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UPDATE Vision 图漾相机点云对齐，异常数据保存

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HJW
2024-10-14 16:32:20 +08:00
parent 2af2bff5e8
commit b88e1937ec
3 changed files with 58 additions and 33 deletions
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36
Vision/camera_coordinate_dete_img.py
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@ -46,12 +46,14 @@ class Detection:
pass
def get_position(self, Point_isVision=False, Box_isPoint=False, save_img_point=0, seg_distance_threshold = 0.01, Height_reduce = 30, width_reduce = 30):
def get_position(self, Point_isVision=False, Box_isPoint=False, First_Depth =True, Iter_Max_Pixel = 30, save_img_point=0, seg_distance_threshold = 0.01, Height_reduce = 30, width_reduce = 30):
"""
检测料袋相关信息
:param Point_isVision: 点云可视化
:param Box_isPoint: True 返回点云值； False 返回box相机坐标
:param save_img_point: 0不保存 ; 1保存原图 ;2保存处理后的图 ; 3保存点云和原图；4 保存点云和处理后的图
:param First_Depth: True 返回料袋中心点深度最小的点云值； False 返回面积最大的料袋中心点云值
:param Iter_Max_Pixel: [int] 点云为NAN时，向该点周围寻找替代值，寻找最大区域（Iter_Max_Pixel×Iter_Max_Pixel）
:param save_img_point: 0不保存 ; 1保存原图 ;2保存处理后的图 ; 3保存点云和原图；4 保存点云和处理后的图; 5 异常数据保存（点云NAN）
:param Height_reduce: 检测框的高内缩像素
:param width_reduce: 检测框的宽内缩像素
:return ret: bool 相机是否正常工作
@ -73,16 +75,21 @@ class Detection:
time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime(time.time()))])
save_img_name = ''.join([save_path, '.png'])
save_point_name = ''.join([save_path, '.xyz'])
if save_img_point == 5:
Abnormal_data_img = img.copy()
if save_img_point == 1 or save_img_point == 3:
cv2.imwrite(save_img_name, img)
if save_img_point == 3 or save_img_point == 4:
if save_img_point == 3 or save_img_point == 4 or save_img_point == 5:
row_list = list(range(1, img.shape[0], 2))
column_list = list(range(1, img.shape[1], 2))
pm_save = pm.copy()
pm_save1 = np.delete(pm_save, row_list, axis=0)
point_new = np.delete(pm_save1, column_list, axis=1)
point_new = point_new.reshape(-1, 3)
np.savetxt(save_point_name, point_new)
if save_img_point == 5:
Abnormal_data_point = point_new.copy()
else:
np.savetxt(save_point_name, point_new)
if self.use_openvino_model == False:
@ -197,7 +204,7 @@ class Detection:
select_point = np.array(select_point)
pm_seg = select_point.reshape(-1, 3)
pm_seg = pm_seg[~np.isnan(pm_seg).all(axis=-1), :] # 剔除 nan
if pm_seg.size < 300:
if pm_seg.size < 100:
print("分割点云数量较少，无法拟合平面")
continue
# cv2.imshow('result', point_result)
@ -226,10 +233,10 @@ class Detection:
box[2][0][1], box[2][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[2][0][1], box[2][0][0])
box[3][0][1], box[3][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[3][0][1], box[3][0][0])
if Box_isPoint == True:
box_point_x1, box_point_y1, box_point_z1 = remove_nan_mean_value(pm, box[0][0][1], box[0][0][0])
box_point_x2, box_point_y2, box_point_z2 = remove_nan_mean_value(pm, box[1][0][1], box[1][0][0])
box_point_x3, box_point_y3, box_point_z3 = remove_nan_mean_value(pm, box[2][0][1], box[2][0][0])
box_point_x4, box_point_y4, box_point_z4 = remove_nan_mean_value(pm, box[3][0][1], box[3][0][0])
box_point_x1, box_point_y1, box_point_z1 = remove_nan_mean_value(pm, box[0][0][1], box[0][0][0], iter_max=First_Depth)
box_point_x2, box_point_y2, box_point_z2 = remove_nan_mean_value(pm, box[1][0][1], box[1][0][0], iter_max=First_Depth)
box_point_x3, box_point_y3, box_point_z3 = remove_nan_mean_value(pm, box[2][0][1], box[2][0][0], iter_max=First_Depth)
box_point_x4, box_point_y4, box_point_z4 = remove_nan_mean_value(pm, box[3][0][1], box[3][0][0], iter_max=First_Depth)
else:
x1, y1, z1 = uv_to_XY(self.cameraType, box[0][0][0], box[0][0][1])
x2, y2, z2 = uv_to_XY(self.cameraType, box[1][0][0], box[1][0][1])
@ -237,7 +244,7 @@ class Detection:
x4, y4, z4 = uv_to_XY(self.cameraType, box[3][0][0], box[3][0][1])
x_rotation_center = int((box[0][0][0] + box[1][0][0] + box[2][0][0] + box[3][0][0]) / 4)
y_rotation_center = int((box[0][0][1] + box[1][0][1] + box[2][0][1] + box[3][0][1]) / 4)
point_x, point_y, point_z = remove_nan_mean_value(pm, y_rotation_center, x_rotation_center)
point_x, point_y, point_z = remove_nan_mean_value(pm, y_rotation_center, x_rotation_center, iter_max=First_Depth)
cv2.circle(img, (x_rotation_center, y_rotation_center), 4, (255, 255, 255), 5) # 标出中心点
if np.isnan(point_x): # 点云值为无效值
continue
@ -268,9 +275,12 @@ class Detection:
cv2.polylines(img, [box], True, (0, 255, 0), 2)
cv2.polylines(img, [box_outside], True, (226, 12, 89), 2)
_idx = find_position(Depth_Z, RegionalArea, 100, True)
_idx = find_position(Depth_Z, RegionalArea, 100, first_depth = Iter_Max_Pixel)
if _idx == None:
if save_img_point == 5:
cv2.imwrite(save_img_name, Abnormal_data_img)
np.savetxt(save_point_name, Abnormal_data_point)
return 1, img, None, None, None
else:
cv2.circle(img, (uv[_idx][0], uv[_idx][1]), 30, (0, 0, 255), 20) # 标出中心点
@ -295,7 +305,9 @@ class Detection:
if save_img_point == 2 or save_img_point == 4:
save_img = cv2.resize(img, (720, 540))
cv2.imwrite(save_img_name, save_img)
if save_img_point == 5:
cv2.imwrite(save_img_name, Abnormal_data_img)
np.savetxt(save_point_name, Abnormal_data_point)
return 1, img, None, None, None
else: