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UPDATE Vision 新增方法：用于寻找拍照点，获取图像目标及中心点云坐标

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HJW
2024-09-29 16:32:27 +08:00
parent 5d526875b2
commit d281b26443
3 changed files with 328 additions and 22 deletions
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172
Vision/camera_coordinate_dete.py
View File

@ -25,12 +25,19 @@ import os
class Detection:
def __init__(self, use_openvino_model=True, cameraIsRVC = True):
def __init__(self, use_openvino_model=True, cameraType = 'RVC'): # cameraType = 'RVC' or cameraType = 'Pe'
"""
初始化相机及模型
:param use_openvino_model: 选择模型，默认使用openvino
:param cameraType: 选择相机 如本相机 'RVC', 图漾相机 'Pe'
"""
self.use_openvino_model = use_openvino_model
if self.use_openvino_model == False:
model_path = ''.join([os.getcwd(), '/Vision/model/pt/best.pt'])
device = 'cpu'
if cameraIsRVC == True:
self.cameraType = cameraType
if self.cameraType == 'RVC':
self.camera_rvc = camera_rvc()
self.seg_distance_threshold = 0.005
else:
@ -41,7 +48,7 @@ class Detection:
else:
model_path = ''.join([os.getcwd(), '/Vision/model/openvino/best.xml'])
device = 'CPU'
if cameraIsRVC == True:
if self.cameraType == 'RVC':
self.camera_rvc = camera_rvc()
self.seg_distance_threshold = 0.005
else:
@ -50,13 +57,20 @@ class Detection:
self.model = yolov8_segment_openvino(model_path, device, conf_thres=0.3, iou_thres=0.3)
def get_position(self, Point_isVision=False, save_img_point=False, Height_reduce = 30, width_reduce = 30):
""
'''
:param api: None
:return: ret , img, (x,y,z), (nx, ny, nz)
'''
"""
检测料袋相关信息
:param Point_isVision: 点云可视化
:param save_img_point: 保存点云和图片
: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
:return img: ndarry 返回img
:return xyz: list 目标中心点云值形如[x,y,z]
:return nx_ny_nz: list 拟合平面法向量，形如[a,b,c]
:return box_list: list 内缩检测框四顶点，形如[[x1,y1],[],[],[]]
"""
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:
@ -196,7 +210,6 @@ class Detection:
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)
@ -227,8 +240,12 @@ class Detection:
[box_point_x2, box_point_y2, box_point_z2],
[box_point_x3, box_point_y3, box_point_z3],
[box_point_x4, box_point_y4, box_point_z4]])
if self.cameraType=='RVC':
xyz.append([point_x*1000, point_y*1000, point_z*1000])
Depth_Z.append(point_z*1000)
else:
xyz.append([point_x, point_y, point_z])
Depth_Z.append(point_z)
nx_ny_nz.append([a, b, c])
RegionalArea.append(cv2.contourArea(max_contour))
uv.append([x_rotation_center, y_rotation_center])
@ -272,6 +289,141 @@ class Detection:
print("RVC X Camera is not opened!")
return 0, None, None, None, None
def get_take_photo_position(self, Height_reduce = 30, width_reduce = 30):
"""
检测当前拍照点能否检测到料袋
:param Height_reduce:
:param width_reduce:
:return ret: bool 相机是否正常工作
:return img: ndarry 返回img
:return find_target: bool 是否有目标
:return xyz: list 目标中心点云值，形如[x,y,z]
"""
ret, img, pm = self.camera_rvc.get_img_and_point_map() # 拍照，获取图像及
find_target = False
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:
flag, det_cpu, scores, masks, category_names = self.model.segment_objects(img)
if flag == 1:
xyz = []
RegionalArea = []
Depth_Z = []
uv = []
for i, item in enumerate(det_cpu):
find_target = True
# 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_point_list = contours_in(contours)
# print(len(all_point_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
'''
拟合最小外接矩形，计算矩形中心
'''
rect = cv2.minAreaRect(max_contour)
rect_reduce = (
(rect[0][0], rect[0][1]), (rect[1][0] - width_reduce, rect[1][1] - Height_reduce), rect[2])
# cv2.boxPoints可以将轮廓点转换为四个角点坐标
box_outside = 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_outside.sum(axis=1).argmin()
box_outside = np.roll(box_outside, 4 - startidx, 0)
box_outside = np.intp(box_outside)
box_outside = box_outside.reshape((-1, 1, 2)).astype(np.int32)
# cv2.boxPoints可以将轮廓点转换为四个角点坐标
box_reduce = cv2.boxPoints(rect_reduce)
startidx = box_reduce.sum(axis=1).argmin()
box_reduce = np.roll(box_reduce, 4 - startidx, 0)
box_reduce = np.intp(box_reduce)
box_reduce = box_reduce.reshape((-1, 1, 2)).astype(np.int32)
box_outside = box_outside + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]],[[box_x1, box_y1]]]
box = box_reduce + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]]]
box[0][0][1], box[0][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[0][0][1], box[0][0][0])
box[1][0][1], box[1][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[1][0][1], box[1][0][0])
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])
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)
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:
if self.cameraType == 'RVC':
xyz.append([point_x * 1000, point_y * 1000, point_z * 1000])
Depth_Z.append(point_z * 1000)
else:
xyz.append([point_x, point_y, point_z])
Depth_Z.append(point_z)
RegionalArea.append(cv2.contourArea(max_contour))
uv.append([x_rotation_center, y_rotation_center])
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)
if _idx == None:
return 1, img, find_target, None
else:
cv2.circle(img, (uv[_idx][0], uv[_idx][1]), 30, (0, 0, 255), 20) # 标出中心点
return 1, img, find_target, xyz[_idx]
else:
return 0, None, None
else:
return 0, None, None
pass
def get_center_position(self):
""

