223 lines
7.7 KiB
Python
223 lines
7.7 KiB
Python
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# -- coding: utf-8 --
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import numpy as np
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import cv2
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import math
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import open3d as o3d
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class rect_roi:
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def __init__(self, x:int, y:int, w:int, h:int):
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self.x = x
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self.y = y
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self.w = w
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self.h = h
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class obj_info:
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def __init__(self, name:str, id:str, points_2d_to_3d:cv2.Mat, bim=None):
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self.name = name
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self.id = id
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self.rect_points_2d_to_3d = points_2d_to_3d
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self.bim = bim
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self.edges = []
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self.orgin_pt = [0.0, 0.0]
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self.centor_pt = [0.0, 0.0]
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def _api_cal_homography(_3d_points, _2d_points):
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ransacReprojThreshold = 5.0 # Homography: 该值越小，精度越高，对时间没有影像
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# 查找单应矩阵
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# 修改第三个参数 1.0 -> 5.0
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# 利用基于RANSAC的鲁棒算法选择最优的四组配对点，再计算转换矩阵H(3*3)并返回,以便于反向投影错误率达到最小
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# ransacReprojThreshold 设置为 1.0，精度最高
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# H, mask = cv2.findHomography(np.array(src_points), np.array(dst_points), cv2.RANSAC, ransacReprojThreshold, maxIters=1500, confidence=0.998) ## 修改 1.0 -> 5.0
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pts_hom = []
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for i in range(len(_2d_points)):
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pt = _2d_points[i]
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x,y = pt[0], pt[1]
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pts_hom.append([x*1.0, y*1.0, 1.0])
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H, _ = cv2.findHomography(np.array(_3d_points), np.array(pts_hom), cv2.RANSAC, ransacReprojThreshold) ## 修改 1.0 -> 5.0
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print(H)
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return H
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# 暂时不需要
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def _api_get_fake_roi_data():
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return [[]]
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def _api_get_obj_2d_roi(im, rois):
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_roi_im_list = []
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for i in range(len(rois)):
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roi = rois[i]
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_roi_im = im[roi.y:roi.y+roi.h, roi.x:roi.x+roi.w]
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_roi_im_list.append(_roi_im)
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return _roi_im_list
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def __get_small_conners(conners, offset):
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# pts = np.array([[conners[0][0] - offset, conners[0][1] - offset],
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# [conners[1][0] - offset, conners[1][1] - offset],
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# [conners[2][0] - offset, conners[2][1] - offset],
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# [conners[3][0] - offset, conners[3][1] - offset]],
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# np.int32)
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pts = [[conners[0][0] + offset, conners[0][1]],
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[conners[1][0] - offset, conners[1][1] + offset],
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[conners[2][0] - offset, conners[2][1]],
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[conners[3][0], conners[3][1] - offset]]
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return pts
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# cv2.polylines(img, [pts], isClosed=True, color=(0, 255, 0), thickness=2)
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def _api_get_sub_rois_with_conners(conners):
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_conners = []
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_conners.append(__get_small_conners(conners, 200))
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return _conners
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def _api_get_obj_2d_to_3d_pts(im_2d_to_3d:cv2.Mat, sub_rois_in_conners):
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in_rect_2d_to_3d_pts = []
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in_rect_2d_pts = []
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in_rect_3d_pts = []
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for i in range(len(sub_rois_in_conners)):
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_sub_rois = sub_rois_in_conners[i]
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_sub_rois_mask = np.zeros_like(im_2d_to_3d)
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# for test
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# normalized_image = np.uint8(_sub_rois_mask)
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# _sub_rois_mask = cv2.normalize(normalized_image, None, 0, 255, cv2.NORM_MINMAX)
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_sub_rois_mask = cv2.fillPoly(_sub_rois_mask, [np.array(_sub_rois)], color=(255, 255, 255))
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im_2d_to_3d_roi = cv2.bitwise_and(im_2d_to_3d, _sub_rois_mask)
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# im_2d_to_3d_roi = cv2.resize(im_2d_to_3d_roi, (int(934*1.5), int(700*1.5)), interpolation=cv2.INTER_LINEAR)
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# cv2.imshow("1", im_2d_to_3d_roi)
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# cv2.waitKey(0)
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normalized_image = np.uint8(im_2d_to_3d_roi)
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normalized_image = cv2.cvtColor(normalized_image, cv2.COLOR_BGR2GRAY)
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_2d_pts = cv2.findNonZero(normalized_image)
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for i in range(len(_2d_pts)):
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_2d_pt = _2d_pts[i][0].tolist()
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if i % 20 == 0:
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in_rect_2d_pts.append(_2d_pt)
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in_rect_3d_pts.append([im_2d_to_3d[_2d_pt[1],_2d_pt[0]][0],
