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UPDATE openvino

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This commit is contained in:
HJW
2024-09-11 14:16:15 +08:00
parent 7fab3b3ffa
commit 2b20732119
2 changed files with 160 additions and 160 deletions
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316
Vision/camera_coordinate_dete.py
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@ -15,29 +15,24 @@ from Vision.tool.CameraRVC import camera
from Vision.yolo.yolov8_pt_seg import yolov8_segment
from Vision.yolo.yolov8_openvino import yolov8_segment_openvino
from Vision.tool.utils import find_position
from Vision.tool.utils import class_names
import os
class Detection:
def __init__(self, use_openvino_model = False):
print(os.getcwd())
self.use_openvino_model = use_openvino_model
if self.use_openvino_model == False:
if self.use_openvino_model==False:
model_path = ''.join([os.getcwd(), '/model/pt/best.pt'])
device = 'cpu'
self.camera_rvc = camera()
self.model = yolov8_segment()
self.model.load_model(model_path, device)
else:
model_path = ''.join([os.getcwd(), '/model/openvino/bset.xml'])
model_path = ''.join([os.getcwd(), '/model/openvino/last-0903.xml'])
device = 'CPU'
self.camera_rvc = camera()
self.model = yolov8_segment_openvino(model_path, device, conf_thres=0.3, iou_thres=0.3)
img_path = ''.join([os.getcwd(), '/model/data/test0910.png'])
point_path = ''.join([os.getcwd(), '/model/data/test0910.xyz'])
self.img = cv2.imread(img_path)
self.point = np.loadtxt(point_path).reshape((1080, 1440, 3))
def get_position(self, Point_isVision=True):
@ -46,169 +41,172 @@ class Detection:
:param api: None
:return: ret , img, (x,y,z), (nx, ny, nz)
'''
#ret, img, pm = self.camera_rvc.get_img_and_point_map() # 拍照，获取图像及
ret = 1
img = self.img
pm = self.point
if ret == 1:
if self.use_openvino_model == False:
flag, det_cpu, dst_img, masks, category_names = self.model.model_inference(img, 0)
else:
flag, det_cpu, scores, masks, category_names = self.model.segment_objects(img)
dst_img = img
if flag == 1:
xyz = []
nx_ny_nz = []
RegionalArea = []
Depth_Z = []
uv = []
seg_point = []
if Point_isVision == True:
pm2 = pm.copy()
pm2 = pm2.reshape(-1, 3)
pm2 = pm2[~np.isnan(pm2).all(axis=-1), :]
pm2[:, 2] = pm2[:, 2] + 0.25
pcd2 = o3d.geometry.PointCloud()
pcd2.points = o3d.utility.Vector3dVector(pm2)
# o3d.visualization.draw_geometries([pcd2])
for i, item in enumerate(det_cpu):
# 画box
box_x1, box_y1, box_x2, box_y2 = item[0:4].astype(np.int32)
if self.use_openvino_model == False:
label = category_names[int(item[5])]
else:
label = class_names[int(item[4])]
rand_color = (0, 255, 255)
score = item[4]
org = (int((box_x1 + box_x2) / 2), int((box_y1 + box_y2) / 2))
x_center = int((box_x1 + box_x2) / 2)
y_center = int((box_y1 + box_y2) / 2)
text = '{}|{:.2f}'.format(label, score)
cv2.putText(img, text, org=org, fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.8,
color=rand_color,
thickness=2)
# 画mask
# mask = masks[i].cpu().numpy().astype(int)
if self.use_openvino_model == False:
mask = masks[i].cpu().data.numpy().astype(int)
else:
mask = masks[i].astype(int)
mask = mask[box_y1:box_y2, box_x1:box_x2]
# mask = masks[i].numpy().astype(int)
h, w = box_y2 - box_y1, box_x2 - box_x1
mask_colored = np.zeros((h, w, 3), dtype=np.uint8)
mask_colored[np.where(mask)] = rand_color
##################################
imgray = cv2.cvtColor(mask_colored, cv2.COLOR_BGR2GRAY)
# cv2.imshow('mask',imgray)
# cv2.waitKey(1)
# 2、二进制图像
ret, binary = cv2.threshold(imgray, 10, 255, 0)
# 阈值 二进制图像
# cv2.imshow('bin',binary)
# cv2.waitKey(1)
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
# all_piont_list = contours_in(contours)
# print(len(all_piont_list))
max_contour = None
max_perimeter = 0
