose(2, 0, 1)
except ImportError:
if tensor.ndimension() == 2:
tensor.unsqueeze_(2)
array = (tensor.expand(tensor.size(0), tensor.size(1), 3).numpy()/max_value).clip(0,1)
elif tensor.ndimension() == 3:
#assert(tensor.size(0) == 3)
#array = 0.5 + tensor.numpy()*0.5
array = 0.5 + tensor.numpy().transpose(1,2,0)*0.5
return array 示例5: main
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_RAINBOW [as 别名]
def main():
height = 368 #544 #368
weight = 1232 #960 #1232
left_img = args.datapath+args.leftimg
right_img = args.datapath+args.leftimg
with tf.Session() as sess:
img_L = cv2.cvtColor(cv2.imread(left_img), cv2.COLOR_BGR2RGB)
img_L = cv2.resize(img_L, (weight, height))
img_R = cv2.cvtColor(cv2.imread(right_img), cv2.COLOR_BGR2RGB)
img_R = cv2.resize(img_R, (weight, height))
img_L = DataLoaderKITTI.mean_std(img_L)
img_L = np.expand_dims(img_L, axis=0)
img_R = DataLoaderKITTI.mean_std(img_R)
img_R = np.expand_dims(img_R, axis=0)
PSMNet = Model(sess, height=height, weight=weight, batch_size=args.batch, max_disp=args.maxdisp)
saver = tf.train.Saver()
saver.restore(sess, args.loadmodel)
pred = PSMNet.predict(img_L, img_R)
pred = np.squeeze(pred,axis=0)
print(pred.shape)
print(pred.max())
#np.save('pred.npy',pred)
pred_disp = pred.astype(np.uint8)
print(pred_disp.shape)
#pred_disp = np.squeeze(pred_disp,axis=0)
cv2.imwrite('pred_disp.png', pred_disp)
pred_rainbow = cv2.applyColorMap(pred_disp, cv2.COLORMAP_RAINBOW)
cv2.imwrite('pred_rainbow.png', pred_rainbow)