54 lines
2.3 KiB
Python
54 lines
2.3 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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'''
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# @Time : 2024/12/26 16:24
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# @Author : hjw
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# @File : Mechanical_arm_error_test.py.py
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'''
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from camera_coordinate_dete import Detection
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from Trace.handeye_calibration import *
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from Vision.tool.CameraPe_depth2color import camera_pe as camera_pe_depth2color
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import cv2
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def dete_center_point_circle(img):
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gay_img = cv2.cvtColor(img, cv2.COLOR_BGRA2GRAY)
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img = cv2.medianBlur(gay_img, 7) # 进行中值模糊，去噪点
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# 要对图像进行滤波去除噪点并转为灰度图像，滤波方法视图像而定
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circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 60, param1=100, param2=30, minRadius=0, maxRadius=0)
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# dp: 图像解析的反向比例。1为原始大小，2为原始大小的一半
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# minDist: 圆心之间的最小距离。过小会增加圆的误判，过大会丢失存在的圆
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# param1: Canny检测器的高阈值
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# param2: 检测阶段圆心的累加器阈值。越小的话，会增加不存在的圆；越大的话，则检测到的圆就更加接近完美的圆形
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# minRadius: 检测的最小圆的半径
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# maxRadius: 检测的最大圆的半径
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print(np.shape(circles))
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circles = np.uint16(np.around(circles))
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print(circles)
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for i in circles[0, :]: # 遍历矩阵每一行的数据
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cv2.circle(img, (i[0], i[1]), i[2], (0, 255, 0), 2)
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cv2.circle(img, (i[0], i[1]), 2, (0, 0, 255), 3)
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print('圆心：', (i[0], i[1]))
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cv2.imshow('circle', img)
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return i[0], i[1]
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detection = Detection(cameraType = 'Pe')
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camera = camera_pe_depth2color()
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while True:
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ret, img, pm, _depth_align = camera.get_img_and_point_map()
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if ret == 1:
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center_x, center_y = dete_center_point_circle(img)
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point_x, point_y, point_z = pm[center_y, center_x]
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# print('xyz点云坐标：', xyz)
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# print('nx_ny_nz法向量：', nx_ny_nz)
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# print('矩形框四顶点：', box)
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# img = cv2.resize(img,(720, 540))
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transformation_matrix = R_matrix(521.534, 0.705, 850.03, 0.0, 90.0, 0.0) # (x, y, z, u, v, w)
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target_position, noraml_base = getPosition(point_x, point_y, point_z, 1, 1, 1, transformation_matrix, None)
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print("target_position:", target_position)
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print("noraml_base", noraml_base)
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cv2.imshow('img', img)
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cv2.waitKey(0) |