for contour in contours: # 排除小分割区域或干扰区域
perimeter = cv2.arcLength(contour, True)
if perimeter > max_perimeter:
max_perimeter = perimeter
max_contour = contour
'''
提取区域范围内的（x, y）
'''
mask_inside = np.zeros(binary.shape, np.uint8)
cv2.drawContours(mask_inside, [max_contour], 0, 255, -1)
pixel_point2 = cv2.findNonZero(mask_inside)
# result = np.zeros_like(color_image)
select_point = []
for i in range(pixel_point2.shape[0]):
select_point.append(pm[pixel_point2[i][0][1] + box_y1, pixel_point2[i][0][0] + box_x1])
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
# cv2.imshow('result', piont_result)
'''
拟合平面，计算法向量
'''
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(pm_seg)
plane_model, inliers = pcd.segment_plane(distance_threshold=0.01,
ransac_n=5,
num_iterations=5000)
[a, b, c, d] = plane_model
# print(f"Plane equation: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0")
# inlier_cloud = pcd.select_by_index(inliers) # 点云可视化
# inlier_cloud.paint_uniform_color([1.0, 0, 0])
# outlier_cloud = pcd.select_by_index(inliers, invert=True)
# outlier_cloud.paint_uniform_color([0, 1, 0])
# o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud])
'''
拟合最小外接矩形，计算矩形中心
'''
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)
# 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]]]
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 = pm[y_rotation_center, x_rotation_center]
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
else:
xyz.append([point_x * 1000, point_y * 1000, point_z * 1000])
Depth_Z.append(point_z * 1000)
nx_ny_nz.append([a, b, c])
RegionalArea.append(cv2.contourArea(max_contour))
uv.append([x_rotation_center, y_rotation_center])
seg_point.append(pm_seg)
cv2.polylines(img, [box], True, (0, 255, 0), 2)
_idx = find_position(Depth_Z, RegionalArea, 100, True)
if _idx == None:
return 1, img, None, None
ret, img, pm = self.camera_rvc.get_img_and_point_map() # 拍照，获取图像及
if self.camera_rvc.caminit_isok == True:
if ret == 1:
if self.use_openvino_model == False:
flag, det_cpu, dst_img, masks, category_names = self.model.model_inference(img, 0)
else:
cv2.circle(img, (uv[_idx][0], uv[_idx][1]), 30, (0, 0, 255), 20) # 标出中心点
flag, det_cpu, scores, masks, category_names = self.model.segment_objects(img)
if flag == 1:
xyz = []
nx_ny_nz = []
RegionalArea = []
Depth_Z = []
uv = []
seg_point = []
if Point_isVision==True:
pm2 = pm.copy()
pm2 = pm2.reshape(-1, 3)
pm2 = pm2[~np.isnan(pm2).all(axis=-1), :]
pm2[:, 2] = pm2[:, 2] + 0.25
pcd2 = o3d.geometry.PointCloud()
pcd2.points = o3d.utility.Vector3dVector(pm2)
# o3d.visualization.draw_geometries([pcd2])
if Point_isVision == True:
for i, item in enumerate(det_cpu):
# 画box
box_x1, box_y1, box_x2, box_y2 = item[0:4].astype(np.int32)
label = category_names[int(item[5])]
rand_color = (0, 255, 255)
score = item[4]
org = (int((box_x1 + box_x2) / 2), int((box_y1 + box_y2) / 2))
x_center = int((box_x1 + box_x2) / 2)
y_center = int((box_y1 + box_y2) / 2)
text = '{}|{:.2f}'.format(label, score)
cv2.putText(img, text, org=org, fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.8,
color=rand_color,
thickness=2)
# 画mask
# mask = masks[i].cpu().numpy().astype(int)
if self.use_openvino_model == False:
mask = masks[i].cpu().data.numpy().astype(int)
else:
mask = masks[i].astype(int)
mask = mask[box_y1:box_y2, box_x1:box_x2]
# mask = masks[i].numpy().astype(int)
h, w = box_y2 - box_y1, box_x2 - box_x1
mask_colored = np.zeros((h, w, 3), dtype=np.uint8)
mask_colored[np.where(mask)] = rand_color
##################################
imgray = cv2.cvtColor(mask_colored, cv2.COLOR_BGR2GRAY)
# cv2.imshow('mask',imgray)
# cv2.waitKey(1)
# 2、二进制图像
ret, binary = cv2.threshold(imgray, 10, 255, 0)
