python opencv 怎么调用'linemod'

要在Python中使用LINEMOD算法，您可以使用OpenCV库中的cv::linemod模块。下面是一个简单的示例，展示了如何使用cv::linemod模块来检测物体：

import cv2
import numpy as np

# 读取训练数据
filename = 'object_templates.yml'
detector = cv2.linemod_Detector()
detector.readTemplates(filename)
num_modalities = detector.numModalities()

# 读取测试图像
img = cv2.imread('test.png', cv2.IMREAD_COLOR)

# 进行特征提取
depth = cv2.imread('test_depth.png', cv2.IMREAD_UNCHANGED)
detection_threshold = 50.0
quantized_images = [np.mod(np.floor((depth + 0.5) / 8.0), 32).astype(np.uint8)]
quantized_images.append(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
num_features = 100
threshold = cv2.linemod_NORM_L2
modalities = [0, 1]

# 进行匹配
matches = []
for i, mod in enumerate(modalities):
    matches.append(list(detector.match(quantized_images[i], detection_threshold, modalities[i])))
    if len(matches[i]) > num_features:
        matches[i].sort(key=lambda x: x.similarity, reverse=True)
        matches[i] = matches[i][:num_features]
    for match in matches[i]:
        match.x = int(match.x)
        match.y = int(match.y)

# 绘制结果
out = img.copy()
for i, mod in enumerate(modalities):
    num_matches = len(matches[i])
    for j in range(num_matches):
        template_id = matches[i][j].template_id
        quantized_angle = matches[i][j].quantized_angle
        R = cv2.linemod_Quaternion(template_id, quantized_angle)
        T = matches[i][j].x, matches[i][j].y
        center = detector.getTemplateCenter(template_id, mod)
        print('Detection:', T, center, matches[i][j].similarity, template_id, mod, quantized_angle)
        pts = np.array([
            [-15, -15, 0],
            [-15, 15, 0],
            [15, 15, 0],
            [15, -15, 0],
            [-15, -15, 30],
            [-15, 15, 30],
            [15, 15, 30],
            [15, -15, 30]
        ], dtype=np.float)
        pts[:, :2] = (pts[:, :2] * R) + center
        pts[:, :2] = np.round(pts[:, :2]).astype(np.int32)
        print(pts)
        color = (0, 255, 0)
        if i == 0:
            color = (255, 0, 0)
        cv2.polylines(out, [pts[:4]], True, color)
        cv2.polylines(out, [pts[4:]], True, color, 2)
cv2.imshow('out', out)
cv2.waitKey(0)

在这个例子中，我们首先读取训练数据，然后读取测试图像，并进行特征提取。然后使用cv::linemod模块中的cv2.linemod_Detector类进行匹配，并将结果绘制在原始图像上。