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UPDATE Vision 新增box[]输出

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HJW
2024-09-18 18:31:08 +08:00
parent 0ddc0c15e8
commit 5f093a2551
3 changed files with 84 additions and 14 deletions
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31
Vision/camera_coordinate_dete_img.py
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@ -18,6 +18,10 @@ from Vision.yolo.yolov8_openvino import yolov8_segment_openvino
from Vision.tool.utils import find_position
from Vision.tool.utils import class_names
from Vision.tool.utils import get_disk_space
from Vision.tool.utils import remove_nan
from Vision.tool.utils import compute_pca_direction
from Vision.tool.utils import pca
import os
class Detection:
@ -34,8 +38,8 @@ class Detection:
device = 'CPU'
self.model = yolov8_segment_openvino(model_path, device, conf_thres=0.3, iou_thres=0.3)
img_path = ''.join([os.getcwd(), '/Vision/model/data/test0910.png'])
point_path = ''.join([os.getcwd(), '/Vision/model/data/test0910.xyz'])
img_path = ''.join([os.getcwd(), '/Vision/model/data/test0911.png'])
point_path = ''.join([os.getcwd(), '/Vision/model/data/test0911.xyz'])
self.img = cv2.imread(img_path)
self.point = np.loadtxt(point_path).reshape((1080, 1440, 3))
@ -81,6 +85,9 @@ class Detection:
Depth_Z = []
uv = []
seg_point = []
box_list = []
pca_list = []
pca_nx_ny_nz = []
if Point_isVision == True:
pm2 = pm.copy()
@ -156,7 +163,8 @@ class Detection:
print("分割点云数量较少，无法拟合平面")
continue
# cv2.imshow('result', piont_result)
pca_list.append(compute_pca_direction(pm_seg))
pca_nx_ny_nz.append(pca(pm_seg))
'''
拟合平面，计算法向量
'''
@ -181,12 +189,17 @@ class Detection:
rect = cv2.minAreaRect(max_contour)
# cv2.boxPoints可以将轮廓点转换为四个角点坐标
box = cv2.boxPoints(rect)
# 这一步不影响后面的画图，但是可以保证四个角点坐标为顺时针
startidx = box.sum(axis=1).argmin()
box = np.roll(box, 4 - startidx, 0)
box1 = np.roll(box, 4 - startidx, 0)
# 在原图上画出预测的外接矩形
box = box.reshape((-1, 1, 2)).astype(np.int32)
box = box + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]]]
box_piont_x1, box_piont_y1, box_piont_z1 = remove_nan(pm, box[0][0][1], box[0][0][0])
box_piont_x2, box_piont_y2, box_piont_z2 = remove_nan(pm, box[1][0][1], box[1][0][0])
box_piont_x3, box_piont_y3, box_piont_z3 = remove_nan(pm, box[2][0][1], box[2][0][0])
box_piont_x4, box_piont_y4, box_piont_z4 = remove_nan(pm, box[3][0][1], box[3][0][0])
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)
@ -195,6 +208,8 @@ class Detection:
if np.isnan(point_x): # 点云值为无效值
continue
else:
box_list.append([[box_piont_x1, box_piont_y1, box_piont_z1], [box_piont_x2, box_piont_y2, box_piont_z2],
[box_piont_x3, box_piont_y3, box_piont_z3], [box_piont_x4, box_piont_y4, box_piont_z4]])
xyz.append([point_x * 1000, point_y * 1000, point_z * 1000])
Depth_Z.append(point_z * 1000)
nx_ny_nz.append([a, b, c])
@ -206,7 +221,7 @@ class Detection:
_idx = find_position(Depth_Z, RegionalArea, 100, True)
if _idx == None:
return 1, img, None, None
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
@ -225,17 +240,17 @@ class Detection:
save_img = cv2.resize(img,(720, 540))
cv2.imwrite(save_img_name, save_img)
return 1, img, xyz[_idx], nx_ny_nz[_idx]
return 1, img, xyz[_idx], nx_ny_nz[_idx], box_list[_idx], pca_list[_idx], pca_nx_ny_nz[_idx]
else:
if save_img_piont == True:
save_img = cv2.resize(img, (720, 540))
cv2.imwrite(save_img_name, save_img)
return 1, img, None, None
return 1, img, None, None, None, None, None
else:
print("RVC X Camera capture failed!")
return 0, None, None, None
return 0, None, None, None, None, None, None
def release(self):
self.model.clear()