# 阈值 二进制图像
# cv2.imshow('bin',binary)
# cv2.waitKey(1)
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
# all_piont_list = contours_in(contours)
# print(len(all_piont_list))
max_contour = None
max_perimeter = 0
for contour in contours: # 排除小分割区域或干扰区域
perimeter = cv2.arcLength(contour, True)
if perimeter > max_perimeter:
max_perimeter = perimeter
max_contour = contour
'''
提取区域范围内的（x, y）
'''
mask_inside = np.zeros(binary.shape, np.uint8)
cv2.drawContours(mask_inside, [max_contour], 0, 255, -1)
pixel_point2 = cv2.findNonZero(mask_inside)
# result = np.zeros_like(color_image)
select_point = []
for i in range(pixel_point2.shape[0]):
select_point.append(pm[pixel_point2[i][0][1]+box_y1, pixel_point2[i][0][0]+box_x1])
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:
print("分割点云数量较少，无法拟合平面")
continue
# cv2.imshow('result', piont_result)
'''
拟合平面，计算法向量
'''
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(seg_point[_idx])
pcd.points = o3d.utility.Vector3dVector(pm_seg)
plane_model, inliers = pcd.segment_plane(distance_threshold=0.01,
ransac_n=5,
num_iterations=5000)
inlier_cloud = pcd.select_by_index(inliers) # 点云可视化
inlier_cloud.paint_uniform_color([1.0, 0, 0])
outlier_cloud = pcd.select_by_index(inliers, invert=True)
outlier_cloud.paint_uniform_color([0, 1, 0])
o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud, pcd2])
[a, b, c, d] = plane_model
#print(f"Plane equation: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0")
# inlier_cloud = pcd.select_by_index(inliers) # 点云可视化
# inlier_cloud.paint_uniform_color([1.0, 0, 0])
# outlier_cloud = pcd.select_by_index(inliers, invert=True)
# outlier_cloud.paint_uniform_color([0, 1, 0])
# o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud])
'''
拟合最小外接矩形，计算矩形中心
'''
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)
# 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]]]
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 = pm[y_rotation_center, x_rotation_center]
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
else:
xyz.append([point_x*1000, point_y*1000, point_z*1000])
Depth_Z.append(point_z*1000)
nx_ny_nz.append([a, b, c])
RegionalArea.append(cv2.contourArea(max_contour))
uv.append([x_rotation_center, y_rotation_center])
seg_point.append(pm_seg)
cv2.polylines(img, [box], True, (0, 255, 0), 2)
_idx = find_position(Depth_Z, RegionalArea, 100,True)
if _idx == None:
return 1, img, None, None
else:
cv2.circle(img, (uv[_idx][0], uv[_idx][1]), 30, (255, 0, 0), 20) # 标出中心点
if Point_isVision==True:
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(seg_point[_idx])
plane_model, inliers = pcd.segment_plane(distance_threshold=0.01,
ransac_n=5,
num_iterations=5000)
inlier_cloud = pcd.select_by_index(inliers) # 点云可视化
inlier_cloud.paint_uniform_color([1.0, 0, 0])
outlier_cloud = pcd.select_by_index(inliers, invert=True)
outlier_cloud.paint_uniform_color([0, 1, 0])
o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud, pcd2])
return 1, img, xyz[_idx], nx_ny_nz[_idx]
else:
return 1, img, None, None
return 1, img, xyz[_idx], nx_ny_nz[_idx]
else:
return 1, img, None, None
print("RVC X Camera capture failed!")
return 0, None, None, None
else:
print("RVC X Camera capture failed!")
print("RVC X Camera is not opened!")
return 0, None, None, None
def release(self):
self.camera_rvc.release()
self.model.clear()

4
Vision/camera_coordinate_dete_img.py
View File

@ -54,7 +54,6 @@ class Detection:
flag, det_cpu, dst_img, masks, category_names = self.model.model_inference(img, 0)
else:
flag, det_cpu, scores, masks, category_names = self.model.segment_objects(img)
dst_img = img
if flag == 1:
xyz = []
nx_ny_nz = []
@ -132,6 +131,9 @@ 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:
print("分割点云数量较少，无法拟合平面")
continue
# cv2.imshow('result', piont_result)
'''