diff --git a/src/maintain/scripts/maintain.py b/src/maintain/scripts/maintain.py index 14d2f0f..392fcd7 100755 --- a/src/maintain/scripts/maintain.py +++ b/src/maintain/scripts/maintain.py @@ -1,4 +1,8 @@ +<<<<<<< HEAD #! /home/da/miniconda3/envs/gsmini/bin/python +======= +#! /home/wxchen/.conda/envs/gsmini/bin/python +>>>>>>> 68e8ba7901e9856d4a89304bc278ab76e8cc0a34 import rospy import numpy as np diff --git a/src/maintain/scripts/test copy.py b/src/maintain/scripts/test copy.py deleted file mode 100755 index 104389a..0000000 --- a/src/maintain/scripts/test copy.py +++ /dev/null @@ -1,148 +0,0 @@ -#! /home/wxchen/.conda/envs/gsmini/bin/python - -import numpy as np -import cv2 as cv -from matplotlib import pyplot as plt -import rospy -from sensor_msgs.msg import Image -import message_filters -from cv_bridge import CvBridge, CvBridgeError -import rospkg - -MIN_MATCH_COUNT = 10 -pkg_path = rospkg.RosPack().get_path('maintain') -rospy.loginfo(pkg_path) -img_template = cv.imread(pkg_path + '/scripts/tt.png',0) - -def callback(rgb, depth): - rospy.loginfo("callback") - bridge = CvBridge() - # rospy.loginfo(rgb.header.stamp) - # rospy.loginfo(depth.header.stamp) - try: - rgb_image = bridge.imgmsg_to_cv2(rgb, 'bgr8') - depth_image = bridge.imgmsg_to_cv2(depth, '16UC1') - - img_matcher = matcher(rgb_image) - cv.imshow("img_matcher", img_matcher) - cv.waitKey(1000) - - except CvBridgeError as e: - print(e) - -def matcher(img): - - try: - # Initiate SIFT detector - sift = cv.SIFT_create() - - # find the keypoints and descriptors with SIFT - kp1, des1 = sift.detectAndCompute(img_template,None) - kp2, des2 = sift.detectAndCompute(img,None) - - FLANN_INDEX_KDTREE = 1 - index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) - search_params = dict(checks = 50) - - flann = cv.FlannBasedMatcher(index_params, search_params) - matches = flann.knnMatch(des1,des2,k=2) - - # store all the good matches as per Lowe's ratio test. - good = [] - for m,n in matches: - if m.distance < 0.7*n.distance: - good.append(m) - - if len(good)>MIN_MATCH_COUNT: - src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2) - dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2) - - M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC,5.0) - matchesMask = mask.ravel().tolist() - - h,w = img_template.shape - pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2) - dst = cv.perspectiveTransform(pts,M) - - roi = img[np.int32(dst)[0][0][1]:np.int32(dst)[2][0][1], np.int32(dst)[0][0][0]:np.int32(dst)[2][0][0]] - # roi = detect_black(roi) - - # img2 = cv.polylines(img2,[np.int32(dst)],True,255,3, cv.LINE_AA) - else: - print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) ) - - return roi - except Exception as e: - print(e) - - - - -if __name__ == "__main__": - - rospy.init_node("maintain") - rospy.loginfo("maintain task start ......") - - rgb_sub = message_filters.Subscriber("/camera/color/image_raw", Image) - depth_sub = message_filters.Subscriber("/camera/aligned_depth_to_color/image_raw", Image) - - ts = message_filters.TimeSynchronizer([rgb_sub, depth_sub], 1) - ts.registerCallback(callback) - - - rospy.spin() - - -# backup -def calculate_image_edge_plane_normal(depth_roi): - # Get the shape of the depth_roi - height, width = depth_roi.shape - - # Get the edges of the ROI - left_edge = [(0, y) for y in range(height)] - right_edge = [(width-1, y) for y in range(height)] - top_edge = [(x, 0) for x in range(width)] - bottom_edge = [(x, height-1) for x in range(width)] - edges = left_edge + right_edge + top_edge + bottom_edge - - # Create a 2D grid of X and Y coordinates - X, Y = np.meshgrid(np.arange(width), np.arange(height)) - - # Reshape the X, Y, and depth_roi arrays into one-dimensional arrays - X = X.reshape(-1) - Y = Y.reshape(-1) - Z = depth_roi.reshape(-1) - - # Stack the X, Y, and depth_roi arrays vertically to create a 3D array of points in the form of [X, Y, Z] - points = np.vstack([X, Y, Z]).T - - # Compute the mean depth value of the edges - edge_depths = [] - for edge_point in edges: - edge_depths.append(depth_roi[edge_point[1], edge_point[0]]) - mean_depth = np.mean(edge_depths) - - # Create a mask to extract the points on the edges - mask = np.zeros_like(depth_roi, dtype=np.uint8) - for edge_point in edges: - mask[edge_point[1], edge_point[0]] = 1 - masked_depth_roi = depth_roi * mask - - # Extract the 3D coordinates of the points on the edges - edge_points = [] - for edge_point in edges: - edge_points.append([edge_point[0], edge_point[1], masked_depth_roi[edge_point[1], edge_point[0]]]) - - # Convert the list of edge points to a numpy array - edge_points = np.array(edge_points) - - # Shift the edge points so that the mean depth value is at the origin - edge_points = edge_points - np.array([width/2, height/2, mean_depth]) - - # Compute the singular value decomposition (SVD) of the edge points - U, S, V = np.linalg.svd(edge_points) - - # Extract the normal vector of the plane that best