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UPDATE Vision 更新点云选取逻辑

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This commit is contained in:
HJW
2024-09-27 17:30:57 +08:00
parent ac3606b8b3
commit a101b52c36
2 changed files with 60 additions and 41 deletions
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43
Vision/camera_coordinate_dete.py
View File

@ -51,7 +51,7 @@ class Detection:
def get_position(self, Point_isVision=False, save_img_point=False):
def get_position(self, Point_isVision=False, save_img_point=False, Height_reduce = 30, width_reduce = 30):
""
'''
:param api: None
@ -145,11 +145,34 @@ class Detection:
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)
'''
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)
cv2.fillPoly(mask_inside, [box_reduce], (255))
pixel_point2 = cv2.findNonZero(mask_inside)
# result = np.zeros_like(color_image)
select_point = []
@ -180,19 +203,8 @@ class Detection:
# 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]]]
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])
@ -222,6 +234,7 @@ class Detection:
uv.append([x_rotation_center, y_rotation_center])
seg_point.append(pm_seg)
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)

58
Vision/camera_coordinate_dete_img.py
View File

@ -37,15 +37,15 @@ 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/1.png'])
point_path = ''.join([os.getcwd(), '/Vision/model/data/1.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))
pass
def get_position(self, Point_isVision=False, save_img_point=True, seg_distance_threshold = 0.01):
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
@ -133,7 +133,7 @@ class Detection:
ret, binary = cv2.threshold(imgray, 10, 255, 0)
# 阈值 二进制图像
# cv2.imshow('bin',binary)
# cv2.waitKey(1)
# cv2.waitKey(0)
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
# all_point_list = contours_in(contours)
# print(len(all_point_list))
@ -144,11 +144,35 @@ class Detection:
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)
'''
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)
cv2.fillPoly(mask_inside, [box_reduce], (255))
# cv2.drawContours(mask_inside, [max_contour], 0, 255, -1)
# cv2.imshow('mask_inside', mask_inside)
# cv2.waitKey(0)
pixel_point2 = cv2.findNonZero(mask_inside)
# result = np.zeros_like(color_image)
select_point = []
@ -178,27 +202,8 @@ class Detection:
# 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)
box = np.int0(box)
# 在原图上画出预测的外接矩形
'''
拟合最大内接矩形，计算矩形中心
'''
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 = box_reduce + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]]]
box_outside = box_outside + [[[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])
@ -226,6 +231,7 @@ class Detection:
uv.append([x_rotation_center, y_rotation_center])
seg_point.append(pm_seg)
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)