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im_2d_to_3d[_2d_pt[1],_2d_pt[0]][1],
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im_2d_to_3d[_2d_pt[1],_2d_pt[0]][2]])
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return in_rect_2d_to_3d_pts, in_rect_2d_pts, in_rect_3d_pts
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# _conners：返回预埋件角点在原图中的坐标（非在roi图中的坐标）
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def _api_get_edges_and_conners(roi_im, roi_rect, fake=True):
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_edges = []
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_conners = []
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rst = "ok" # 如果识别改区域不是预埋件区域，则返回错误
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if fake:
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# 人工定好4个角点
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_conners = [[465,436],[615,260],[783,405],[630,581]]
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for i in range(4):
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_conners[i] = [int(_conners[i][0]*9344/934/1.5), int(_conners[i][1]*7000/700/1.5)]
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return rst, _edges, _conners
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# 在未来使用roi_im, roi_rect进行优化
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# def _api_cal_3d_edge_scale(roi_im, roi_rect, conners, H):
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def _api_cal_3d_edge_scale(conners, H, _2pts, _3pts):
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_edges = []
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_conners_3d = []
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if len(conners) != 4:
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assert("_cal_scale: len(conners) is not 4")
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# 计算点云的拟合平面
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_3pts_cloud = o3d.geometry.PointCloud()
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_3pts_cloud.points = o3d.utility.Vector3dVector(_3pts)
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[A,B,C,D], inliers = _3pts_cloud.segment_plane(distance_threshold=0.01, ransac_n=3, num_iterations=1500)
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# [a,b,c,d]= plane model
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H_inv = np.linalg.inv(H)
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for i in range(4):
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conner = conners[i]
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x, y = conner[0], conner[1]
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pts_hom = np.array([x, y, 1])
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# pts_hom = cv2.convertPointsToHomogeneous(pts)
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pts_3d = np.dot(H_inv, pts_hom.T)
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pts_3d = pts_3d/pts_3d[2]
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# 求线与平面的交点
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t = -D / (A * pts_3d[0] + B * pts_3d[1] + C * pts_3d[2])
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pts_3d = np.array([t * pts_3d[0], t * pts_3d[1], t * pts_3d[2]])
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_conners_3d.append(list(pts_3d))
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for i in range(4):
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_conner_3d = _conners_3d[i]
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_conner_3d_next = _conners_3d[(i+1)%4]
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_edge = math.sqrt(pow((_conner_3d[0] - _conner_3d_next[0]), 2)
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+ pow((_conner_3d[1] - _conner_3d_next[1]), 2)
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+ pow((_conner_3d[2] - _conner_3d_next[2]), 2))
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_edges.append(_edge)
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return _edges
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# 主处理流程
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# for test: roi_rects = [[0,0,0,0]]
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def api_cal_info(im, im_2d_to_3d, roi_rects):
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print("api_cal_info start!")
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_obj_info_list = []
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# for test，人工定义roi区域
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# roi_rects = _api_get_fake_roi_data()
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# 图像剪切到一个个roi图像
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_roi_im_list = _api_get_obj_2d_roi(im, roi_rects)
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# 遍历所有预埋件的子区域图像，这里可以分线程跑，提升效率
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for i in range(len(_roi_im_list)):
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# 获取单个预埋件图像
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_roi_im = _roi_im_list[i]
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# 在单个预埋件图像中计算区域内预埋件边缘和角点
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_rst, _, _2d_conners = _api_get_edges_and_conners(_roi_im, roi_rects[i])
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print("api_cal_info _2d_conners ==> ")
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print(_2d_conners)
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# assert(_rst != "ok")
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# 计算预埋件内部用于检测 H矩阵的2d 3d区域
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# _sub_rois_in_conners：全局坐标系
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_sub_rois_in_conners = _api_get_sub_rois_with_conners(_2d_conners)
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# 挑选合适的预埋件区域内2d和3d点映射，挑选算法需要设计和优化
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_, _2pts, _3pts = _api_get_obj_2d_to_3d_pts(im_2d_to_3d, _sub_rois_in_conners)
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# 计算H矩阵
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H = _api_cal_homography(_3pts, _2pts)
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# 通过角点和边缘计算尺寸
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# _2d_conners：为全局坐标，不是roi坐标
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# _edgs_scale = _api_cal_3d_edge_scale(_roi_im, roi_rects[i], _2d_conners, H)
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_edgs_scale = _api_cal_3d_edge_scale(_2d_conners, H, _2pts, _3pts)
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print(_edgs_scale)
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# _obj_info = obj_info()
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# _obj_info_list.append(_obj_info)
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print("api_cal_info end!")
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return _obj_info_list
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if __name__ == '__main__':
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pass
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# _edge = math.sqrt(pow(-0.19919018 - 0.271936, 2)
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# + pow(-0.48743263 - -0.32997242, 2)
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# + pow(3.513779 - 3.527755, 2))
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# print(_edge)
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