158
Vision/camera_coordinate_dete_img.py
View File

@ -25,8 +25,9 @@ import os
class Detection:
def __init__(self, use_openvino_model = True, cameraIsRVC = True):
def __init__(self, use_openvino_model=True, cameraType = 'RVC'):
self.use_openvino_model = use_openvino_model
self.cameraType = cameraType
if self.use_openvino_model == False:
model_path = ''.join([os.getcwd(), '/Vision/model/pt/best.pt'])
device = 'cpu'
@ -44,14 +45,20 @@ class Detection:
pass
def get_position(self, Point_isVision=False, save_img_point=True, 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 save_img_point: 保存点云和图片
: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
:return img: ndarry 返回img
:return xyz: list 目标中心点云值形如[x,y,z]
:return nx_ny_nz: list 拟合平面法向量，形如[a,b,c]
:return box_list: list 内缩检测框四顶点，形如[[x1,y1],[],[],[]]
def get_position(self, Point_isVision=True, save_img_point=True, seg_distance_threshold = 0.01, Height_reduce = 30, width_reduce = 30):
""
'''
: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
@ -267,6 +274,141 @@ class Detection:
print("RVC X Camera capture failed!")
return 0, None, None, None, None
def get_take_photo_position(self, Point_isVision=False, save_img_point=False, Height_reduce = 30, width_reduce = 30):
"""
专用于拍照位置点查找，检测当前拍照点能否检测到料袋
:param Height_reduce:
:param width_reduce:
:return ret: bool 相机是否正常工作
:return img: ndarry 返回img
:return find_target: bool 是否有目标
:return xyz: list 目标中心点云值，形如[x,y,z]
"""
ret = 1
img = self.img
pm = self.point
find_target = False
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)
if flag == 1:
xyz = []
RegionalArea = []
Depth_Z = []
uv = []
for i, item in enumerate(det_cpu):
find_target = True
# 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_point_list = contours_in(contours)
# print(len(all_point_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
'''
拟合最小外接矩形，计算矩形中心
'''
rect = cv2.minAreaRect(max_contour)
rect_reduce = (
(rect[0][0], rect[0][1]), (rect[1][0] - width_reduce, rect[1][1] - Height_reduce), rect[2])
# cv2.boxPoints可以将轮廓点转换为四个角点坐标
box_outside = 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_outside.sum(axis=1).argmin()
box_outside = np.roll(box_outside, 4 - startidx, 0)
box_outside = np.intp(box_outside)
box_outside = box_outside.reshape((-1, 1, 2)).astype(np.int32)
# cv2.boxPoints可以将轮廓点转换为四个角点坐标
box_reduce = cv2.boxPoints(rect_reduce)
startidx = box_reduce.sum(axis=1).argmin()
box_reduce = np.roll(box_reduce, 4 - startidx, 0)
box_reduce = np.intp(box_reduce)
box_reduce = box_reduce.reshape((-1, 1, 2)).astype(np.int32)
box_outside = box_outside + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]],[[box_x1, box_y1]]]
box = box_reduce + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]]]
box[0][0][1], box[0][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[0][0][1], box[0][0][0])
box[1][0][1], box[1][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[1][0][1], box[1][0][0])
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])
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)
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:
if self.cameraType == 'RVC':
xyz.append([point_x * 1000, point_y * 1000, point_z * 1000])
Depth_Z.append(point_z * 1000)
else:
xyz.append([point_x, point_y, point_z])
Depth_Z.append(point_z)
RegionalArea.append(cv2.contourArea(max_contour))
uv.append([x_rotation_center, y_rotation_center])
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)
if _idx == None:
return 1, img, find_target, None
else:
cv2.circle(img, (uv[_idx][0], uv[_idx][1]), 30, (0, 0, 255), 20) # 标出中心点
return 1, img, find_target, xyz[_idx]
else:
return 0, None, None
def release(self):
self.model.clear()

14
get_position_test.py
View File

@ -19,7 +19,6 @@ import os
"""
def detectionPosition_test():
detection = Detection()
while True:
ret, img, xyz, nx_ny_nz, box = detection.get_position()
if ret==1:
@ -35,6 +34,18 @@ def detectionPosition_test():
cv2.imshow('img', img)
cv2.waitKey(1)
def take_photo_position_test():
detection = Detection()
while True:
ret, img, find_target, xyz = detection.get_take_photo_position()
if ret==1:
print('是否检测到目标：', find_target)
if xyz!=None:
print('xyz点云坐标：', xyz)
else:
print('目标点云无效')
cv2.imshow('img', img)
cv2.waitKey(1)
def detectionPerson_test():
detectionPerson = DetectionPerson()
@ -50,5 +61,6 @@ def detectionPerson_test():
else:
break
if __name__ == '__main__':
detectionPosition_test()