写一个单目深度可视化代码

我可以提供一个简单的单目深度可视化代码，它的核心是OpenCV库： import cv2
import numpy as np # Load the image you want to visualize
img = cv2.imread('image.jpg') # Create a grayscale version of the image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Calculate the depth map from the grayscale image
depth = cv2.imread('depth.jpg',cv2.IMREAD_ANYDEPTH) # Create an 8-bit version of the depth map
depth8 = np.right_shift(depth, 8) # Normalize the 8-bit depth map
depth8_norm = cv2.normalize(depth8, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U) # Display the depth map
cv2.imshow('Depth Map', depth8_norm)
cv2.waitKey(0)
cv2.destroyAllWindows()

相关问题

写一个单张深度图可视化代码

可以参考下面的代码：import matplotlib.pyplot as plt
from sklearn.datasets import make_classificationX, y = make_classification(n_samples=500, n_features=2, n_informative=2, n_redundant=0, n_repeated=0, n_classes=2, n_clusters_per_class=1, class_sep=0.5, random_state=0)plt.figure(figsize=(6, 6))
plt.scatter(X[:, 0], X[:, 1], marker='o', c=y, s=25, edgecolor='k')plt.show()

写一个单张深度图可视化代码python

代码如下：import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_iris data = load_iris()
features = data.data
feature_names = data.feature_names
target = data.target
target_names = data.target_names for t in range(3):
if t == 0:
c = 'r'
marker = '>'
elif t == 1:
c = 'g'
marker = 'o'
elif t == 2:
c = 'b'
marker = 'x'
plt.scatter(features[target == t,0],
features[target == t,1],
marker=marker,
c=c)plt.legend(target_names, loc = 'lower right')
plt.xlabel(feature_names[0])
plt.ylabel(feature_names[1])
plt.show()