fits the edge points from the right-singular vector corresponding to the smallest singular value - normal = V[2] - - return normal diff --git a/src/maintain/scripts/test.py b/src/maintain/scripts/test.py index 606321e..450d673 100755 --- a/src/maintain/scripts/test.py +++ b/src/maintain/scripts/test.py @@ -60,6 +60,17 @@ def compute_plane_normal(box, depth, color_intrinsics): normal += np.cross(v3, v4) normal += np.cross(v4, v1) normal /= np.linalg.norm(normal) + # 计算法向量相对于参考向量的旋转角度和旋转轴 + ref_vector = np.array([0, 0, 1]) + normal_vector = normal + angle = math.acos(np.dot(ref_vector, normal_vector) / (np.linalg.norm(ref_vector) * np.linalg.norm(normal_vector))) + axis = np.cross(ref_vector, normal_vector) + axis = axis / np.linalg.norm(axis) + + # 将旋转角度和旋转轴转换为四元数 + qx, qy, qz, qw = tf.transformations.quaternion_about_axis(angle, axis) + quaternion = [qx, qy, qz, qw] + return quaternion # 计算法向量相对于参考向量的旋转角度和旋转轴 ref_vector = np.array([0, 0, 1]) @@ -73,30 +84,76 @@ def compute_plane_normal(box, depth, color_intrinsics): quaternion = [qx, qy, qz, qw] return quaternion -def compute_normal_vector(p1, p2, p3, p4): - # Compute two vectors in the plane - v1 = np.array(p2) - np.array(p1) - v2 = np.array(p3) - np.array(p1) - # Compute the cross product of the two vectors to get the normal vector - n = np.cross(v1, v2) - # Compute the fourth point in the plane - p4 = np.array(p4) - # Check if the fourth point is on the same side of the plane as the origin - if np.dot(n, p4 - np.array(p1)) < 0: - n = -n - # Normalize the normal vector to obtain a unit vector - n = n / np.linalg.norm(n) - # 计算法向量相对于参考向量的旋转角度和旋转轴 - ref_vector = np.array([0, 0, 1]) - normal_vector = n - angle = math.acos(np.dot(ref_vector, normal_vector) / (np.linalg.norm(ref_vector) * np.linalg.norm(normal_vector))) - axis = np.cross(ref_vector, normal_vector) - axis = axis / np.linalg.norm(axis) +def calculate_image_edge_plane_normal(depth_roi): + # Get the shape of the depth_roi + height, width = depth_roi.shape + + # Get the edges of the ROI + left_edge = [(0, y) for y in range(height)] + right_edge = [(width-1, y) for y in range(height)] + top_edge = [(x, 0) for x in range(width)] + bottom_edge = [(x, height-1) for x in range(width)] + edges = left_edge + right_edge + top_edge + bottom_edge - # 将旋转角度和旋转轴转换为四元数 - qx, qy, qz, qw = tf.transformations.quaternion_about_axis(angle, axis) - quaternion = [qx, qy, qz, qw] - return quaternion + # Create a 2D grid of X and Y coordinates + X, Y = np.meshgrid(np.arange(width), np.arange(height)) + + # Reshape the X, Y, and depth_roi arrays into one-dimensional arrays + X = X.reshape(-1) + Y = Y.reshape(-1) + Z = depth_roi.reshape(-1) + + # Stack the X, Y, and depth_roi arrays vertically to create a 3D array of points in the form of [X, Y, Z] + points = np.vstack([X, Y, Z]).T + + # Compute the mean depth value of the edges + edge_depths = [] + for edge_point in edges: + edge_depths.append(depth_roi[edge_point[1], edge_point[0]]) + mean_depth = np.mean(edge_depths) + + # Create a mask to extract the points on the edges + mask = np.zeros_like(depth_roi, dtype=np.uint8) + for edge_point in edges: + mask[edge_point[1], edge_point[0]] = 1 + masked_depth_roi = depth_roi * mask + + # Extract the 3D coordinates of the points on the edges + edge_points = [] + for edge_point in edges: + edge_points.append([edge_point[0], edge_point[1], masked_depth_roi[edge_point[1], edge_point[0]]]) + + # Convert the list of edge points to a numpy array + edge_points = np.array(edge_points) + + # Shift the edge points so that the mean depth value is at the origin + edge_points = edge_points - np.array([width/2, height/2, mean_depth]) + + # Compute the singular value decomposition (SVD) of the edge points + U, S, V = np.linalg.svd(edge_points) + + # Extract the normal vector of the plane that best fits the edge points from the right-singular vector corresponding to the smallest singular value + normal = V[2] + + return normal + +# def compute_normal_vector(p1, p2, p3, p4): +# # Compute two vectors in the plane +# v1 = np.array(p2) - np.array(p1) +# v2 = np.array(p3) - np.array(p1) +# # Compute the cross product of the two vectors to get the normal vector +# n = np.cross(v1, v2) +# # Compute the fourth point in the plane +# p4 = np.array(p4) +# # Check if the fourth point is on the same side of the plane as the origin +# if np.dot(n, p4 - np.array(p1)) < 0: +# n = -n +# # Normalize the normal vector to obtain a unit vector +# n = n / np.linalg.norm(n) +# theta = math.acos(n[2]) +# sin_theta_2 = math.sin(theta/2) +# quaternion = [math.cos(theta/2), sin_theta_2 * n[0], sin_theta_2 * n[1], sin_theta_2 * n[2]] +# return quaternion def filter_quaternion(quat, quat_prev, alpha): if quat_prev is None: diff --git a/src/yolov5_ros/launch/yolov5.launch b/src/yolov5_ros/launch/yolov5.launch index 7a85c2e..4c6539a 100644 --- a/src/yolov5_ros/launch/yolov5.launch +++ b/src/yolov5_ros/launch/yolov5.launch @@ -50,7 +50,8 @@ - + +